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

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