Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Vasudevan, Nisha | Venkatraman, Vasudevan | Ramkumar, A | Sheela, A

Article Type: Research Article

Abstract: Smart grid is a sophisticated and smart electrical power transmission and distribution network, and it uses advanced information, interaction and control technologies to build up the economy, effectiveness, efficiency and grid security. The accuracy of day-to-day power consumption forecasting models has an important impact on several decisions making, such as fuel purchase scheduling, system security assessment, economic capacity generation scheduling and energy transaction planning. The techniques used for improving the load forecasting accuracy differ in the mathematical formulation as well as the features used in each formulation. Power utilization of the housing sector is an essential component of the overall …electricity demand. An accurate forecast of energy consumption in the housing sector is quite relevant in this context. The recent adoption of smart meters makes it easier to access electricity readings at very precise resolutions; this source of available data can, therefore, be used to build predictive models., In this study, the authors have proposed Prophet Forecasting Model (PFM) for the application of forecasting day-ahead power consumption in association with the real-time power consumption time series dataset of a single house connected with smart grid near Paris, France. PFM is a special type of Generalized Additive Model. In this method, the time series power consumption dataset has three components, such as Trend, Seasonal and Holidays. Trend component was modelled by a saturating growth model and a piecewise linear model. Multi seasonal periods and Holidays were modelled with Fourier series. The Power consumption forecasting was done with Autoregressive Integrated Moving Average (ARIMA), Long Short Term Neural Memory Network (LSTM) and PFM. As per the comparison, the improved RMSE, MSE, MAE and RMSLE values of PFM were 0.2395, 0.0574, 0.1848 and 0.2395 respectively. From the comparison results of this study, the proposed method claims that the PFM is better than the other two models in prediction, and the LSTM is in the next position with less error. Show more

Keywords: Energy management, smart home, energy forecast, power management, efficiency

DOI: 10.3233/JIFS-189886

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Krishnamoorthy, Amrutha | Sindhura, Vijayasimha Reddy | Gowtham, Devarakonda | Jyotsna, C. | Amudha, J.

Article Type: Research Article

Abstract: Extraction of eye gaze events is highly dependent on automated powerful software that charges exorbitant prices. The proposed open-source intelligent tool StimulEye helps to detect and classify eye gaze events and analyse various metrics related to these events. The algorithms for eye event detection in use today heavily depend on hand-crafted signal features and thresholding, which are computed from the stream of raw gaze data. These algorithms leave most of their parametric decisions on the end user which might result in ambiguity and inaccuracy. StimulEye uses deep learning techniques to automate eye gaze event detection which neither requires manual decision …making nor parametric definitions. StimulEye provides an end to end solution which takes raw streams of data from an eye tracker in text form, analyses these to classify the inputs into the events, namely saccades, fixations, and blinks. It provides the user with insights such as scanpath, fixation duration, radii, etc. Show more

Keywords: Eye tracking, fixations, saccades, scanpath, deep learning

DOI: 10.3233/JIFS-189893

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Sujatha, M. | Geetha, K. | Balakrishnan, P.

Article Type: Research Article

Abstract: The widespread adoption of cloud computing by several companies across diverse verticals of different sizes has led to an exponential growth of Cloud Service Providers (CSP). Multiple CSPs offer homogeneous services with a vast array of options and different pricing policies, making the suitable service selection process complex. Our proposed model simplifies the IaaS selection process that can be used by all users including clients from the non-IT background. In the first phase, requirements are gathered using a simple questionnaire and are mapped with the compute services among different alternatives.In the second phase, we have implemented the Sugeno Fuzzy inference …system to rank the service providers based on the QoS attributes to ascertain the appropriate selection. In the third phase, we have applied the cost model to identify the optimal CSP. This framework is validated by applying it for a gaming application use case and it has outperformed the online tools thus making it an exemplary model. Show more

Keywords: Cloud computing, IaaS selection, Sugeno Fuzzy inference system, CSP selection, compute service, MCDM

DOI: 10.3233/JIFS-189883

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Jeyanthi, R. | Sahithi, Madugula | Sireesha, N.V.L. | Srinivasan, Mangala Sneha | Devanathan, Sriram

Article Type: Research Article

Abstract: In process industries, measurements usually contain errors due to the improper instrumental variation, physical leakages in process streams and nodes, and inaccurate recording/reporting. Thus, these measurements violate the laws of conservation, and do not conform to process constraints. Data reconciliation (DR) is used to resolve the difference between measurements and constraints. DR is also used in reducing the effect of random errors and more accurately estimating the true values. A multivariate technique that is used to obtain estimates of true values while preserving the most significant inherent variation is Principal Component Analysis (PCA). PCA is used to reduce the dimensionality …of the data with minimum information loss. In this paper, two new DR techniques are proposed moving-average PCA (MA-PCA) and exponentially weighted moving average PCA (EWMA-PCA) to improve the performance of DR and obtain more accurate and consistent data. These DR techniques are compared based on RMSE. Further, these techniques are analyzed for different values of sample size, weighting factor, and variances. Show more

Keywords: Data reconciliation, MA-PCA, EWMA-PCA

DOI: 10.3233/JIFS-189892

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-6, 2021

Authors: Shabir, Muhammad | Mubarak, Asad | Naz, Munazza

Article Type: Research Article

Abstract: The rough set theory is an effective method for analyzing data vagueness, while bipolar soft sets can handle data ambiguity and bipolarity in many cases. In this article, we apply Pawlak’s concept of rough sets to the bipolar soft sets and introduce the rough bipolar soft sets by defining a rough approximation of a bipolar soft set in a generalized soft approximation space. We study their structural properties and discuss how the soft binary relation affects the rough approximations of a bipolar soft set. Two sorts of bipolar soft topologies induced by soft binary relation are examined. We additionally discuss …some similarity relations between the bipolar soft sets, depending on their roughness. Such bipolar soft sets are very useful in the problems related to decision-making such as supplier selection problem, purchase problem, portfolio selection, site selection problem etc. A methodology has been introduced for this purpose and two algorithms are presented based upon the ongoing notions of foresets and aftersets respectively. These algorithms determine the best/worst choices by considering rough approximations over two universes i.e. the universe of objects and universe of parameters under a single framework of rough bipolar soft sets. Show more

Keywords: Rough sets, bipolar soft sets, rough bipolar soft sets, bipolar soft topology, decision making

DOI: 10.3233/JIFS-202958

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-1, 2021

Authors: Adu, Kwabena | Yu, Yongbin | Cai, Jingye | Dela Tattrah, Victor | Adu Ansere, James | Tashi, Nyima

Article Type: Research Article

Abstract: The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic …routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time. Show more

Keywords: Artificial intelligence, capsule network, convolutional neural network, deep learning, pneumonia, x-ray imaging

DOI: 10.3233/JIFS-202638

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-25, 2021

Authors: Recal, Füsun | Demirel, Tufan

Article Type: Research Article

Abstract: Although Machine Learning (ML) is widely used to examine hidden patterns in complex databases and learn from them to predict future events in many fields, utilization of it for predicting the outcome of occupational accidents is relatively sparse. This study utilized diversified ML algorithms; Multinomial Logistic Regression (MLR), Support Vector Machines (SVM), Single C5.0 Tree (C5), Stochastic Gradient Boosting (SGB), and Neural Network (NN) in classifying the severity of occupational accidents in binary (Fatal/NonFatal) and multi-class (Fatal/Major/Minor) outcomes. Comparison of the performance of models showed Balanced Accuracy to be the best for SVM and SGB methods in 2-Class and SGB …in 3-Class. Algorithms performed better at predicting fatal accidents compared to major and minor accidents. Results obtained revealed that, ML unveils factors contributing to severity to better address the corrective actions. Furthermore, taking action related to even some of the most significant factors in complex accidents database with many attributes can prevent majority of severe accidents. Interpretation of most significant factors identified for accident prediction suggest the following corrective measures: taking fall prevention actions, prioritizing workplace inspections based on the number of employees, and supplementing safety actions according to worker’s age and experience. Show more

Keywords: Accidents severity, classification, data mining, feature selection; machine learning

DOI: 10.3233/JIFS-202099

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Wang, Shuang | Ding, Lei | Sui, He | Gu, Zhaojun

Article Type: Research Article

Abstract: Cybersecurity risk assessment is an important means of effective response to network attacks on industrial control systems. However, cybersecurity risk assessment process is susceptible to subjective and objective effects. To solve this problem, this paper introduced cybersecurity risk assessment method based on fuzzy theory of Attack-Defense Tree model and probability cybersecurity risk assessment technology, and applied it to airport automatic fuel supply control system. Firstly, an Attack-Defense Tree model was established based on the potential cybersecurity threat of the system and deployed security equipment. Secondly, the interval probability of the attack path was calculated using the triangular fuzzy quantification of …the interval probabilities of the attack leaf nodes and defensive leaf nodes. Next, the interval probability of the final path was defuzzified. Finally, the occurrence probability of each final attack path was obtained and a reference for the deployment of security equipment was provided. The main contributions of this paper are as follows: (1) considering the distribution of equipment in industrial control system, a new cybersecurity risk evaluation model of industrial control system is proposed. (2) The experimental results of this article are compared with other assessment technologies, and the trend is similar to that of other evaluation methods, which proves that the method was introduced in this paper is scientific. However, this method reduces the subjective impact of experts on cybersecurity risk assessment, and the assessment results are more objective and reasonable. (3) Applying this model to the airport oil supply automatic control system can comprehensively evaluate risk, solve the practical problems faced by the airport, and also provide an important basis for the cybersecurity protection scheme of the energy industry. Show more

Keywords: Cybersecurity risk assessment, fuzzy set theory, attack-defense tree

DOI: 10.3233/JIFS-201126

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: An, Qing | Tang, Ruoli | Su, Hongfeng | Zhang, Jun | Li, Xin

Article Type: Research Article

Abstract: Due to the promising performance on energy-saving, the building integrated photovoltaic system (BIPV) has found an increasingly wide utilization in modern cities. For a large-scale PV array installed on the facades of a super high-rise building, the environmental conditions (e.g., the irradiance, temperature, sunlight angle etc.) are always complex and dynamic. As a result, the PV configuration and maximum power point tracking (MPPT) methodology are of great importance for both the operational safety and efficiency. In this study, some famous PV configurations are comprehensively tested under complex shading conditions in BIPV application, and a robust configuration for large-scale BIPV system …based on the total-cross-tied (TCT) circuit connection is developed. Then, by analyzing and extracting the feature variables of environment parameters, a novel fast MPPT methodology based on extreme learning machine (ELM) is proposed. Finally, the proposed configuration and its MPPT methodology are verified by simulation experiments. Experimental results show that the proposed configuration performs efficient on most of the complex shading conditions, and the ELM-based intelligent MPPT methodology can also obtain promising performance on response speed and tracking accuracy. Show more

Keywords: Building integrated photovoltaic system, maximum power point tracking, PV configuration, intelligent control, extreme learning machine

DOI: 10.3233/JIFS-210424

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Rajavel, Rajkumar | Ravichandran, Sathish Kumar | Nagappan, Partheeban | Ramasubramanian Gobichettipalayam, Kanagachidambaresan

Article Type: Research Article

Abstract: A major demanding issue is developing a Service Level Agreement (SLA) based negotiation framework in the cloud. To provide personalized service access to consumers, a novel Automated Dynamic SLA Negotiation Framework (ADSLANF) is proposed using a dynamic SLA concept to negotiate on service terms and conditions. The existing frameworks exploit a direct negotiation mechanism where the provider and consumer can directly talk to each other, which may not be applicable in the future due to increasing demand on broker-based models. The proposed ADSLANF will take very less total negotiation time due to complicated negotiation mechanisms using a third-party broker agent. …Also, a novel game theory decision system will suggest an optimal solution to the negotiating agent at the time of generating a proposal or counter proposal. This optimal suggestion will make the negotiating party aware of the optimal acceptance range of the proposal and avoid the negotiation break off by quickly reaching an agreement. Show more

Keywords: Service level agreement, broker-based negotiation framework, game theory decision system, E-commerce application

DOI: 10.3233/JIFS-189882

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Pradhan, Rosy | Khan, Mohammad Rafique | Sethy, Prabir Kumar | Majhi, Santosh Kumar

Article Type: Research Article

Abstract: The field of optimization science is proliferating that has made complex real-world problems easy to solve. Metaheuristics based algorithms inspired by nature or physical phenomena based methods have made its way in providing near-ideal (optimal) solutions to several complex real-world problems. Ant lion Optimization (ALO) has inspired by the hunting behavior of antlions for searching for food. Even with a unique idea, it has some limitations like a slower rate of convergence and sometimes confines itself into local solutions (optima). Therefore, to enhance its performance of classical ALO, quantum information theory is hybridized with classical ALO and named as QALO …or quantum theory based ALO. It can escape from the limitations of basic ALO and also produces stability between processes of explorations followed by exploitation. CEC2017 benchmark set is adopted to estimate the performance of QALO compared with state-of-the-art algorithms. Experimental and statistical results demonstrate that the proposed method is superior to the original ALO. The proposed QALO extends further to solve the model order reduction (MOR) problem. The QALO based MOR method performs preferably better than other compared techniques. The results from the simulation study illustrate that the proposed method effectively utilized for global optimization and model order reduction. Show more

