Journal of Intelligent & Fuzzy Systems - Volume 43, issue 5

Authors: Yang, Jing | Su, Wei

Article Type: Research Article

Abstract: Interval-valued neutrosophic set (IVNS) plays an important role in dealing with imprecise judgment information. For a multi-attribute decision making problem, the information of alternatives under different attributes is given in the form of interval valued neutrosophic number(IVNN). The objective of the presented paper is to develop a multiple-attribute decision making (MADM) method under interval-valued neutrosophic sets(IVNSs) using the new similarity measurement. The similarity measurement of IVNSs has always been a research hotspot. A new similarity measurement of IVNSs is first proposed in this paper based on Chebyshev distance. The proposed method enriches the existing similarity measurement methods. It can be …applied to not only IVNSs, but also single-valued neutrosophic sets(SVNSs). The influence of each attribute on the decision-making result can be described by the weight. How to formulate the weight scientifically is vital as well. In this paper, the objective weight is calculated by normalizing the grey correlation coefficient obtained by a score function which can be applied to IVNSs. The objective weight is then combined with the subjective one by considering an adjustment factor with the weighted summation method. The adjustment factor is determined by the importance of subjective weight. Finally, an example is used to illustrate the comparison results of the proposed algorithm and other three ones. The comparison shows that the proposed algorithm is effective and can identify the optimal scheme quickly. Show more

Keywords: Fuzzy multi-attribute decision making, similarity measure, chebyshev distance, interval-valued neutrosophic sets

DOI: 10.3233/JIFS-220534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6549-6559, 2022

Authors: Tan, Guimei | Yu, Xichang

Article Type: Research Article

Abstract: As an important tool to measure the degree of difficulty of predicting the realization of an uncertain set, entropy theory of uncertain set has been investigated by many scholars. In order to measure the uncertainty associated with some uncertain sets, this paper first proposes the arc entropy for an uncertain set. Then a computational arc entropy formula via inverse membership function is introduced to calculate the arc entropy more quickly, and some properties of arc entropy are studied. Furthermore, some applications are also provided to illustrate the superiority of the arc entropy.

Keywords: Uncertainty theory, uncertain set, arc entropy, portfolio selection

DOI: 10.3233/JIFS-220564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6561-6574, 2022

Authors: Tang, Jianfei | Zhao, Hui

Article Type: Research Article

Abstract: The focus of a large amount of research on malware detection is currently working on proposing and improving neural network structures, but with the constant updates of Android, the proposed detection methods are more like a race against time. Through the analysis of these methods, we found that the basic processes of these detection methods are roughly the same, and these methods rely on professional reverse engineering tools for malware analysis and feature extraction. These tools generally have problems such as high time-space cost consumption, difficulty in achieving concurrent analysis of a large number of Apk, and the output results …are not convenient for feature extraction. Is it possible to propose a general malware detection process implementation platform that optimizes each process of existing malware detection methods while being able to efficiently extract various features on malware datasets with a large number of APK? To solve this problem, we propose an automated platform, AmandaSystem, that highly integrates the various processes of deep learning-based malware detection methods. At the same time, the problem of over privilege due to the openness of Android system and thus the problem of excessive privileges has always required the accurate construction of mapping relationships between privileges and API calls, while the current methods based on function call graphs suffer from inefficiency and low accuracy. To solve this problem, we propose a new bottom-up static analysis method based on AmandaSystem to achieve an efficient and complete tool for mapping relationships between Android permissions and API calls, PerApTool. Finally, we conducted tests on three publicly available malware datasets, CICMalAnal2017, CIC-AAGM2017, and CIC-InvesAndMal2019, to evaluate the performance of AmandaSystem in terms of time efficiency of APK parsing, space occupancy, and comprehensiveness of extracted features, respectively, compared with existing methods were compared. Show more

Keywords: Cybersecurity, android malware analysis, static analysis, dynamic analysis, least privilege

DOI: 10.3233/JIFS-220567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6575-6589, 2022

Authors: Cypto, J. | Karthikeyan, P.

