Journal of Intelligent & Fuzzy Systems - Volume 46, issue 3

Authors: Wang, Daiwen | Sun, Jie | Wei, Cuiping

Article Type: Research Article

Abstract: Spent lithium-ion battery (LIB) recycling can create great pollution to the environment. Understanding the safety, environment, technique, and regulation factors’ impact on the recycling process is crucial. Due to the complexity of the relevant factors, and there is a certain degree of correlation and dependence between the factors, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyze the factors’ degree of impact in this study. As the experts are ambiguous about some relations between the factors, it is impossible to conduct integrated evaluation. The improved DEMATEL method is proposed in this study to make up the …missing relations. Further, the weights of the factors will be calculated. In the improved DEMATEL method, the numerical scale of a linguistic term set is introduced. Therefore, the numerical scale used by experts can not only be uniform and symmetrical, but can also be non-uniform symmetric, non-uniform asymmetric, etc. Finally, both reusing and recycling companies are included in this study and their factors’ importance weights were analyzed with the fuzzy comprehensive evaluation method. Show more

Keywords: Automotive waste lithium-ion battery recycling, numerical scale function, DEMATEL method, fuzzy comprehensive evaluation

DOI: 10.3233/JIFS-224124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6149-6169, 2024

Authors: Mohan Raj, K.R. | Katiravan, Jeevaa

Article Type: Research Article

Abstract: Recently, security has been necessary in this computer world due to the fast development of technology and enormous user strength. The different kinds of security mechanisms including the Intrusion Detection System (IDS) were developed by many researchers to confirm the security of the data in the communication process. In general, the IDS are used to detect anomalous nodes, and attacks and increase the security level. Even though, the various disadvantages are available to ensure the data reliability on different kinds of applications. For this purpose, this work proposes a cross-layer IDS that is a combination of the trust-based secure routing …method, attribute selection and classification algorithms. This study introduces a novel attribute selection approach known as the Weighted Genetic Feature Selection Algorithm (WGFSA). This method is designed to identify and prioritize valuable attributes within the context of network, physical, and data link layers. And introduce a deep classifier called the Hyperparameter-Tuned Fuzzy Temporal Convolutional Neural Network (HFT-CNN) for efficient categorization. Additionally, we propose a pioneering secure routing algorithm known as the Fuzzy Logic and Time-Constrained Dynamic Trusted Cross-Layer-Based Secure Routing Algorithm (FCSRA) to ensure the secure transmission of data packets. The effectiveness of the newly developed system is proved by conducting experiments with the network, standard Aegean Wi-Fi intrusion dataset (AWID) and proved superior to other systems in delay, energy consumption, packet delivery rate, and prediction accuracy. Show more

Keywords: Fuzzy temporal logic, intrusion detection systems, trust score, cross-layer, deep learning, attribute selection, secure routing

DOI: 10.3233/JIFS-233275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6171-6183, 2024

Authors: Wei, Ying | Gong, Kaixin | Chen, Chunfang | Zhu, Xianghong

Article Type: Research Article

Abstract: This research proposes a new method to solve group decision-making(GDM) problems with intuitionistic fuzzy preference relations(IFPRs). First, a new definition of multiplicative consistency of IFPR is presented to address the defects of the existing consistency definitions. Then, two programming models are established to obtain the most optimistic and pessimistic consistent IFPRs and corresponding intuitionistic fuzzy priority weights. Also, in order to improve the accuracy of aggregate information, a new method to determine the weights of decision-makers(DMs) is offered by considering the interaction among DMs. Subsequently, by combining the vagueness and non-vagueness of the aggregated information, a multiplicative consistency definition of …the collective IFPR is provided. Moreover, to simplify the GDM process, a programming model for solving the priority weight is established, which effectively avoids the consistency test and correction of IFPRs. Finally, the values of the proposed method are illustrated by comparative analysis. Show more

Keywords: Group decision-making, interaction, risk preference, intuitionistic fuzzy preference relation, consistency

DOI: 10.3233/JIFS-233543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6185-6199, 2024

Authors: Hou, Xianyu | Chen, Yumin | Wu, Keshou | Zhou, Ying | Lu, Junwen | Weng, Xuan

Article Type: Research Article

Abstract: Neighborhood granulation is a classical granulation method. Although it is adequate for clustering and classification tasks, its granules are more complex, and the data representation is binary. This paper proposes a new granulation method based on the neighborhood granulation. Firstly, a detailed definition of the granular form is given with fuzzy rough set theory. Then, a modified fuzzy rough discriminant function is proposed based on neighborhood systems. The samples are globally granulated on single features to construct granules and on multiple features to construct granular vectors. Also, a feature selection technique based on the Chi-square, which strikingly reduces the complexity …of the fuzzy rough granular vectors, is introduced to address the disadvantage of the fuzzy rough granular vectors. An ensemble model structure is also proposed in the paper for the mixed nature of fuzzy rough granular vectors. The paper makes a detailed comparison between the fuzzy rough granulation and the neighborhood granulation. The results show that fuzzy rough granulation has higher computational efficiency and classification performance. Finally, a detailed comparison is made between the fuzzy rough granular ensemble model and various classical ensemble algorithms. The final results show that the fuzzy rough granular ensemble model has better robustness and generalization. Show more

Keywords: Granular computing, fuzzy rough granulation, neighborhood granulation, granular ensemble learning, granular selection

DOI: 10.3233/JIFS-234510

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6201-6217, 2024

Authors: Sun, Hong | Zhang, Xianyong

Article Type: Research Article

Abstract: Z-numbers contain fuzzy restrictions, credibility measures, and probability distributions to effectively represent uncertain information. Converting Z-numbers to fuzzy numbers facilitates extensive applications (such as multi-attribute decision-making (MADM)), thus becoming valuable for research purposes. Regarding Z-number conversions, the original method never considers the association probability, while probabilistic strategies offer better informatization. Recently, a probability-driven conversion starts with a linear transformation of the centroid difference between the fuzzy restriction and probabilistic distribution. However, it has the invalidation weakness of edge information due to underlying non-normalization. To improve this probability-linear conversion, a Z-number conversion is proposed by using underlying probability-exponential descriptions, and this …new method is further applied to MADM. At first, the current probability-linear conversion is analyzed based on the initial non-probabilistic conversion, and its intrinsic weakness and correctional improvement are revealed. Then, the novel probability-exponential conversion resorts to an exponential characterization of centroid difference between the restriction and distribution, and it gains information enrichment due to underlying normalization. The refined method preserves the inherent characteristics of Z-numbers more effectively, facilitating their application in subsequent engineering practices. This is especially pertinent in decision-making systems based on expert input and initial value problems. The proposed method for converting Z-numbers aims to minimize information loss in transitions between Z-numbers and classical fuzzy numbers. This approach will be further explored in future research. Furthermore, the probability-exponential conversion induces an ExpTODIM algorithm for MADM, called PE-ExpTODIM. Three Z-number conversions (i.e., the non-probabilistic, probability-linear, and probability-exponential types) and three decision algorithms (i.e., ExpTODIM, EDAS, MOORA) are combined to establish a 3 × 3 framework of Z-number-driven MADM. Finally, the systematical 9 algorithms are applied to the problem of site selection of carbon storage. They are validated by criss-cross contrast analyses and statistical significance tests. Thus, PE-ExpTODIM exhibits the desired optimization. The last technology of statistical testing is original, ingenious, and valuable for MADM. Show more

Keywords: Z-numbers, fuzzy numbers, probability-exponential conversion, multi-attribute decision making, ExpTODIM/EDAS/MOORA

DOI: 10.3233/JIFS-235304

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6219-6233, 2024

Authors: Kavitha, D. | Radha, V.

Article Type: Research Article

Abstract: This work proposes an unique hardware design for a multi-line Refreshable Braille Display (RBD) device using Imprint Punch Head Technology with Optical Character Recognition (OCR) capabilities for learning and reading text in Braille codes. The device uses a microprocessor board to seamlessly integrate stepper/servo motors with OCR algorithms. A thin flexible metal sheet coated with rubber is used as a display surface on which the raised points for Braille codes are repeatedly formed and deformed. The device is designed in such a way that the material used for its construction are low cost which makes them economical and affordable. The …device was evaluated in the lab setup and showed promising results, and had prospects of becoming a vital Assistive Technology for vision impaired people. Show more

Keywords: OCR, refreshable braille display, raspberry pi, text detection

DOI: 10.3233/JIFS-236527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6235-6248, 2024

Authors: Li, Heng

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-237196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6249-6263, 2024

Authors: Li, Bo | Lu, TongWei | Min, Feng

Article Type: Research Article

Abstract: 3D point cloud has irregularity and disorder, which pose challenges for point cloud analysis. In the past, the projection or point cloud voxelization methods often used were insufficient in accuracy and speed. In recent years, the methods using Transformer in the NLP field or ResNet in the deep learning field have shown promising results. This article expands these ideas and introduces a novel approach. This paper designs a model AaDR-PointCloud that combines self-attention blocks and deep residual point blocks and operates iteratively to extract point cloud information. The self-attention blocks used in the model are particularly suitable for point cloud …processing because of their order independence. The deep residual point blocks used provide the expression of depth features. The model performs point cloud classification and segmentation tests on two shape classification datasets and an object part segmentation dataset, achieving higher accuracy on these benchmarks. Show more

Keywords: PointCloud, transformer, ResNet, point cloud classification, point cloud part segmentation

DOI: 10.3233/JIFS-231997

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6265-6277, 2024

Authors: Yu, Huan

Article Type: Research Article

Abstract: With the acceleration of urbanization and the significant improvement of people’s living standards, the motorization of urban transportation in China has developed rapidly, and the number of urban motor vehicles has sharply increased. This has also caused a series of problems such as increasingly severe urban road traffic congestion, increased traffic energy consumption, and atmospheric environmental pollution. Unprecedented social and environmental pressures have put forward higher requirements for the development model of urban transportation. Against the backdrop of increasingly severe conflicts between urban transportation and resource environment in China, green transportation with the goal of “meeting maximum demand with minimum …consumption” has gradually received widespread attention from the academic community. The urban green transportation development level evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional geometric BM (GBM) operator and generalized weighted GBM (GWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy generalized GBM (TPFGGBM) operator and triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for urban green transportation development level evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity. Show more

Keywords: Multiple attribute decision making (MADM), Triangular Pythagorean fuzzy set, geometric BM (GBM) operator, Triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM), green transportation development level

DOI: 10.3233/JIFS-232579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6279-6297, 2024

