Journal of Intelligent & Fuzzy Systems - Volume 45, issue 4

Authors: Du, Kang | Fan, Ruguo | Xue, Hu | Wang, Yitong | Bao, Xuguang

Article Type: Research Article

Abstract: The mechanism of promoting cooperation in the public goods game has always been concerned by scholars. However, most of the existing studies are based on the premise that participants are self-interested. In order to explore why some sellers on e-commerce platforms voluntarily maintain the platform’s reputation, we incorporate heterogeneous social preferences of sellers into the spatial public goods game. We find that heterogeneous social preferences can enhance cooperation by improving collective rationality. Specifically, the altruistic preference of sellers can greatly reduce free-riding behavior, while the inequality aversion preference has a little inhibitory effect. Interestingly, when the benefit of maintaining the …platform’s reputation is relatively high, the reciprocal preference can inhibit cooperation, but it can promote cooperation when the benefit is relatively small. This is due to the existence of some loosely connected but stable cooperative or defective clusters of sellers in e-commerce platforms. Furthermore, we propose a dynamic punishment mechanism to punish free riders. We observe that the dynamic punishment mechanism is more effective than the static punishment mechanism in solving the second-order free-riding problem faced by punishers. Increasing the enhancement factor of public goods is identified as a fundamental approach to mitigating this problem. Show more

Keywords: E-commerce platform, altruism, inequality aversion, reciprocity, spatial public goods game

DOI: 10.3233/JIFS-232322

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6453-6467, 2023

Authors: Thao, Le Quang | Diep, Nguyen Thi Bich | Bach, Ngo Chi | Linh, Le Khanh | Giang, Nguyen Do Hoang

Article Type: Research Article

Abstract: In this study, we introduce a new method to address the pressing issue of school violence using Artificial Intelligence (AI). School violence is a critical issue that affects the safety and well-being of students, teachers, and the school community as a whole. Violent behaviors, such as bullying, physical assaults, and weapon use, can have long-term effects on students’ psychological health and academic performance. To reduce these issues, we developed a lightweight Deep Learning model that can be integrated into a school’s surveillance camera system to quickly detect violent fighting behaviors for timely intervention by school staff. The proposed FightNet model …consists of three components: MobileNetV2 backbone, Feature Pyramid Network (FPN) neck, and Centernet Object as a Point (COaP) head. By optimizing the hyperparameters of the model to extract keypoints in image frames from the COCO dataset, we applied an LSTM model to determine the temporal dependence of actions and classify them as “fighting” or “normal” using the UBI-Fights dataset. The FightNet model achieved mAP@0.5 of 45.34% and mAP@0.95 of 55.89% in estimating keypoints, and 72.68% accuracy and 71.69% F1-score in predicting actions. Based on these results, we conclude that the proposed model can effectively address the issue of school violence. Show more

Keywords: School fighting violence, multi-keypoints, FightNet, light-weight model, LSTM

DOI: 10.3233/JIFS-232480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6469-6483, 2023

Authors: Javeed, M.D. | Nagaraju, Regonda | Chandrasekaran, Raja | Rajulu, Govinda | Tumuluru, Praveen | Ramesh, M. | Suman, Sanjay Kumar | Shrivastava, Rajeev

Article Type: Research Article

Abstract: The process of partitioning into different objects of an image is segmentation. In different major fields like face tracking, Satellite, Object Identification, Remote Sensing and majorly in medical field segmentation process is very important to find the different objects in the image. To investigate the functions and processes of human boy in radiology magnetic resonance imaging (MRI) will be used. MRI technique is using in many hospitals for the diagnosis purpose widely in finding the stage of a particular disease. In this paper, we proposed a new method for detecting the tumor with enhanced performance over traditional techniques such as …K-Means Clustering, fuzzy c means (FCM). Different research methods have been proposed by researchers to detect the tumor in brain. To classify normal and abnormal form of brain, a system for screening is discussed in this paper which is developed with a framework of artificial intelligence with deep learning probabilistic neural networks by focusing on hybrid clustering for segmentation on brain image and crystal contrast enhancement. Feature’s extraction and classification are included in the developing process. Performance in Simulation of proposed design has shown the superior results than the traditional methods. Show more

Keywords: Segmentation, brain tumor, probabilistic neural networks, feature extraction, classification

DOI: 10.3233/JIFS-232493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6485-6500, 2023

Authors: Zhan, Huawei | Pei, Xinyu | Zhang, Tianhao | Zhang, Linqing

Article Type: Research Article

Abstract: A flame detection algorithm based on the improved SSD (Single Shot Multibox Detector) is proposed in response to the issues with the limited detection distance, delayed reaction, and high false alarm rate of previous flame detection systems. First, the ResNet-50-SPD model was added to the original backbone network to improve the detection of low resolution and tiny objects. After that, incorporate feature fusion between layers to improve the bond between contexts. Before the feature entered the prediction, the impact of channel number reduction was eliminated using the adaptive module AAM. According to experimental findings, the modified SSD algorithm’s mAP value …on on the random division dataset and K-fold verification dataset reaches 87.89% and 89.63%, respectively, which is 3.97% and 5.17% higher than the original SSD, while the FPS remains at 64.9 f/s. It is helpful to improve the time of the fire alarm, find the ignition point in time, and better meet the actual engineering needs of fire monitoring. Show more

Keywords: Flame detection, SSD, ResNet-50-SPD, feature fusion, AAM

DOI: 10.3233/JIFS-232645

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6501-6512, 2023

Authors: Zhang, Boqiang | Gao, Tianzhi | Chen, Yanbin | Jin, Xin | Feng, Tianpei | Chen, Xinming

Article Type: Research Article

Abstract: A large number of grain machinery and vehicle equipment are usually required in the raw grain storage phase, and these objects together form the path planning map environment for the unmanned grain transfer vehicle. After using LiDAR to build a map of the environment for path planning, these dense and cluttered obstacles tend to affect the path planning effect making the unmanned transfer vehicle create a crossing from the impenetrable dense obstacles. To address this problem, this paper firstly deals with obstacles by fusing the DBSCAN clustering algorithm and K-means clustering algorithm, clustering obstacles, and extracting the cluster centroid and …boundary points of each obstacle class to avoid the above situation. Secondly, the specific A* algorithm is improved, the search field way of the A* algorithm is optimized, and the optimized 5×5 field search way is used instead of the traditional 3×3 field search way of A* to improve the node search efficiency of the algorithm. Finally, the repulsion function of the artificial potential field algorithm is added to the A* heuristic function as a safety function to increase the obstacle avoidance capability of the A* algorithm. After verification, the improvement can operate better in the dense and cluttered obstacle environment. Show more

Keywords: Grain depot, food logistics, clustering algorithm, A* algorithm, artificial potential field, raster map

DOI: 10.3233/JIFS-232780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6513-6533, 2023

Authors: Xiaozhen, Zheng | Le, Xuong

Article Type: Research Article

Abstract: Carbon dioxide is produced during the manufacture of normal Portland cement; however, this gas may be minimized by utilizing ground granulated blast furnace slag (GGBFS ). When planning and constructing concrete buildings, compressive strength (f c ), a crucial component of concrete mixtures, is a need. It is essential to assess this GGBFS -blended concrete property precisely and consistently. The major objective of this research is to provide a practical approach for a comprehensive evaluation of machine learning algorithms in predicting the f c of concrete containing GGBFS . The research used the Equilibrium optimizer (EO ) …to enhance and accelerate the performance of the radial basis function (RBF ) network (REO ) and support vector regression (SVR ) (SEO ) analytical methodologies. The novelty of this work is particularly attributed to the application of the EO , the assessment of f c including GGBFS , the comparison with other studies, and the use of a huge dataset with several input components. The combined SEO and REO systems demonstrated proficient estimation abilities, as evidenced by coefficient of determination (R 2 ) values of 0.9946 and 0.9952 for the SEO ’s training and testing components and 0.9857 and 0.9914 for the REO , respectively. The research identifies the SVR optimized with the EO algorithm as the most successful system for predicting the f c of GGBFS concrete. This finding has practical implications for the construction industry, as it offers a reliable method for estimating concrete properties and optimizing concrete mixtures. Show more

Keywords: Compressive strength, ground granulated blast furnace slag, prediction, equilibrium optimizer, support vector regression

DOI: 10.3233/JIFS-233428

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6535-6547, 2023

Authors: Umaamaheshvari, A. | Sivasankari, K. | Suguna, N. | Kshirsagar, Pravin R. | Tirth, Vineet | Rajaram, A.