Keywords: Antlion optimization, quantum information theory, model order reduction, metahueristic optimization, CEC benchmark

DOI: 10.3233/JIFS-189894

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Lili, Xu | Feng, Liu | Xuejian, Chu

Article Type: Research Article

Abstract: This study examines the application of the business model of supply chain finance depending on the core enterprise, to the credit financing of transportation capacity enterprises. It studies the credit transmission characteristics regarding core enterprise credit radiation, presents the core enterprise credit segmentation and credit pricing, and transforms them into the calculation of credit guarantee and the default probability of core enterprises. Credit guarantee is regarded as a constraint of financial institutions’ credit decisions. Using probability density and logistic tools, we construct a profit maximization model for financial institutions and solve their optimal credit decision for a specific interest rate. …Through numerical experiments, we verify the validity of the model and conclude that increasing the business volume between financing enterprises and core enterprises or reducing the probability of default can effectively improve financial institutions’ credit line. Show more

Keywords: Credit segmentation, credit pricing, default probability, transportation capacity financing, credit decision

DOI: 10.3233/JIFS-201818

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Özgül, Ercan | Dinçer, Hasan | Yüksel, Serhat

Article Type: Research Article

Abstract: Healthy life is recognized as one of the most fundamental human rights. However, even today, millions of people around the world are forced to choose between their basic needs and fundamental rights. Half of the world’s population does not have access to the healthcare they need. Universal Health Coverage (UHC) aims to ensure that all individuals receive the quality health services they need without incurring a financial burden, and to protect them from risk factors that threaten their health. The aim of this study is to identify the significant factors to improve UHC in the countries. For this purpose, house …of quality (HoQ) approach is used in the analysis process so that both customer expectations and technical requirements are considered. Within this framework, a novel hybrid model has been proposed which has three different stages. Firstly, 3 groups of diseases and 4 clinical services for each group are determined regarding the customer needs. Secondly, these factors are weighted by using interval-valued intuitionistic hesitant 2-tuple fuzzy decision making and trial evaluation laboratory (DEMATEL). In the final stage, 9 different technical requirements are ranked by using interval-valued intuitionistic hesitant 2-tuple fuzzy technique for order preference by similarity to ideal solution (TOPSIS). Additionally, another evaluation has also been conducted by considering Spherical fuzzy sets. Similarly, a comparative analysis has also been performed with VIKOR while ranking the alternatives. It is concluded that analysis results of both evaluations are quite similar. This situation gives an information about the coherency and consistency of the analysis results. The findings indicate that treatment services in noncommunicable diseases play the most significant role in this respect. Moreover, according to the ranking results, it is concluded that strategic policies should be related to improving the social security and special physician capacity as well as decreasing the out-of-pocket payment. Show more

Keywords: House of quality, UHC, hesitant linguistic terms, DEMATEL, TOPSIS

DOI: 10.3233/JIFS-202818

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Sarraf, Gaurav | Srivatsa, Anirudh Ramesh | Swetha, MS

Article Type: Research Article

Abstract: With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep …learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques. Show more

Keywords: Cybersecurity, multimedia steganography, steganalysis, convolutional neural networks, cryptography

DOI: 10.3233/JIFS-189879

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Anushiadevi, R. | Amirtharajan, Rengarajan

Article Type: Research Article

Abstract: Reversible Data Hiding (RDH) schemes have recently gained much interest in protecting the secret information and sensitive cover images. For cloud security applications, the third party’s data embedding can be done (e.g., cloud service). In such a scenario, to protect the cover image from unauthorized access, it is essential to encrypt before embedding it. It can be overcome by combining the RDH scheme with encryption. However, the key challenge in integrating RDH with encryption is that the correlation between adjacent pixels begins to disappear after encryption, so reversibility cannot be accomplished. RDH with elliptic curve cryptography is proposed to overcome …this challenge. In this paper (ECC-RDH) by adopting additive homomorphism property; the proposed method, the stego image decryption gives the sum of the original image and confidential data. The significant advantages of this method are, the cover image is transferred with high security, the embedding capacity is 0.5 bpp with a smaller location map size of 0.05 bpp. The recovered image and secrets are the same as in the original, and thus 100% reversibility is proved. Show more

Keywords: Elliptic curve cryptography, reversible data hiding, additive homomorphism, lossless data hiding, reversible steganography

DOI: 10.3233/JIFS-189878

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Zhang, Shao-Yu

Article Type: Research Article

Abstract: This paper introduces a special Galois connection combined with the wedge-below relation. Furthermore, by using this tool, it is shown that the category of M -fuzzifying betweenness spaces and the category of M -fuzzifying convex spaces are isomorphic and the category of arity-2 M -fuzzifying convex spaces can be embedded in the category of M -fuzzifying interval spaces as a reflective subcategory.

Keywords: Fuzzy convex structure, fuzzy betweenness space, fuzzy interval space, arity-2 fuzzy convexity

DOI: 10.3233/JIFS-210060

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Liu, Peide | Khan, Qaisar | Mahmood, Tahir | Khan, Rashid Ali | Khan, Hidayat Ullah

Article Type: Research Article

Abstract: Pythagorean fuzzy set (PyFS) is an extension of various fuzzy concepts, such as fuzzy set (FS), intuitionistic FS, and it is enhanced mathematical gizmo to pact with uncertain and vague information. In this article, some drawbacks in the Dombi operational rules for Pythagorean fuzzy numbers (PyFNs) are examined and some improved Dombi operational laws for PyFNs are developed. We also find out that the value aggregated using the existing Dombi aggregation operators (DAOs) is not a PyFN. Furthermore, we developed two new aggregations, improved existing aggregation operators (AOs) for aggregating Pythagorean fuzzy information (PyFI) and are applied to multiple-attribute decision …making (MADM). To acquire full advantage of power average (PA) operators proposed by Yager, the Pythagorean fuzzy Dombi power average (PyFDPA) operator, the Pythagorean fuzzy Dombi weighted power average (PyFDWPA) operator, Pythagorean fuzzy Dombi power geometric (PyFDPG) operator, Pythagorean fuzzy Dombi weighted geometric (PyFDPWG) operator, improved the existing AOs and their desirable properties are discussed. The foremost qualities of these developed Dombi power aggregation operators is that they purge the cause of discomfited data and are more supple due to general parameter. Additionally, based on these Dombi power AOs, a novel MADM approach is instituted. Finally, a numerical example is given to show the realism and efficacy of the proposed approach and judgment with the existing approaches is also specified. Show more

Keywords: Pythagorean fuzzy set, PA operator, Dombi t-norm and Dombi t-conorm, MADM

DOI: 10.3233/JIFS-201723

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Yang, Jie | Luo, Tian | Zhao, Fan | Li, Shuai | Jin, Xin

Article Type: Research Article

Abstract: Based on the granular computing and three-way decisions theory, the sequential three-way decisions (S3WD) model implements the idea of progressive computing. However, almost S3WD models are established based on labeled information system, and there is still a lack of S3WD model for processing unlabeled information system (UIS). In this paper, to solve the issue of given accepted number for UIS, a data-driven sequential three-way decisions (DDS3WD) model is proposed. Firstly, from the perspective of similarity computed by TOPSIS, a general three-way decisions model for UIS based on decision risk is presented and its shortcomings are analyzed. Then, a concept of …optimal density difference is defined to establish the DDS3WD model for UIS by updating attributes. Finally, the related experiments show that DDS3WD is feasible and effective for dealing with UIS under the condition of given accepted number of objects. Show more

Keywords: Sequential three-way decisions, unlabeled information system, data-driven, optimal density difference

DOI: 10.3233/JIFS-201527

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Alansari, Monairah | Shagari Mohammed, Shehu | Azam, Akbar

Article Type: Research Article

Abstract: As an improvement of fuzzy set theory, the notion of soft set was initiated as a general mathematical tool for handling phenomena with nonstatistical uncertainties. Recently, a novel idea of set-valued maps whose range set lies in a family of soft sets was inaugurated as a significant refinement of fuzzy mappings and classical multifunctions as well as their corresponding fixed point theorems. Following this new development, in this paper, the concepts of e -continuity and E -continuity of soft set-valued maps and α e -admissibility for a pair of such maps are introduced. Thereafter, we present some generalized quasi-contractions …and prove the existence of e -soft fixed points of a pair of the newly defined non-crisp multivalued maps. The hypotheses and usability of these results are supported by nontrivial examples and applications to a system of integral inclusions. The established concepts herein complement several fixed point theorems in the framework of point-to-set-valued maps in the comparable literature. A few of these special cases of our results are highlighted and discussed. Show more

Keywords: 46S40, 47H10, 54H25, e-soft fixed point, e-continuous, F-contraction, soft set-valued map, αe-admissible, integral inclusion

DOI: 10.3233/JIFS-202154

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Rajavel, Rajkumar | Ravichandran, Sathish Kumar | Nagappan, Partheeban | Venu, Sivakumar

Article Type: Research Article

Abstract: Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling …algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms. Show more

Keywords: Cloud computing, task scheduling, Qos aware task scheduling, cost enabled scheduling, cloud utilization, cloud utility

DOI: 10.3233/JIFS-189881

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Biswas, Kusan

Article Type: Research Article

Abstract: In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems  (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy …channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature. Show more

Keywords: Data hiding, JPEG, ECC, SVD, Turbo codes, PSNR, SSIM

DOI: 10.3233/JIFS-189877

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Ayub, Mohammed | El-Alfy, El-Sayed M.

Article Type: Research Article

Abstract: Web technology has become an indispensable part in human’s life for almost all activities. On the other hand, the trend of cyberattacks is on the rise in today’s modern Web-driven world. Therefore, effective countermeasures for the analysis and detection of malicious websites is crucial to combat the rising threats to the cyber world security. In this paper, we systematically reviewed the state-of-the-art techniques and identified a total of about 230 features of malicious websites, which are classified as internal and external features. Moreover, we developed a toolkit for the analysis and modeling of malicious websites. The toolkit has implemented several …types of feature extraction methods and machine learning algorithms, which can be used to analyze and compare different approaches to detect malicious URLs. Moreover, the toolkit incorporates several other options such as feature selection and imbalanced learning with flexibility to be extended to include more functionality and generalization capabilities. Moreover, some use cases are demonstrated for different datasets. Show more

Keywords: Web security, malicious websites, malicious URL, machine learning, feature extraction, toolkits

DOI: 10.3233/JIFS-189874

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Wang, Fubin | Liu, Peide | Wang, Peng

Article Type: Research Article

Abstract: A scientific evaluation model can be effectively used for the evaluation of regional talent development level. This paper proposes a set of scientific index systems for evaluating rural science and technology talents, which can be used for understanding the development status and level of rural science and technology talents in various regions; putting forward the corresponding talent cultivation and introduction policies, and; promoting the development of rural economic construction. Moreover, in order to avoid the shortcoming of over-subjective indicator weight in analytic hierarchy process (AHP), this paper uses the entropy weight method to determine indicator weight. Furthermore, giving the fact …that the evaluation experts may have individual personal preferences, this paper proposes an extended TODIM method based on hybrid index values, for achieving more scientific and effective evaluation results of rural science and technology talents. Finally, the proposed methods are evaluated on an actual case, where relevant analysis and suggestions are given. Show more

Keywords: Rural scientific and technological talents, TODIM method, entropy weight method, hybrid indicator, multi-attribute decision-making

DOI: 10.3233/JIFS-202847

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Alpana, | Chand, Satish | Mohapatra, Subrajeet | Mishra, Vivek

Article Type: Research Article

Abstract: Coal is the mixture of organic matters, called as macerals, and inorganic matters. Macerals are categorized into three major groups, i.e., vitrinite, inertinite, and liptinite. The maceral group identification serves an important role in coking and non-coking coal processes that are used mainly in steel and iron industries. Hence, it becomes important to efficiently characterize these maceral groups. Currently, industries use the optical polarized microscope to distinguish the maceral groups. However, the microscopical analysis is a manual method which is time-consuming and provides subjective outcome due to human interference. Therefore, an automated approach that can identify the maceral groups accurately …in less processing time is strongly needed in industries. Computer-based image analysis methods are revolutionizing the industries because of its accuracy and efficacy. In this study, an intelligent maceral group identification system is proposed using markov-fuzzy clustering approach. This approach is an integration of fuzzy sets and the markov random field, which is employed towards maceral group identification in a clustering framework. The proposed model shows better results when compared with the standard cluster-based segmentation techniques. The results from the suggested model have also been validated against the outcome of manual methods, and the feasibility is tested using performance metrics. Show more

Keywords: Coal, macerals, image segmentation, clustering, fuzzy sets, markov random field

DOI: 10.3233/JIFS-189889

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Richa, | Bedi, Punam

Article Type: Research Article

Abstract: Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user …has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure. Show more

Keywords: Recommender system, cross domain, serendipity, trust, fuzzy sets

DOI: 10.3233/JIFS-189872

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Hamdoun, Hala | Sagheer, Alaa | Youness, Hassan