Authors: Bolourchi, Pouya | Gholami, Mohammadreza

Article Type: Research Article

Abstract: Alzheimer’s disease (AD) is the most prevalent brain disorder which affects millions of people worldwide. Early detection is crucial for possible treatment. In this regard, machine learning (ML) approaches are widely utilized for AD detection. In this paper, we propose an ML-based method that drastically reduces the dimensionality of features while maintaining the relevant features and boosting the overall performance. To remove irrelevant features, first statistical feature extraction method is applied, and then further reduction among remaining features is applied by utilizing the harmony search method (HSM). The selected features are the most informative features that are fed to the …different classifiers. To test the effectiveness of the proposed method, we deployed three classification techniques including support vector machine (SVM), k -nearest neighbor (k -NN), and decision tree (DT). The experimental results show that the proposed method has a higher performance while decreasing the dimensionality of feature space. To guarantee that the performance of the proposed method is accurate, we applied an ensemble of three classifiers (SVM, KNN, and DT) for classification. The results of the proposed method verify that this method can be successfully deployed for AD detection, due to its high performance and low dimensional features, and can help improve the accuracy and efficiency of Alzheimer’s disease diagnosis. The proposed method demonstrated a significant improvement, achieving high performance in AD/HC classification, with accuracy, sensitivity, specificity, F1 -score, MCC , and Cohen’s Kappa rates reaching 95.5%, 97%, 94%, 95.56%, 0.9104, and 0.9109, respectively. AD/HC classification displayed the highest performance. Additionally, in the more challenging pMCI/sMCI classification, the method achieved an accuracy of 78.50%, sensitivity of 84.00%, specificity of 73.00%, F1 -score of 79.62%, MCC of 0.57, and Cohen’s Kappa of 0.59. Show more

Keywords: Alzheimer’s disease, ensemble of classifiers, harmony search, statistical feature extraction, sMRI

DOI: 10.3233/JIFS-233000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6299-6312, 2024

Authors: Khenglawt, Vanlalmuansangi | Laskar, Sahinur Rahman | Pakray, Partha | Khan, Ajoy Kumar

Article Type: Research Article

Abstract: Low-resource language in machine translation systems poses multiple complications regarding accuracy in translation due to insufficient incorporation of linguistic information. The difference in the linguistic information between the language pair also significantly impacts the dataset creation for improving translation accuracy. Although neural machine translation achieves a state-of-the-art approach, dealing with low-resource language is challenging since it struggled with limited resources. This paper attempts to address the data scarcity problem using augmentation of synthetic parallel sentences, source-target phrase pairs, and language models at the target side for English-to-Mizo and Mizo-to-English translation via transformer-based neural machine translation. We have attained state-of-the-art results …for both directions of translation. Show more

Keywords: English–Mizo, NMT, transformer, augmentation, language model

DOI: 10.3233/JIFS-235740

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6313-6323, 2024

Authors: Jin, Yongbing | Ran, Teng | Yuan, Liang | Lv, Kai | Wang, Guoliang | Xiao, Wendong

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-237275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6325-6335, 2024

Authors: You, Haoyang

Article Type: Research Article

Abstract: Students’ English learning ability depends on the knowledge and practice provided during the teaching sessions. Besides, language usage improves the self-ability to scale up the learning levels for professional communication. Therefore, the appraisal identification and ability estimation are expected to be consistent for different English learning levels. This paper introduces Performance Data-based Appraisal Identification Model (PDAIM) to support such reference. This proposed model is computed using fuzzy logic to identify learning level lags. The lag in performance and retains in scaling-up are identified using different fuzzification levels. The study suggests a fuzzy logic model pinpointing learning level gaps and consistently …evaluating performance across various English learning levels. The PDAIM model gathers high and low degrees of variance in the learning process to give students flexible learning knowledge. Based on the student’s performance and capacity for knowledge retention, it enables scaling up the learning levels for professional communication. The performance measure in the model is adjusted to accommodate the student’s diverse grades within discernible assessment boundaries. This individualized method offers focused education and advancement to students’ unique requirements and skills. The model contains continuous normalization to enhance the fuzzification process by employing prior lags and retentions. Several indicators, including appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time, are used to validate the PDAIM model’s performance. The model may adjust to the various performance levels and offer pertinent feedback using fuzzification. The high and low variation levels in the learning process are accumulated to provide adaptable learning knowledge to the students. Therefore, the performance measure is modified to fit the student’s various grades under distinguishable appraisal limits. If a consistent appraisal level from the fuzzification is observed for continuous sessions, then the learning is scaled up to the next level, failing, which results in retention. This proposed model occupies constant normalization for improving the fuzzification using previous lags and retentions. Hence the performance of this model is validated using appraisal rate, lag detection, number of retentions, data analysis rate, and analysis time. Show more

Keywords: Appraisal model, big data, English learning, fuzzy logic and fuzzification

DOI: 10.3233/JIFS-233414

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6337-6353, 2024

Authors: Men, Rui | FAN, Xiumei | Yan, Jun | Shan, Axida | Fan, Shujia

Article Type: Research Article

Abstract: Vehicle Edge Computing (VEC) is a promising technique to improve the quality of service (QoS) and quality of experience (QoE) in autonomous driving by exploiting the resources at the network edge. However, the high mobility of the vehicles leads to stochastic communication link duration, and the tasks generated by various applications in autonomous driving incur fierce competition for resources. These challenges cause excessive task completion delays. In this paper, we propose a vehicle-to-vehicle (V2V) partial computation offloading scheme that leverages the prediction results of the communication link lifetime between vehicles. A History track, Current interactions and Future planning trajectory-aware Gated …Recurrent Units (HCF-GRU) network is built to capture the essential factors to improve the prediction accuracy. Then, we design a GRU-based Proximal Policy Optimization (GRU-PPO) algorithm to obtain an optimal one-to-many offloading decision to minimize the task execution cost. The HCF-GRU prediction algorithm is evaluated on a real world vehicle trajectory dataset, and the performance of the GRU-PPO algorithm is analyzed on extensive numerical simulations. Experimental results demonstrate that our prediction network and offloading decision algorithm outperform the baseline methods in terms of prediction accuracy and task execution cost. Show more

Keywords: Communication link lifetime prediction, partial offloading decision, machine learning, autonomous driving

DOI: 10.3233/JIFS-235954

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6355-6368, 2024

Authors: Amin, Umair | Fahmi, Aliya | Yaqoob, Naveed | Farid, Aqsa | Hassan, Muhammad Arshad Shehzad

Article Type: Research Article

Abstract: The concept of domination in graphs is very ancient. Several types of notions of domination in graphs have been discussed by many researchers. In this work, the concept of domination and some notions of domination sets, minimal dominating sets, independence sets, and maximal independence sets are introduced in bipolar fuzzy soft graphs. Additionally, several properties of dominating sets are discussed and some theorems in bipolar fuzzy soft graphs are proved.

Keywords: Domination in bipolar fuzzy soft graphs, minimal domination set in bipolar fuzzy soft graphs, maximal independence set in bipolar fuzzy soft graphs

DOI: 10.3233/JIFS-236485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6369-6382, 2024

Authors: Xu, Fang

Article Type: Research Article

Abstract: In the context of globalization, cross-border e-commerce platforms have become the main way for enterprises to achieve international trade transformation and overseas investment. From this, it can be seen that cross-border e-commerce platforms are of great importance to the development of enterprises, and the development of cross-border e-commerce platforms is also a necessary choice for the development of the times. In the new era, in order to make cross-border e-commerce platforms better serve enterprises and bring economic benefits to their development. The sustainable development capability evaluation of third-party cross-border e-commerce (TPCBEC) platform is a MAGDM. Recently, the Exponential TODIM (ExpTODIM) …technique and Evaluation Based on Distance from Average Solution (EDAS) technique has been employed to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are employed as a tool for portraying uncertain information during the sustainable development capability evaluation of TPCBEC platform. In this paper, the 2-tuple linguistic neutrosophic number Exponential TODIM-EDAS (2TLNN-ExpTODIM-EDAS) technique is implemented to manage the MAGDM under 2TLNSs. Finally, a numerical study for sustainable development capability evaluation of TPCBEC platform is constructed to validate the implemented technique. Thus, the main advantages of the proposed 2TLNN-ExpTODIM-EDAS technique are outlined: (1) the proposed 2TLNN-ExpTODIM-EDAS technique not only handles the distances information from the 2TLNNAS, but also portrays the DMs’ psychological behavior during the sustainable development capability evaluation of TPCBEC platform. (2) the proposed 2TLNN-ExpTODIM-EDAS technique analyze the behavior of the TODIM technique and EDAS technique as MADM techniques when they are hybridized. Show more

Keywords: Multiple-attribute group decision-making (MAGDM), 2-tuple linguistic neutrosophic sets (2TLNSs), Exponential TODIM (ExpTODIM) technique, EDAS technique, sustainable development capability evaluation

DOI: 10.3233/JIFS-237170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6383-6398, 2024

Authors: Gao, Jun | Peng, Zhiyuan | Cao, Qiang | Zhang, Jie

Article Type: Research Article

Abstract: The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge …was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study. Show more

Keywords: Energy management strategy, Markov chain, minimum principle, optimal control

DOI: 10.3233/JIFS-238713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6399-6409, 2024

Authors: Zhang, Nan | Yin, Jiayi | Zhang, Ning | Sun, Tongtong | Yin, Shi | Wan, Lijun

Article Type: Research Article

Abstract: Digital technologies, such as big data, the Internet, and artificial intelligence, are rapidly advancing. Photovoltaic building materials enterprises (PBMEs) have been leveraging digital transformation to enhance their technological innovation capabilities and gain a competitive edge. In the global context of transitioning towards a low-carbon economy, the deep integration of digital technology offers a new solution for the green transformation of PBMEs. The synergy between green traction digitalization and digitalization enables green practices, making collaborative integration crucial for the far-reaching development of PBMEs. Within the framework of China’s “double carbon” policy, domestic PBMEs are experiencing exponential growth, where digital green innovation …(DGI) has become their primary objective. In this DGI context, selecting the right partners is the first step that significantly impacts the efficiency and effectiveness of DGI implementation. Therefore, the purpose of this study is to assist PBMEs in selecting high-quality partners, promoting the DGI process, enhancing technological innovation capabilities, and gaining a competitive advantage. To achieve this, the paper proposes constructing a theoretical framework for evaluating the DGI cooperation ability of PBMEs using the theory of ecological reciprocity. Based on this framework, an evaluation index system is established to assess the DGI cooperation ability of potential partners The interval intuitionistic fuzzy evaluation method, combined with a double combination weighting approach, is employed to evaluate the DGI ability of selected partners. Furthermore, by applying field theory, a dynamic selection model for strategic alliance partners is developed to aid PBMEs in selecting high-quality partners for DGI and facilitating the DGI process. The research findings indicate that: i) The evaluation standard framework for DGI cooperation ability of PBMEs encompasses “symbiosis,” “mutualism,” and “regeneration,” along with the crucial environmental element of mutual trust. ii) The evaluation method based on double combination weighting effectively assesses the comprehensive DGI capabilities of selected PBME partners. The application of field theory enables scientific and effective dynamic partner selection for PBMEs through resource complementarity. iii) The proposed framework and partner selection model can be employed in real partner selection scenarios for PBMEs, allowing them to choose high-quality partners, enhance their DGI capabilities, and attain practical selection outcomes. This paper presents novel partner selection model that integrates decision rules and resource complementarity, enabling PBMEs to efficiently select DGI partners from a pool of potential candidates and improve their innovation efficiency. The utilization of the double combination weighting method and field theory in the partner selection paradigm of D extends the theoretical foundation, while the establishment of the DGI capability evaluation index system for PBME partners contributes to empirical applications. Show more

Keywords: Photovoltaic building materials enterprises, digital green innovation, partner selection, double combination weighting, field theory

DOI: 10.3233/JIFS-234838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6411-6437, 2024