Article Type: Research Article

Abstract: The optimization algorithms mimic the process of natural evolution. In watermarking, appropriate positions to insert the watermark is identified by the image that covers. These positions represent the populations of genetic algorithms. The major drawback in genetic algorithm are that it may get stuck-up at a local optimum while moving towards the best global solution and hence the result is poor when compared to other local optimization techniques. The proposed work based on Bandelet based biogeography firefly hybrid algorithms. The Number of pixels, Intensity of the pixel and contrast are considered for watermarking. The redundancy is reduced by Bandelet and …used to determine the best location to embed the information into an image both locally and globally. Results of these techniques are compared based on coefficient correlation, index structural similarity, and noise ratio from peak signal. Show more

Keywords: Biogeography firefly algorithm, genetic algorithm, optimization, peak signal to noise ratio

DOI: 10.3233/JIFS-224590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6549-6559, 2023

Authors: Birong, Zhang

Article Type: Research Article

Abstract: In this paper, a bi-objective mixed-integer linear programming model is constructed to manage the pharmaceutical supply chain of a hospital. The proposed model aims to concurrently reduce the overall cost of obtaining drugs from several vendors and choose the best suitable source. The suggested model takes into account supplier distance, inventory management, and multi-product and multi-period. The major assumptions of the proposed model are product storage for future periods of decreased demand and supplier capacity. The results indicate that the ideal approach can minimize hospital supply and pharmaceutical planning expenses. The Best-Worst and TOPSIS methods determine which pharmaceutical supplier should …be selected for future orders. The suggested model identifies human resource capability as an essential factor that might significantly affect the system’s total cost. The results of applying the model and the sensitivity analysis validate the efficacy and validity of the suggested mathematical model and solution strategy. Show more

Keywords: Optimization, pharma supply chain, uncertainty, robust programming

DOI: 10.3233/JIFS-230017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6561-6574, 2023

Authors: Arulselvan, G. | Rajaram, A.

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-231905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6575-6590, 2023

Authors: Xiao, Huimin | Gao, Xiaosong | Yang, Peng | Wei, Meng

Article Type: Research Article

Abstract: In the face of multi-attribute decision problems in complex situations, most traditional multi-attribute group decision methods are based on the assumption that the decision maker is perfectly rational, while in the face of complex decision problems, the decision maker usually has the psychological characteristics of limited rationality and may use more than one linguistic term to describe the decision information when expressing the decision information To this end, this paper selects probabilistic language term sets to describe complex preference information. First, to address the problem that the current probabilistic linguistic term set correlation coefficient cannot appropriately measure the degree of …correlation among probabilistic linguistic term sets, this paper proposes a new probabilistic linguistic term set correlation coefficient from three characteristic factors of probabilistic linguistic term sets: mean, variance, and length rate. To integrate the attribute index weights, probabilistic linguistic term set weighted mixed correlation coefficients are proposed. Second, this paper introduces the TODIM method, which can consider the psychological behavior of decision makers, and proposes a TODIM multi-attribute decision making method based on probabilistic linguistic term sets with mixed correlation coefficients. Finally, through an empirical analysis of four Internet listed companies in a new first-tier city in China, this study verifies the rationality and validity of the proposed method. The results show that the mixed correlation coefficient can comprehensively measure the correlation between probabilistic linguistic term sets, which provides an important method for future multi-attribute decision making problems. Show more

Keywords: Multi-attribute decision making, probabilistic linguistic term sets, mixed correlation coefficient, TODIM method

DOI: 10.3233/JIFS-232042

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6591-6604, 2023

Authors: Suresh Babu, D. | Ramakrishnan, M.

Article Type: Research Article

Abstract: A severe problem that regularly affects cloud systems are intrusions. Ignore how the expansion of Internet of Things (IoT) devices will result in enormous intrusions. To distinguish intrusions from authorized network activity, detection is a crucial procedure. An Enhanced Lion Optimization Algorithm (ELOA) is utilized in this research, IoT intrusion detection system. Intrusions are classified using the Deep Belief Network (DBN) and an SDN controller technique. The proposed ELOA-based Intrusion Detection System uses the optimal weight in DBN to train the neurons to categorize the data in a network as normal and attacked during the training phase. In the testing …step that follows training, data from nodes are examined, and by contrasting the training results, they are categorized as normal and attacked data. By using the proposed ELOA and DBN algorithms, our intrusion detection system can successfully identify intrusions. Based on the creation of blacklists for detecting IoT intrusions, the (SDN) Software Defined Networking controller can effectively prohibit harmful devices. In order to demonstrate that the proposed ELOA finds network intrusions more successfully, its performance is compared to that of other existing techniques. The node sizes of the algorithms are run and evaluated for 1000, 2000, 3000, 4000, and 5000 respectively. At highest node 5000, the Proposed ELOA and DPN have precision, recall, f-score and accuracy becomes as 97.8, 96.22, 97.5 and 98.67 respectively. Show more

Keywords: Internet of Things, intrusion detection, Enhanced Lion Optimization Algorithm, deep belief network, SDN controller

DOI: 10.3233/JIFS-232532

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6605-6615, 2023

Authors: Suganya, S. | Selvamuthukumaran, S.

Article Type: Research Article

Abstract: Hadoop is a big data processing system that enables the distributed processing of massive data sets across multiple computers using straightforward programming techniques. Hadoop has been extensively investigated in many attacks as a result of its growing significance in industry. A company may learn about the actions of invaders as well as the weaknesses of the Hadoop cluster by examining a significant quantity of data from the log file. In a Big Data setting, the goal of the paper is to generate an analytical classification for intrusion detection. In this study, Hadoop log files were examined based on assaults that …were recorded in the log files. Prior to analysis, the log data is cleaned and improved using a Hadoop preprocessing tool. For feature extraction, the hybrid Improved Sparrow Search Algorithm with Mutual Information Maximization (H-ISSA-MIM). Then the CNN (Convolutional Neural Network) classifier will detect the intrusions. The implementation is performed using the MATLAB 2020a software. The performance metrics like accuracy, precision, F-score, recall, specificity, FPR, FNR are calculated for the proposed methodology and it is compared with the existing techniques like Decision Tree (DT), Principal Components Analysis (PCA)- K means, Long Short Time Memory (LSTM). The maximum value of accuracy finds out in the proposed method 98% . Show more

Keywords: Hadoop attacks, log file, intrusion detection, big data environment and feature extraction, convolutional neural networks

DOI: 10.3233/JIFS-233579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6617-6628, 2023

Authors: Baqer, Ihsan A. | Jaber, Alaa Abdulhady | Soud, Wafa A.