Article Type: Research Article

Abstract: Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Recently, deep learning methods have been emerged in the artificial intelligence field attaining astonishing performance in a wide range of applications. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. Most of the review articles that handle the energy TSF problem are systematic reviews, however, a qualitative and quantitative study for the energy TSF problem is not yet available in the literature. The purpose …of this paper is twofold, first it provides a comprehensive analytical assessment for conventional, machine learning, and deep learning methods that can be utilized to solve various energy TSF problems. Second, the paper carries out an empirical assessment for many selected methods through three real-world datasets. These datasets related to electrical energy consumption problem, natural gas problem, and electric power consumption of an individual household problem. The first two problems are univariate TSF and the third problem is a multivariate TSF. Compared to both conventional and machine learning contenders, the deep learning methods attain a significant improvement in terms of accuracy and forecasting horizons examined. In the meantime, their computational requirements are notably greater than other contenders. Eventually, the paper identifies a number of challenges, potential research directions, and recommendations to the research community may serve as a basis for further research in the energy forecasting domain. Show more

Keywords: Energy time series forecasting, conventional forecasting methods, machine learning, deep learning, energy management systems

DOI: 10.3233/JIFS-201717

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-26, 2021

Authors: Zhang, Mu | Li, Si-si | Zhao, Bi-bin

Article Type: Research Article

Abstract: In view of the problem that it is difficult to quantitatively assess the interactivity between attributes in the identification process of 2-order additive fuzzy measure, this work uses the intuitionistic fuzzy sets (IFSs) to describe and deal with the interactivity between attributes. Firstly, the interactivity between attributes is defined by the supermodular game theory. On this basis, the experts employ the intuitionistic fuzzy number (IFN) to assess the interactivity between attributes, Secondly, the opinions of all experts are aggregated by using the intuitionistic fuzzy weighted average operator (IFWA). Finally, based on the aggregated results, the intuitionistic fuzzy interaction degree between …attributes is defined and calculated by the score function of IFN. Thus, a 2-order additive fuzzy measure identification method based on IFSs is further proposed. Based on the proposed method, using the Choquet fuzzy integral as nonlinear integration operator, a multi-attribute decision making (MADM) process is presented. Taking the credit evaluation of the big data listed companies in China as an application example, the feasibility and effectiveness of the proposed method is verified by the analysis results of application example. Show more

Keywords: Interactivity between attributes, intuitionistic fuzzy sets, 2-order additive fuzzy measure, choquet fuzzy integral, multi-attribute decision making, credit evaluation

DOI: 10.3233/JIFS-201368

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Jeena, R. S | Shiny, G. | Sukesh Kumar, A. | Mahadevan, K.

Article Type: Research Article

Abstract: Stroke is a major reason for disability and mortality in most of the developing nations. Early detection of stroke is highly significant in bio-medical research. Research illustrates that signs of stroke are reflected in the eye and may be analyzed from fundus images. A custom dataset of fundus images has been compiled for formulating an automated stroke detection algorithm. In this paper, a comparative study of hand-crafted texture features and convolutional neural network (CNN) has been recommended for stroke diagnosis. The custom CNN model has also been compared with five pre-trained models from ImageNet. Experimental results reveal that the recommended …custom CNN model gives the best performance by achieving an accuracy of 95.8 %. Show more

Keywords: Stroke, convolutional neural network (CNN)

DOI: 10.3233/JIFS-189855

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Luo, D. | Zhang, G.Z.

Article Type: Research Article

Abstract: The purpose of this paper is to solve the prediction problem of nonlinear sequences with multiperiodic features, and a multiperiod grey prediction model based on grey theory and Fourier series is established. For nonlinear sequences with both trend and periodic features, the empirical mode decomposition method is used to decompose the sequences into several periodic terms and a trend term; then, a grey model is used to fit the trend term, and the Fourier series method is used to fit the periodic terms. Finally, the optimization parameters of the model are solved with the objective of obtaining a minimum mean …square error. The novel model is applied to research on the loss rate of agricultural droughts in Henan Province. The average absolute error and root mean square error of the empirical analysis are 0.3960 and 0.5086, respectively. The predicted results show that the novel model can effectively fit the loss rate sequence. Compared with other models, the novel model has higher prediction accuracy and is suitable for the prediction of multiperiod sequences. Show more

Keywords: Nonlinear sequences, multiperiod, grey model, empirical mode decomposition, Fourier series

DOI: 10.3233/JIFS-202775

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Touqeer, Muhammad | Umer, Rimsha | Ali, Muhammad Irfan

Article Type: Research Article

Abstract: Pythagorean fuzzy sets and interval-valued Pythagorean fuzzy sets are more proficient in handling uncertain and imprecise information than intuitionistic fuzzy sets and fuzzy sets. In this article, we put forward a chance-constraint programming method to solve linear programming network problems with interval-valued Pythagorean fuzzy constraints. This practice is developed using score function and upper and lower membership functions of interval-valued Pythagorean fuzzy numbers. The feasibility of the anticipated approach is illustrated by solving an airway network application and shown to be used to solve different types of network problems with objective function having interval-valued Pythagorean fuzzy numbers by employing it …on shortest path problem and minimum spanning tree problem. Furthermore, a comparative examination was performed to validate the effectiveness and usefulness of the projected methodology. Show more

Keywords: Interval-valued pythagorean fuzzy number (IVPFN), interval-valued trapezoidal pythagorean number (IVTrPFN), linear programming problem (LPP), chance-constraint programming (CCP)

DOI: 10.3233/JIFS-202383

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Zhang, Yongjie | Cao, Kang | Liang, Ke | Zeng, Yongqi

Article Type: Research Article

Abstract: Commonality, a typical commercial feature of serialized civil aircraft study and development, refers to a series of methods of reusing and sharing assets, which were developed based on broad similarity. The common design of serialized civil aircraft is capable of maximally saving R&D, production, operation, and disposal. To maximize the total benefits of manufacturers and operators, the common design of serialized civil aircrafts primarily exploits the commercial experience of serialized products in other fields (e.g., automobiles and mobile phones), whereas a scientific index system and quantitative evaluation model has not been formed. Accordingly, this study proposes a new civil aircraft …commonality index evaluation model in accordance with fuzzy set theory and methods. The model follows two branches, i.e., attribute commonality and structural commonality, to develop a multi-level civil aircraft commonality index system. The proposed model can split the commonality into six commonality sub-intervals and build the corresponding standard fuzzy set with the characteristic attribute parameters of the civil aircraft as the elements. Next, based on considerable civil aircraft sample data, a fuzzy test is designed to yield the membership function of the fuzzy set. Thus, a model of evaluating civil aircraft commonality is constructed, taking the characteristic parameters of the civil aircraft to be evaluated as input, and selecting the degree of commonality of each level as output. Lastly, this study employs the evaluation model to evaluate the commonality of Boeing 757-200 with other civil aircrafts. Furthermore, the evaluated results well explain the actual situation, which verifies the effectiveness and practicability of the proposed model. Show more

Keywords: Cost benefit analysis, serialized civil aircraft, commonality index, fuzzy set, membership function

DOI: 10.3233/JIFS-202749

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Pathak, Vinay | Singh, Karan

Article Type: Research Article

Abstract: Due to the rapid growth in sensor technology and embedded technology, wireless body area network WBANs plays a vital role in monitoring the human body system and the surrounding environment. It supports many healthcare applications on the one hand and are very much help full in pandemic scenarios. It has become the most innovative health care area, which is intriguing to many researchers because of its vast future prospective and potential. Data collected by different wireless sensors or nodes is very personal, critical, and important because of human life involvement. WBANs can minimize human to human contact, which helps stop …the spread of severe infectious diseases. The biggest concern is the maintenance of privacy and accuracy of data is still a hot area of research due to nature of attacks, which are changing day by day and increasing, as well as for the sake of better performance. A suitable security mechanism is a way to address above issues, for achieving data security, it is expedient to propose a mechanism. It is essential to update the patient’s regular data. WBANs help to deliver truthful reports related to the patient’s health regularly and individually. This paper proposes an algorithm that shows a better result than the existing algorithm in their previous works. This work is all about proposing a mechanism which needs comparatively less resource. Only authentic entities can interact with the server, which has become obligatory for both sides, keeping data safe. Several authentication schemes have been proposed or discussed by different researchers. This paper has proposed a Secure and Efficient WBANs Authentication Mechanism (SEAM). This security framework will take care of the authentication and the security of transmitted data. Show more

Keywords: WBANs, wearable sensors, eHealth privacy & security, threat, WSN, security

DOI: 10.3233/JIFS-189873

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Shabir, Muhammad | Din, Jamalud | Ganie, Irfan Ahmad

Article Type: Research Article

Abstract: The original rough set model, developed by Pawlak depends on a single equivalence relation. Qian et al, extended this model and defined multigranulation rough sets by using finite number of equivalence relations. This model provide new direction to the research. Recently, Shabir et al. proposed a rough set model which depends on a soft relation from an universe V to an universe W  . In this paper we are present multigranulation roughness based on soft relations. Firstly we approximate a non-empty subset with respect to aftersets and foresets of finite number of soft binary relations. In this way we …get two sets of soft sets called the lower approximation and upper approximation with respect to aftersets and with respect to foresets. Then we investigate some properties of lower and upper approximations of the new multigranulation rough set model. It can be found that the Pawlak rough set model, Qian et al. multigranulation rough set model, Shabir et al. rough set model are special cases of this new multigranulation rough set model. Finally, we added two examples to illustrate this multigranulation rough set model. Show more

Keywords: Rough set, multigranulation rough set, soft set, soft relation and approximation by soft binary relation

DOI: 10.3233/JIFS-201910

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Jithendra, K.B. | Kassim, Shahana T.

Article Type: Research Article

Abstract: Security of a recently proposed bitwise block cipher GIFT is evaluated in this paper. In order to mount full round attacks on the cipher, biclique cryptanalysis method is applied. Both variants of the block cipher are attacked using Independent biclique approach. For recovering the secret keys of GIFT-64, the proposed attack requires 2127.45 full GIFT-64 encryption and 28 chosen plain texts. For recovering the secret keys of GIFT-128, the proposed attack requires 2127.82 full GIFT-128 encryption and 218 chosen plain texts. The attack complexity is compared with that of other attacks proposed previously. The security level …of GIFT is also compared with that of the parent block cipher PRESENT, based on the analysis. Show more

Keywords: Block cipher, cryptanalysis, biclique, complexity

DOI: 10.3233/JIFS-189875

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Cai, LiHua | Cao, Jin | Wang, MingQiang | Zhou, Ta | Fang, HaiFeng

Article Type: Research Article

Abstract: Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane could be recognized, improving the classification performance of SVM classifier …in this layer. The loss function defined in this paper could control the depth of Linear Multi Hierarchical SVM (LMHSVM), the generalization parameters are added in the loss function and the input matrix to enhance the generalization performance of LMHSVM. The process of creating Bottle dataset by Histogram of Oriented Gradient (HOG) and Principal Component Analysis (PCA) is introduced meanwhile, reducing the data size of bottles. Experiments are conducted on LMHSVM and multiple typical classification algorithms with Bottle dataset and UCI datasets, the results indicated that LMHSVM has excellent classification performances than FNN classifier, LIBSVM (Gaussian) and GFS-AdaBoost-C in KEEL. Show more

Keywords: Recycling plastic bottles, deep learning structure, SVM, Linear multi hierarchical, extract dataset

DOI: 10.3233/JIFS-202729

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Pavan Kumar, C S | Dhinesh Babu, L D

Article Type: Research Article

Abstract: Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. …There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks. Show more

Keywords: Dementia, sentiment analysis, machine learning, FDA, feature-split, feature engineering, trapezoid membership function

DOI: 10.3233/JIFS-202874

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Chen, Heng | Li, Guanyu | Sun, Yunhao | Jiang, Wei

Article Type: Research Article

Abstract: Capturing the composite embedding representation of a multi-hop relation path is an extremely vital task in knowledge graph completion. Recently, rotation-based relation embedding models have been widely studied to embed composite relations into complex vector space. However, these models make some over-simplified assumptions on the composite relations, resulting the relations to be commutative. To tackle this problem, this paper proposes a novel knowledge graph embedding model, named QuatGE, which can provide sufficient modeling capabilities for complex composite relations. In particular, our method models each relation as a rotation operator in quaternion group-based space. The advantages of our model are twofold: …(1) Since the quaternion group is a non-commutative group (i.e., non-Abelian group), the corresponding rotation matrices of composite relations can be non-commutative; (2) The model has a more expressive setting with stronger modeling capabilities, which is flexible to model and infer the complete relation patterns, including: symmetry/anti-symmetry, inversion and commutative/non-commutative composition. Experimental results on four benchmark datasets show that the proposed method outperforms the existing state-of-the-art models for link prediction, especially on composite relations. Show more

Keywords: Knowledge graph embedding, quaternion group, link prediction

DOI: 10.3233/JIFS-202546

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Chen, Kuen-Suan | Yu, Chun-Min

Article Type: Research Article

Abstract: Many corporations purchase components from suppliers, which can reduce operating costs and enable firms to focus their resources on core advantages. Studies have indicated that process quality and manufacturing time performance are two crucial indicators for supplier selection. We used the process quality index and a manufacturing time performance index to create a dual dimensional fuzzy supplier selection model. First, the upper confidence limits of these two indices were derived, and a fuzzy membership function based on these limits was constructed. Based on the fuzzy test rules for process quality and manufacturing time performance, we divided the fuzzy supplier selection …matrix into nine evaluation zones. Using the upper confidence limits of these two indices, we created evaluation coordinates and assigned weights based on the location of the coordinates. Then, the total of all the weights was employed to form a supplier selection index for which a higher value means a higher ranking. The use of confidence limits decreased the chance of misjudgment resulting from sampling errors while the fuzzy test rules increased the applicability of the model. Consequently, the proposed model can be used to select suppliers efficiently so as to form partnerships in which corporations and suppliers can grow together. Show more