Authors: You, Fang | Li, Yaru | Fu, Qianwen | Zhang, Jun

Article Type: Research Article

Abstract: With the increasing levels of intelligence and automation, the relationship between humans and vehicles has evolved from a utilitarian perspective to a partnership. Among the crucial factors for enhancing user experiences are the analysis of driving tasks, the construction of user needs models, and the design of intelligent interfaces. Based on this background, this paper proposes a cognitive task analysis model using intelligent steering wheel information interaction design as the vehicle. The model aims to extract key design elements to assist designers in making design decisions, thereby improving the human-machine cooperation performance of intelligent automobiles and enhancing user perceptual experiences. …Firstly, within the context of human-machine cooperation systems, a cognitive task analysis method integrating the SRK model is proposed. By analyzing the behavioral decision characteristics between the vehicle and the user, a framework for the human-machine interface (HMI) logic of the steering wheel and a dynamic layout prototype are established. Secondly, the design of the steering wheel’s HMI interaction is based on an analysis of users’ affective needs and rational physiological characteristics. This paper integrates the analysis of users’ affective needs to identify design elements that align with a high level of user satisfaction. Lastly, the design methodology model is applied to a navigation scenario, resulting in the creation of a steering wheel HMI prototype within a human-machine cooperation system. The prototype is then subjected to a combined subjective and objective experimental analysis, thereby validating the superiority of the steering wheel HMI’s detection indicators over those of the central control HMI and establishing the design pattern for the steering wheel HMI. Show more

Keywords: Intelligent cockpit, steering wheel, cognitive task analysis, human-machine interaction interface

DOI: 10.3233/JIFS-233500

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6439-6464, 2024

Authors: Zhao, Dongping

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-235734

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6465-6478, 2024

Authors: Ouyang, Zhiyuan | Wan, Yanling | Zhang, Tao | Wu, Wen-Ze

Article Type: Research Article

Abstract: The introduction of fractional order accumulation has played a crucial role in the development of grey forecasting methods. However, accurately identifying a single fractional order accumulation for modeling diverse sequences is challenging due to the dependence of different fractional order accumulations on data structure over time. To address this issue, we propose a novel fractional grey model abbreviated as FGMMA, incorporating a model averaging method. The new model combines existing fractional grey models by using four judgment criteria, including Akaike information criteria, Bayesian information criteria, Mallows criteria, and Jackknife criteria. Meanwhile, the cutting-edge algorithm named breed particle swarm optimization is …employed to search the optimal fractional order for each candidate model to enhance the effectiveness of the designed model. Subsequently, we conduct a Monte Carlo simulation for verification and validation purposes. Finally, empirical analysis based on energy consumption in three countries is conducted to verify the applicability of the proposed model. Compared with other benchmark models, we can conclude that the proposed model outperforms the other competitive models. Show more

Keywords: Grey forecasting model, fractional order accumulation, model averaging, breed particle swarm optimization

DOI: 10.3233/JIFS-237479

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6479-6490, 2024

Authors: Zhang, Yihao | Wang, Yuhao | Lan, Pengxiang | Xiang, Haoran | Zhu, Junlin | Yuan, Meng

Article Type: Research Article

Abstract: Conversational recommender systems use natural language conversations to elicit user preferences and recommend items proactively. Existing methods based on graph neural networks have been proven to be effective in exploiting knowledge graphs. However, node positions are often treated as constants, which leads to the neglect of graph connectivity due to fuzzy processing. In addition, although the transformer has significant advantages in understanding the text, its secondary computational complexity may be incapable when dealing with long texts. In order to solve these problems, we propose an additive positional conversational recommender model called APCR. This model converts the pair product of transformer …into a linear operation, and uses the Laplacian eigenvector to build a location graph. The extended graph neural network captures the topology structure of the location knowledge graph. Specifically, we design an encoder based on additive attention to break through the bottleneck of long text. Furthermore, we develop a recommendation model based on a positional graph neural network to match items with dialogue context, thereby capturing the graph topology. Extensive experiments on the REDIAL dataset show significant improvements in our proposed model over the state-of-the-art methods in recommendation and dialogue generation evaluations. Show more

Keywords: Interactive recommender systems, graph neural networks, knowledge graphs, additive attention

DOI: 10.3233/JIFS-230905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6491-6503, 2024

Authors: Qian, Jin | Wang, Taotao | Lu, Yuehua | Yu, Ying

Article Type: Research Article

Abstract: Multi-granularity hesitant fuzzy linguistic terms set is an effective expression of linguistic information, which can utilize some fuzzy linguistic terms to evaluate various common qualitative information and plays an important role when experts provide linguistic information to express hesitancy. Since the alternative description in the decision-making information system is characterized by multi-granularity, uncertainty, and vagueness, this paper proposes a multi-granularity hesitant fuzzy linguistic decision-making VIKOR method based on entropy weight and information transformation. Specifically, this paper firstly adopts fuzzy information entropy to obtain the weights of different attributes and introduces a multi-granularity hesitant fuzzy linguistic term set conversion method to …realize the semantic information conversion between different granularities. Then for the converted affiliation linguistic decision matrix, the entropy weighting method is used to obtain the weights of different affiliation granularity layers, and a weight optimization VIKOR method based on the affiliation linguistic decision matrix is further proposed to rank the alternatives. Finally, the feasibility of the proposed method verified by arithmetic examples, experimental analysis is carried out in terms of parameter sensitivity analysis and comparison with other methods. The experimental results prove the rationality and effectiveness of the proposed method. Show more

Keywords: Multi-granularity hesitant fuzzy term set, affiliation degree, information transformation, VIKOR method

DOI: 10.3233/JIFS-237951

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6505-6516, 2024

Authors: Prasath, J.S. | Shyja, V. Irine | Chandrakanth, P. | Kumar, Boddepalli Kiran | Raja Basha, Adam

Article Type: Research Article

Abstract: Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and intrusion detection systems (IDS) cannot be directly adapted to the …IoT with acceptable security performance and are vulnerable to various attacks that do not benefit. In this paper we propose an optimal secure defense mechanism for DDoS in IoT network using feature optimization and intrusion detection system (OSD-IDS). In OSD-IDS mechanism, first we introduce an enhanced ResNet architecture for feature extraction which extracts more deep features from given traffic traces. An improved quantum query optimization (IQQO) algorithm for is used feature selection to selects optimal best among multiple features which reduces the data dimensionality issues. The selected features have given to the detection and classification module to classify the traffic traces are affected by intrusion or not. For this, we design a fast and accurate intrusion detection mechanism, named as hybrid deep learning technique which combines convolutional neural network (CNN) and diagonal XG boosting (CNN-DigXG) for the fast and accurate intrusion detection in IoT network. Finally, we validate the performance of proposed technique by using different benchmark datasets are BoNeSi-SlowHTTPtest and CIC-DDoS2019. The simulation results of proposed IDS mechanism are compared with the existing state-of-art IDS mechanism and analyze the performance with respects to different statistical measures. The results show that the DDoS detection accuracy of proposed OSD-IDS mechanism is high as 99.476% and 99.078% for BoNeSi-SlowHTTPtest, CICDDoS2019, respectively. Show more

Keywords: Defense mechanism, DDoS intrusion, intrusion detection system, feature selection, IoT

DOI: 10.3233/JIFS-235529

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6517-6534, 2024

Authors: Ye, Qing | Song, Zihan | Zhao, Yuqi | Zhang, Yongmei

Article Type: Research Article

Abstract: Video anomaly detection refers to the automatic identification of abnormal behaviors, objects, or events in videos. However, current methods for anomaly detection based on original frames lack a comprehensive understanding of the importance of foreground information, making it challenging to efficiently address video anomaly detection in the presence of complex background interference. In this paper, we propose a video anomaly detection algorithm based on Background Separation Network (BSN) to address this issue. Firstly, we utilize a video stabilization algorithm to reduce video jitter and enhance the quality of input video frames. Secondly, BSN shifts the focus from the entire frame …to the foreground region with higher anomaly detection value. BSN utilizes the motion pixel distribution of the video as the basis for foreground extraction, enabling pixel-level background separation to obtain more accurate and complete foreground targets. Lastly, a certain proportion of foreground targets in the foreground image are masked as background, reducing the interference caused by redundant targets on the detection results. The proposed method achieves an accuracy of 96.2% on the UCSD ped2 dataset, demonstrating its effectiveness. This method contributes to accurately detecting abnormal behaviors in real-world surveillance videos to protect the safety of public lives and assets. Show more

Keywords: Video anomaly detection, auto encoder, background separation network, video jitter elimination

DOI: 10.3233/JIFS-235717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6535-6551, 2024

Authors: Sheikh, Ansar Isak | Sadish Sendil, M. | Sridhar, P. | Thariq Hussan, M.I. | Abidin, Shafiqul | Kumar, Ravi | Irshad, Reyazur Rashid | Muniyandy, Elangovan | Phani Kumar, Solleti

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-237474

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6553-6564, 2024

Authors: Runkler, Thomas A.

Article Type: Research Article

Abstract: Pairwise fuzzy preference matrices can be constructed using expert ratings. The number of pairwise preference values to be specified by the experts increases quadratically with the number of options. Consistency (transitivity) allows to reduce this quadratic complexity to linear complexity which makes this approach feasible also for large scale applications. Preference values are usually expected to be on a fixed finite interval. Additive preference is defined on such a finite interval. However, completing preference matrices using additive consistency may yield preferences outside this finite interval. Multiplicative preference is defined on an infinite interval and is therefore not suitable here. …To overcome this problem we extend the concept of consistency beyond additive and multiplicative to arbitrary commutative, associative, and invertible operators. Infinitely many of such operators induce infinitely many types of consistency. As one example, we examine Einstein consistency, which is induced by the Einstein sum operator. Completing preference matrices using Einstein consistency always yields preferences inside the finite interval, which yields the first method that allows to construct large scale finite preference matrices using expert ratings. A case study with the real–world car preference data set indicates that Einstein consistency also yields more accurate preference estimates than additive or multiplicative consistency. Show more

Keywords: Fuzzy preference relations, consistent preference, additive preference, multiplicative preference, Einstein sum

DOI: 10.3233/JIFS-224179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6565-6576, 2024

Authors: Bu, Yanbin | Chen, Ting | Duan, Hongxiu | Liu, Mei | Xue, Yandan

Article Type: Research Article

Abstract: In the modern world, structured and semi-structured knowledge bases hold a considerable amount of data. There-fore, people who are familiar with formal query languages should not be the only ones who can efficiently and clearly query them. Semantic Parsing (SP) is converting natural language utterances into formal meaning representations. The paper suggests a model for SP that uses a novel method of utilizing the Semi-Supervised Generative Adversarial Network (SS-GAN) to enhance the classifier performance. The proposed SS-GAN extends the fine-tuning of word embedding architectures using unlabeled examples in a generative adversarial environment. We provide a regularization strategy for addressing the …mode missing problem and unstable training in SS-GAN. The main viewpoint is to use the extracted feature vectors from the discriminator. Hence, the generator produces outputs by aiding the discriminator’s learned features. A reconstruction loss is added to the loss function of the SS-GAN to drive the genera-tor to reconstruct outputs from the discriminator’s features, hence steering the generator toward actual data configurations. The proposed reconstruction loss improves the performance of SS-GAN, produces high-quality outputs, and may be combined with other regularization loss functions to improve the performance of diverse GANs. We employ BERT word embedding for our model, which can be included in a downstream task and fine-tuned as a model, while the pre-trained BERT model can capture various linguistic properties. We examine the suggested model using the WikiSQL and SparC datasets, and the analysis findings reveal our model outperforms its rivals. The findings from our experiments indicate that the need for labeled samples can be minimized, down to as few as 100 instances, while still achieving commendable classification outcomes. Show more