Article Type: Research Article

Abstract: Belt drive contamination is considered one of the most common failure modes that could be developed in the belts due to harsh operation conditions, high humidity, and sunlight exposure, reducing the belt’s performance. If the belt failure has not been detected early, a sudden shutdown may happen, producing safety and economic consequences. However, most maintenance personnel use their senses of sight, hearing, smell, and touch to identify the cause of the problem while diagnosing a belt drive condition. Hence, this research involves developing an intelligent contamination status detection system based on vibration signal analysis for a pulley-belt rotating system. Time-domain …signal analysis was employed to extract some suggestive features such as the root mean square, kurtosis, and skewness from the vibration data. An artificial neural network (ANN) model was built to detect the simulated different operating conditions. The vibration data was gathered with the help of two MEMS accelerometers (ADXL335) interfaced with an NI USB-6009 data acquisition device. A signal capture, analysis, and feature extraction system was developed using Matlab Simulink. The simulated operating conditions include clean, wet, and powder-contaminated belts. The results showed that the designed system could identify the pulley-belt operation conditions with 100% overall accuracy. Show more

Keywords: Condition monitoring, fault diagnosis, preventive maintenance, time-domain signal analysis, machine learning

DOI: 10.3233/JIFS-222438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6629-6643, 2023

Authors: Lin, Tao | Chen, Biao | Wang, Ruixia | Zhang, Yabo | Shi, Yu | Jiang, Nan

Article Type: Research Article

Abstract: Vision-based Continuous Sign Language Recognition (CSLR) is a challenging and weakly supervised task aimed at segmenting sign language from weakly annotated image stream sequences for recognition. Compared with Isolated Sign Language Recognition (ISLR), the biggest challenge of this work is that the image stream sequences have ambiguous time boundaries. Recent CSLR works have shown that the visual-level sign language recognition task focuses on image stream feature extraction and feature alignment, and overfitting is the most critical problem in the CSLR training process. After investigating the advanced CSLR models in recent years, we have identified that the key to this study …is the adequate training of the feature extractor. Therefore, this paper proposes a CSLR model with Multi-state Feature Optimization (MFO), which is based on Fully Convolutional Network (FCN) and Connectionist Temporal Classification (CTC). The MFO mechanism supervises the multiple states of each Sign Gloss in the modeling process and provides more refined labels for training the CTC decoder, which can effectively solve the overfitting problem caused by training, while also significantly reducing the training cost in time. We validate the MFO method on the popular CSLR dataset and demonstrate that the model has better performance. Show more

Keywords: Continuous sign language recognition, fully convolutional network, multi-state feature optimization, connectionist temporal classification, adequate training

DOI: 10.3233/JIFS-223601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6645-6654, 2023

Authors: Wang, Libo | Zhao, Jun | Guo, Shizhong

Article Type: Research Article

Abstract: Concrete is known as one of the most important materials in the world. Concrete composites consisting of cement, water, aggregates, and some additives are used to improve the properties of concrete. These concrete have a certain compressive strength that can be increased depending on the type of concrete. In line with these ideas, high-performance concrete (HPC) has been produced, which can have high compressive strength by adding materials such as fly ash, silica fume, etc. This type of concrete is used in bridges, dams, and special constructions. However, obtaining the mixture design of HPC is problematic and complex, for this …reason, the machine learning methods can make it easy to achieve the output by saving time and energy. This study has used support vector regression (SVR) to predict the compressive strength of HPC. Moreover, this study provided two meta-heuristic algorithms for obtaining suitable and optimized results, which are contained the artificial hummingbird algorithm (AHA) and Sine Cosine Algorithm (SCA). The model by coupling with algorithms created the hybrid method in the framework of SVR-AHA and SVR-SCA. Furthermore, some criteria indicators have been used for determining the most desirable hybrid model, which is included coefficient of correlation (R2 ), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and weight absolute percentage error (WAPE). As a result, the AHA algorithm could have a more satisfying association model with the SVR model, and the results were RMSE = 2.00 (MPa), R2  = 98.59%, MAE = 0.717 (MPa), MAPE = 1.22 (MPa), and WAPE = 0.114 (MPa). Show more

Keywords: High-performance concrete, sine cosine algorithm, artificial hummingbird algorithm, support vector regression, compressive strength

DOI: 10.3233/JIFS-230132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6655-6666, 2023

Authors: Li, Jiacheng | Wang, Jianhua | Liu, Wenjie | Gao, Shengxia | Du, Shiqiang

Article Type: Research Article

Abstract: The Dunhuang murals, notably the paintings on the interior walls of China’s Dunhuang Grottoes, are considered international cultural treasure. The Dunhuang murals were ruined to varied degrees after a lengthy period of erosion. Deep learning networks were utilized to reconstruct broken parts of murals in order to better preserve their important historical and cultural values. Due to the presence of various damages, such as large peeling, mold and scratches, and multi-scale objects in the mural, a simple porting of existing working methods is suboptimal. In this paper, we propose a progressive Dunhuang murals inpainting (PDMI) based on recurrent feature reasoning …network to progressively infer the pixel values of hole centers by a progressive approach, aiming to obtain visually reasonable and semantically consistent inpainted results. PDMI consists mainly of the FFC-based recurrent feature reasoning (RFR) module and Multi-scale Knowledge Consistent Attention (MKCA) module. The RFR module first fills in the feature value at the feature map’s hole border, then utilizes the obtained feature value as a clue for further inference. The module steadily improved the limitation of hole centers, making the inpainted results more explicit; MKCA enables feature maps in RFR to handle richer background information from distant location information in a flexible manner while preventing misuse. After several round-robin inferences provide multiple feature maps, these feature maps are fused using an adaptive feature weighted fusion mechanism, then the fused feature maps decode back to RGB image. Experiments on a publicly available dataset and a self-made Dunhuang mural dataset reveal that the proposed method outperforms the comparison algorithm in both qualitative and quantitative aspects. Show more

Keywords: Image inpainting, Dunhuang murals, progressive inpainting, feature reasoning

DOI: 10.3233/JIFS-230320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6667-6678, 2023

Authors: Du, Jinze | Wang, Chang

Article Type: Research Article

Abstract: Based on the quaternion system, we give a new representation of the complex vague soft set, and related logical operations. This new representation contains more information than before. Three quaternion distance measures are proposed and a decision model is established. The disease diagnosis of breast cancer is applied to the model to reflect the superiority of the model. By comparing the diagnostic errors under the different distance measures, the most suitable distance measure for this dataset is selected.

Keywords: Quaternion, vague soft set, complex vague soft set

DOI: 10.3233/JIFS-231270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6679-6690, 2023

Authors: Mythily, M. | David, Beaulah | Venkatesan, R. | Joseph, Iwin Thanakumar

Article Type: Research Article

Abstract: Emerging daily, new devices and software-driven advancements pose challenges in software development, including errors, bugs, and evolving requirements. This leads to delays in delivery. Ensuring software security within the Software Development Life Cycle (SDLC) is crucial. To address this, the research focuses on incorporating security aspects early in the SDLC through model transformation. Platform-independent models with security attributes like Integrity, Privacy, Security Audit, non-repudiation, and authentication are generated. A template-based source code generator is utilized to create the structure of the source model. The Secure Business Process Model (SBPM) encompasses Unified Modeling Language (UML) artifacts, such as analysis level classes …and sequence diagrams, enriched with security attributes derived from the activity model. Security requirements are linked to elements extracted from the source model, and structural codes with security-enabled members are produced. Automation in software development is inevitable, though not complete, as it plays a vital role in addressing these challenges and improving the security of software applications. Show more

Keywords: Index Terms: Object-oriented modeling, software design, software safety, software reusability, software tools.