Article Type: Research Article

Abstract: With the growth in vehicular traffic, there is a greater risk of road accidents. Over speeding, intoxicated driving, driver distractions, red-light runners, ignoring safety equipment such as seat belts and helmets, non-adherence to lane driving, and improper overtaking are the leading causes of accidents. Speed violation, in particular, has a significant influence on today’s transportation. Also, detecting this speed violation and punishing this violator are more time-consuming tasks. For that reason, a novel automatic speed violation detection in traffic based on Deep learning is proposed in this paper. This proposed method is separated into two working modules: object detection and …license plate recognition. The object detection module uses the most efficient PP YOLO neural networks. It utilizes open ALPR (Automatic License Plate Recognition) for the vehicle’s number plate identification, which passes the traffic above maximum speed. With the number plate details, the authorities can take action against the rule violator with less time and effort. The simulation results show that the proposed automatic speed violation detection system also has an accuracy rate of 98.8% for speed violation detection and 99.3% for license plate number identification, demonstrating that the approach described in this work has a higher performance in terms of accuracy. Furthermore, the proposed technique was compared to recent existing results. Show more

Keywords: Speed violation, intoxicated driving, deep learning, PP YOLO, object detection, license plate recognition

DOI: 10.3233/JIFS-220577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6591-6606, 2022

Authors: Shi, Xuecheng | Lin, Zhichao | Zhou, Ligang | Bao, Hengjia

Article Type: Research Article

Abstract: Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are an effective tool for representing fuzzy linguistic information, and they can obtain a wider expression scope than linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers by increasing the value of parameter q . In this paper, we propose a new similarity measure called the grey similarity degree between any two Lq-ROFNs based on the concept of the grey correlation degree. Considering the significance of determining unknown weights, we also propose a grey correlation method to determine each expert’s weight under different alternatives and attributes, and we construct an optimization model to determine …incompletely known attribute weights. Furthermore, an approach to linguistic q-rung orthopair fuzzy multiple-attribute group decision making is proposed that combines the grey similarity degree with the PROMETHEE II method. Finally, a numerical example is given to illustrate the effectiveness of the proposed method, and a sensitivity analysis and comparison analysis are also performed. Show more

Keywords: Linguistic q-rung orthopair fuzzy numbers, grey correlation degree, grey similarity degree, PROMETHEE, group decision making

DOI: 10.3233/JIFS-220579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6607-6625, 2022

Authors: An, Qing | Tang, Ruoli | Li, Xueyan | Zhang, Xiaodi | Li, Xin

Article Type: Research Article

Abstract: In order to optimally control the marine hybrid power system (HPS) under increasingly complex regulation constraints or hardware constraints, an efficient power-flow scheduling model and optimization algorithm are of great importance. This work focuses on the optimal power-flow scheduling of marine HPS, especially on the efficiency improvement of the penalty functions for satisfying complex constraints. To be specific, an optimal operation model of marine HPS is discussed, and the complex model constraints are described as various penalty functions. Secondly, a novel optimization algorithm, namely adaptive multi-context cooperatively coevolving differential evolution algorithm with random topology and mutated context vector (AMCCDE - rt - mcv ) …is developed to optimize the aforementioned model. In order to ensure the satisfaction of the complex model constraints, the detailed forms for penalty functions are researched and the optimal parameters for penalty functions are comprehensively compared, analysed and tested by a set of numerical experiments. Finally, the developed methodologies are tested by simulation experiments. Experimental results show that the damping factor, exponent parameter and punish strength constant effect the efficiency of penalty functions a lot, and the developed penalty functions can effectively satisfy all the model constraints with fast response speed. With the integration of penalty functions, the developed methodology can obtain promising performance on the optimal scheduling of the evaluated marine HPS. Show more

Keywords: Hybrid power system, optimal energy management, penalty function, optimization algorithm, differential evolution

DOI: 10.3233/JIFS-220645

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6627-6649, 2022

Authors: Suresh, K. | Jagatheeswari, P.