Keywords: Fuzzy supplier selection model, fuzzy membership function, manufacturing time performance, process quality, upper confidence limit

DOI: 10.3233/JIFS-202349

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Dhanani, Jenish | Mehta, Rupa | Rana, Dipti

Article Type: Research Article

Abstract: Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O  (n 2 ). However, there is a limited number of pairs consisting of strong relevance among …the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems. Show more

Keywords: Legal document recommender systems, Pairwise similarity, Graph Clustering, Semantic similarity

DOI: 10.3233/JIFS-189871

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Naderi, Katayoun | Ahari, Roya M. | Jouzdani, Javid | Amindoust, Atefeh

Article Type: Research Article

Abstract: Fierce competition in the global markets forced companies to improve the design and management of supply chains, because companies are always looking for more profit and higher customer satisfaction. The emergence of the green supply chain is one of the most important developments of the last decade. It provides an opportunity for companies to adjust their supply chains according to environmental goals and sustainability. The integrated production-inventory-routing is a new field that aims to optimize these three decision-making levels. It can be described as follow: a factory produces one or more products, and sells them to several customers (by direct …delivery or a specific customer chain). The current study aims to model a production-inventory-routing system using a system dynamics approach to design a green supply chain under uncertain conditions. For this purpose, first, the association between selected variables was determined. Then, the proposed model was validated. Finally, to identify variables with the highest influence, four scenarios were developed. The results indicated that minimum total transportation cost, the total warehouse capacity of the supply chain, and the maximum production rate are the most influential strategies to achieve ideal condition. Show more

Keywords: System dynamics, integrated production-inventory-routing problem, green supply chain, uncertainty

DOI: 10.3233/JIFS-202622

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Zhang, Kai | Zheng, Jing | Wang, Ying-Ming

Article Type: Research Article

Abstract: Case-based reasoning (CBR) is one of the most popular methods used in emergency decision making (EDM). Case retrieval plays a key role in EDM processes based on CBR and usually functions by retrieving similar historical cases using similarity measurements. Decision makers (DMs), thus, choose the most appropriate historical cases. Although uncertainty and fuzziness are present in the EDM process, in-depth research on these issues is still lacking. In this study, a heterogeneous multi-attribute case retrieval method based on group decision making (GDM) with incomplete weight information is developed. First, the case similarities between historical and target cases are calculated, and …a set of similar historical cases is constructed. Six formats of case attributes are considered, namely crisp numbers, interval numbers, linguistic variables, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (NNs) and interval-valued NNs. Next, the evaluation information from the DMs is expressed using single-valued NNs. Additionally, the evaluation utilities of similar historical cases are obtained by aggregating the evaluation information. The comprehensive utilities of similar historical cases are obtained using case similarities and evaluation utilities. In this process, the weights of incomplete information are determined by constructing optimization models. Furthermore, the most appropriate similar historical case is selected according to the comprehensive utilities. Finally, the proposed method is demonstrated using two examples; its performance is then compared with those of other similar methods to demonstrate its validity and efficacy. Show more

Keywords: Case retrieval, group decision making, single-valued neutrosophic number, incomplete weight information, emergency decision making

DOI: 10.3233/JIFS-201817

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Lalitha, S. | Gupta, Deepa

Article Type: Research Article

Abstract: Automatic recognition of human affective state using speech has been the focus of the research world for more than two decades. In the present day, with multi-lingual countries like India and Europe, population are communicating in various languages. However, majority of the existing works have put forth different strategies to recognize affect from various databases, with each comprising single language recordings. There exists a great demand for affective systems to serve the context of mixed-language scenario. Hence, this work focusses on an effective methodology to recognize human affective state using speech samples from a mixed language framework. A unique cepstral …and bi-spectral speech features derived from the speech samples classified using random forest (RF) are applied for the task. This work is first of its kind with the proposed approach validated and found to be effective on a self-recorded database with speech samples comprising from eleven various diverse Indian languages. Six different affective states of angry, fear, sad, neutral, surprise and happy are considered. Three affective models have been investigated in the work. The experimental results demonstrate the proposed feature combination in addition to data augmentation show enhanced affect recognition. Show more

Keywords: Affective state, cepstral, mixed-lingual, recognition, Indian languages

DOI: 10.3233/JIFS-189868

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Han, Lin | Han, Lu

Article Type: Research Article

Abstract: With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent …system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better. Show more

Keywords: Fuzzy theory, city brand image, visual recognition technology, intelligent system, neural network, design and development

DOI: 10.3233/JIFS-189833

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Preethi, D. | Vimala, J. | Rajareega, S. | Al-Tahan, M.

Article Type: Research Article

Abstract:  This article deals with a fuzzy hypercompositional structure called fuzzy hyperlattice ordered δ - group ( FHLO δ - G ) , the extension of the fuzzy hypercompositional structure namely fuzzy hyperlattice ordered group (FHLOG ). Using FHLO δ - G , we can involve one additional non-empty set δ with FHLOG , which helps to develop new results and applications. The structural characteristics and properties of FHLO δ - G are analysed. Furthermore, an application of FHLO δ - G …for ABO blood group system is proposed. Show more

Keywords: Lattice ordered group, fuzzy lattice ordered group, fuzzy hyperlattice, fuzzy hyperlattice ordered group, 𝒜ℬ𝒪 blood group system

DOI: 10.3233/JIFS-189853

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Wu, Jian-Zhang | Beliakov, Gleb

Article Type: Research Article

Abstract: Nonmodularity is a prominent property of capacity that deeply links to the internal interaction phenomenon of multiple decision criteria. Following the common architectures of the simultaneous interaction indices as well as of the bipartition interaction indices, in this paper, we construct and study the notion of probabilistic nonmodularity index and also its particular cases, such as Shapely and Banzhaf nonmodularity indices, which can be used to describe the comprehensive interaction situations of decision criteria. The connections and differences among three categories of interaction indices are also investigated and compared theoretically and empirically. It is shown that three types of interaction …indices have the same roots in their first and second orders, but meanwhile the nonmodularity indices have involved less amount of subsets and can be adopted to describe the interaction phenomenon in decision analysis. Show more

Keywords: Fuzzy measure, interaction representation, nonmodularity, nonadditivity, decision analysis

DOI: 10.3233/JIFS-201583

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Yang, Yimin | Hu, Di

Article Type: Research Article

Abstract: Serving is the most important hitting technique in tennis, and a good service receiving can instantly reverse the active and passive relationship between serve and receive on the tennis court, and control the rhythm of the court. The purpose of this study is to use an adaptive neuron fuzzy intelligent system to analyze some techniques of tennis serve. In this study, eight male players from the school tennis team were selected as the experimental subjects, whose sports level was above the national tennis level II. Ten weeks before the simulation test, the training time and frequency of 8 subjects were …the same. In other words, 5 times a week, 2.5 hours±0.5 hours. The work engineering of adaptive fuzzy system firstly, in the off-line modeling stage, the adaptive fuzzy system uses the rule self splitting technology to generate the initial fuzzy rules, and uses the improved adaptive neural network algorithm to optimize the calculation; then according to the error between the system input and the predicted output, the independent variable is adjusted and replaced; at the same time, the adaptive fuzzy system is further used for calculation In the process of tennis serving, the nonlinear control variables are obtained online and applied to the fuzzy system for control. Next, in the experiment, the system was used to record the body’s movement and service scores during service. The experimental results show that during the service process, the maximum trunk torsion amplitude can reach 48.26 ° and the minimum is only 5.41 ° and the service score accounts for 81.41% and 80.47% of the total scores of the two sections respectively. This shows that the fuzzy system in this study can effectively analyze the service posture and score of athletes. It is concluded that the accurate calculation and analysis of tennis serve by adaptive neuron intelligent fuzzy system in this study is conducive to improve the tennis serviceability and competition performance of players. This research has made a certain contribution to the intellectualization of sports. Show more

Keywords: Adaptive neuron, fuzzy system, tennis serve, sports technique analysis

DOI: 10.3233/JIFS-189828

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Guo, Erying

Article Type: Research Article

Abstract: With the development of globalization, people’s demand for English audio interaction is increasing. In order to overcome the shortcomings of traditional translation methods in grammatical variables, such as semantic ambiguity, quantifier errors, low translation accuracy, improve the quality and speed of English translation, and get more accurate and speed guaranteed translation, this study proposes an artificial intelligence English audio translation cross language system based on fuzzy algorithm. In this experiment, the collected analog speech signal is converted into a digital speech signal, and then, the speech features are modeled and digitized, and the whole set of speech samples are integrated …and modified to eliminate the interference caused by noise as far as possible. After that, the collected voice will be stored in the text format, and then the text will be translated to achieve English audio translation. The DNN-HMM speech recognition model and the traditional GMM-HMM speech recognition model are used to preprocess the original corpus, and the accuracy of the corpus processing is compared. After that, the accuracy and utilization of the fuzzy algorithm are evaluated between the first type TSK and the second type TSK. For speech synthesis in which the corpus lacks language, it is meaningful to explore the least amount of training data for the synthesis of acceptable speech. The experimental results show that the accuracy of the fuzzy algorithm is about 97.34%, and the utilization rate is about 98.14%. The accuracy rate of type 1 and type 2 algorithms are about 85.77% and 76.87% respectively, and the utilization rate is about 83.25% and 78.63% respectively. The fuzzy algorithm based artificial intelligence English audio translation cross language system is obviously better than the other two algorithms. Show more

Keywords: Fuzzy algorithm, artificial intelligence, english translation, cross language system

DOI: 10.3233/JIFS-189829

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Arivudainambi, D. | Pavithra, R.

Article Type: Research Article

Abstract: Wireless Sensor Network (WSN) has emerged recently due to its advancements and applications in various scientific and industrial fields. WSN consists a set of low cost and readily deployable sensors to monitor targets and recognise the physical phenomena. The principal challenge in WSN is to deploy these sensor nodes in optimal positions to achieve efficient network. Such network should satisfy the quality of service requirements in order to achieve high performance levels. Hence, this paper focuses on target Q-coverage problem where each target requires different number of sensors to monitor them. A Sequential Vertex Coloring based Sensor Placement (SVC-SP) algorithm …is proposed to determine the number of sensors required and its optimal spot to satisfy the coverage quality requirement. The SVC-SP algorithm determines sensor requirement by partitioning the target set into independent subsets depending on the target’s position and the sensor’s sensing range. Each independent set consists set of targets that are nearer in the network such that a common sensor is sufficient to monitor them. The cardinality of such independent subsets provides the sensor requirement for target coverage. The optimal spot for each target is determined by the mean positioning of the targets in each independent set. This process is repeated until the q-requirement for each target is satisfied. Further, to improve the optimal spot for sensors, the random based SVC-SP algorithm, cuckoo search based SVC-SP algorithm and the genetic algorithm based SVC-SP algorithm are utilized. The simulation results show that genetic algorithm based SVC-SP algorithm performs better than other existing algorithms. Show more

Keywords: Optimal sensor placement, target coverage, Q-coverage, vertex coloring, sequential vertex coloring

DOI: 10.3233/JIFS-191795

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Malik, Hasmat | Alotaibi, Majed A. | Almutairi, Abdulaziz

Article Type: Research Article

Abstract: The electric load forecasting (ELF) is a key area of the modern power system (MPS) applications and also for the virtual power plant (VPP) analysis. The ELF is most prominent for the distinct applications of MPS and VPP such as real-time analysis of energy storage system, distributed energy resources, demand side management and electric vehicles etc. To manage the real-time challenges and map the stable power demand, in different time steps, the ELF is evaluated in yearly, monthly, weekly, daily, and hourly, etc. basis. In this study, an intelligent load predictor which is able to forecast the electric load for …next month or day or hour is proposed. The proposed approach is a hybrid model combining empirical mode decomposition (EMD) and neural network (NN) for multi-step ahead load forecasting. The model performance is demonstrated by suing historical dataset collected form GEFCom2012 and GEFCom2014. For the demonstration of the performance, three case studies are analyzed into two categories. The demonstrated results represents the higher acceptability of the proposed approach with respect to the standard value of MAPE (mean absolute percent error). Show more

Keywords: Feature extraction, decomposition, intelligent data analytics, short-term forecasting, power system planning

DOI: 10.3233/JIFS-189775

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Singh, Rajvir | Rama Krishna, C. | Sharma, Rajnish | Vig, Renu

Article Type: Research Article

Abstract: Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense …to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are more by 56% and 41%, respectively. Show more

Keywords: Wireless sensor network, clustered architecture

DOI: 10.3233/JIFS-192177

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Ramírez-Mendoza, Abigail María Elena | Yu, Wen | Li, Xiaoou

Article Type: Research Article

Abstract: The identification of nonlinear systems is a complex task. This article presents a method comparison between the new Fuzzy Adaptive Neurons (FAN), Radial Basis Function Network (RBF), and Adaptive Network-Based Fuzzy Inference System (ANFIS). The nonlinear systems presented are solved with stable and optimal learning. The simulation of the results for two models presented, are carried out in Matlab ® , the optimization of the system identification for the first and second systems were obtained with great success.