DOI: 10.3233/JIFS-231359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6691-6705, 2023

Authors: Yang, Ze | Jiang, Xianliang | Jin, Guang | Bai, Jie

Article Type: Research Article

Abstract: Accurate and fast pest detection is crucial for ensuring high crop yield and quality in modern agriculture. However, there are significant challenges in using deep learning for pest detection, such as the small proportion of pest individuals in the image area, complex backgrounds in light-trapped pest images, and an unbalanced distribution of pest species. To address these problems, we propose MFSPest, a multi-scale feature selection network for detecting agricultural pests in trapping scenes. We design a novel selective kernel spatial pyramid pooling structure (SKSPP) in the feature extraction stage to enhance the network’s feature extraction ability for key regions and …reduce its focus on irrelevant background information. Furthermore, we present the equalized loss to increase the loss weights of rare categories and improve the distribution imbalance among pest categories. Finally, we build LAPD, a light-trapping agricultural pest dataset containing nine pest categories. We conducted experiments on this dataset and demonstrated that our proposed method achieves state-of-the-art performance, with Accuracy, Recall, and mean Average Precision (mAP) of 89.9%, 92.8%, and 93.6%, respectively. Our method satisfies the requirements of pest detection applications in practical scenarios and has practical value and economic benefits for use in agricultural pest trapping and management. Show more

Keywords: Deep learning, object detection, agricultural light-trapped pests, pest detection

DOI: 10.3233/JIFS-231590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6707-6720, 2023

Authors: Duan, Qi | Zong, Yi

Article Type: Research Article

Abstract: It is worth exploring how “novices in academic entrepreneurship” can more clearly judge their performance in academic entrepreneurship process, self-diagnose the interaction effect between internal and external factors, and improve the effectiveness of entrepreneurial activities. This research takes Chinese academic entrepreneurs as the object, through the qualitative research method of grounded theory analysis, constructs the behavior map of academic entrepreneurship. The main stages of academic entrepreneurship chain are divided, including four stages: starting point, finding technology application, stabilizing technology application, and enterprise mature development. The common decision logic of academic entrepreneurs in each stage is explained. At the same time, …the map shows the main influencing factors of academic entrepreneurial behavior and the logic of these factors’ influence on academic entrepreneurial behavior. The above results not only enrich the research theory in the field of academic entrepreneurship process, but also have guiding significance for the practical activities of “novices in academic entrepreneurship”. Show more

Keywords: Academic entrepreneurship, behavior map, grounded theory analysis

DOI: 10.3233/JIFS-232240

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6721-6733, 2023

Authors: Thao, Le Quang | Diep, Nguyen Thi Bich | Bach, Ngo Chi | Cuong, Duong Duc | Linh, Le Khanh | Linh, Nguyen Viet | Linh, Tran Ngoc Bao

Article Type: Research Article

Abstract: Vietnamese students are facing significant academic pressure due to societal and familial expectations, which leads to an unfavorable learning environment. We aim to employ a temporary spatial-temporal stress monitoring system. Using Wireless Sensor Network (WSN) technology, it collects data on students’ emotional states and incorporates a prediction model, “Reduce Students’ Stress in School” (R3 S), to detect students’ emotional states across school premises. The integration of R3 S and WSN is conducted in three stages. Initially, sensor nodes are deployed in schools to collect emotional data. Subsequently, we introduce a novel hybrid model combining a one-dimensional Convolutional Neural Network with Long Short-Term …Memory networks (1D-CNN-LSTM) to generate a predictive emotional map. This model’s performance, evaluated using RMSE and MAE metrics, shows exceptional precision compared to other LSTM models. When predicting the “stress” condition, the R3 S model achieved a Mean Absolute Error (MAE) of 10.30 and a Root Mean Square Error (RMSE) of 0.041. Lastly, we generate a comprehensive map of cumulative emotional conditions, serving as a guide for school counselors. This map aids in fostering a healthy, conducive learning environment. Show more

Keywords: Monitor student emotion, wireless sensor network, LSTM, 1DCNN, prediction stress

DOI: 10.3233/JIFS-232256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6735-6749, 2023

Authors: Arun Kumar, A. | Manikandan, B.V. | Kannan, S. | Bhuvanesh, A.

Article Type: Research Article

Abstract: This paper proposed a multi-objective-based Generation Expansion Planning (GEP) for the real-word power generation system of Tamil Nadu, an Indian state. GEP aims to solve numerous conflicting problems for constructing new power plants. The proposed approaches are Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) and Circle Search algorithm. The key objectives of the proposed method is to reduce budget, to maximize reliability and to minimize the pollutant discharge. Therefore, the apt formulations are modeled and solved to establish the conflicting facets of GEP problem. This paper implements MOCLPSO algorithm to solve Multi-Objective GEP (MOGEP) problem for 7-year and 14-year planning …horizon. By then, the proposed model is implemented at MATLAB/Simulink platform and the implementation is calculated. The proposed method shows better results in all approaches like Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO) and Cuckoo Search algorithm. The outcomes establish the competence of MOCLPSO and Circle Search Algorithm to offer good-ranged Pareto optimal non-dominated solutions. Show more

Keywords: CLPSO, recuperation, GEP, Tamil Nadu, power station, utility

DOI: 10.3233/JIFS-232909

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6751-6766, 2023

Authors: Nisha, A.S. | Siva Rani, T.S.

Article Type: Research Article

Abstract: The process of fusing different images from various imaging modalities into a single, fused image that contains a wealth of information and improves the usability of medical images in real-world applications is known as medical image fusion. The most useful features from data can be automatically extracted by deep learning models. In the recent past, the field of image fusion has been preparing to introduce a deep learning model. In this work we can achieve the multi-Focus medical image fusion by hybrid deep learning models. Here the relevant health care data are collected from database (CT & MRI brain images). …Following the input images are pre-processed using sliding window and the abnormal data is eliminated using distribution map method. Further the proposed work comprises 3 steps, 1) the proposed method is used to extract the features from the input image using the modified Tetrolet transform (MMT), which uses a brain image as an input image. This model is capable of identifying anomalous trends in time series data and automatically deriving from the input data characteristics that characterise the system state.2) Propose a novel hybrid model based on CNN with Bi-LSTM (Bi-directional Short Term Memory) multi-focus image fusion method to overcome the difficulty faced by the existing fusion methods. 3) This hybrid model are used to predict the brain tumor present in the fused image. Finally, experimental results are evaluated using a variety of performance measures. From the results, we can see that our suggested model contributes to an increase in predictive performance while also lowering the complexity in terms of storage and processing time. Show more

Keywords: CNN with Bi-LSTM, hierarchical data fusion, deep learning, health care applications, sliding window, modified tetrolet transform, multi-focus image fusion

DOI: 10.3233/JIFS-224439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6767-6783, 2023

Authors: Thomas, Julia T. | Kumar, Mahesh

Article Type: Research Article

Abstract: In industry, for the quality inspection processes, acceptance sampling plans proved to be economically viable, but the unpredictability of the plan’s characteristics made the use of conventional acceptance sampling plans less reliable. The generalized fuzzy multiple deferred state sampling plan (GFMDSSP) is suggested in this study for qualities that consider the difficulty in calculating the precise value of the percentage of defectives in a batch. The strategy is created with a minimal average sample size in mind and the performance measures have already been determined. An analysis of the current fuzzy acceptance sampling plans for characteristics is conducted, and an …important conclusion is drawn regarding the effectiveness of the proposed scheme. Analysis of the impact of inspection errors on the sampling process reveals a decline in plan acceptance standards that is correlated with escalating inspection errors. Finally, some numerical examples are provided to support the findings. Show more

Keywords: Fuzzy acceptance sampling plans, average sample number, acceptable quality level, limiting quality level, inspection errors

DOI: 10.3233/JIFS-224487

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6785-6796, 2023

Authors: Samy, V.S. | Thenkanidiyoor, Veena

Article Type: Research Article

Abstract: Due to the unpredictable nature of the weather and the complexity of atmospheric movement, extreme weather has always been a significant and challenging meteorological concern. Meteorological problems and the complexity of how the atmosphere moves have made it necessary to find a technological solution. Deep learning techniques can automatically learn and train from vast quantities of data to provide enhanced feature expression. This is frequently used in computer vision, natural language processing, and other domains to enhance the performance of numerous real-time problems. The purpose of this research is to propose a deep learning-based approach for effectively predicting extreme weather …events such as blizzards. To recognize weather patterns and forecast blizzards, the proposed deep learning-based method primarily employs RNN with LSTM. Real-time datasets from the Polar Regions were used to test the proposed approach’s accuracy, and tests were conducted to compare it to existing weather forecasting models. The accuracy of the model is 49.60% (univariate) and 55.19% (bivariate) using bivariate attributes of wind speed and air pressure based on the calculated RMSE values such as 0.0023 and 0.0021. Show more

Keywords: Weather patterns analytics, machine learning, deep learning, extreme prediction and weather forecasting

DOI: 10.3233/JIFS-224543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6797-6812, 2023