Article Type: Research Article

Abstract: Renewable energy has seen a substantial increase in deployment as an alternative to traditional power sources. However, two fundamental constraints exist that preclude widespread adoption: the availability of the generated power and the expense of the equipment. One of the most critical difficulties with this sort of hybrid system is to appropriately design the Hybrid Renewable Energy System (HRES) elements so that they fulfill all load requirements while requiring the least amount of investment and running expenditures. This research proposes a novel technique for evaluating the optimal smart grid linking Hybrid Renewable Energy (Solar photovoltaic and wind) with battery, to …increase profitability, dependability, and feasibility. A multiobjective function is suggested and constructed to be optimized utilizing two optimization algorithms: Enhanced Particle Swarm Optimization (EPSO) and Harris Hawks Optimization (HHO) algorithm with Fuzzy-Extreme Learning Machine (ELM). The primary goal for the HRES is to operate optimally to reduce the cost of energy generat ion through hourly day-ahead. Here, the Fuzzy-ELM is utilized to predict the required load of the smart grid-connected system and hybrid EPSO-HHO, which are introduced to solve the problem of HRES economic analysis. Finally, the suggested EPSO-EHO method is implemented in the MATLAB software, and its performance comparison is made with other existing methods such as PSO, WOA, and HHO. The simulation result shows that the cost of the newly suggested EPSO-HHO technique-based Hybrid Renewable Energy System is less than PSO, WOA, and HHO by 4.89 %, 4.51 %, and 4.05 %, respectively. Show more

Keywords: Harris Hawks’ Optimization, economic analysis, renewable energy sources, Extreme Learning Machine, smart grid

DOI: 10.3233/JIFS-220726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6651-6662, 2022

Authors: Durmaz, Nida | Budak, Ayşenur

Article Type: Research Article

Abstract: This study aims to define the adoption barriers to Industry 4.0 for sustainable supply chain and define their causalities and, dependencies, hierarchical levels of these barriers. Firstly, a framework for critical barriers to Industry 4.0 for sustainable supply chain management is created with literature review and experts for the first time. Then an integrated approach of Grey DEMATEL – ANP is proposed to analyze the adoption barriers to Industry 4.0 in sustainable supply chain management. The proposed method determines the cause-effect relationship among barriers, the strength of interactions, and the relative weights of critical barriers to Industry 4.0 in a …sustainable supply chain. The results show that uncertainty about economic benefits, resistance to change, and lack of infrastructure and tools for Industry 4.0 in the Sustainable supply chain are crucial barriers to implementing Industry 4.0 technologies in SSC. This study can help decision-makers and managers define the barriers and provide the theoretical guideline to implement Industry 4.0 technologies across the sustainable supply chain successfully. Show more

Keywords: Sustainable supply chain management, Grey DEMATEL, ANP, Industry 4.0 adoption barriers

DOI: 10.3233/JIFS-220732

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6663-6682, 2022

Authors: Jiang, Ruiyang

Article Type: Research Article

Abstract: The Pile motion seems to be one of the most critical in pile failure that requires appraisal before installing piles. The variables to estimate the Pile Settlement parameter, there are several methods. Among existing theoretical ways to investigate the pile movement mathematically, most studies have tried to model the piles’ settlement overloading period using artificial intelligence. Thus, this research has used the Artificial Neural Network to have the actual status of pile motion vertically over the loading periods dynamically and statically. Therefore, the present research has utilized the Radial Basis Function Neural Network joint with Equilibrium Optimizer Algorithm and Grasshopper …Optimization Algorithm to figure out the optimum number of neurons within the hidden layer. Kuala Lumpur’s Klang Valley Mass Rapid Transit transportation network, Malaysia, opted to model the piles’ settlement and earth properties via the proposed hybrid RBF-GOA and RBF-EOA frameworks. By modeling both frameworks, the error index of RMSE for RBF-GOA and HRBF-EOA were gained to 0.6312 and 0.5947, respectively. However, the VAF indicator showed identical results of the rates 96.98 and 97.33, respectively. Overly, the RBF-EOA represented better than RBF-GOA by little efficiency. Show more