Keywords: Fuzzy adaptive neurons, identification of systems, learning algorithm, level sets, ANFIS, nonlinear systems

DOI: 10.3233/JIFS-201782

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Indu, V. | Thampi, Sabu M.

Article Type: Research Article

Abstract: Social networks have emerged as a fertile ground for the spread of rumors and misinformation in recent times. The increased rate of social networking owes to the popularity of social networks among the common people and user personality has been considered as a principal component in predicting individuals’ social media usage patterns. Several studies have been conducted to study the psychological factors influencing the social network usage of people but only a few works have explored the relationship between the user’s personality and their orientation to spread rumors. This research aims to investigate the effect of personality on rumor spread …on social networks. In this work, we propose a psychologically-inspired fuzzy-based approach grounded on the Five-Factor Model of behavioral theory to analyze the behavior of people who are highly involved in rumor diffusion and categorize users into the susceptible and resistant group, based on their inclination towards rumor sharing. We conducted our experiments in almost 825 individuals who shared rumor tweets on Twitter related to five different events. Our study ratifies the truth that the personality traits of individuals play a significant role in rumor dissemination and the experimental results prove that users exhibiting a high degree of agreeableness trait are more engaged in rumor sharing activities and the users high in extraversion and openness trait restrain themselves from rumor propagation. Show more

Keywords: Social networks, rumor propagation, personality, five-factor model, user behavior analysis

DOI: 10.3233/JIFS-189864

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Banerjee, Tulika | Yagnik, Niraj | Hegde, Anusha

Article Type: Research Article

Abstract: Human communication is not limited to verbal speech but is infinitely more complex, involving many non-verbal cues such as facial emotions and body language. This paper aims to quantitatively show the impact of non-verbal cues, with primary focus on facial emotions, on the results of multi-modal sentiment analysis. The paper works with a dataset of Spanish video reviews. The audio is available as Spanish text and is translated to English while visual features are extracted from the videos. Multiple classification models are made to analyze the sentiments at each modal stage i.e. for the Spanish and English textual datasets as …well as the datasets obtained upon coalescing the English and Spanish textual data with the corresponding visual cues. The results show that the analysis of Spanish textual features combined with the visual features outperforms its English counterpart with the highest accuracy difference, thereby indicating an inherent correlation between the Spanish visual cues and Spanish text which is lost upon translation to English text. Show more

Keywords: Multimodal analysis, natural language processing, non-verbal cues, classification algorithms, cultural-shift

DOI: 10.3233/JIFS-189870

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Kartik, P. V. S. M. S. | Sumanth, Konjeti B. V. N. S. | Sri Ram, V. N. V. | Jeyakumar, G.

Article Type: Research Article

Abstract: The encoding of a message is the creation of the message. The decoding of a message is how people can comprehend, and decipher the message. It is a procedure of understanding and interpretation of coded data into a comprehensible form. In this paper, a self-created explicitly defined function for encoding numerical digits into graphical representation is proposed. The proposed system integrates deep learning methods to get the probabilities of digit occurrence and Edge detection techniques for decoding the graphically encoded numerical digits to numerical digits as text. The proposed system’s major objective is to take in an Image with digits …encoded in graphical format and give the decoded stream of digits corresponding to the graph. This system also employs relevant pre-processing techniques to convert RGB to text and image to Canny image. Techniques such as Multi-Label Classification of images and Segmentation are used for getting the probability of occurrence. The dataset is created, on our own, that consists of 1000 images. The dataset has the training data and testing data in the proportion of 9 : 1. The proposed system was trained on 900 images and the testing was performed on 100 images which were ordered in 10 classes. The model has created a precision of 89% for probability prediction. Show more

Keywords: Image processing, deep learning, convolutional neural network, multi-label classification, image segmentation, edge detection, contours, graphical encoding

DOI: 10.3233/JIFS-189859

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Sil, Riya | Alpana, | Roy, Abhishek | Dasmahapatra, Mili | Dhali, Debojit

Article Type: Research Article

Abstract: It is essential to provide a structured data feed to the computer to accomplish any task so that it can process flawlessly to generate the desired output within minimal computational time. Generally, computer programmers should provide a structured data feed to the computer program for its successful execution. The hardcopy document should be scanned to generate its corresponding computer-readable softcopy version of the file. This process also proves to be a budget-friendly approach to disengage human resources from the entire process of record maintenance. Due to this automation, the workload of existing manpower is reduced to a significant level. This …concept may prove beneficial for the delivery of any type of services to the ultimate beneficiary (i.e., citizen) in a minimal time frame. The administration has to deal with various issues of citizens due to the pressure of a huge population who seek legal help to resolve their issues, thereby leading to the filing of large numbers of pending legal cases at several courts of the country. To assist the victims with prompt delivery of justice and legal professionals in reducing their workload, this paper proposed a machine learning based automated legal model to enhance the efficiency of the legal support system with an accuracy of 94%. Show more

Keywords: Machine learning, image processing, document analysis, argument, text summarization

DOI: 10.3233/JIFS-189867

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Islam, Sk Rabiul | Pal, Madhumangal

Article Type: Research Article

Abstract: The Zagreb index (ZI) is a very important graph parameter and it is extensively used in molecular chemistry, spectral graph theory, network theory and several fields of mathematics and chemistry. In this article, the first ZI is studied for several fuzzy graphs like path, cycle, star, fuzzy subgraph, etc. and presented an ample number of results. Also, it is established that the complete fuzzy graph has maximal first ZI among n -vertex fuzzy graphs. Some bounds of first ZI are discussed for Cartesian product, composition, union and join of two fuzzy graphs. An algorithm has been designed to calculate the …first ZI of a fuzzy graph. At the end of the article, a multi-criteria decision making (MCDM) method is provided using the first ZI of a fuzzy graph to find the best employee in a company. Also a comparison is provided among related indices on the result of application and shown that our method gives better results. Show more

Keywords: Fuzzy graph, First Zagreb index, MCDM

DOI: 10.3233/JIFS-201293

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Tripathi, Diwakar | Ramachandra Reddy, B. | Padmanabha Reddy, Y.C.A. | Shukla, Alok Kumar | Kumar, Ravi Kant | Sharma, Neeraj Kumar

Article Type: Research Article

Abstract: Credit scoring plays a vital role for financial institutions to estimate the risk associated with a credit applicant applied for credit product. It is estimated based on applicants’ credentials and directly affects to viability of issuing institutions. However, there may be a large number of irrelevant features in the credit scoring dataset. Due to irrelevant features, the credit scoring models may lead to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with large number of features. In this work, we emphasized on the role of feature selection to enhance the predictive …performance of credit scoring model. Towards to feature selection, Binary BAT optimization technique is utilized with a novel fitness function. Further, proposed approach aggregated with “Radial Basis Function Neural Network (RBFN)”, “Support Vector Machine (SVM)” and “Random Forest (RF)” for classification. Proposed approach is validated on four bench-marked credit scoring datasets obtained from UCI repository. Further, the comprehensive investigational results analysis are directed to show the comparative performance of the classification tasks with features selected by various approaches and other state-of-the-art approaches for credit scoring. Show more

Keywords: BAT algorithm, credit score, feature selection

DOI: 10.3233/JIFS-189876

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Li, Hongyan | Wu, Peng | Zhou, Ligang | Chen, Huayou

Article Type: Research Article

Abstract: The consensus problem is a very important aspect of group decision making (GDM). In order to deal with the multiple criteria group decision consensus problem in the interval type-2 fuzzy environment, a consensus measure based on similarity measurement is proposed in this paper. In this paper, first, a new similarity measure of two interval type-2 fuzzy sets (IT2FSs) is defined and the consensus measure is defined by the similarity measure between two IT2FSs. Then, a new consensus feedback mechanism is proposed. In the stage of alternatives selection, the entropy of IT2FSs is defined, and the entropy weight method is used …to determine the weights of the criteria. Finally, the feasibility of the method proposed in this paper is illustrated by a comprehensive evaluation of old-age institutions. Show more

Keywords: IT2FSs, similarity measure, consensus, MCGDM

DOI: 10.3233/JIFS-201979

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Nair, Rekha R. | Singh, Tripty | Sankar, Rashmi | Gunndu, Klement

Article Type: Research Article

Abstract:  The multi-sensor, multi-modal, composite design of medical images merged into a single image, contributes to identifying features that are relevant to medical diagnoses and treatments. Although, current image fusion technologies, including conventional and deep learning algorithms, can produce superior fused images, however, they will require huge volumes of images of various modalities. This solution may not be viable for some situations, where time efficiency is expected or the equipment is inadequate. This paper addressed a modified end-to-end Generative Adversarial Network(GAN), termed Loss Minimized Fusion Generative Adversarial Network (LMF-GAN), a triple ConvNet deep learning architecture for the fusion of medical images …with a limited sampling rate. The encoding network is combined with a convolutional neural network layer and a dense block called GAN, in contrast to conventional convolutional networks. The loss is minimized by training GAN’s discriminator with all the source images by learning more parameters to generate more features in the fused image. The LMF-GAN can produce fused images with clear textures through adversarial training of the generator and discriminator. The proposed fusion method has the ability to achieve state-of-the-art quality in objective and subjective evaluation, in comparison with current fusion methods. The model has experimented with standard data sets. Show more

Keywords: Medical image fusion, generative adversarial network, generator, discriminator, ADAM optimizer

DOI: 10.3233/JIFS-189860

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Jia, Xiang | Wang, Xinfan | Zhu, Yuanfang | Zhou, Lang | Zhou, Huan

Article Type: Research Article

Abstract: This study proposes a two-sided matching decision-making (TSMDM) approach by combining the regret theory under the intuitionistic fuzzy environment. At first, according to the Hamming distance of intuitionistic fuzzy sets and regret theory, superior and inferior flows are defined to describe the comparative preference of subjects. Hereafter, the satisfaction degrees are obtained by integrating the superior and inferior flows of the subjects. The comprehensive satisfaction degrees are calculated by aggregating the satisfaction degrees, based on which, a multi-objective TSMDM model is built. Furthermore, the multi-objective TSMDM model is converted to a single-objective model, the optimal solution of the latter is …derived. Finally, an illustrative example and several analyses are provided to verify the feasibility and the effectiveness of the proposed approach. Show more

Keywords: Multi-criteria decision-making, intuitionistic fuzzy sets, regret theory, two-sided matching, optimal model

DOI: 10.3233/JIFS-202720

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Jaiseeli, C. | Raajan, N.R.

Article Type: Research Article

Abstract: The neurological disorders are developed in adults due to reduced visual perception. Opto Kinetic Nystagmus (OKN) is a clinical method to detect visual perception. For objective measurements, a computational algorithm based OKN detection is preferable rather than clinical practice. In this paper, a memory-efficient Subsampled Lucas-Kanade Optical Flow (SLKOF) is proposed. The proposal employs the Subsampling of images for various levels. The proposal deals with the computation of OKN gain for different image Subsampling factors using the MATLAB platform. The experimental set up to observe OKN is done using computer-based rotation control of the drum through a stepper motor. The …results are compared with the well established Lucas-Kanade (LK) method for Optical flow. It is observed that OKN gain corresponds to 1/4th of a subsampled image of the SLKOF method correlates with the LK method for the majority of the cases. This validation evidently elucidates that the proposal is computationally efficient. Show more

Keywords: Opto Kinetic Nystagmus, Lucas-Kanade Optical Flow, eye movements, subsampling, objective method

DOI: 10.3233/JIFS-189849

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Chirgaiya, Sachin | Sukheja, Deepak | Shrivastava, Niranjan | Rawat, Romil

Article Type: Research Article

Abstract: The decisions and approaches of renowned personality used to impress the real world are to a great extent adapted to how others have seen or assessed the world with opinion and sentiment. Examples could be any opinion and sentiment of people view about Movie audits, Movie surveys, web journals, smaller scale websites, and informal organizations. In this research classifies the movie review into its correct category, classifier model is proposed that has been trained by applying feature extraction and feature ranking. The focus is on how to examine the sentiment expression and classification of a given movie review on a …scale of (–) negative and (+) positive sentiments analysis for the IMDB movie review database. Due to the lack of grammatical structures to comments on movies, natural language processing (NLP) has been used to implement proposed model and experimentation is performed to compare the present study with existing learning models. At the outset, our approach to sentiment classification supplements the existing movie rating systems used across the web to an accuracy of 97.68%. Show more

Keywords: Machine learning, artificial intelligence, movie reviews, sentiment analysis

DOI: 10.3233/JIFS-189866

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Ojha, Chinmayee | Venugopalan, Manju | Gupta, Deepa

Article Type: Research Article

Abstract: Fast growth of technology and the tremendous growth of population has made millions of people to be active participants on social networking forums. The experiences shared by the participants on different websites is highly useful not only to customers to make decisions but also helps companies to maintain sustainability in businesses. Sentiment analysis is an automated process to analyze the public opinion behind certain topics. Identifying targets of user’s opinion from text is referred to as aspect extraction task, which is the most crucial and important part of Sentiment Analysis. The proposed system is a rule-based approach to extract aspect …terms from reviews. A sequence of patterns is created based on the dependency relations between target and its nearby words. The system of rules works on a benchmark of dataset for Hindi shared by Akhtar et al., 2016. The evaluated results show that the proposed approach has significant improvement in extracting aspects over the baseline approach reported on the same dataset. Show more