Authors: Thao, Le Quang | Bach, Ngo Chi | Cuong, Duong Duc | Linh, Le Khanh

Article Type: Research Article

Abstract: Babies who can’t communicate through language use crying as a way to express themselves. By identifying the unique characteristics of their cries, parents can quickly meet their needs and ensure their health. This study aimed to create a lightweight deep learning model called Bbcry to classify the cries of babies and determine their needs, such as hunger, pain, normal, deafness, or asphyxia. The model was trained using the Chillanto dataset and underwent three stages of development. Initially, the Wav2Vec 2.0 model was utilized as a teacher for the Knowledge Distillation (KD) method and applied to the transformer and prediction layers …to reduce the number of required parameters. Then, a projection head layer was added and linked to the transformer layers to control their impact on the Wav2Vec 2.0 model. This resulted in the first version of the Bbcry model with an accuracy of 93.39% and an F1-score of 87.60%. Finally, the number of transformer layers was reduced to create the Bbcry-v4 model with only 9.23 million parameters, which used only 10% of the parameters of Wav2Vec 2.0 while only slightly reducing accuracy and F1-score. The study concludes with a software demonstration that shows the proposed model’s ability to accurately recognize and determine the needs of infants based on their cries. Show more

Keywords: Dunstan baby language, infant cry classification, knowledge distillation, Wav2Vec

DOI: 10.3233/JIFS-232118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6813-6824, 2023

Authors: Fan, Jianping | Yuan, Jiu | Wu, Meiqin

Article Type: Research Article

Abstract: This paper studies a large-scale group decision-making method (LSGMD) based on incomplete hesitant fuzzy linguistic preference relations (IHFLPRs) and proposes an improved model for additive consistency of hesitant fuzzy linguistic preference relations (HFLPRs). Additionally, consistency control and fuzzy C-means (FCM) clustering are utilized to enhance efficiency and reliability. Firstly, a model is proposed to address the issues of missing elements in IHFLPRs and insufficient additive consistency in HFLPRs, aiming to more accurately reflect decision makers’ preference relationships towards candidate alternatives. Subsequently, the FCM method is employed to cluster decision experts’ preference information and obtain the overall preference information. Finally, the …rationality and accuracy of our proposed method are demonstrated through a case study and comparative analysis. Show more

Keywords: Incomplete hesitant fuzzy linguistic preference relations, consistency control, large-scale group decision making, Fuzzy C-means clustering

DOI: 10.3233/JIFS-232615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6825-6836, 2023

Authors: Lin, Pao-Ching | Huang, Jui-Chan | Ho, Ping-Tsan

Article Type: Research Article

Abstract: In recent years, tourism has developed rapidly and made great contributions to the economic enhancement of various regions; While tourism environment carrying ability assessment is the key to tourism sustainable development. The randomness and fuzziness of the traditional multi-level fuzzy comprehensive tourism environmental carrying ability assessment model cannot be combined effectively. In view of this, to construct a reasonable and objective assessment model, this study improves the multi-level fuzzy comprehensive tourism environmental carrying ability assessment model based on cloud model. The results indicate that the unimproved model judges that this tourism environment carrying ability level corresponds to level 2 for …selecting tourism destination. And it is in a suitable load state. The evaluation results on the foundation of cloud model improved multi-level fuzzy comprehensive tourism environmental carrying ability assessment show that its Ex is 5.748, En is 1,296 and He is 0.1, which is between moderate to slightly overloaded, and the overall state is moderate, but there is a tendency to develop towards slightly overloaded. The evaluation results of the improved model are more intuitive in showing the carrying capacity of the tourism environment, and these evaluation results are more objective and reliable, which verifies the applicability of the research model. This research model provides a theoretical basis and data support for the study of tourism environment carrying capacity. Show more

Keywords: Tourism, environmental carrying ability, cloud model, fuzzy integrated assessment, assessment model

DOI: 10.3233/JIFS-232982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6837-6847, 2023

Authors: Li, Kunpeng | Xu, Junjie | Zhao, Huimin | Deng, Wu

Article Type: Research Article

Abstract: Most of the flight accident data have uneven distribution of categories. When the traditional classifier is applied to this data, it will pay less attention to the minority class data. Synthetic Minority Over-sampling Technique (SMOTE), and its improvements are well-known methods to address this imbalance problem at the data level. However, traditional algorithms still have the problems in blurring the boundary of positive and negative classes and changing the distribution of original data. In order to overcome these problems and accurately predict flight accidents, a new Clustered Biased Borderline SMOTE(CBB-SMOTE) is proposed for Quick Access Recorder (QAR) Go-Around data. It …generates more obvious positive and negative class boundaries by using K-means for boundary minority class data and safety minority class data respectively, and maintains the original data distribution to the greatest extent through a biased oversampling method. Experiments were carried out on a group of QAR Go-Around data. The data set is balanced by CBB-SMOTE, SMOTE, Cluster-SMOTE algorithm respectively, and the random forest algorithm is used to predict the new data set. The experimental results show that CBB-SMOTE outperforms the SMOTE in terms of G-means value, Recall and AUC. Show more

Keywords: Imbalanced learning, oversampling, SMOTE, QAR Go-Around data, data generation

DOI: 10.3233/JIFS-233548

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6849-6862, 2023

Authors: Suresh Kumar, P. | Barkathulla, A.

Article Type: Research Article

Abstract: A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed …in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively. Show more

Keywords: Wireless sensor network, cluster head selection, energy efficiency, clustering, network lifetime

DOI: 10.3233/JIFS-221370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6863-6873, 2023

Authors: Tekin, Özlem

Article Type: Research Article

Abstract: Spherical fuzzy sets are an advanced tool of three-dimensional membership functions which consist of membership, non-membership and hesitancy degrees. In this paper, it is introduced a new approach via proximal spaces for spherical fuzzy sets. To do this, the spherical fuzzy proximity axioms are defined on proximal relator spaces. Also, spherical fuzzy spatial Lodato proximity relation is studied. By using spherical fuzzy proximity relation, it is defined that descriptive proximity relation. An example is given how people are proximal(near) to each other via their description features.

Keywords: Proximity space, relator space, fuzzy relation, fuzzy proximity, spherical fuzzy sets, spherical fuzzy proximity

DOI: 10.3233/JIFS-230314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6875-6886, 2023

Authors: Guo, Aiyin | Xu, Yunjian | Li, Gang

Article Type: Research Article

Abstract: In order to simultaneously calculate the temporal and spatial characteristics of behavior sequence samples, a convolutional neural network recognition model based on a multi-scale convolutional operator is proposed. Firstly, the skeleton vector information in the sequence samples is integrated into a behavior matrix by superposition, and then the matrix is input into the recognition model. In order to explore the role of bone points with different adjacencies in describing human behavior, the convolutional operator in each layer of the convolutional neural network is extended to a multi-scale convolutional operator, and the features obtained by the network are used for classification. …Good recognition rates were obtained in the MSR-Action3D dataset and HDM05 dataset. Show more

Keywords: Behavior recognition, spatiotemporal characteristics, deep convolutional neural network, deep learning, behavior matrix

DOI: 10.3233/JIFS-231220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6887-6896, 2023

Authors: Prabhu, S. | Mary Anita, E.A. | Mohanageetha, D.