Keywords: Pile in rock, settlement, prediction, radial basis function, equilibrium optimizer algorithm, grasshopper optimization, R-value correlation

DOI: 10.3233/JIFS-220741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6683-6695, 2022

Authors: Kalaichelvi, V. | Vimala Devi, P. | Meenakshi, P. | Swaminathan, S. | Suganya, S.

Article Type: Research Article

Abstract: The billions of bits of information are transferred each second through the internet every day. The information may be text, image, audio or video etc, accordingly, we need some protection mechanism while sharing confidential data. Generally, RSA algorithm is used for encrypting the Secret images. However, the security provided by Elliptic Curve Cryptography (ECC) is higher with lower sized key than the RSA algorithm. So, this article proposes an extended Elliptic Curve encryption approach for encrypting the secret images. In this system, the secret image is partitioned into three color image planes such as Red, Green and Blue. By applying …Radix-64 encoding and Mapping table, these planes are converted into elliptic curve points and then these points are encrypted using ECC algorithm. Again, these points are applied to the Radix-64 decoding and the mapping table to get ciphered-image. At last, the key parameters such as a, b, p and Generator point (G) are embedded in the last four pixel positions of the ciphered-image. In order to get the original secret image, the recipient must extract these key parameters from the encrypted image and then apply the remaining processes to the encrypted image in the opposite order. Experimental results tested using MATLAB R2021b and it shows that the NPCR and UACI values are 99.54% and 28.73 % and better quality feature is attained since the entropy value is almost closer to eight. So, the proposed image encryption has robust capacity to fight against the differential attack. Show more

Keywords: ECC, Radix-64 conversion, image encryption, image decryption, security

DOI: 10.3233/JIFS-220767

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6697-6708, 2022

Authors: Mishra, Anju | Singh, Laxman | Pandey, Mrinal | Lakra, Sachin

Article Type: Research Article

Abstract: Diabetic Retinopathy (DR) is a disease that damages the retina of the human eye due to diabetic complications, resulting in a loss of vision. Blindness may be avoided If the DR disease is detected at an early stage. Unfortunately, DR is irreversible process, however, early detection and treatment of DR can significantly reduce the risk of vision loss. The manual diagnosis done by ophthalmologists on DR retina fundus images is time consuming, and error prone process. Nowadays, machine learning and deep learning have become one of the most effective approaches, which have even surpassed the human performance as well as …performance of traditional image processing-based algorithms and other computer aided diagnosis systems in the analysis and classification of medical images. This paper addressed and evaluated the various recent state-of-the-art methodologies that have been used for detection and classification of Diabetic Retinopathy disease using machine learning and deep learning approaches in the past decade. Furthermore, this study also provides the authors observation and performance evaluation of available research using several parameters, such as accuracy, disease status, and sensitivity. Finally, we conclude with limitations, remedies, and future directions in DR detection. In addition, various challenging issues that need further study are also discussed. Show more

Keywords: Retinal fundus images, machine learning, deep learning, classification, Diabetic retinopathy

DOI: 10.3233/JIFS-220772

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6709-6741, 2022

Authors: Niu, Guo | Ma, Zhengming | Liu, Xi

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-220785

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6743-6754, 2022

Authors: Aras, Cigdem G. | Al-shami, Tareq M. | Mhemdi, Abdelwaheb | Bayramov, Sadi