Keywords: Sentiment analysis (SA), aspect term, sequential pattern, dependency parser (DP), part of speech (POS)

DOI: 10.3233/JIFS-189869

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Ghosh, Arijit | Dey, Munmun | Mondal, Sankar Prasad | Shaikh, Azharuddin | Sarkar, Anirban | Chatterjee, Banashree

Article Type: Research Article

Abstract: E-Rickshaw is an E-vehicle that has three wheels, a rechargeable battery driven electric motor as engine. E-rickshaw has become very popular due to low operating cost, low maintenance cost, eco-friendliness and ease of driving. It is perfect for small distance transport. As a last mile connector, it has transformed the public transport system in India. The low cost electric vehicle carries enough people to make a decent income and hence has become a source of livelihood for many. For considering the issues in this paper, detailed attributes of E-rickshaw are studied and Analytical Hierarchy Process (AHP) has been applied to …calculate criteria weights for the sorted attributes. Subsequently, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi Criteria Decision Making (MCDM) technique has been applied for the selection of best E-Rickshaw. In this paper, sensitivity analysis and comparative analysis have been conducted for further insight. Show more

Keywords: AHP method, E-Rickshaws selection, sensitivity analysis, TOPSIS method

DOI: 10.3233/JIFS-202406

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Raj, Vinoth | Janakiraman, Siva | Amirtharajan, Rengarajan

Article Type: Research Article

Abstract: Digitized forms of images do widely used for medical diagnostics. To maintain the privacy of an individual in e-health care applications, securing the medical image becomes essential. Hence exclusive encryption algorithms have been developed to protect the confidentiality of medical images. As an alternative to software implementations, the realization of image encryption architectures on hardware platforms such as FPGA offers significant benefit with its reconfigurable feature. This paper presents a lightweight image encryption scheme for medical image security feasible to realize as concurrent architectural blocks on reconfigurable hardware like FPGA to achieve higher throughput. In the proposed encryption scheme, Lorentz …attractor’s chaotic keys perform the diffusion process. Simultaneously, the pseudo-random memory addresses obtained from a Linear Feedback Shift Register (LFSR) circuit accomplishes the confusion process. The proposed algorithm implemented on Intel Cyclone IV FPGA (EP4CE115F29C7) analyzed the optimal number of concurrent blocks to achieve a tradeoff among throughput and resource utilization. Security analyses such as information entropy, histogram, correlation, and PSNR confirms the algorithm’s encryption quality. The strength of diffusion keys was ensured by randomness verification through the standard test suite from the National Institute of Standards and Technology (NIST). The proposed scheme has a larger keyspace of 2384 that guarantees good confusion through near-zero correlation, and successful diffusion with a PSNR of <5 dB towards the statistical attacks. Based on the hardware analysis, the optimal number of concurrent architectural blocks (2 N ) on the chosen FPGA to achieve higher throughput (639.37 Mbps), low power dissipation (138.85 mW), minimal resource utilization (1268 Logic Elements) and better encryption quality for the proposed algorithm is recommended as 4 (with N = 2). Show more

Keywords: Concurrency, lightweight, lorentz attractor, FPGA and encryption

DOI: 10.3233/JIFS-200203

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Wang, Huiru | Zhou, Zhijian

Article Type: Research Article

Abstract: Multi-view learning utilizes information from multiple representations to advance the performance of categorization. Most of the multi-view learning algorithms based on support vector machines seek the separating hyperplanes in different feature spaces, which may be unreasonable in practical application. Besides, most of them are designed to balanced data, which may lead to poor performance. In this work, a novel multi-view learning algorithm based on maximum margin of twin spheres support vector machine (MvMMTSSVM) is introduced. The proposed method follows both maximum margin principle and consensus principle. By following the maximum margin principle, it constructs two homocentric spheres and tries to …maximize the margin between the two spheres for each view separately. To realize the consensus principle, the consistency constraints of two views are introduced in the constraint conditions. Therefore, it not only deals with multi-view class-imbalanced data effectively, but also has fast calculation efficiency. To verify the validity and rationlity of our MvMMTSSVM, we do the experiments on 24 binary datasets. Furthermore, we use Friedman test to verify the effectiveness of MvMMTSSVM. Show more

Keywords: Multi-view learning, twin spheres, SVM, maximum margin, consensus principle

DOI: 10.3233/JIFS-202427

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Aydoğmuş, Hacer Yumurtacı | Kamber, Eren | Kahraman, Cengiz

Article Type: Research Article

Abstract: The purpose of this study is to develop an extension of CODAS method using picture fuzzy sets. In this respect, a new methodology is introduced to figure out how picture fuzzy numbers can be applied to CODAS method. COmbinative Distance-based Assessment (CODAS) is a new MCDM method proposed by Ghorabaee et al. Picture fuzzy sets (PFSs) are a new extension of ordinary fuzzy sets for representing human’s judgments having possibility more than two answers such as yes, no, refusal and neutral. Compared with other studies, the proposed method integrates multi-criteria decision analysis with picture fuzzy uncertainty based on Euclidean and …Taxicab distances and negative ideal solution. ERP system selection problem is handled as the application area of the developed method, picture fuzzy CODAS. Results indicate that the new proposed method finds meaningful rankings through picture fuzzy sets. Comparative analyzes show that the presented method gives successful and robust results for the solutions of MCDM problems under fuzziness. Show more

Keywords: Fuzzy, picture fuzzy sets, CODAS method, ERP selection

DOI: 10.3233/JIFS-202564

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Shahbazi, Zeinab | Byun, Yung-Cheol

Article Type: Research Article

Abstract: Understanding the real-world short texts become an essential task in the recent research area. The document deduction analysis and latent coherent topic named as the important aspect of this process. Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are suggested to model huge information and documents. This type of contexts’ main problem is the information limitation, words relationship, sparsity, and knowledge extraction. The knowledge discovery and machine learning techniques integrated with topic modeling were proposed to overcome this issue. The knowledge discovery was applied based on the hidden information extraction to increase the suitable dataset for further analysis. …The integration of machine learning techniques, Artificial Neural Network (ANN) and Long Short-Term (LSTM) are applied to anticipate topic movements. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. We demonstrate general information from different techniques applied in short text topic modeling. We proposed three categories based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation using representative design and analysis of all categories’ performance in different tasks. Finally, the proposed system evaluates with state-of-art methods on real-world datasets, comprises them with long document topic modeling algorithms, and creates a classification framework that considers further knowledge and represents it in the machine learning pipeline. Show more

Keywords: Machine Learning, knowledge discovery, Topic Modeling, Latent Dirichlet Allocation, Short Text, Long Short Term Memory

DOI: 10.3233/JIFS-202545

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Chen, Xiaojun | Jia, Shengbin | Ding, Ling | Xiang, Yang

Article Type: Research Article

Abstract: Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson , spouse Of , Ryan Reynolds ) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransRILP , which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming. Specifically, we …utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransRILP can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on [email protected] Show more

Keywords: Knowledge graph reasoning, temporal information, temporal consistency constraints, integer linear programming

DOI: 10.3233/JIFS-210064

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Lu, Hanchuan | Khalil, Ahmed Mostafa | Alharbi, W. | El-Gayar, M. A.

Article Type: Research Article

Abstract:  In this article, we propose a novel concept of the generalized picture fuzzy soft set by combining the picture fuzzy soft set and the fuzzy parameter set. For possible applications, we explain five kinds of operations (e.g., subset, equal, union, intersection, and complement) based on generalized picture fuzzy soft sets. Then, we establish several theoretical operations of generalized picture fuzzy soft sets. In addition, we present the new type by using the AND operation of the generalized picture fuzzy soft set for fuzzy decision-making and clarify its applicability with a numerical example. Finally, we give a comparison between the picture …fuzzy soft set theory and the generalized picture fuzzy soft set theory. It is shown that our proposed (i.e., generalized picture fuzzy soft set theory) is viable and provide decision makers a more mathematical insight before making decisions on their options. Show more

Keywords: Picture fuzzy set, soft set, generalized picture fuzzy soft set, Algorithm 1, Algorithm 2, decision-making

DOI: 10.3233/JIFS-201706

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Priyanga, V.T | Sanjanasri, J.P | Menon, Vijay Krishna | Gopalakrishnan, E.A | Soman, K.P

Article Type: Research Article

Abstract: The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine …learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis. Show more

Keywords: Fake news, social media, word embedding, cosine similarity, Auto-encoders, network analysis

DOI: 10.3233/JIFS-189865

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Aarthi, R. | Amudha, J.

Article Type: Research Article

Abstract: Computer vision research aims at building models which mimic human systems. The recent development in visual information have been used to derive computational models which address a variety of applications. Biological models help to identify the salient objects in the image. But, the identification of non-salient objects in a heterogeneous environment is a challenging task that requires a better understanding of the visual system. In this work, a weight modulation based top-down model is proposed that integrates the visual features that depend on its importance for the target search application. The model is designed to learn the optimal weights such …that it biases the features of the target from the other surrounding regions. Experimental analysis is performed on various scenes on a standard dataset with the selected object in the scene. Metrics such as area under curve, average hit number and correlation reveal that the method is more suitable in target identification, by suppressing the other region. Show more

Keywords: Target search, visual attention, saliency, top down approach

DOI: 10.3233/JIFS-189863

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Akila, K. | Indra Priyadharshini, S. | Ulaganathan, Pradheeba | Prem Priya, P. | Yuvasri, B. | Suriya Praba, T. | Veeramuthuvenkatesh,

Article Type: Research Article

Abstract: The primary objective is to identify and segments the multiple, partly occluded objects in the image. The subsequent stage carry out our approach, primarily start with frame conversion. Next in the preprocessing stage, the Gaussian filter is employed for image smoothening. Then from the preprocessed image, Multi objects are segmented through modified ontology-based segmentation, and the edge is detected from the segmented images. After that, from the edge detected frames area is extracted, which results in object detected frames. In the feature extraction stage, attributes such as area, contrast, correlation, energy, homogeneity, color, perimeter, circularity are extorted from the detected …objects. The objects are categorized as human or other objects (bat/ball) through the feed-forward back propagation neural network classifier (FFBNN) based upon the extracted attributes. Show more

Keywords: Object segmentation, gaussian filtering, object classification, object detection, feature extraction, ontology

DOI: 10.3233/JIFS-189862

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Rao, Adish | Mysore, Aniruddha | Ajri, Siddhanth | Guragol, Abhishek | Sarkar, Poulami | Srinivasa, Gowri

Article Type: Research Article

Abstract: We present an automated approach to segment key structures of the eye, viz., the iris, pupil and sclera in images obtained using an Augmented Reality (AR)/ Virtual Reality (VR) application. This is done using a two-step classifier: In the first step, we use an auto encoder-decoder network to obtain a pixel-wise classification of regions that comprise the iris, sclera and the background (image pixels that are outside the region of the eye). In the second step, we perform a pixel-wise classification of the iris region to delineate the pupil. The images in the study are from the OpenEDS challenge and …were used to evaluate both the accuracy and computational cost of the proposed segmentation method. Our approach achieved a score of 0.93 on the leaderboard, outperforming the baseline model by achieving a higher accuracy and using a smaller number of parameters. These results demonstrate the great promise pipelined models hold along with the benefit of using domain-specific processing and feature engineering in conjunction with deep-learning based approaches for segmentation tasks. Keywords Show more

DOI: 10.3233/JIFS-189858

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Chandra Sekhar Author, P. N. R. L. | Shankar Author, T. N.

Article Type: Research Article

Abstract: In the era of digital technology, it becomes easy to share photographs and videos using smartphones and social networking sites to their loved ones. On the other hand, many photo editing tools evolved to make it effortless to alter multimedia content. It makes people accustomed to modifying their photographs or videos either for fun or extracting attention from others. This altering brings a questionable validity and integrity to the kind of multimedia content shared over the internet when used as evidence in Journalism and Court of Law. In multimedia forensics, intense research work is underway over the past two decades …to bring trustworthiness to the multimedia content. This paper proposes an efficient way of identifying the manipulated region based on Noise Level inconsistencies of spliced mage. The spliced image segmented into irregular objects and extracts the noise features in both pixel and residual domains. The manipulated region is then exposed based on the cosine similarity of noise levels among pairs of individual objects. The experimental results reveal the effectiveness of the proposed method over other state-of-art methods. Show more

Keywords: Splicing localization, object segmentation, residual images, noise level inconsistencies, cosine dissimilarity

DOI: 10.3233/JIFS-189861

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Wei, Shanting | Zhang, Zhuo | Chen, Xintong

Article Type: Research Article

Abstract: Network-based incubation has undergone rapid developments and the incubation mechanism has begun to change recently. To incentive the start-ups on the basis of ensuring its own interests, the incubator need to design a feasible contract. According to network theory, a single network cannot adequately describe the heterogeneous alliances of incubated start-ups in the business incubator. Therefore, by constructing super-network structure of incubated start-ups, this paper designs two types of linear incentive contracts and uses numerical simulation to further discuss the model. The results indicate that the business incubator should design the contract according to the different capability levels and risk …preference degree of start-ups: linear screening contract (LSC) is more effective to motivate the incubated start-ups to improve the capability, while the incentive effect will be weakened by the increasing proportion of high-capability start-ups; for high risk-preference start-ups, linear pooling contract (LPC) is superior than LSC. The results can serve as a theoretical direction for the business incubator to effectively distinguish different capability levels of start-ups and make better decision on contract design to motivate start-ups on the basis of ensuring the maximization of its own utility. Show more

Keywords: Business incubator, start-up, super-network, dynamic capability, cooperative network, knowledge network

DOI: 10.3233/JIFS-202279

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Sabahi, Farnaz | Akbarzadeh–T., Mohammad-R.