Article Type: Research Article

Abstract: Wireless sensor nodes (WSN) combine sensing and communication capabilities in the smallest sensor network component. Sensor nodes have basic networking capabilities, such as wireless connection with other nodes, data storage, and a microcontroller to do basic processing. The intrusion detection problem is well analyzed and there exist numerous techniques to solve this issue but suffer will poor intrusion detection accuracy and a higher false alarm ratio. To overcome this challenge, a novel Intrusion Detection via Salp Swarm Optimization based Deep Learning Algorithm (ID-SODA) has been proposed which classifies intrusion node and non-intrusion node. The proposed ID-SODA technique uses the k-means …clustering algorithm to perform clustering. The Salp Swarm Optimization (SSO) technique takes into residual energy, distance, and cost while choosing the cluster head selection (CHS). The CHS is given the input to a multi-head convolutional neural network (MHCNN), which will classify into intrusion node and non-intrusion node. The performance analysis of the suggested ID-SODA is evaluated based on the parameters like accuracy, precision, F1 score, detection rate, recall, false alarm rate, and false negative rate. The suggested ID-SODA achieves an accuracy range of 98.95%. The result shows that the suggested ID-SODA improves the overall accuracy better than 6.56%, 2.94%, and 2.95% in SMOTE, SLGBM, and GWOSVM-IDS respectively. Show more

Keywords: Wireless sensor nodes, k-means clustering, Salp Swarm optimization, multi-head convolutional neural network

DOI: 10.3233/JIFS-231756

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6897-6909, 2023

Authors: Durgam, Revathi | Devarakonda, Nagaraju

Article Type: Research Article

Abstract: In machine learning, a crucial task is feature selection in that the computational cost will be increased exponentially with increases in problem complexity. To reduce the dimensionality of medical datasets and reduce the computational cost, multi-objective optimization approaches are mainly utilized by researchers. Similarly, for improving the population diversity of the Flamingo Search Algorithm, the neighbourhood centroid opposition-based learning mutation is employed. In this paper, to improve the classification accuracy, enhance their exploration capability in the search space and reduce the computational cost while increasing the size of dataset, neighbourhood centroid opposition-based learning (NCOBL) is integrated into the multi-objective optimization …based Flamingo Search Algorithm (MOFSA). The optimal selected datasets are classified by using the weighted K-Nearest Neighbour classifier. With the use of fifteen benchmark medical datasets, the efficacy of the suggested strategy is assessed in terms of recall, precision, accuracy, running time, F-measure, hamming loss, ranking loss, standard deviation, mean value error, and size of the selected features. Then the performance of the suggested feature selection technique is compared to that of the existing approaches. The suggested method produced a minimum mean value, standard deviation, mean hamming loss, and maximum accuracy of about 99%. The experimental findings demonstrate that the suggested method may enhance classification accuracy and also eliminate redundancy in huge datasets. Show more

Keywords: Flamingo search algorithm, K-Nearest Neighbour, feature selection, multi-objective optimization, disease classification

DOI: 10.3233/JIFS-232128

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6911-6922, 2023

Authors: Lin, Youping | Wang, Wenxin | Chen, Yanling | Li, Feng

Article Type: Research Article

Abstract: The evaluation of teaching quality plays a crucial role in promoting the improvement of education quality and ensuring the healthy development of education. This study presents a novel teaching quality evaluation model based on improved interval-valued intuitionistic fuzzy Best-Worst method (IVIF-BWM) and interval-valued intuitionistic fuzzy weighted Maclaurin symmetric mean operators (IVIFWMSM). The study is divided into three parts. Firstly, to derive the optimal interval-valued intuitionistic fuzzy weights of criteria, we develop an improved IVIF-BWM by establishing a goal programming model based on the multiplicative consistent interval-valued intuitionistic fuzzy preference relation(IVIFPR), and then we propose the new consistency index (CI) and …the consistency ratio (CR) under interval-valued intuitionistic fuzzy environment to verify the reliability of the derived results. Secondly, with regard to the importance and interaction relationships among criteria, IVIFWMSM is used to aggregate evaluation values of alternatives on each evaluation criteria in multi-criteria decision making process. Finally, the proposed teaching quality evaluation model is applied to a case of teaching quality evaluation in higher education and a comparison study with other existing methods are performed. The results demonstrate that the proposed teaching quality evaluation model not only overcomes the shortcomings of previous methods, but also is more accuracy, effective and reasonable for dealing with the teaching quality evaluation under the intuitionistic fuzzy environments. Show more

Keywords: Teaching quality evaluation model, interval-valued intuitionistic fuzzy Best-Worst method, interval-valued intuitionistic fuzzy preference relation, interval-valued intuitionistic fuzzy weighted Maclaurin symmetric mean operator

DOI: 10.3233/JIFS-232272

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6923-6941, 2023

Authors: Ashwini, A. | Purushothaman, K.E. | Rosi, A. | Vaishnavi, T.

Article Type: Research Article

Abstract: The most common challenge faced by dermoscopy images is the automatic detection of lesion features. All the existing solutions focus on complex algorithms to provide accurate detections. In this research work, proposed Online Tigerclaw Fuzzy Region Segmentation with Deep Learning Classification model, an intellectual model is proposed that provides discrimination of features with classification even in fine-grained samples. This model works on four different stages, which include the Boosted Anisotropic Diffusion filter with Recursive Pixel Histogram Equalization (BADF-RPHE) in the preprocessing stage. The next step is the proposed Online Tigerclaw Fuzzy Region Segmentation (OTFRS) algorithm for lesion area segmentation of …dermoscopic images, which can achieve 98.9% and 97.4% accuracy for benign and malignant lesions, respectively. In the proposed OTFRS, an accuracy improvement of 1.4% is achieved when compared with previous methods. Finally, the increased robustness of lesion classification is achieved using Deep Learning Classification –DenseNet 169 with 500 images. The proposed approach was evaluated with accuracy classifications of 100% and 98.86% for benign and malignant lesions, respectively, and a processing time of less than 18 sec. In the proposed DensetNet-169 classification technique, an accuracy improvement of 3% is achieved when compared with other state-of-art methods. A higher range of true positive values is obtained for the Region of Convergence (ROC) curve, which indicates that the proposed work ensures better performance in clinical diagnosis for accurate feature visualization analysis. The methodology has been validated to prove its effectiveness and throw light on the lives of affected patients so they can resume normalcy and live long. The research work was tested in real-time clinical samples, which delivered promising and encouraging results in skin cell detection procedures. Show more

Keywords: Boosted Anisotropic Diffusion filter with Recursive Pixel Histogram Equalization (BADF-RPHE), Deep learning Classification - DenseNet 169, Proposed Online Tigerclaw fuzzy Region Segmentation (OTFRS), Skin tumor

DOI: 10.3233/JIFS-233024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6943-6958, 2023

Authors: Jyothi, Kilari | Dubey, R.B.

Article Type: Research Article

Abstract: This manuscript proposes a hybrid method to solve the job shop scheduling problem (JSP). Here, the machine consumes different amounts of energy for processing the tasks. The proposed method is the joint execution of Feedback Artificial Tree (FAT) and Atomic Orbital Search (AOS), hence it is called the FAT-AOS method. The aim of the proposed multi-objective method is to lessen the non-processing energy consumption (NEC), total weighted tardiness and earliness (TWET), and makespan (Cmax). Depending on the machine’s operating status, such as working, standby, off, or idle, the energy-consumption model of the machine is constructed. The NEC is the essential …metric and the Cmax and TWET are the classical performance metrics used to predict the effects of energy effectiveness in JSP. The proposed AOS technique optimizes the objective of the system and FAT is used to predict the optimal outcome. The proposed method’s performance is implemented in MATLAB and is compared with various existing methods. From this simulation, under the 15x15_1 instance, the proposed method makes the span the best value of 1370, the median is 1720, and the worst value become 2268 is obtained. Show more

Keywords: Hybrid approach, total weighted tardiness and earliness, job shop scheduling, machine status, non-processing energy consumption, makespan

DOI: 10.3233/JIFS-222362

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6959-6981, 2023

Authors: Fan, Jianping | Wang, Min | Wu, Meiqin

Article Type: Research Article

Abstract: Virtual teams (VT) have become increasingly popular due to modern technology. VT allows talented people from different places with different skills to work towards a common goal through network media. In order to form a more versatile VT, selecting VT members becomes a critical step. Based on the linguistic Pythagorean fuzzy sets (LPFS), this paper proposes an integrated approach to select VT members by means of the method based on standard removal effects (MEREC) and the method based on the mean solution distance of direct and indirect uncertainty (DIUEDAS). Firstly, decision information is described by LPFS. Secondly, MEREC is used …to determine the criteria weights. Finally, the decision-making and evaluation laboratory (DEMATEL), failure mode and effects analysis (FMEA), and EDAS are combined to select the optimal VT members under the premise of evaluating the uncertainty in selecting VT members. In addition, this paper proposes a new method for determining expert weights. At the end of the paper, the model and the expert weight determination method are applied to the case of a port selecting VT members, and the effectiveness of the model proposed is demonstrated by comparative analysis in this paper. Show more

Keywords: VT members, LPFS, MEREC, DEMATEL, FMEA, EDAS

DOI: 10.3233/JIFS-232494

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6983-7003, 2023

Authors: Ramachandran, Dhanagopal | Venkatesh, J. | Jothilakshmi, R. | Gugapriya, G.