Article Type: Research Article

Abstract: A bipolar soft set is given by helping not only a chosen set of “parameters” but also a set of oppositely meaning parameters called “not set of parameters”. It is known that a structure of bipolar soft set is consisted of two mappings such that F  : E  → P  (X ) and G  :⌉ E  → P  (X ), where F explains positive information and G explains opposite approximation. In this study, we first introduce a new definition of bipolar soft points to overcome the drawbacks of the previous definition of bipolar soft points given in [34]. Then, we explore …the structures of bipolar soft locally compact and bipolar soft paracompact spaces. We investigate their main properties and illuminate the relationships between them. Also, we define the concept of a bipolar soft compactification and investigate under what condition a bipolar soft topology forms a bipolar soft compactification for another bipolar soft topology. To elucidate the presented concepts and obtained results, we provide some illustrative examples. Show more

Keywords: bipolar soft set, bipolar soft topology, bipolar soft locally compactness, bipolar soft paracompactness

DOI: 10.3233/JIFS-220834

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6755-6763, 2022

Authors: Xu, Qin | Xu, Shumeng | Wang, Dongyue | Yang, Chao | Liu, Jinpei | Luo, Bin

Article Type: Research Article

Abstract: Representing features at multiple scales is of great significance for hyperspectral image classification. However, the most existing methods improve the feature representation ability by extracting features with different resolutions. Moreover, the existing attention methods have not taken full advantage of the HSI data, and their receptive field sizes of artificial neurons in each layer are identical, while in neuroscience, the receptive field sizes of visual cortical neurons adapt to the neural stimulation. Therefore, in this paper, we propose a Res2Net with spectral-spatial and channel attention (SSCAR2N) for hyperspectral image classification. To effectively extract multi-scale features of HSI image at a …more granular level while ensuring a small amount of calculation and low parameter redundancy, the Res2Net block is adopted. To further recalibrate the features from spectral, spatial and channel dimensions simultaneously, we propose a visual threefold (spectral, spatial and channel) attention mechanism, where a dynamic neuron selection mechanism that allows each neuron to adaptively adjust the size of its receptive fields based on the multiple scales of the input information is designed. The comparison experiments on three benchmark hyperspectral image data sets demonstrate that the proposed SSCAR2N outperforms several state-of-the-art deep learning based HSI classification methods. Show more

Keywords: Hyperspectral image classification, deep learning, convolutional neural networks (CNNs), Res2Net, visual attention mechanism

DOI: 10.3233/JIFS-220863

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6765-6781, 2022

Authors: Sanjana, R. | Ramesh, G.

Article Type: Research Article

Abstract: This paper is concerned with the solution mechanism to solve the transportation problem under unpredictability by using interval valued intuitionistic fuzzy parameters. The parameters are chosen as intervals in which costs are modeled by intuitionistic fuzzy numbers, whereas source and destination are taken as crisp values. Various methods of transportation problem like VAM, Monalisha’s Approximation method, Zero point method are used to illustrate the cost in interval numbers by using the interval arithmetic operations. For each method, a solution is derived without converting into crisp expression followed by a graphical representation.

Keywords: Interval valued intuitionistic fuzzy numbers, inteval valued intuitionistic fuzzy transportation problem, interval arithmetic, interval VAM, interval ZPM

DOI: 10.3233/JIFS-220946

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6783-6792, 2022

Authors: Lakkshmanan, Ajanthaa | Anbu Ananth, C. | Tiroumalmouroughane, S.

Article Type: Research Article

Abstract: Pancreatic tumor is the deadliest disease which needs earlier identification to reduce the mortality rate. With this motivation, this study introduces a Multi-Objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis (MOM-IDL) model. The proposed MOM-IDL technique encompasses an adaptive Weiner filter based pre-processing technique to enhance the image quality and get rid of the noise. In addition, multi-level thresholding based segmentation using Kapur’s entropy is employed where the threshold values are optimally chosen by the barnacles mating optimizer (BMO). Besides, densely connected network (DenseNet-169) is employed as a feature extractor and fuzzy support vector machine (FSVM) is …utilized as a classifier. For improving the classification performance, the BMO technique was implemented for fine-tuning the parameters of the FSVM model. The design of MOBMO algorithm for threshold selection and parameter optimization processes shows the novelty of the work. A wide range of simulations take place on the benchmark dataset and the experimental results highlighted the enhanced performance of the MOM-IDL technique over the recent state of art techniques. Show more