Keywords: Semantic parsing, generative adversarial network, semi-supervised learning, BERT

DOI: 10.3233/JIFS-233212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6577-6588, 2024

Authors: Li, Feng | Zhu, Mozhong | Lin, Ling

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-234686

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6589-6605, 2024

Authors: Ma, Fanglan | Zhu, Changsheng | Liu, Dukui

Article Type: Research Article

Abstract: Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student’s knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and …learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students’ performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior. Show more

Keywords: Knowledge tracing, cognitive law, learning behavior, ResNet, deep learning

DOI: 10.3233/JIFS-235723

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6607-6617, 2024

Authors: Yang, Shuyi | Li, Lusu | Feng, Libo

Article Type: Research Article

Abstract: Currently, scientific big data management is generally faced with the problems of scattered data resources, inconsistent data standards, and the inability to share and circulate data safely. Research personnel attaches great importance to whether sharing the first-hand property is secure under clear ownership and whether it can contribute to the large society. The isolation of the data management system is the obvious obstacle to collecting and managing across-disciplinary data. To a large extent, sharing and trading scientific big data is the primary purpose to realize the clarity of property rights, secure data sharing, and the value of the data assets …step by step. We propose to construct a public platform for scientific big data management. The system is managed to unify and authorize the on-chain data, on which data sharing and trading is tracked throughout the process. Smart contracts are executed with vital functions and guarantee price matching in data transactions. We design the incentive mechanism which measures the incentive yield of data cost quality based on Evolutionary Game Theory and Data Quality Control Theory (EGQCY), considering how the cost of data quality performs in controlling and impacting the rational release of the incentive yields in the sharing and trading process. The experiments found that the design of incentive yield and incentive coefficients only significantly affected the transition from low-quality data to medium-quality data. Both parameters converged to fixed values as the cost of data quality increased. Show more

Keywords: Scientific big data, blockchain, smart contract, data sharing and transaction, data incentive mechanism, the cost of data quality control

DOI: 10.3233/JIFS-236521

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6619-6635, 2024

Authors: Lin, Shanlang | Lin, Zeyu

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-233366

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6637-6655, 2024

Authors: Li, Dongmei | Yang, Lehua | Liu, Shaojun | Tan, Ruipu

Article Type: Research Article

Abstract: Emergency rescue decisions in case of a typhoon disaster can be considered multi-attribute decision-making problems. Considering the need for the timeliness and authenticity of decision-making information sources after such a disaster, this study proposed using learning methods to process real-time online data and interval-valued neutrosophic numbers (NNs) to express the classification results. Using Typhoon Hagupit as an example, a trained text classification model was used to classify real-time data (online comments), following which the classification results were used as weights to convert these data into interval-valued NNs. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) …method was adopted to rank the extent of damage caused by the typhoon in each region; the sorting results were consistent with the official statistical data, proving the effectiveness of the proposed method. A detailed sensitivity analysis was conducted to determine the optimal parameter settings of the classification model. Furthermore, the proposed method was compared with existing methods in terms of data conversion and deep learning efficiency; the results confirmed the superior capabilities of the proposed method. Notably, the proposed method can provide support to disaster management professionals in their post-disaster emergency relief work. Show more

Keywords: Deep learning, interval-valued neutrosophic numbers, multi-attribute decision making, typhoon disaster

DOI: 10.3233/JIFS-235315

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6657-6677, 2024

Authors: Liu, Huilin | Wang, Yansi | Yang, Gaoming | Xu, Huan | Wang, Tao

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-235903

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6679-6696, 2024

Authors: Zhan, Linjie | Tang, Zhenpeng

Article Type: Research Article

Abstract: Effective energy futures price prediction is an important work in the energy market. However, the existing research on the application of “decomposition-prediction” framework still has shortcomings in noise processing and signal reconstruction. In view of this, this paper first uses PSO to optimize VMD to improve the effectiveness of single decomposition, and further uses SGMD to capture the remaining key information after extracting low-frequency modal components by using PSO-VMD technology. Further, combined with LSTM to predict each component, a new PSO-VMD-SGMD-LSTM hybrid model is innovatively constructed. The empirical research results based on the real energy market transaction price show that …compared with the benchmark model, the hybrid model proposed in this paper has obvious forecasting advantages in different forecasting scenarios. Show more

Keywords: Energy futures price forecast, secondary decomposition technique, long short term memory

DOI: 10.3233/JIFS-236019

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6697-6713, 2024

Authors: Koam, Ali N.A. | Ahmad, Ali | Azeem, Muhammad | Qahiti, Raed

Article Type: Research Article

Abstract: Let G be a graph and R  = {r 1 , r 2 , …, r k } be an ordered subset of vertices of G , if every two vertices of G have different representation r  (v |R ) = (d  (v , r 1 ) , d  (v , r 2 ) , …, d  (v , r k )) with respect to R , then R is said to be a metric-based resolving parameter or resolving set of G and its minimum cardinality is called the metric dimension of graph G  . Metric dimension …is considered as an important applied concept of graph theory especially in the localization of a network and also in the chemical graph theoretical study of molecular compounds. Therefore, it is hot topic to study for different families of graphs as well. Convex polytopes play an important role both in various branches of mathematics and in applied areas, most notably in linear programming. In this paper, we determine the metric-based resolving parameter of line graph of a convex polytope S n , and conclude that it has constant metric dimension but vary with the parity of n  . This article presents a measurement of the line graph of a convex polytope, denoted as ( S n ) . The subsequent section provides the metric dimension of the resulting graph. There are two scenarios pertaining to the metric dimension of a selected graph with respect to the metric dimension. The metric dimension of even cycle-based convex polytopes is three, whereas for other values, the metric dimension is four. Show more

Keywords: Convex polytope, metric dimension, resolving set, constant metric dimension, line graph of convex polytope

DOI: 10.3233/JIFS-236517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6715-6727, 2024

Authors: Arul, A. | Kathirvelu, M.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-237475

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6729-6746, 2024

Authors: Sun, Shaoye

Article Type: Research Article

Abstract: In recent years, the lack of coordination in cross-border logistics has been one of the challenges and challenges faced by cross-border e-commerce. As the primary link in cross-border logistics, the selection of logistics service providers is an important foundation for promoting the development of cross-border e-commerce, and also a key link in improving the competitiveness of cross-border e-commerce enterprises. How to choose suitable and effective cross-border e-commerce logistics service providers has important theoretical significance and practical application value. The cross-border e-commerce logistics service providers evaluation is a multiple-attributed decision-making (MADM) problem. In this paper, the Type-2 neutrosophic number cross-entropy (T2NN-CE) …technique is designed with help of cross-entropy and Type-2 neutrosophic number (T2NN). Furthermore, Then, T2NN-CE technique is built to solve the MADM. Finally, a numerical example for cross-border e-commerce logistics service providers evaluation is given and some comparisons are conducted to illustrate advantages of the designed T2NN-CE technique. The research contribution of the paper is outlined: (1) The T2NN-CE is managed under T2NNs; (2) the T2NN-CE method is implemented for MADM under T2NNs; (3) the T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is constructed and were compared with some existing techniques; (4) Through the comparison, it is known that T2NN-CE technique for cross-border e-commerce logistics service providers evaluation is effective. Show more

Keywords: Multiple-attributed decision-making (MADM), Type-2 neutrosophic number (T2NN), cross entropy, cross-border e-commerce logistics service providers evaluation

DOI: 10.3233/JIFS-238592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6747-6762, 2024

Authors: Rao, Fengshuo | Chung, Sung-Pil | Xing, Kailin

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-238836

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6763-6780, 2024

Authors: Radhakrishnan, P. | Senthilkumar, G.

Article Type: Research Article

Abstract: Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text …is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively. Show more

Keywords: Text summarization, automatic text summarization, Nesterov-accelerated Adaptive Moment Estimation, Long Short-Term Memory

DOI: 10.3233/JIFS-224299

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6781-6793, 2024

Authors: Liu, Qi

Article Type: Research Article

Abstract: In the era of advanced technology, integrating and distributing data are crucial in smart grid-connected systems. However, as energy loads continue to increase, practical implementation of these systems faces challenges in resource allocation and lacks efficient data collaboration. In this study, the ant colony optimization algorithm is further investigated for stochastic crossover systems and cluster nodes in intelligent path planning management. To improve the pheromone setting method in smart grid-connected systems, we propose an adaptive intelligent ant colony optimization algorithm called the Group Allocation Optimization Algorithm (GAOA). This algorithm expands the pheromone transmission rate of network nodes, establishes a multi-constrained …adaptive model with data mining as the pheromone target, and analyzes the accuracy of resource allocation to import the optimal scheme for smart grid-connected systems. Through experimental results, we demonstrate that the optimized adaptive ant colony algorithm leads to effective improvements in grid-connected systems, pheromone evaluation, data throughput, convergence speed, and data load distribution. These findings provide evidence that the optimized ant colony algorithm is both feasible and effective for resource allocation in smart grid-connected systems. Show more

Keywords: Smart Grid-connected system, data-driven allocation, ant colony algorithm, group allocation optimization algorithm

DOI: 10.3233/JIFS-235091

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6795-6805, 2024

Authors: Pandey, Raksha | Kushwaha, Alok Kumar Singh | Kumar, Vinay

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-235818

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6807-6820, 2024

Authors: Ma, Mengyuan | Huang, Huiling | Han, Jun | Feng, Yanbing | Yang, Yi

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-235929

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6821-6834, 2024

Authors: Zhang, Zhifei | Wang, Shenmin

Article Type: Research Article

Abstract: The focus of attention has shifted to land use and land cover changes as a result of the world’s fast urbanisation, and logical planning of urban land resources depends greatly on the forecast and analysis of these changes. In order to more precisely forecast and assess patterns of land use change, the study suggests a grey Markov land pattern analysis and prediction model that incorporates social aspects. The study builds a land pattern analysis and prediction model using a major city as the research object. The outcomes demonstrated the high accuracy and reliability of the grey Markov land pattern analysis …and prediction model incorporating social factors, which can more accurately reflect and predict the land use pattern of the study area, with an average relative error of less than 0.01, an accuracy of more than 98%, and an overall fit that has increased by more than 3%. The overall pattern of change is very consistent with the reality. The model predicts that the main trend of future land use in the study area is the continued expansion of urban land such as industrial land, land for transport facilities and land for settlements, while non-construction land such as agricultural land and forest land will continue to decrease. The optimized land pattern analysis and prediction model of the study has a good application environment. Show more

Keywords: Grey system theory, land use change, prediction model, socio-economic factors

DOI: 10.3233/JIFS-235965

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6835-6850, 2024

Authors: Arivanandhan, Rajesh | Ramanathan, Kalaivani | Chellamuthu, Senthilkumar

Article Type: Research Article

Abstract: Users possess the option to rent instances of various sorts, in a variety of regions, and a variety of availability zones, thanks to cloud service carriers like AWS, GCP, and Azure. In the cloud business right now, fixed price models are king when it comes to pricing. However, as the diversity of cloud providers and users grows, this approach is unable to accurately reflect the market’s current needs for cost savings. As a consequence, a dynamic pricing strategy has become a desirable tactic to better handle the erratic cloud demand. In this study, a deep learning model was used to …propose a dynamic pricing structure that ensures service providers are treated fairly in a multi-cloud context. The computational optimization of DL approaches can be severely hampered by the requirement for human hyperparameter selection. Traditional automated solutions to this issue have inadequate durability or fail in specific circumstances. To choose the hyper-parameters in the Dueling Deep Q-Network (DDQN), the hybrid DL approach in this study uses the concept-based wild horse optimization (WHO) method. A community of untamed horses is evolved, and the fitness of the population is evaluated concurrently to estimate the optimum hyper-parameters. The plan changes the price appropriately to promote the use of underutilized resources and discourage the use of overutilized resources. The evaluation’s findings demonstrated that the suggested strategy can lower end-user costs while conducting compute- and data-intensive activities in a multi-cloud environment. The research was concluded by comparing current models after the results were analyzed using various performance indicators. Show more

Keywords: Cloud providers, dynamic pricing scheme, Deep Learning, hyper-parameter selection, Oppositional-Based Learning, Wild Horse Optimization and Dueling Deep Q-Network

DOI: 10.3233/JIFS-236043

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6851-6865, 2024

Authors: Essaki Muthu, A. | Saravanan, K.