Article Type: Research Article

Abstract: Since there is no central controller, preserving the security and energy efficiency of wireless sensor networks (WSN) is challenging. They also have a flexible configuration. A network of this type is vulnerable to several attacks. The main goal of this paper is to focus on a well-known attack known as the sinkhole attack. Sensors are installed and positioned equally in a WSN to communicate sensed data to a centralized station regularly. So, the sinkhole attack is a big danger to the WSN network layer, and it is still a difficult issue on sensor networks, where even the malicious node collects …packets from other regular sensor nodes and dumps them. To maintain the integrity and authentication of data during its travel in wireless sensor networks overcoming sinkhole attacks we propose a novel approach. In our approach besides overcoming sinkhole attack using a threshold-based method, authentication, and data integrity is maintained using a watermarking-based technique. Show more

Keywords: Ad-hoc On-demand Distance Vector (AODV), Binary Logistic Regression (BLR), Intrusion Detection System (IDS), Low Energy Adaptive Clustering Hierarchy (LEACH), Machine Learning, Wireless Sensor Network (WSN), Network Simulator (NS), Statistical Analysis (SA), Support Vector Machine (SVM)

DOI: 10.3233/JIFS-224463

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7005-7023, 2023

Authors: Zuo, Yandi | Wang, Pan | Fan, Zhun | Li, Ming | Guo, Xinhua | Gao, Shijie

Article Type: Research Article

Abstract: Assembly flow shop scheduling problem (AFSP) in a single factory has attracted widespread attention over the past decades; however, the distributed AFSP with DPm  → 1 layout considering uncertainty is seldom investigated. In this study, a distributed assembly flow shop scheduling problem with fuzzy makespan minimization (FDAFSP) is considered, and an efficient artificial bee colony algorithm (EABC) is proposed. In EABC, an adaptive population division method based on evolutionary quality of subpopulation is presented; a competitive employed bee phase and a novel onlooker bee phase are constructed, in which diversified combinations of global search and multiple neighborhood search are executed; the …historical optimization data set and a new scout bee phase are adopted. The proposed EABC is verified on 50 instances from the literature and compared with some state-of-the-art algorithms. Computational results demonstrate that EABC performs better than the comparative algorithms on over 74% instances. Show more

Keywords: distributed assembly flow shop scheduling, uncertainty, artificial bee colony algorithm, fuzzy makespan

DOI: 10.3233/JIFS-230592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7025-7046, 2023

Authors: Zhu, Wenxi | Zhang, Jing | Zeng, Ying | Chen, Jie | Ma, Chongsen

Article Type: Research Article

Abstract: This paper extracts the causes of collusion behavior based on literature analysis and expert interviews and obtains collusion causation data. The Apriori algorithm is used to mine the relationship between the causes of collusion behavior, and the network model of the causes of collusion behavior is constructed. The successive failures theory mines the most easily evolved causation chain of collusion behavior. The study results showed that: (1) The critical causes of the formation of collusion are self-discipline consciousness and difficulty of investigation. The strong control ability of causation network of collusion behavior is self-discipline consciousness, difficulty of investigation, and transparency …of rights operation. (2) Based on the analysis of the group case data, eight causation chains are most likely to form collusion in actual cases, among which the causation chain of collusion behavior that occurs frequently is “difficulty of investigation⟶self-discipline consciousness⟶interest chain”. (3) In view of the causation nodes in the causation chain of collusion behavior, we propose more effective preventive and preventive control measures for collusion between bidders and tenderers in construction projects from three aspects, namely, behavior awareness binding, collusion implementation dilemma and collusion supervision deterrence. Show more

Keywords: Construction project, collusive behavior, causation network, successive failures

DOI: 10.3233/JIFS-231802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7047-7063, 2023

Authors: Zhao, Lixia

Article Type: Research Article

Abstract: Purpose: The purpose of this study is to systematically review the research hotspots and frontiers in the field of international child and adolescent mental health education over the past 22 years. Furthermore, based on the changes in these hotspots, it aims to predict future research directions, providing valuable references for scholars engaged in subsequent research in this field. Methods : Using analytical tools such as CiteSpace, R-Tool, and VOSviewer, a quantitative analysis was conducted on 10,231 research papers in the field of children’s mental health education from the WoSCC database published between 2000 and 2022. Results : The results indicate …that mental health problems among children and adolescents have become a global public health issue, with a continuous increase in related research publications over the years. The COVID-19 pandemic has exacerbated mental health problems among children and adolescents during periods of lockdown. The United States is a core research country in this field, and influential journals in this area include "Pediatrics" and "Social Science & Medicine." Ford, Tamsin is an authoritative author in this field. Popular research topics in this field include family education, children with disabilities, and substance abuse. Future research is likely to focus on the impact of physical activity on mental health. Show more

Keywords: Children, adolescent, mental health, visualisation analysis

DOI: 10.3233/JIFS-232204

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7065-7082, 2023

Authors: Soni, Santosh | Chandra, Pankaj | Singh, Devendra Kumar | Sharma, Prakash Chandra | Saini, Dinesh

Article Type: Research Article

Abstract: Recent research emphasized the utilization of rechargeable wireless sensor networks (RWSNs) in a variety of cutting-edge fields like drones, unmanned aerial vehicle (UAV), healthcare, and defense. Previous studies have shown mobile data collection and mobile charging should be separately. In our paper, we created an novel algorithm for mobile data collection and mobile charging (MDCMC) that can collect data as well as achieves higher charging efficiency rate based upon reinforcement learning in RWSN. In first phase of algorithm, reinforcement learning technique used to create clusters among sensor nodes, whereas, in second phase of algorithm, mobile van is used to visit …cluster heads to collect data along with mobile charging. The path of mobile van is based upon the request received from cluster heads. Lastly, we made the comparison of our proposed new MDCMC algorithm with the well-known existing algorithms RLLO [32 ] & RL-CRC [33 ]. Finally, we found that, the proposed algorithm (MDCMC) is effectively better collecting data as well as charging cluster heads. Show more

Keywords: Mobile sink, mobile charger, charging efficiency, reinforcement learning, rechargeable wireless sensor node, mobile data collection and mobile charging

DOI: 10.3233/JIFS-224473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7083-7093, 2023

Authors: Liang, Zhongyuan | Zhong, Peisi | Liu, Mei | Zhang, Chao

Article Type: Research Article

Abstract: Optimal allocation of production resources is an urgent need for the development of industrialization. Reasonable production scheduling algorithm and excellent scheduling scheme can efficiently plan production resources, reduce production costs and shorten order completion time. Genetic algorithm has become one of the most popular algorithms for solving job shop scheduling problem because of its simplicity, versatility and good robustness. However, the genetic algorithm for solving NP-hard problems such as job shop scheduling has the problem of falling into local optimum, which leads to the decrease of solution accuracy. This study focused on the problem and proposed a generic enhanced search …framework based on genetic algorithm, which named niche adaptive genetic algorithm. The niche selection mechanism and adaptive genetic operators were used to enrich the diversity of population, balance the genetic probability and enhance the global search performance of the algorithm. The working mechanism of this algorithm is analysed by testing data, and the proposed algorithm was tested on job-shop scheduling problem instances. The results show that the performance of the proposed method is 0.79 percentage points higher than that of the standard genetic algorithm, and it has the ability to search for the global optimum. Show more

Keywords: Job shop scheduling, genetic algorithm, enhanced search, optimization

DOI: 10.3233/JIFS-230076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7095-7111, 2023

Authors: Sathya Janaki, R. | Nagarajan, V.