Keywords: Pancreatic tumor, computer aided diagnosis, deep learning, image classification, parameter optimization

DOI: 10.3233/JIFS-221171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6793-6804, 2022

Authors: Qiu, Chenye | Fang, Huixing | Liu, Ning

Article Type: Research Article

Abstract: Microgrid (MG) systems are growing at a rapid pace since they can accommodate the high amount of renewable energy. Since the MG consists of small distributed generators (DG) with volatile characteristics, an efficient energy management system is the main requisite in MG. In this paper, a chaotic sine cosine algorithm with crossover operator (CSCAC) is proposed for the day-ahead MG optimal energy scheduling problem. CSCAC includes a novel non-linear transition parameter based on the chaos system which can help the algorithm escape from local optima. A chaotic search operator is proposed to enhance the local search ability. Furthermore, a crossover …operator is devised to combine the advantages of different search strategies and achieve a comparatively better balance of exploration and exploitation. First, the effectiveness of CSCAC is validated on several benchmark functions. Then, it is applied to the day-ahead energy scheduling in a MG with three wind power plants, two photovoltaic power plants and a combined heat and power plant (CHP). Furthermore, it is implemented in two more cases considering the uncertainty and stochastic nature of the renewable power sources. Experimental results demonstrate the superiority of CSCAC over other comparative algorithms in the optimal MG energy management problem. Show more

Keywords: Sine cosine algorithm, microgrid, chaotic system, energy scheduling, uncertainty

DOI: 10.3233/JIFS-221178

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6805-6819, 2022

Authors: Zhang, Taoyun | Zhang, Yugang | Zhang, Guangdong | Xue, Ling | Wang, Jin

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-221185

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6821-6830, 2022

Authors: Li, Xiang | Yu, Junqi | Wang, Qian | Dong, Fangnan | Cheng, Renyin | Feng, Chunyong

Article Type: Research Article

Abstract: Short-term energy consumption prediction of buildings is crucial for developing model-based predictive control, fault detection, and diagnosis methods. This study takes a university library in Xi’an as the research object. First, a time-by-time energy consumption prediction model is established under the supervised learning approach, which uses a long short-term memory (LSTM) network and a Multi-Input Multi-Output (MIMO) strategy. The experimental results validate the model’s validity, which is close enough to physical reality for engineering purposes. Second, the potential of the people flows factor in energy consumption prediction models is explored. The results show that people flow has great potential in …predicting building energy consumption and can effectively improve the prediction model performance. Third, a diagnostic method, which can recognize abnormal energy consumption data is used to diagnose the unreasonable use of the building during each hour of operation. The method is based on differences between actual and predicted energy consumption data derived from a short-term energy consumption prediction model. Based on actual building operation data, this work is enlightening and can serve as a reference for building energy efficiency management and operation. Show more

Keywords: Deep learning, energy consumption prediction, energy consumption diagnosis, people flows

DOI: 10.3233/JIFS-221188

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6831-6848, 2022

Authors: Senthil, P. | Selvakumar, S.