Article Type: Research Article

Abstract: Cataract, a common eye disease, causes lens opacification, which can lead to blindness. Early cataract detection in a privacy-preserving approach has led us to investigate the concept of Federated Learning (FL) and its prominent technique, known as Federated Averaging (FedAVG). Federated learning has the potential to solve the privacy issues by allowing data servers to train their models natively and distribute them without invading patient confidentiality. This research introduces an interactive federated learning framework that permits multiple medical institutions to screen cataract from split lamp images utilising convolutional neural network (CNN) without sharing patient data, as well as grade normal, …mild, moderate, and severe cataracts. The CNN is developed based on Modified-ResNet-50 and FedAVG technique could achieve relatively high accuracy. The experimental results demonstrate that the proposed modification reduces the processing time to a greater extent. Show more

Keywords: Federated learning, confidentiality, accuracy, CNN

DOI: 10.3233/JIFS-223465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6867-6880, 2024

Authors: Wang, Haohao | Li, Wei | Yang, Bin

Article Type: Research Article

Abstract: Rosenfeld defined a fuzzy subgroup of a given group as a fuzzy subset with two special conditions and Mustafa Demirci proposed the idea of fuzzifying the operations on a group through a fuzzy equality and a fuzzy equivalence relation. This paper mainly focuses on fuzzy subsets and vague sets of monoids with several extended algebraic properties. Firstly, we generalize some algebraic properties of t -norms to fuzzy t -norms, this allows for a broader analysis and classification of fuzzy t -norms, enabling their wider application. Furthermore, we explore specific research on the properties of vague t -norms. Finally, selected conclusions …about fuzzy t -norms are extended to bounded lattices. Show more

Keywords: t-norm, t-conorm, uninorm, nullnorm, aggregation function, fuzzy monoid, vague monoid

DOI: 10.3233/JIFS-231401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6881-6891, 2024

Authors: Du, Wen Sheng

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-231452

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6893-6910, 2024

Authors: Xu, Xiaohui

Article Type: Research Article

Abstract: In the new normal period, the trend changes and adjustments of the environment such as international trade, production capacity, labor supply and resource constraints have put forward new requirements for China’s industrial development, which have brought new challenges and given new opportunities. In the new normal stage where economic growth continues to decline, industrial growth is still an important support for economic growth. The advancement of industrial technology is the main driving force for improving the total factor productivity of the industrial industry. Therefore, the most important thing to promote industrial growth is to upgrade the level of industrial technology. …In response to the above-mentioned problems, this paper analyzed the relationship between industrial technology and industrial output in the new normal environment by using the BP neural network (BPNN) algorithm. The connection between the two has been found, which provided a clear direction for the functional adjustment of economic law. Experimental studies have shown that there is a positive relationship between industrial technological progress and industrial output. When other conditions are the same, and when the non-new normal is selected, industrial output increases by about 0.36% for every 1% increase in industrial technological progress. When choosing to be in the new normal, industrial technological progress has a higher impact on industrial output. For every 1% increase in technological progress, industrial output increases by about 0.39%. Show more

Keywords: Sustainable development, new industrial normal, economic law, functional adjustment, artificial neural network

DOI: 10.3233/JIFS-233251

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6911-6924, 2024

Authors: Wang, Fei

Article Type: Research Article

Abstract: Recently, there has been a lot of interest in using the wearable sensors for tracking the exercise progress because of the unbiased accuracy and precision they are provided throughout the continual monitoring. For those with physical impairments, the system’s non-intrusive, lightweight ways of the monitoring activity may ease their load and enhance the quality of their decision-making. As a different measuring unit measures the exercise activity levels recorded by the each wearable sensor, it is challenging to assess the monitoring system. Hence, this paper proposes a Hybridized Fuzzy Multi-Attribute for Exercise Monitoring System (HFMA-EMS) to address the uncertainty issues of …the wearable sensors. The Triangular Fuzzy membership function is proposed to begin classifying the observed values. Pair-wise attribute comparison and evaluator weighting in a T-spherical uncertain linguistic set setting utilizing the Techniques for Ordering of Preferences by Similarities to Ideal Solutions (TOPSIS). In the suggested method, a utility function is used to assess the merits of a model in which attribute the weights are calculated, followed by an exercise in which the attributes are ordered employing the Measurements of the Alternative and Ranking Compromise Solutions model (MARCOS). The performance is performed to analyze the proposed method’s accuracy, precision, recall, f1-score, and correct and incorrect exercise assessment by an accelerometer, gyroscope, and magnetic field sensor unit. The application scenario of the HFMA-EMS can be used in the clinical applications, healthcare management, and sports injury detection. Show more

Keywords: Exercise monitoring system, wearable sensors, disabled individuals, TOPSIS, MARCOS, fuzzy multi-attribute model

DOI: 10.3233/JIFS-235112

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6925-6938, 2024

Authors: Zhou, Yuzhong | Lin, Zhengping | Wu, Zhengrong | Zhang, Zifeng

Article Type: Research Article

Abstract: Due to the complexity of the calculation process of the existing methods, the efficiency of data fusion of the power grid model is low. In order to improve the knowledge fusion effect of power grid model, this paper studied the knowledge fusion method of power grid model based on Seq2seq half pointer and half label method. The Text Rank algorithm is used to calculate the weight of semantic nodes of each grid model, and combined with the topological potential method, the semantic information of the grid model is extracted according to the final weight value, and the Seq2Seq semi-pointer semi-label …model framework is constructed. The data of the scheduling automation system OMS and the production management system PMS are used as input. The extracted candidate mesh model semantics and the original mesh model semantics are encoded by Seq2Seq half-pointer half-label model. The semantic data of the power grid model is fused and sent to the Seq2Seq encoder. After the training is completed, the effective information is extracted from the power grid model through the Seq2Seq model to complete the knowledge fusion of the power grid model. Experimental results show that this method eliminates the redundant part of the basic attributes of each data source in the substation grid model after knowledge fusion, and the description of each basic attribute is more standardized, unified and perfect. Under different mesh model data dimensions, the support of the proposed method is all above 98%. The model trained by the proposed method tends to be stable after 120 iterations, and the precision, recall and F1 of the test set are 0.98, 0.93 and 0.91, respectively. At the same time, this method has high efficiency in the knowledge fusion processing of the power grid model, and its data processing speed is less than 160 s. The average integrity of the private data of the power grid model is 98.86%, indicating that the proposed method can better ensure the integrity of the data. Finally, compared with the application of other methods under different data amounts, the mean square error obtained by the proposed method is the smallest, indicating that the proposed method effectively improves the fusion accuracy. Show more

Keywords: Grid model, knowledge fusion method, half label method, LSTM neural network, Seq2seq half pointer, TPC TextRank algorithm

DOI: 10.3233/JIFS-236465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6939-6950, 2024

Authors: Xiao, Yuteng | Liu, Zhaoyang | Yin, Hongsheng | Wang, Xingang | Zhang, Yudong

Article Type: Research Article

Abstract: Multivariate Time Series (MTS) forecasting has gained significant importance in diverse domains. Although Recurrent Neural Network (RNN)-based approaches have made notable advancements in MTS forecasting, they do not effectively tackle the challenges posed by noise and unordered data. Drawing inspiration from advancing the Transformer model, we introduce a transformer-based method called STFormer to address this predicament. The STFormer utilizes a two-stage Transformer to capture spatio-temporal relationships and tackle the issue of noise. Furthermore, the MTS incorporates adaptive spatio-temporal graph structures to tackle the issue of unordered data specifically. The Transformer incorporates graph embedding to combine spatial position information with long-term …temporal connections. Experimental results based on typical finance and environment datasets demonstrate that STFormer surpasses alternative baseline forecasting models and achieves state-of-the-art results for single-step horizon and multistep horizon forecasting. Show more

Keywords: Multivariate time series forecasting, Spatio-temporal structure, transformer, graph embedding, recurrent neural network

DOI: 10.3233/JIFS-237250

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6951-6967, 2024

Authors: Behera, Santi Kumari | Rao, Mannava Srinivasa | Amat, Rajat | Sethy, Prabira Kumar

Article Type: Research Article

Abstract: Mineral classification is a crucial task for geologists. Minerals are identified by their characteristics. In the field, geologists can identify minerals by examining lustre, color, streak, hardness, crystal habit, cleavage, fracture, and specific features. Geologists sometimes use a magnifying hand lens to identify minerals in the field. Surface color can assist in identifying minerals. However, it varies widely, even within a single mineral family. Some minerals predominantly show a single color. So, identifying minerals is possible considering surface color and texture. But, again, a limited database of minerals is available with large-scale images. So, the challenges arise to identify the …minerals using their images with limited images. With the advancement of machine learning, the deep learning approach with bi-layer feature fusion enhances the dimension of the feature vector with the possibility of high accuracy. Here, an experimental analysis is reported with three possibilities of bi-layer feature fusion of three CNN models like Alexnet, VGG16 & VGG19, and a framework is suggested. Alexnet delivers the highest performance with the bi-layer fusion of fc6 and fc7. The achieved accuracy is 84.23%, sensitivity 84.23%, specificity 97.37%, precision 84.7%, FPR 2.63%, F1 Score 84.17%, MCC 81.75%, and Kappa 53.59%. Show more

Keywords: Mineral identification, deep learning, bi-layer feature fusion, deep feature

DOI: 10.3233/JIFS-221987

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6969-6976, 2024

Authors: Danielraj, A. | Venugopal, P. | Padmapriya, N.