Article Type: Research Article

Abstract: It would be hard to deny the importance of fuzzy number ranking in fuzzy-based applications. The definition of fuzzy ranking, however, evades an easy description due to the overlapping of fuzzy sets. While many researchers have addressed this subject, close examination reveals that their results suffer from one or more shortcomings such as image-ranking problems or ranking two equally embedded fuzzy numbers with the same centroid and different spreads. This paper proposes a new fast and straightforward computational approach to ranking fuzzy numbers that aims to overcome such problems. The proposed approach considers several important factors such as spread, skewness …and center, in addition to human intuition. Further, the proposed ranking approach involves a composition of these factors as demonstrated in the several examples provided and in comparison with other existing approaches. Show more

Keywords: Center, human intuition, skewness, spread, ranking fuzzy numbers

DOI: 10.3233/JIFS-201591

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Xu, Di | Wang, Zhili

Article Type: Research Article

Abstract: This paper proposes a better semi-supervised semantic segmentation network using an improved generative adversarial network. It is important for the discriminator on the pixel level to know whether it correctly distinguishes the predicted probability map. However, currently there is no correlation between the actual credibility and the confidence map generated by the pixel-level discriminator. We study this problem and a new network is proposed, which includes one generator and two discriminators. One of the discriminators can output more reliable confidence maps on the pixel level and the other is trained to generate the probability on the image level, which is …used as the dynamic threshold in the semi-supervised module instead of being set manually. In addition, the trusted region shared by the two discriminators is used to provide the semi-supervised reference. Through experiments on the PASCAL VOC 2012 and Cityscapes datasets, the proposed network brings better gains, proving the effectiveness of the network. Show more

Keywords: Semi-supervise semantic segmentation, generative adversarial network, confidence map

DOI: 10.3233/JIFS-202220

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: AnithaKumari, T. | Venkateswarlu, B. | Akilbasha, A.

Article Type: Research Article

Abstract: An innovative method, namely modified slice-sum method using the principle of zero point method is proposed for finding an optimal solution to fully rough interval integer solid transportation problems (FRIISTP). The proposed method yields an optimal solution to the fully rough interval integer solid transportation problem directly. In this method, there is no necessity to find an initial basic feasible solution to FRIISTP and also need not to use the existing MODI and stepping stone methods for testing the optimality to improve the basic feasible solution to the FRIISTP but directly obtain an optimal solution to the given FRIISTP by …using the proposed method. The optimal values of decision variables and the objective function of the fully rough interval integer solid transportation problems provided by the proposed method are rough interval integers. The advantages of the proposed method over existing method are discussed in the context of an application example. The modified slice-sum method has been applied to calculate the optimal compromise solutions of FRIISTP, and then it was solved by using TORA software. The proposed method can be served as an appropriate tool for the decision makers when they are handling logistic models of real life situations involving three items with rough interval integer parameters. Show more

Keywords: Solid transportation problem, rough interval integer, optimal solution, conveyance

DOI: 10.3233/JIFS-202373

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Mousa, A.A. | Higazy, M. | Abdel-Khalek, S. | Hussein, Mohamed A. | Farouk, Ahmed

Article Type: Research Article

Abstract: The application and analysis of effective blood supply chain network under natural disaster imposed many critical challenges which addressed through an optimization of multiple objectives functions. In this article, relies on reference point algorithm, a user-preference based enriched swarm optimization algorithm is proposed where, inner reference points were produced depending on the perturbed reference point. For each inner reference point, weakly/ɛ -properly Pareto optimal solution was generated using augmented achievement function. All the generated solutions (points) are presented as potential positions for particles in the particle swarm optimization PSO. The proposed algorithm has been reinforced with a novel chaotic contraction …operator to retain the feasibility of the particles. To prove the validity of our algorithm, the obtained results are compared with true Pareto optimal front and three of the most salient evolutionary algorithms using inverted generational distance metric IGD. In addition it was implement to detect the most cost and time efficient blood supply chain to provide the required blood types demand on the blood transfusion center in emergence situation, where, it is required to solve this real life application with predefined supply time and predefined supply cost, which is considered as reference point to get the nearby Pareto optimal solution. By the experimental outcomes, we proved that the proposed algorithm is capable to find the set of Paetro optimal solutions nearby the predefined reference points. Show more

Keywords: Particle swam optimization, reference point, multi-objective optimization, blood supply chain

DOI: 10.3233/JIFS-202529

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Remya Revi, K. | Wilscy, M. | Antony, Rahul

Article Type: Research Article

Abstract: Forged portraits of people are widely used for creating deceitful propaganda of individuals or events in social media, and even for cooking up fake pieces of evidence in court proceedings. Hence, it is very important to find the authenticity of the images, and image forgery detection is a significant research area now. This work proposes an ensemble learning technique by combining predictions of different Convolutional Neural Networks (CNNs) for detecting forged portrait photographs. In the proposed method seven different pretrained CNN architectures such as AlexNet, VGG-16, GoogLeNet, Res-Net-18, ResNet-101, Inception-v3, and Inception-ResNet-v2 are utilized. As an initial step, we fine-tune …the seven pretrained networks for portrait forgery detection with illuminant maps of images as input, and then uses a majority voting ensemble scheme to combine predictions from the fine-tuned networks. Ensemble methods had been found out to be good for improving the generalization capability of classification models. Experimental analysis is conducted using two publicly available portrait splicing datasets (DSO-1 and DSI-1). The results show that the proposed method outperforms the state-of-the-art methods using traditional machine learning techniques as well as the methods using single CNN classification models. Show more

Keywords: Image splicing detection, deep learning, convolutional neural networks, transfer learning, ensemble classifier

DOI: 10.3233/JIFS-189857

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Zeng, Qingtian | Zhao, Xishi | Hu, Xiaohui | Duan, Hua | Zhao, Zhongying | Li, Chao

Article Type: Research Article

Abstract: Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE ) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, …which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models. Show more

Keywords: Sentiment analysis, word embedding, classification, representation learning

DOI: 10.3233/JIFS-201993

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Ahmed, Dliouah | Dai, Binxiang

Article Type: Research Article

Abstract: In this paper, we give a new notion of the picture m-polar fuzzy sets (Pm-PFSs) (i.e, combination between the picture fuzzy sets (PFSs) and the m-polar fuzzy sets (m-PFSs)) and study several of the structure operations including subset, equal, union, intersection, and complement. After that, the basic definitions, theorems, and examples on Pm-PFSs are explained. Also, the certain distance between two Pm-PFSs and a novel similarity measure for Pm-PFSs based on distances are defined. MCDM is animated for Pm-PFS data that take into account the distances for the best alternative (solution) by proposed an application of similarity measure for Pm-PFSs …in decision-making. Finally, we construct a new methodology to extend the TOPSIS to Pm-PFS in which capable of different objects recognizing belonging to the same family and illustrate its applicability via a numerical example. Show more

Keywords: Picture m-polar fuzzy set, distance measure, similarity measure, decision-making, multi-expert TOPSIS technique

DOI: 10.3233/JIFS-202309

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Goel, Sonia | Tushir, Meena

Article Type: Research Article

Abstract: Semi-supervised clustering technique partitions the unlabeled data based on prior knowledge of labeled data. Most of the semi-supervised clustering algorithms exist only for the clustering of complete data, i.e., the data sets with no missing features. In this paper, an effort has been made to check the effectiveness of semi-supervised clustering when applied to incomplete data sets. The novelty of this approach is that it considers the missing features along with available knowledge (labels) of the data set. The linear interpolation imputation technique initially imputes the missing features of the data set, thus completing the data set. A semi-supervised clustering …is now employed on this complete data set, and missing features are regularly updated within the clustering process. In the proposed work, the labeled percentage range used is 30, 40, 50, and 60% of the total data. Data is further altered by arbitrarily eliminating certain features of its components, which makes the data incomplete with partial labeling. The proposed algorithm utilizes both labeled and unlabeled data, along with certain missing values in the data. The proposed algorithm is evaluated using three performance indices, namely the misclassification rate, random index metric, and error rate. Despite the additional missing features, the proposed algorithm has been successfully implemented on real data sets and showed better/competing results than well-known standard semi-supervised clustering methods. Show more

Keywords: Semi-supervised clustering, labeled and unlabeled data, incomplete data, and interpolation

DOI: 10.3233/JIFS-189744

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Durairaj, M. | Hirudhaya Mary Asha, J.

Article Type: Research Article

Abstract: Biometric features are used to verify the people identity in the living places like smart apartments. To increase the chance of classification and recognition rate, the recognizing procedure contains various steps such as detection of silhouette from the gait profile, silhouette segmentation, reading features from the silhouette, classification of features and finally recognition of person using its probability value. Person recognition accuracy will be oscillated and declined due to blockage, radiance and posture variance problems. In the proposed work, the gait profile will be formed by capturing the gait of a targeted person in stipulated time to reach the destination. …From the profile the silhouettes are detected using frame difference and segmented from the background using immediate thresholding and features are extracted from the silhouette using gray-level covariance matrix and optimized feature set is formed using PSO. These optimized features are fused, trained and classified using nearest neighbor support vectors. The fuzzy probability method is used for recognizing the person based on the probability value of the authentic and imposter scores. The relationship between the CMS, TPR, TNR and F-rate are calculated for 1 : 1 matcher from the gallery set. The performance of the classifiers are found to be perfect by plotting the DET graph and ROC curve. The proposed fuzzy probability theory is mingled with GLCMPSO and NSFV method for human recognition purpose. The performance of the proposed is proved to be acceptable for recognition with the optimal parameters (Entropy, SSIM, PSNR, CQM) calculation From the work, it is clear that, the rank probability is proportional to the match score value of the silhouette stored in the gallery. Show more

Keywords: Gait cycle, silhouette image, feature detection, feature extraction, feature classification, person recognition using fuzzy

DOI: 10.3233/JIFS-201913

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Gosain, Anjana | Dahiya, Sonika

Article Type: Research Article

Abstract: DKIFCM (Density Based Kernelized Intuitionistic Fuzzy C Means) is the new proposed clustering algorithm that is based on outlier identification, kernel functions, and intuitionist fuzzy approach. DKIFCM is an inspiration from Kernelized Intuitionistic Fuzzy C Means (KIFCM) algorithm and it addresses the performance issue in the presence of outliers. It first identifies outliers based on density of data and then clusters are computed accurately by mapping the data to high dimensional feature space. Performance and effectiveness of various algorithms are evaluated on synthetic 2D data sets such as Diamond data set (D10, D12, and D15), and noisy Dunn data set …as well as on high dimension real-world data set such as Fisher-Iris, Wine, and Wisconsin Breast Cancer Data-set. Results of DKIFCM are compared with results of other algorithms such as Fuzzy-C-Means (FCM), Intuitionistic FCM (IFCM), Kernel-Intuitionistic FCM (KIFCM), and density-oriented FCM (DOFCM), and the performance of proposed algorithm is found to be superior even in the presence of outliers and noise. Key advantages of DKIFCM are outlier identification, robustness to noise, and accurate centroid computation. Show more

Keywords: Fuzzy clustering, identification of outlier, FCM, IFCM, DOFCM, KIFCM, kernel functions

DOI: 10.3233/JIFS-201858

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Li, Dong | Liu, Shulin | Gao, Furong | Sun, Xin

Article Type: Research Article

Abstract: Classification methods play an important role in many fields. However, they cannot effectively classify the samples from sample spaces that are varying with time, for they lack continual learning ability. A continual learning classification method for time-varying data space based on artificial immune system, CLCMTVD, is proposed. It is inspired by the intelligent mechanism that memory cells of the biological immune system can recognize and eliminate previous invaders when they attack again very fast and more efficiently, and these memory cells can evolve with the evolution of previous invaders. Memory cells were continuously updated by learning testing data during the …testing stage, thus realize the self-improvement of classification performance. CLCMTVD changes a linearly inseparable spatial problem into many classification problems of several different times, and it will degenerate into a common supervised learning classification method when all data independent of time. To assess the performance and possible advantages of CLCMTVD, the experiments on well-known datasets from UCI repository, synthetic data and XJTU-SY rolling element bearing accelerated life test datasets were performed. Results show that CLCMTVD has better classification performance for time-invariant data, and outperforms the other methods for time-varying data space. Show more

Keywords: Artificial immune system, classification, continual learning, machine learning, time-varying data

DOI: 10.3233/JIFS-200044

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Li, Yang | Shi, Xianliang | Diao, Hongdong | Zhang, Min | Wu, Yadong

