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

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