Article Type: Research Article

Abstract: Graph Neural Networks (GNNs) have gained popularity across various research fields in recent years. GNNs utilize graphs to construct an embedding that includes details about the nodes and edges in a graph’s neighborhood. In this work, a set of Region Adjacency Graphs (RAG) derives the attribute values from Static Signature (SS) images. These attribute values are used to label the nodes of the complete graph, which is formed by considering each signature as a node taken from the sample of signatures of a specific signer. The complete graph is trained by using GraphSAGE, an inductive representation learning method. This trained …model helps to determine any newly introduced node (static signature to be tested) as genuine or fake. Standard static signature datasets, notably GPDSsynthetic and MCYT-75 are used to test the prevailing model. Experimental results on genuine and counterfeit signature networks demonstrate that our computed model enables a high rate of accuracy (GPDSsynthetic 99.91% and MCYT-75 99.56%) and minimum range of loss (GPDSsynthetic 0.0061 and MCYT-75 0.0070) on node classification. Show more

Keywords: Signature verification, GNN, region adjacency graph, complete graph, GraphSAGE Node classifications

DOI: 10.3233/JIFS-231369

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6977-6994, 2024

Authors: Li, Jing | Hu, Yifan | Fan, Jiulun | Yu, Haiyan | Jia, Bin | Liu, Rui | Zhao, Feng

Article Type: Research Article

Abstract: The Fuzzy C-means (FCM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition. As the intensity of observation noise increases, FCM tends to produce large center deviations and even overlap clustering problems. The relative entropy fuzzy C-means algorithm (REFCM) adds relative entropy as a regularization function to the fuzzy C-means algorithm, which has a good ability for noise detection and membership assignment to observed values. However, REFCM still tends to generate overlapping clusters as the size of the cluster increases and becomes imbalanced. Moreover, the convergence speed of this algorithm is slow. To solve this problem, …modified suppressed relative entropy fuzzy c-means clustering (MSREFCM) is proposed. Specifically, the MSREFCM algorithm improves the convergence speed of the algorithm while maintaining the accuracy and anti-noise capability of the REFCM algorithm by adding a suppression strategy based on the intra-class average distance measurement. In addition, to further improve the clustering performance of MSREFCM for multidimensional imbalanced data, the center overlapping problem and the center offset problem of elliptical data are solved by replacing the Euclidean distance in REFCM with the Mahalanobis distance. Experiments on several synthetic and UCI datasets indicate that MSREFCM can improve the convergence speed and classification performance of the REFCM for spherical and ellipsoidal datasets with imbalanced sizes. In particular, for the Statlog dataset, the running time of MSREFCM is nearly one second less than that of REFCM, and the accuracy of MSREFCM is 0.034 higher than that of REFCM. Show more

Keywords: Fuzzy c-means clustering, relative entropy fuzzy c-means clustering, modified suppressed relative entropy fuzzy c-means, Mahalanobis distance

DOI: 10.3233/JIFS-231515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6995-7019, 2024

Authors: Liu, Ziwei | Xu, Ziyu | Zheng, Xiyu | Zhao, Yongxing | Wang, Jinghua

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-232076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7021-7034, 2024

Authors: Behera, Santi Kumari | Anitha, Komma | Amat, Rajat | Sethy, Prabira Kumar

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-233910

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7035-7045, 2024

Authors: Suganthi, J. Roselin | Rajeswari, K.

Article Type: Research Article

Abstract: Communication is an essential component of human nature. It connects humans, allowing them to learn, grow, col-laborate, and resolve conflicts. Several aspects of human society, relationships, and growth would be significantly hampered in the absence of efficient communication. Hand gesture recognition is a way to interact with technology that can be particularly useful for individuals with disabilities. This hand gesture recognition is mainly employed in sign language translation, healthcare, rehabilitation, prosthesis, and Human-Computer Interaction (HCI). The high degree of dexterity is a main challenge for prosthetic limbs. In order to meet this challenge, hand gesture recognition is employed for the …prosthetic limb, which can be used for rehabilitation. The objective of this article is to show the methodology for the recognition of hand gestures using Electromyography (EMG) signals. This article uses the pro-posed time domain feature extraction method called Absolute Fluctuation Analysis (AFA) along with the Root Mean Square (RMS) for the feature extraction method. Along with these feature extraction methods, repeated stratified K-fold cross validation is used for the validation of the classifiers such as the XGB classifier, the K-Nearest Neighbour (KNN) classifier, the Decision Tree classifier, the Random Forest classifier, and the SVM classifier, whose mean recognition accuracy is given by 93.26%, 87.42%, 85.26%, 92.23%, and 91.78%, respectively. The recognition accuracy of machine learning classifiers is being compared with state-of-the-art networks such as artificial neural net-works (ANN), long short-term memory (LSTM), bidirectional LSTM, gated recurrent units (GRU), and convolution-al neural networks (CNN), which provide recognition accuracy of 96.65%, 99.16%, 99.94%, and 99.99%, respectively. Show more

Keywords: Human computer interaction(HCI), Absolute fluctuation analysis, LSTM, GRU, CNN

DOI: 10.3233/JIFS-234196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7047-7059, 2024

Authors: Lian, Jing | Chen, Shi | Li, Linhui | Sui, Duo | Ren, Weiwei

Article Type: Research Article

Abstract: Intelligent vehicles require accurate identification of traversable road areas and the ability to provide precise and real-time localization data in unstructured road environments. To address these issues, we propose a system for traversable map construction and robust localization in unstructured road environments based on a priori knowledge. The proposed method performs traversable area segmentation on the LiDAR point cloud and employs a submap strategy to jointly optimize multiple frames of data to obtain a reliable and accurate point cloud map of the traversable area, which is then rasterized and combined with the vehicle kinematic model for global path planning. Then, …it integrates priori map information and real-time sensor information to provide confidence and priori constraints to ensure the robustness of localization, and it fuses multi-sensor heterogeneous data to improve real-time localization. Experiments are conducted in a mining environment to evaluate the performance of the proposed method on an unstructured road. The experimental results demonstrate that the traversable map and localization results based on the proposed method can meet the requirements for autonomous vehicle driving on unstructured roads and provide reliable priori foundation and localization information for autonomous vehicle navigation. Show more

Keywords: Autonomous vehicles, traversability analysis, map construction, robust localization

DOI: 10.3233/JIFS-235063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7061-7075, 2024

Authors: Srinivasa Rao Illapu, Sankara | Mula, Aswini | Malarowthu, Padmaja

Article Type: Research Article

Abstract: Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized …by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR. Show more

Keywords: Energy efficiency, life expectancy, multiobjective-energy centric honey badger optimization, time division multiple access, wireless body area network

DOI: 10.3233/JIFS-235387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7077-7091, 2024

Authors: Qin, Zhi | Liu, Enyang | Zhang, Shibin | Chang, Yan | Yan, Lili

Article Type: Research Article

Abstract: Currently, word segmentation errors and polysemy problems are common in the field of Chinese relationship extraction. Although character-based model input can avoid word segmentation errors, in order to obtain the word information of a sentence, it is often necessary to introduce a dictionary or an external knowledge base to expand the word information, which requires a lot of manpower and time. In response to the above existing problems, this article uses characters as input, uses multiple embedding models to jointly form a character vector sequence, and obtains features containing character information through BiLSTM and attention layers; considering that convolutional neural …networks are good at extracting local features, obtain features containing word information through multi-kernel convolutional layers and multi-head self-attention layers, and finally use a gating mechanism to fuse the features. The model was tested on the public SanWen data set and our own cultural-travel data set, and obtained F1 values of 61.22% and 60.26% respectively. Experimental results show that our method can achieve better relationship extraction effects without using word segmentation tools and without building a dictionary or external knowledge base, and the effect is better than most commonly used models currently. Show more

Keywords: Chinese relation extraction, multiple embedded representations, muti-head self-attention, gating mechanism

DOI: 10.3233/JIFS-237391

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7093-7107, 2024

Authors: Sundar, R. | Choudhury, Ziaul Haque | Chiranjivi, M. | Parasa, Gayatri | Ravuri, Praseeda | Sivaram, M. | Subramanian, Balambigai | Muppavaram, Kireet | Lakshmi.Challa, Vijaya Madhavi

Article Type: Research Article

Abstract: Embracing Artificial Intelligence (AI) is becoming more common in a variety of areas, including healthcare, banking, and transportation, and it is based on substantial data analysis. However, utilizing data for AI raises a number of obstacles. This extensive article examines the challenges connected with using data for AI, including data quality, volume, privacy and security, bias and fairness, interpretability and ethical considerations, and the required technical knowledge. The investigation delves into each obstacle, providing insightful solutions for businesses and organizations to properly handle these complexities. Organizations may effectively harness AI’s capabilities to make educated decisions by understanding and proactively tackling …these difficulties, obtaining a competitive edge in the digital era. This review study, which provides a thorough examination of numerous solutions developed over the last decade to address data difficulties for AI, is expected to be a helpful resource for the scientific research community. It not only provides insights into current difficulties, but it also serves as a platform for creating novel ideas to alter our approaches to data strategies for AI. Show more

Keywords: Artificial intelligence, data quality, privacy, security, ethical consideration

DOI: 10.3233/JIFS-238830

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7109-7122, 2024

Authors: Xing, Haihua | Zhang, Min | Tong, Qixiang | Zeng, Xiya | Chen, Huannan

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-231883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7123-7141, 2024

Authors: Ding, Ling | Xiong, Xiaobing | Bao, Zhenyu | Hu, Luokai | Chen, Yu | Li, Bijun | Cheng, Yong

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-234248

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7143-7153, 2024

Authors: Wei, Xiaohong

Article Type: Research Article

Abstract: Higher vocational mathematics education is advanced and related to real-time applications providing vast knowledge. Teaching and training peculiar mathematical problems improve their educational and career-focused performance. Therefore optimal performance assessment methods are required for reducing the lack of knowledge in mathematics learning. This article hence introduces an Articulated Performance Assessment Model (APAM) for consenting mathematics assessment. In this model, fuzzy optimization is used for consenting different factors such as understandability, problem-solving, and replication. The understandability is identified using similar problem progression by the students, whereas replication is the application of problem-solving skills for articulated mathematical models. From perspectives, problem-solving and …solution extraction is the theme that has to be met by the student. The assessments hence generate a perplexed outcome due to which the fuzzy optimization for high and low-level understandability is evaluated. The optimization recommends the change in varying steps in problem explanation and iterated replication for leveraging the students’ performance. This process swings between irrelevant and crisp inputs during fuzzification. In this process, the crisp inputs are the maximum replications produced by the students for better understanding. Therefore, the proposed model is evaluated using efficiency, maximum replication, fuzzification rate, and analytical time. Show more

Keywords: Fuzzy optimization, mathematics education, performance evaluation, student assessment

DOI: 10.3233/JIFS-235564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7155-7171, 2024

Authors: Zhao, RongLe | Tang, Xiao

Article Type: Research Article

Abstract:  The theory of interval-valued formal contexts was originally derived from fuzzy formal contexts. While the fuzzy formal context can extract information from fuzzy formal contexts more precisely, it lacks theoretical analysis of formal contexts with interval-valued data types. This paper incorporates the three-way concept into interval-valued formal contexts, and partitions the interval value range of objects and attributes into three regions utilizing the notion of three-way decisions. On the basis of interval-valued information granules, the concepts of negative operators and interval-valued three-way concepts are proposed. They can conduct profounder knowledge discovery in interval-valued formal contexts, and a generation algorithm of …interval-valued three-way concepts is devised. Finally, the effectiveness of the algorithm is substantiated through experimentation Show more

Keywords: Three-way decision model, formal concept analysis, interval-valued, three-way concept, granular computing

DOI: 10.3233/JIFS-236146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7173-7184, 2024