Article Type: Research Article

Abstract: This paper analyzes the artificial intelligence algorithms related to the storage path optimization problem and focuses on the ant colony algorithm and genetic algorithm with better applicability. The genetic algorithm is used to optimize the parameters of the ant colony algorithm, and the performance of the ant colony algorithm is improved. A typical route optimization problem model is taken as an example to prove the effectiveness of parameter optimization. This paper proposes a combined forecasting method through data preprocessing algorithm and artificial intelligence optimization. The combined prediction method first uses wavelet transform threshold processing to remove the noise data in …the original data and then uses three separate methods to reduce noise. Forecast warehouse data and obtain intermediate forecast results. This article analyzes warehouse management and can solve the problems in the company’s warehouse management from the aspects of warehouse design and planning, warehouse design, and integrated warehouse management. After comparative analysis and selection, this paper uses the SLP method to rationally adjust and arrange the relative position and area of each functional area of the warehouse, and improve the evaluation index system. Experimental research shows that under the guidance of this article to optimize storage strategy, cargo location layout, and warehousing workflow, the employee reward mechanism mobilizes the enthusiasm of employees, improves work efficiency, and reduces storage costs. The above-mentioned various optimization and storage improvement measures finally reduced the total storage cost by 17%, effectively achieving the goal of cost control. Show more

Keywords: Warehouse management, optimized design, artificial intelligence algorithm, warehouse operation

DOI: 10.3233/JIFS-189843

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Li, Zuoshan

Article Type: Research Article

Abstract: With the continuous progress of society, the level of science and technology of the country has made a leap forward development, the research energy of various industries on new science and technology continues to deepen, greatly promoting the promotion of science and technology. At the same time, with the increase in social pressure, more and more people pursue spiritual relaxation, and appropriate leisure and entertainment activities have gradually become a part of people’s life. Film plays an irreplaceable role in leisure and entertainment. Mainly from the background of the development of the film industry towards intelligent direction, and then use …machine learning technology to study the application of film animation production and film virtual assets analysis and investigation. Based on the Internet of things technology, we also vigorously develop the ways and methods of visual expression of movies, and at the same time introduce new expression modes to promote the expression effect of the intelligent system. Finally, by comparing various algorithms in machine learning technology, the results of intelligent expression of random number forest algorithm in machine learning technology are more accurate. The system is also applied to 3D animation production to observe the measurement error of 3D motion data and facial expression data. Show more

Keywords: Machine learning technology, movie new vision, combinatorial algorithm

DOI: 10.3233/JIFS-189835

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Naik, Amrita | Edla, Damodar Reddy

Article Type: Research Article

Abstract: Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are …further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%. Show more

Keywords: CNN, GLCM, GLRLM, SVM

DOI: 10.3233/JIFS-189847

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Zhu, Wenhua | Peng, Hu | Leng, Chaohui | Deng, Changshou | Wu, Zhijian

Article Type: Research Article

Abstract: Breast cancer is a severe disease for women health, however, with expensive diagnostic cost or obsolete medical technique, many patients are hard to obtain prompt medical treatment. Thus, efficient detection result of breast cancer while lower medical cost may be a promising way to protect women health. Breast cancer detection using all features will take a lot of time and computational resources. Thus, in this paper, we proposed a novel framework with surrogate-assisted firefly algorithm (FA) for breast cancer detection (SFA-BCD). As an advanced evolutionary algorithm (EA), FA is adopted to make feature selection, and the machine learning as classifier …identify the breast cancer. Moreover, the surrogate model is utilized to decrease computation cost and expensive computation, which is the approximation function built by offline data to the real object function. The comprehensive experiments have been conducted under several breast cancer dataset derived from UCI. Experimental results verified that the proposed framework with surrogate-assisted FA significantly reduced the computation cost. Show more

Keywords: Breast cancer detection, firefly algorithm, machine learning, surrogate model, feature selection

DOI: 10.3233/JIFS-201124

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Jiao, Guangshi

Article Type: Research Article

Abstract: Volleyball has been developing rapidly since 1996. It has been widely used both in competitive events and among ordinary people. However, with the continuous improvement of the sports level, the traditional manual training methods cannot meet the existing technical requirements. It is a mainstream method to analyze the track of volleyball by the computer in volleyball training. However, there are still some technical problems such as low precision and incomplete analysis. Therefore, this paper puts forward the research of volleyball track acquisition and intelligent analysis technology. In this paper, the shortcomings of the existing technology are systematically analyzed, and on …this basis, the optimization and improvement scheme is proposed. The core technology of this project is to improve the original image preprocessing technology and strengthen the system’s feature extraction ability. Finally, combined with the CAMSHAFT moving object tracking algorithm, the technical scheme of this paper is formed. Through a series of technical improvements, the system effectively improves the ability to extract and analyzing the track of volleyball. In order to further verify the practical effectiveness of this scheme, a number of comparative experiments including algorithm accuracy comparison experiment, trajectory recognition detection, and algorithm signal-to-noise ratio verification are carried out. The object of comparison is the current mainstream common filtering algorithm. Through the analysis of experimental data, this method is more accurate than the common filtering algorithm in the extraction of volleyball trajectory, which effectively improves the comprehensive performance and robustness of the traditional method. Show more

Keywords: Volleyball training, trajectory analysis, image preprocessing, target tracking technology

DOI: 10.3233/JIFS-189842

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zuo, Jiankai | Zhang, Yaying

Article Type: Research Article

Abstract: In the field of intelligent robot engineering, whether it is humanoid, bionic or vehicle robots, the driving forms of standing, moving and walking, and the consciousness discrimination of the environment in which they are located have always been the focus and difficulty of research. Based on such problems, Naive Bayes Classifier (NBC), Support Vector Machine(SVM), k-Nearest-Neighbor (KNN), Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were introduced to conduct experiments. The six individual classifiers have an obvious effect on a particular type of ground, but the overall performance is poor. Therefore, the paper proposes a “Novel Hybrid …Evolutionary Learning” method (NHEL) which combines every single classifier by means of weighted voting and adopts an improved genetic algorithm (GA) to obtain the optimal weight. According to the fitness function and evolution times, this paper designs the adaptively changing crossover and mutation rate and applies the conjugate gradient (CG) to enhance GA. By making full use of the global search capabilities of GA and the fast local search ability of CG, the convergence speed is accelerated and the search precision is upgraded. The experimental results show that the performance of the proposed model is significantly better than individual machine learning and ensemble classifiers. Show more

Keywords: Hybrid classification model, improved GA, machine learning, ground recognition

DOI: 10.3233/JIFS-202940

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Chen, Huiping

Article Type: Research Article

Abstract:  This paper aims to put forward a hesitant fuzzy multi-attribute group decision making (MAGDM) method based on the weighted power aggregation operators in social network. From the point of view of social network analysis, decision makers (DMs) are interconnected in the process of MAGDM. Furthermore, the dimension of the obtained hesitant fuzzy element (HFE) by original power operators will be greater with the increasing number of attributes and alternatives and DMs, which will lead to the problem of “intermediate expression swell". This paper combines the order operation laws with the power operators to redefine two novel hesitant fuzzy power aggregation …operators to simplify the involved calculation and explore new operators’ properties. Meanwhile, when two given elements have different number of values, we use the strength of social ties and social influence to develop an algorithm for extending the HFEs objectively. On the other hand, the PageRank algorithm and the deviation method are used to determine DMs’ combined weights. The feasibility of the proposed hesitant fuzzy MAGDM method based on social network is illustrated by the application to the actual issue of decision making and the comparative analysis with the existing method. Show more

Keywords: Group decision making, hesitant fuzzy set, social network, power operator, PageRank algorithm

DOI: 10.3233/JIFS-201859

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Di, Xiaolian

Article Type: Research Article

Abstract:  With the improvement of software system complexity and frequent updating of user requirements, the requirements of the information software development industry for information construction are constantly improved, and the quality and management requirements of software products researched and developed are also constantly improved. Project managers in the information software development industry gradually realize the importance and necessity of software system deployment. It requires scientific, timely, effective and clear work. Software system deployment system for task division and task monitoring. Based on the research results at home and abroad, this paper studies the deployment of computer software system based on event-driven …architecture by using a discrete Fourier transform algorithm, decision tree algorithm and parallel algorithm. By comparing and optimizing the advantages and disadvantages of discrete Fourier transform algorithm, decision tree algorithm and parallel algorithm. This paper studies the unified management, scheduling and allocation of computer software resources. The results show that after using the research model, the data error is controlled within 5%, and the overall data accuracy is improved by 15% compared with the previous methods, which have certain practical value. Show more

Keywords: System architecture, system deployment, computer software, software system deployment

DOI: 10.3233/JIFS-189841

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Krishnan, Sajitha | Amudha, J. | Tejwani, Sushma

Article Type: Research Article

Abstract: It is quite alarming that the increase of glaucoma is due to the lack of awareness of the disease and the cost for glaucoma screening. The primary eye care centers need to include a comprehensive glaucoma screening and include machine learning models to elaborate it as decision support system. The proposed system considers the state of art of eye gaze features to understand cognitive processing, direction and restriction of visual field. There is no significant difference in global and local ratio and skewness value of fixation duration and saccade amplitude, which suggest that there is no difference in cognitive processing. …The significance value of saccadic extent along vertical axis, Horizontal Vertical ratio (HV ratio), convex hull area and saccadic direction show that there is restriction in vertical visual field. The statistical measures (p <  0.05) and Spearman correlation coefficient with class label validate the results. The proposed system compared the performance of seven classifiers: Naïve Bayes classifier, linear and kernel Support Vector classifiers, decision tree classifier, Adaboost, random forest and eXtreme Gradient Boosting (XGBoost) classifier. The discrimination of eye gaze features of glaucoma and normal is efficiently done by XGBoost with accuracy 1.0. The decision support system is cost-effective and portable. Show more

Keywords: Restriction, quality of life, decision support system, eye tracking, glaucoma

DOI: 10.3233/JIFS-189846

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Jiang, Zhiwei | Wei, Guiwu | Wu, Jiang | Chen, Xudong

Article Type: Research Article

Abstract: With the development of society, people’s living standard is constantly improving. Meanwhile, people need various food to satisfy their needs in daily life. Under this situation, more and more food enterprises are appearing in the market. However, some issues about food safety come out. Because of the huge number of food company, managers are difficult in achieving profitability. Therefore, some of the managers try to use some unhealthy materials to produce food in the society. So, it is important for people to distinguish healthy and unhealthy food enterprises in their daily life. In order to help government discern and control …the quality of healthy food enterprises in the market, we need to propose an effective evaluation system in estimating food enterprises. In this paper, we introduce a method of evaluating the quality degree of food enterprises which can help us to distinguish enterprises effectively. As we all know, the method of TODIM is widely used in multiple attribute decision making (MADM). In this article, we describe the extended TODIM which based on the cumulative prospect theory (CPT) with picture fuzzy numbers (PF-CPT-TODIM) and use it to evaluate food companies. What’s more, we use entropy method to decide the weights of various attributes. Finally, we select optimal enterprise by using the PF-CPT-TODIM method. Furthermore, we use the comparison of the results of classical PF-TODIM method and PFWA operators to test the availability of PF-CPT-TODIM. It not only can enrich decision-making methods but also make up for the traditional PF-TODIM method in considering the psychological aspects of decision makers. Show more

Keywords: Multiple attribute group decision making (MAGDM), CPT-TODIM, picture fuzzy sets (PFSs), food enterprise, quality credit evaluation

DOI: 10.3233/JIFS-202839

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Thampi, Sabu M. | El-Alfy, El-Sayed M. | Trajkovic, Ljiljana

Article Type: Editorial

DOI: 10.3233/JIFS-189845

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Sukumaran, Poornima | Govardhanan, Kousalya

Article Type: Research Article

Abstract: Voice processing has proven to be an eminent way of recognizing the various emotions of the people. The objective of this research is to identify the presence of Autism Spectrum Disorder (ASD) and to analyze the emotions of autistic children through their voices. The presented automated voice-based system can detect and classify seven basic emotions (anger, disgust, neutral, happiness, calmness, fear and sadness) expressed by children through source parameters associated with their voices. Various prime voice features such as Mel-frequency Cepstral Coefficients (MFCC) and Spectrogram are extracted and utilized to train a Multi-layer Perceptron (MLP) Classifier to identify possible emotions …exhibited by the children thereby assessing their behavioral state. This proposed work therefore helps in the examination of emotions in autistic children that can be used to assess the kind of training and care required to enhance their lifestyle. Show more

Keywords: Emotion recognition, Autism Spectrum Disorder, voice processing, MFCC

DOI: 10.3233/JIFS-189854

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhao, Fuqiang | Zhu, Zhengyu | Han, Ping

Article Type: Research Article

Abstract: To measure semantic similarity between words, a novel model DFRVec that encodes multiple semantic information of a word in WordNet into a vector space is presented in this paper. Firstly, three different sub-models are proposed: 1) DefVec: encoding the definitions of a word in WordNet; 2) FormVec: encoding the part-of-speech (POS) of a word in WordNet; 3) RelVec: encoding the relations of a word in WordNet. Then by combining the three sub-models with an existing word embedding, the new model for generating the vector of a word is proposed. Finally, based on DFRVec and the path information in WordNet, a …new method DFRVec+Path to measure semantic similarity between words is presented. The experiments on ten benchmark datasets show that DFRVec+Path can outperform many existing methods on semantic similarity measurement. Show more

Keywords: Semantic similarity, WordNet, word embedding, POS, synset

DOI: 10.3233/JIFS-202337

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021