Article Type: Research Article

Abstract: Wireless sensor networks (WSN) is a popularly emerging technology with several opportunities to sustain in various field that require multipurpose sensor nodes, less energy and non-expensive system. But in the WSN, the radio transmission needs high amount of energy and this creates the critical problem. Hence consumption of energy has to be decreased to extend the network durability. Even though there are so many techniques existing for clustering approach of WSN, they have limitations like increased energy consumption, less delivery rate of data, redundancy and unbalanced network load. Hence, these problems are solved by introducing the energy efficient deep learning …techniques for clustering and finding the optimal route. Initially the initialization process of system model is performed with the implementation of energy model. In WSN, energy consumption should be reduced to enhance the QoS and balance the network traffic. Hence clustering method is used to group up the sensor nodes and the optimal cluster head is selected with the proposed technique of hybrid cuckoo search and particle swarm optimization (CSO-PSO). As the CH is chosen, the optimal path of routing data should be found in addition with the procedure of optimization and it is done through the proposed model of Optimization based routing protocol that incorporates the Energy Aware Multi Point Routing (EAMPR) protocol along with the Improved Tuna Search Optimization (ITSO) algorithm. Finally, by the use of ITSO-EAMPR technique the energy consumption will get reduced with the decrease in relative mobility and high stability of nodes would be achieved. The simulations are proceeded and the outcomes are validated. The result obtained is compared with the traditional methods to show the effectiveness of proposed technique. As per the results obtained the proposed ITSO-EAMPR attains maximized PDR and Throughput, higher energy efficiency with extension in lifetime of WSN along with decrease in BER, end-to-end latency as compared to the existing techniques. Show more

Keywords: Energy consumption, optimization, cluster path, sensor nodes, clustering, throughput, end-to-end delay

DOI: 10.3233/JIFS-231342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7113-7127, 2023

Authors: Padmapriya, S. | Umamageswari, A. | Deepa, S. | Faritha Banu, J.

Article Type: Research Article

Abstract: Exploration of underwater resource play a vital role for nation development. Underwater surveillance systems play a crucial role in security applications, requiring accurate detection of suspicious objects in underwater images. However, the presence of noise, poor visibility, and uneven lighting conditions in underwater environments pose significant challenges for reliable object detection. This work proposes an integrated approach for underwater image de-noising, pre-processing, enhancement, and subsequent suspicious object detection by combining the DnCNN (Deep Convolutional Neural Network), CLAHE (Contrast Limited Adaptive Histogram Equalization), and additional image enhancement techniques. In addition to de-noising and pre-processing, it incorporate various image enhancement techniques to …further improve object detection performance. These techniques include color correction, contrast adjustment, and edge enhancement, aiming to enhance the visual characteristics and saliency of suspicious objects in underwater images. To evaluate the effectiveness of proposed approach, this work conducted extensive experiments on an underwater image dataset containing diverse scenes and suspicious objects. The work compares proposed method with existing de-noising, preprocessing, and object detection techniques, analyzing the results using quantitative performance metrics, including precision, recall, and F1 score. The experimental results demonstrate that proposed integrated approach outperforms individual methods and achieves superior detection performance by enhancing the quality of underwater images and improving the visibility of suspicious objects. Show more

Keywords: Dn-CNN, CLAHE, red compensation, white balancing, gamma correction, image sharpening

DOI: 10.3233/JIFS-234002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7129-7144, 2023

Authors: Ponsam, J. Godwin | Nimala, K. | Mohammad, Gousebaig | Shitharth, S. | Radha, Vijaya Kumar Reddy | Srinivasa Rao, B. | Srihari, K. | Chandragandhi, S.

Article Type: Research Article

Abstract: The creation of sensor-based software for health monitoring using Internet of Things (IoT) technology is the main goal of this project. The program’s objective is to continuously monitor human physiological data, including ECG, SPO2, heart rate, and respiration, by employing biomedical sensor networks. These sensors collect data, which is then processed by a processor and transmitted to an edge server through a transceiver. A node of corner facilitates for real transmission has processed each data will be patient’s phone and the clinicians’ LED display. To address the optimization challenge, the program utilizes a Double Deep-Q-Network approach, with parameters optimized using …a hybrid genetic algorithm-based simulated annealing technique. However, healthcare records obtained from the sensors are susceptible to change due to environmental factors, leading to potential performance issues. In order to overcome this challenge, an optimization approach is employed to refine the proposed technique, ensuring accurate prediction of readings. The study conducted experiments to evaluate the program’s performance, utilizing various metrics and different parameters. The results to provide light on how well the program that was created for leveraging IoT technologies for health monitoring is working. This study presents an innovative sensor-based program for IoT technology-based health monitoring, which continuously monitors human physiological data. The program incorporates a hybrid optimization approach to ensure accurate prediction of readings, accounting for environmental factors. The proposed Double Deep-Q-Network and the evaluation metrics employed demonstrate the originality and contributions of this research in advancing health monitoring systems. Show more

Keywords: Biomedical record system, double DQN, bio-sensors, edge computing, hybrid optimization algorithm

DOI: 10.3233/JIFS-221076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7145-7159, 2023

Authors: Shekar Goud, D. | Beenarani, B.B. | Brijilal Ruban, C. | Fathima, Rani | Bharathi, M.L. | Rajaram, A. | Kshirsagar, Pravin R. | Tirth, Vineet

Article Type: Research Article

Abstract: Architectural, cognitive, and service layers are the three components that come together to form the system as a whole. The data that is acquired by the instruments at the application layer is processed by the system that is in charge of the network. The conceptual layer, which is where edge sensors are put, is responsible for managing radio resource management and intersensor connections in order to solve the issues raised by the physical layer about increasing power consumption and increased latency. In response to the processed data provided by the logical layer, the application layer will make judgements. The key …objective is to lower prices so that they are more accessible to regular people. Patients will not only be able to maintain their financial stability, but they will also have easy access to private therapy. This research presents a solution based on the Internet of Things (IoT), which will simplify the usage of a generally complicated medical device while allowing you to do it at a reasonable cost and in the comfort of your own home. The Elephant Herding Optimizations using Convolutional Neural Networks (CNNs) method is discussed here in order to differentiate between healthy and unhealthy patterns of behavior. The scoring function, also known as fuzzy logic, is used in order to arrive at a conclusion on the severity of the irregularity. In the end, tests were carried out to see how well the recommended work fared in contrast to the existing approaches in terms of specificity, recall, f1-score, and ROC curve. These metrics were examined. Show more

Keywords: IoT based smart healthcare monitoring system, edge computing, deep learning techniques, smart wearables and implantable devices

DOI: 10.3233/JIFS-231239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7161-7175, 2023

Authors: Jeganathan, Aruna | Chellaiah, Jeyalakshmi

Article Type: Research Article

Abstract: Most recently, Human fall detection systems using deep learning models find major applications in all fields, especially in the held of healthcare. Even without doctor analysis, most Neurological and musculoskeletal diseases such as oncoming strokes and gait problems can be identified using these models and computer vision. In this article, automatic human fall detection is proposed using a convolutional neural network by applying real-time videos. In general, most of the research has been carried out using standard videos which will not apply to real-time applications. Hence this work concentrates about using convolutional neural networks as a system has real-time videos …for the Human Fall Detection and monitoring system using three pre-trained models: (i) TinyYOLOv3-ones, (ii) AlphaPose and (iii) ST-GCN. The proposed Spatial temporal graph convolutional networks produce better accuracy with captured real-time video for human fall detection. The same method was also utilized for classification with different epochs. The results were compared and maximum accuracy of 100% is obtained for 500 epochs. Hence it is proved that the existing method can be utilized for human fall detection with greater accuracy. Show more

Keywords: Fall detection, Deep Convolution Neural Network-DCNN, Spatial-Temporal Graph Convolution Network-ST-GCN, Daily Living Activities-ADL

DOI: 10.3233/JIFS-232842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 7177-7190, 2023