Authors: Feng, Rui | Weng, Lie’en

Article Type: Research Article

Abstract:  The text information processing technology of public health service is one of the hot research topics at present. To improve the defects of public health service texts, such as inaccurate word segmentation, spelling errors and professional vocabulary understanding, this study designed a character-level deep neural network model on the characteristics of public health service texts. In this model, the bidirectional short and short time memory and the attention pooling operation layer are introduced to make the model better classify the text according to the context. In addition, counter perturbation is introduced in this study to improve the robustness and generalization …ability of the model, thus improving its classification effect. The performance verification results show that the proposed model has better classification performance on the public health service text data set. The anti-disturbance samples generated by the model are all in the range of 0–0.2 when WMD deviation degree is measured, while most of the other methods are in the range of 0.4–0.6. The experimental object of this study is ultrasonic examination data. The experimental results show that the automatic analysis model of public health service text based on character level convolutional neural network constructed in this study has excellent accuracy and convergence speed, and has excellent performance in the classification of public health service text in different subject areas. Show more

Keywords: Public health service text, character level convolutional neural network, automatic analysis, counter sample, text classification

DOI: 10.3233/JIFS-236470

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7185-7197, 2024

Authors: Alrasheedi, Adel Fahad | Mishra, Arunodaya Raj | Pamucar, Dragan | Devi, Sarita | Cavallaro, Fausto

Article Type: Research Article

Abstract: In the theory of interval-valued intuitionistic fuzzy set (IVIFS), the rating/grade of an element is the subset of the closed interval [0, 1], therefore the IVIFS doctrine is more useful for the decision expert to present their judgments in terms of intervals rather than the crisp values. The present work develops an integrated decision-making methodology for evaluating sustainable wastewater treatment technologies within the context of IVIFS. The proposed decision-making framework is divided into three stages. First, some Yager weighted aggregation operators and their axioms are developed to combine the interval-valued intuitionistic fuzzy information. These operators can offer us a flexible …way to solve the realistic multi-criteria decision-making problems under IVIFS context. Second, an extension of Symmetry Point of Criterion model is introduced to determine the criteria weights under IVIFS environment. Third, an integrated alternative ranking order model accounting for two-step normalization (AROMAN) approach is proposed from IVIF information perspective. Next, the practicability and efficacy of the developed model is proven by implementing it on a case study of sustainable wastewater treatment technologies evaluation problem with multiple criteria and decision experts. Finally, comparative analysis is discussed to illustrate the consistency and robustness of the obtained outcomes. Show more

Keywords: Interval-valued intuitionistic fuzzy set, sustainability, waste water treatment, Yager aggregation operators, score function, AROMAN

DOI: 10.3233/JIFS-236697

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7199-7222, 2024

Authors: Shanthini, J. | Poovizhi, P. | Kavitha, M.S. | Karthik, S.

Article Type: Research Article

Abstract: PURPOSE: Increasing technological advancements in processing and storage have made it easier to handle formerly difficult jobs like disease diagnosis or semantic segmentation. Eye cancer is a rare but deadly disorder that, if misdiagnosed, can cause blindness or even death. It is essential to find eye cancer early in order to successfully treat it and enhance patient outcomes. The usage of DL methods for medical image analysis, particularly the identification of eye cancer, has fascinated increasing consideration in current era. The demand for efficient tool to detect the eye cancer emphasize the need for reliable detection systems. Examining how explainable …deep learning techniques, in which the model’s decision-making process can be understood and visualized, can increase confidence in and adoption of the deep learning-based approach for detecting eye cancer. Expert input is necessary to train machine learning algorithms properly. As it necessitates knowledge of ophthalmology, radiography, and pathology, this can be difficult for eye cancer identification. The main purpose of the study is to detect the eye cancer with at most accuracy with the utilization of Deep learning-based approach. METHODS: There are four steps involved to achieve the efficient detection system. They are pre-processing, segmentation, augmentation, feature extraction with classification. The Circle Hough Transform is applied to detect the edges in the image. The dataset size is increased by shifting, rotating and flipping augmentation techniques. Deep learning-based approach is suggested for the automatic detection of eye cancer. The two methods named 3XConPool and 10XCon5XPool were investigated using Python learning environment. The two techniques 3XConPool and 10XCon5XPool imply on the Sine Cosine Fitness Grey Wolf Optimization (SCFGWO) algorithm for the adjustment of the hyperparameters. The 3XConPool and 10XCon5XPool methods with SCFGWO are compared with each other and also with the other existing methods. RESULTS: As a comparison to the earlier techniques, the suggested configured Convolution Neural Network with SCFGWP exceeds with regard to high accuracy, recall and precision. The suggested 10XCon5XPool with SCFGWO obtains 98.01 as accuracy compared to other method 3XConPool which results 97.23% accuracy. CONCLUSION: The Proposed Method 1 and Proposed Method 2 is presented here, where Proposed Method 2 with 5 times convolution layer with pooling layer yields high accuracy compared to proposed method 1. The main contribution by the SCFGWO algorithm resulted in the achievement of accuracy. This study will open the door for further investigation and the creation of deep learning-based techniques with optimization for ophthalmic processing. Show more

Keywords: Eye cancer, deep learning model, Sine Cosine Fitness, Grey Wolf Optimizer, fully connected layer

DOI: 10.3233/JIFS-237083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7223-7239, 2024

Authors: Selvin Prem Kumar, S. | Agees Kumar, C. | Venugopal, Anita | Sharma, Aditi

Article Type: Research Article

Abstract: The central nervous system can develop complex and deadly neoplastic growths called brain tumors. Despite being relatively uncommon in comparison to other cancers, brain tumors pose particular challenges because of their delicate anatomical placement and interactions with critical brain regions. The data are taken from TCIA (The Cancer Image Archive) and Kaggle Datasets. Images are first pre-processed using amplified median filter techniques. The pre-processed images are then segmented using the Grabcut method. Feature extraction is extracted using the Shape, ABCD rule, and GLCM are the features were retrieved. The MRI images are then classified into several classes using the Bi-directional …Encoder Representations from Transformers-Bidirectional Long Short Term Memory (BERT-Bi-LSTM) model. Kaggle and TICA datasets are used to simulate the proposed approach, and the results are evaluated in terms of F1-score, recall, precision and accuracy. The proposed model shows improved brain tumour identification and classification. To evaluate the expected technique’s efficacy, a thorough comparison of the current techniques with preceding methods is made. The trial results showed that an efficient hybrid bert model for brain tumor classification suggested strategy provided precision of 98.65%, F1-score of 98.25%, recall of 99.25%, and accuracy of 99.75%. Show more

Keywords: Brain tumor, BERT, Bi-LSTM, grabcut algorithm, classification, feature extraction

DOI: 10.3233/JIFS-237653

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7241-7258, 2024

Authors: Liu, Yang | Yi, Ran | Ma, Ding | Wang, Yongfu

Article Type: Research Article

Abstract: Due to the complexity of the maritime environment and the diversity of the volume and shape of monitored objects in the maritime, existing object detection algorithms based on Convolutional Neural Networks (CNN) are challenging to balance the requirements of high accuracy and high real-time simultaneously in the field of maritime object detection. In response to the characteristics of complex backgrounds, significant differences in object size between categories, and the characteristic of having a large number of small objects in maritime surveillance videos and images, the Maritime dataset with rich scenes and object categories was self-made, and the OS-YOLOv7 algorithm was …proposed based on the YOLOv7 algorithm. Firstly, a feature enhancement module named the TC-ELAN module based on the self-attention mechanism was designed, which enables the feature map used for detection to obtain enhanced semantic information fused from multiple scale features. Secondly, in order to enhance the attention to the area of dense small objects and further improve the positioning accuracy of occluded small objects, this study redesigned the SPPCSPC structure. Then, the network structure was improved to alleviate the problem of decreased object detection accuracy caused by the loss of semantic feature information. Finally, experimental results on self-made datasets and mainstream maritime object detection datasets show that OS-YOLOv7 has a better object detection effect compared to other state-of-the-art (SOTA) object detection algorithms at the cost of reasonable inference time and parameter quantity and can achieve good object detection accuracy on mainstream datasets with high real-time performance. Show more

Keywords: Object detection, real-time, maritime, object recognition, multi-scale

DOI: 10.3233/JIFS-237263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7259-7271, 2024

Authors: Feng, Yue | Zhu, Yuanguo | He, Liu

Article Type: Research Article

Abstract: In recent years, there has been a great development in parameter estimation methods for uncertain differential equations (UDEs). However, the observations we can obtain in real life are limited, in which case the form of function in a UDE is unknown. When dealing with such UDEs, we may use observational data to make nonparametric estimates. There are many nonautonomous systems in real life, and nonautonomous UDEs can simulate some uncertain nonautonomous dynamical systems well. In this paper, a nonparametric estimation method based on the nonautonomous UDEs of the binary Legendre polynomial is proposed. Then, three numerical examples are given to …verify the reliability of nonparametric estimation. As an application, a real data example of global average monthly temperatures is used to illustrate the effectiveness of our method. Show more

Keywords: Uncertainty theory, uncertain differential equations, nonparametric estimation, global average monthly temperature

DOI: 10.3233/JIFS-235022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7273-7281, 2024

Authors: Devulapalli, Praveen Kumar | Boppidi, Srikanth | Sake, Pothalaiah | Matta, Jagadeesh Chandra Prasad | Gopal, Dhanalakshmi | Maganti, Sushanth Babu

Article Type: Research Article

Abstract: High bit-error rates and high transmission rates are required for the Multimedia Wireless Sensor Networks (MWSN) to transmit high-quality pictures through smart devices. To fully utilize the advantages of the technology known as Multiple-Input Multiple-Output, MWSN heavily relies on cooperative communication. Large-scale wireless networks use multi-radio-multi-channel to improve performance by simultaneous broadcasts across symmetrical channels to reduce interference. Expanding cooperative communication in vast networks is subject to severe interference. And, as each node in the network is mobile, routing and transmission delay pose significant problems for cooperative multimedia wireless sensor networks. Mobility increases the MWSNs’ dynamic nature, which reflects in …the overhead control traffic. To address above issues, a Cluster-based Delay Aware Cooperative Relay Selection (CDACRS) was proposed by employing mobility and distance metrics and channel assignment (CA) using dynamic Global Table (GT). To minimize the end-to-end transmission latency without compromising aggregate throughput, our approach chooses a relay-node depending on the mobility and the maximum available channel capacity. Further, to improve the end-to-end energy consumption, Power Aware Transmission (PAT) protocol is developed by calculating maximum transmission power required to meet target bit error rate (BER). The proposed method’s performance is evaluated against the Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS); energy efficient and quality aware multi-hop cooperative image transmission; and Energy Aware Cooperative Image Transmission (EACIT) algorithms and observed that our approach improves the transmission delay by 37.5% (approx.) and end-to-end energy consumption by 48.8%. Show more

Keywords: Wireless multimedia sensor networks, Delay aware routing, Cooperative image transmission, Energy efficiency, Optimal relay selection

DOI: 10.3233/JIFS-234312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7283-7293, 2024

Authors: Srivastava, Jyoti | Srivastava, Ashish Kumar | Muthu Kumar, B. | Anandaraj, S.P.