Article Type: Research Article

Abstract: Digital evidence is an integral part of any trial. Data is critical facts, encrypted information that requires explanation in order to gain meaning and knowledge. The current process of digital forensic research cannot effectively address the various aspects of a complex infrastructure. Therefore, digital forensics requires the optimal processing of a complex infrastructure that differs from the current process and structure. For a long time, digital forensic research has been utilized to discuss these issues. In this research, we offer a forensic investigation hybrid deep learning approach based on integrated multi-model data fusion (HDL-DFI). First, we concentrate on digital evidence …collection and management systems, which can be achieved by an integrated data fusion model with the help of an improved brain storm optimization (IBSO) algorithm. Here, we consider several multimedia data’s for evidence purposes, i.e. text, image, speech, physiological signals, and video. Then, we introduce a recurrent multiplicative neuron with a deep neural network (RM-DNN) for data de-duplication in evidence collection, which avoids repeated and redundant data. After that, we design a multistage dynamic neural network (MDNN) for sentimental analysis to decide what type of crime has transpired and classify the action on it. Finally, the accuracy, precision, recall, F1-score, G-mean, and area under the curve of our proposed HDL-DFI model implemented with the standard benchmark database and its fallouts are compared to current state-of-the-art replicas (AUC). The results of our experiments show that the computation time of the proposed model HDL-DFI is 20% and 25% lower than the previous model’s for uploading familiar and unfamiliar files, 22% and 29% lower for authentication generation, 23% and 31% lower for the index service test scenario, and 24.097% and 32.02% lower for familiarity checking . Show more

Keywords: Digital forensics, evidence collection, evidence protection, deep learning, multi model fusion

DOI: 10.3233/JIFS-221307

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6849-6862, 2022

Authors: Muruganandham, R. | Sheik Abdullah, A. | Selvakumar, S.

Article Type: Research Article

Abstract: The primary goal of this study is to optimize web content for a positive user experience and to develop a data-driven methodology to assess the success of visitor flow on a website for school children. Through Vision-Based Page Segmentation, the suggested study work intends to broaden the stated web approach’s reach and statistical inference. The improvisation has been made accordingly with the semantic structure observed from each node with the designated degree of coherence to indicate the content in spatial and block based on visual perception for each event. The click count (number of clicks) is calculated for all the …possibilities of Quest Software. The most frequently accessed event is displayed at the top to enhance usability and visibility with an accuracy of about 92.80%. From the experimental analysis, it has been observed that most of the students preferred events corresponding to drawing, rhymes, and rangoli with a willingness rate of above 80%, respectively. Statistical analysis has been made using chi-square analysis, and it has been found that the levels from A to D are significant for three years with a P -value < 0.001. Sentimental analysis of feedback collected from the participants about the events is also done, and the most preferred event is suggested for the upcoming years. Show more

Keywords: Data driven model, event analysis, optimization, page segmentation, web analytics

DOI: 10.3233/JIFS-221392

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6863-6875, 2022

Authors: Mohana Sundaram, K.D. | Shankar, T. | Sudhakar Reddy, N.

Article Type: Research Article

Abstract: Computer vision functions like object detection, image segmentation, and image classification were recently getting advance due to Convolutional Neural Networks (CNNs). In the food and agricultural industries, image classification plays a critical role in quality control. CNNs are made up of layers that alternate between convolutional, nonlinearity, and feature pooling. In this article, we proposed Fuzzy Pooling, a novel pooling approach that works based on fuzzy logic, that can increase the accuracy of the CNNs by replacing the conventional pooling layer. This proposed Fuzzy Pooling was put to the test with CIFAR-10 and SVHN data sets on single layer CNN, …and it outperformed previous pooling strategies by achieving 92% and 97% classification accuracy. This proposed Fuzzy Pooling layer was replaced the Max Pooling layer in the ThinNet architecture, and it was trained using the back propagation method. It was demonstrated experimentally on the Lemon fruit data set to classify the fruits into three categories such as Good, Medium, and Poor. In order to classify the lemon fruit into three categories, the 1000 Fully Connected layer in ThinNet architecture was replaced with three Fully Connected layers. The Modified ThinNet architecture called ThinNet_FP was trained with a learning rate of 0.001 and achieved 97% accuracy in classifying the images and outperformed previous CNN architectures when trained on the same data set. Show more

Keywords: Convolutional neural network (CNN), fuzzy logic, fuzzy pooling, back propagation, fruit classification

DOI: 10.3233/JIFS-221550

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6877-6891, 2022