Article Type: Research Article

Abstract: Text summarizing (TS) takes key information from a source text and condenses it for the user while retaining the primary material. When it comes to text summaries, the most difficult problem is to provide broad topic coverage and diversity in a single summary. Overall, text summarization addresses the fundamental need to distill large volumes of information into more manageable and digestible forms, making it a crucial technology in the era of information abundance. It benefits individuals, businesses, researchers, and various other stakeholders by enhancing efficiency and comprehension in dealing with textual data. In this paper, proposed a novel Modified Generative …adversarial network (MGAN) for summarize the text. The proposed model involves three stages namely pre-processing, Extractive summarization, and summary generation. In the first Phase, the Text similarity dataset is pre-processed using Lowering Casing, Tokenization, Lemmatization, and, Stop Word Removal. In the second Phase, the Extractive summarization is done in three steps Generating similarity metrics, Sentence Ranking, and Sentence Extractive. In the third stage, a generative adversarial network (GAN) employs summary generation to jointly train the discriminative model D and the generative model G. To classify texts and annotate their syntax, Generative Model G employs a convolutional neural network called Bidirectional Gated Recursive Unit (CNN-BiGRU). The performance analysis of the proposed MGAN is calculated based on the parameters like accuracy, specificity, Recall, and Precision metrics. The proposed MGAN achieves an accuracy range of 99%. The result shows that the proposed MGAN improves the overall accuracy better than 9%, 6.5% and 5.4% is DRM, LSTM, and CNN respectively. Show more

Keywords: Text summarization, convolutional neural network, bidirectional gated recurrent unit

DOI: 10.3233/JIFS-236813

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7295-7306, 2024

Authors: Shu, Wenhao | Li, Shipeng | Qian, Wenbin

Article Type: Research Article

Abstract: In real-world scenarios, datasets generally exhibit containing mixed-type of attributes and imbalanced classes distribution, and the minority classes in the data are the primary research focus. Attribute reduction is a key step in the data preprocessing process, but traditional attribute reduction methods commonly overlook the significance of minority class samples, causing the critical information possessed in minority class samples to damage and decrease the performance of classification. In order to address this issue, we develop an attribute reduction algorithm based on a composite entropy-based uncertainty measure to handle imbalanced mixed-type data. To begin with, we design a novel oversampling method …based on the three-way decisions boundary region to synthesize the samples of minority class, for the boundary region to contain more high-quality samples. Then, we propose an attribute measure to select candidate attributes, which considers the boundary entropy, degree of dependency and weight of classes. On this basis, a composite entropy-based uncertainty measure guided attribute reduction algorithm is developed to select the attribute subset for the imbalanced mixed-type data. Experimental on UCI imbalanced datasets, as well as the results indicate that the developed attribute reduction algorithm is significantly outperforms compared to other attribute reduction algorithms, especially in total AUC, F1-Score and G-Mean. Show more

Keywords: imbalanced data, three-way decisions, neighborhood rough set, uncertainty measure, attribute reduction

DOI: 10.3233/JIFS-237211

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7307-7325, 2024

Authors: Li, Jingyan | Mo, Yuanbin | Hong, Lila | Gong, Rong

Article Type: Research Article

Abstract: Dynamic optimization problems exist widely in chemical industry, and its operational variables change with the evolution of both space and time. Therefore, dynamic optimization problems have important research significance and challenges. To solve this problem, a multi-strategy mayfly optimization algorithm (MMOA) combined with control variable parameterization method(CVP) is proposed in this paper. MMOA introduces three improvements on the basis of the original algorithm, namely, circle chaos crossover strategy, center wandering strategy and boundary correction strategy. The hybrid strategy can better balance the exploration and exploitation ability of the algorithm. Based on MATLAB simulation environment, MMOA was evaluated. The experimental results …show that MMOA has excellent performance in solving precision, convergence speed and stability for the benchmark function. For the six classical chemical dynamic optimization problems, MMOA obtained the performance indexes of 0.61071, 0.4776, 0.57486, 0.73768, 0.11861 and 0.13307, respectively. Compared with the data in the previous literature, MMOA can obtain more accurate control trajectory and better performance indicators. It provides an effective way to solve the dynamic optimization problem. Show more

Keywords: Chemical dynamic system, process control, dynamic optimization, mayfly optimization algorithm, control vector parameterization

DOI: 10.3233/JIFS-237786

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7327-7352, 2024

Authors: Zhang, Hong | Liu, Shaojie

Article Type: Research Article

Abstract: The amount of used new energy vehicle transactions is increasing quickly as the social economy matures, yet prices are typically low, making it increasingly difficult to select a fair trading system. Enhancing the score function is crucial in order to account for how different people’s attitudes affect the outcome of decisions and to choose an acceptable trading strategy that is applicable to other scenarios and has a favorable impact on transaction flow. The choice of a trading scheme for new energy-using vehicles is usually regarded as a multi-attribute decision problem. In this paper, the Intuitionistic Fuzzy Hybrid Averaging (IFHA) operator …integration operator with an improved score function is proposed based on the influence of herd mentality on decision-makers. In order to examine the correlation between the score function and the decision outcome using the Spearman rank correlation coefficient, an application to a real situation and some comparative analyses are provided. The outcomes demonstrate that the decision-making process for used car trading schemes can make use of the proposed improved score function. Show more

Keywords: Intuitionistic fuzzy set, multi-characteristic decision making, used new energy car, improved score function, spearman rank correlation coefficient

DOI: 10.3233/JIFS-231358

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7353-7365, 2024

Authors: Xiong, Yu | Cai, Ting | Zhong, Xin | Zhou, Song | Cai, Linqin

Article Type: Research Article

Abstract: Speech emotion recognition is of great significance in the industry such as social robots, health care, and intelligent education. Due to the obscurity of emotional expression in speech, most works on speech emotion recognition (SER) ignore the consistency of speech emotion recognition, leading to fuzzy expression and low accuracy in emotional recognition. In this paper, we propose a semantic aware speech emotion recognition model to alleviate this issue. Specifically, a speech feature extraction module based on CNN and Transformer is designed to extract local and global information from the speech. Moreover, a semantic embedding support module is proposed to use …text semantic information as auxiliary information to assist the model in extracting emotional features of speech, and can effectively overcome the problem of low recognition rate caused by emotional ambiguity. In addition, the model uses a key-value pair attention mechanism to fuse the features, which makes the fusion of speech and text features preferable. In experiments on two benchmark corpora IEMOCAP and EMO-DB, the recognition rates of 74.3% and 72.5% were obtained under respectively, which show that the proposed model can significantly improve the accuracy of emotion recognition. Show more

Keywords: Speech emotion recognition, obscure emotion, semantic awareness, deep learning, pattern recognition

DOI: 10.3233/JIFS-232280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7367-7377, 2024

Authors: Song, Kun | Yi, Huai’an | Song, Xinru | Shu, Aihua | Huang, Jiefeng

Article Type: Research Article

Abstract: The surface roughness of the workpiece is one of the important indicators to measure the quality of the workpiece. Vision-based detection methods are mainly based on human-designed image feature indicators for detection, while the self-extraction method of milling surface features based on deep learning has problems such as poor perception of details, and will be affected by surface rust. In order to solve these problems, this paper proposes a visual inspection method for surface roughness of milling rusted workpieces combined with local equilibrium histogram and CBB-yolo network. Experimental results show that local equilibrium histogram can enhance the milling texture and …improve the accuracy of model detection when different degrees of rust appear on the surface of the milled workpiece. The detection accuracy of the model can reach 97.9%, and the Map can reach 99.3. The inference speed can reach 29.04 frames per second. And the inspection of workpieces without rust, this method also has high detection accuracy, can provide automatic visual online measurement of milling surface roughness Theoretical basis. Show more

Keywords: Surface roughness detection, CBB-yolo, milling workpieces, local equilibrium histosquare

DOI: 10.3233/JIFS-233590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7379-7388, 2024

Authors: Zhou, Yancong | Ma, Yumei | Sun, Xiaochen | Peng, Aihuan | Zhang, Bo | Gu, Xiaoying | Wang, Yan | He, Xingxing | Guo, Zhen

Article Type: Research Article

Abstract: Kiwifruit has a high decay rate, in part because quality changes during storage cannot be easily monitored in real time. In order to better monitor the shelf life of kiwifruit and understand the quality changing process during storage, internal quality indexes such as hardness, respiratory intensity and TSS(Total Soluble Solid) were considered into the prediction models. The prediction models were constructed based on BPNN (Back Propagation Neural Network), Random Forest (RF) and XGBoost (eXtreme Gradient Boosting) respectively. And transfer learning algorithm was used to construct the quality prediction models with BPNN, RF, and XGBoost algorithms as the base learner. In …the experiments, sample data were augmented by adding Gaussian noise, which effectively prevented the model from over-fitting. The experimental results showed that the prediction accuracy of each index based on transfer learning was better than that of individual BPNN, RF and XGBoost. Moreover, the average prediction accuracy of the models based on transfer learning was 96.2%, and that of respiratory intensity was as high as 99.4%. Therefore transfer learning can be used to effectively analyze and predict changes of kiwifruit quality indexes during storage. Show more

Keywords: BPNN, RF, XGBoost, transfer learning, kiwifruit, quality prediction

DOI: 10.3233/JIFS-233718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7389-7400, 2024

Authors: Yang, Ying | Liu, Shaoshuai | Wang, Xiaolong | Guo, Xiaopeng

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-236130

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7401-7412, 2024

Authors: Li, Shanglin | Xiao, Juan | Li, Yalan | Chen, Xuegang

Article Type: Research Article

Abstract: Favorite-longshot and reverse favorite-longshot biases have become widespread in various traditional sports betting markets in recent years. However, there is a limited number of investigations that have been conducted on the eSports betting market or the bettors that operate within it. In the present research, we have made efforts to re-examine the bias and market inefficiency in four typical eSport games: League of Legends , Counter-Strike: Global Offensive , Dota 2 , and King of Glory . Due to the natural characteristics of e-sports, we analyze the reasons for the market biases from 4 aspects: commission, region, match format, and …tournaments. We find that both the favorite-longshot and reverse favorite-longshot bias occur in eSports. Moreover, the distribution of these betting biases is completely different among different eSports game titles and tournaments. The results of the weighted linear regression model reveal that long match format is the important factor of long-short bias, while regional tournaments are the important factor of reverse long-short bias in League of Legends . Show more

Keywords: eSports, betting market, favorite-longshot, market bias, market inefficiency, statistical analysis

DOI: 10.3233/JIFS-232932

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7413-7426, 2024

Authors: Xing, Cheng | Wang, Jie-Sheng | Liu, Yu

Article Type: Research Article

Abstract: With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. An improved Fireworks Algorithm (FWA) with elitism-based selection and optimal particle guidance strategies (EO-FWA) was proposed to address the limitations of the traditional FWA in terms of optimization accuracy and convergence speed, which not only improves the efficiency of the searching agent but also accelerates its convergence speed. In addition, by adopting boundary-based mapping rules, EO-FWA eliminates the randomness of traditional modulo operation mapping rules, which improves its stability and reliability. Twelve benchmark functions …in CEC-BC-2022 are used to test the performance of EO-FWA, and the welded beam design problem is optimized at the end. The results show that EO-FWA exhibits stronger competitiveness than other algorithms in dealing with high-dimensional optimization problems and engineering optimization problem, and it can balance exploitation and exploration effectively so as to prevent the algorithm from falling into local optimal solutions. Show more

Keywords: Fireworks algorithm, elitist-based selection strategy, optimal particle guidance, optimization problem

DOI: 10.3233/JIFS-234536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7427-7446, 2024