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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Vallabhaneni, Nagalakshmi | Prabhavathy, Panneer
Article Type: Research Article
Abstract: Numerous people are interested in learning yoga due to the increased tension levels in the modern lifestyle, and there are a variety of techniques or resources available. Yoga is practiced in yoga centers, by personal instructors, and through books, the Internet, recorded videos, etc. As the aforementioned resources may not always be available, a large number of people will opt for self-study in fast-paced lifestyles. Self-learning makes it impossible to recognize an incorrect posture. Incorrect poses will have a negative effect on the patient’s health, causing severe agony and long-term chronic issues. Computer vision (CV)-related techniques derive pose features and …conduct pose analysis using non-invasive CV methods. The application of machine learning (ML) and artificial intelligence (AI) techniques to an inter-disciplinary field like yoga becomes quite difficult. Due to its potent feature learning ability, deep learning (DL) has recently achieved an impressive level of performance in classifying yoga poses. In this paper, an artificial algae optimizer with hybrid deep learning-based yoga pose estimation (AAOHDL-YPE) model is presented. The presented AAOHDL-YPE model analyzes yoga video clips to estimate pose. Utilizing Part Confidence Map and Part Affinity Field with bipartite equivalent and parsing, OpenPose can be employed to determine the joint location. The deep belief network (DBN) model is then used for Yoga recognition. Finally, the AAO algorithm is utilized to enhance the EfficientNet model’s recognition performance. The results of a comprehensive experimentation analysis reveal that the AAOHDL-YPE technique produces superior results in comparison to existing methods. Show more
Keywords: Yoga posture, activity recognition, deep learning, metaheuristics, computer vision
DOI: 10.3233/JIFS-233583
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Sendhil, R. | Arulmurugan, A. | Jose Moses, G. | Kaviarasan, R. | Ramadoss, P.
Article Type: Research Article
Abstract: Occult peritoneal metastasis often emerges in sick persons having matured gastric cancer (GC) and is inexpertly detected with presently feasible instruments. Due to the existence of peritoneal metastasis that prevents the probability of healing crucial operation, there relies upon a discontented requirement for an initial diagnosis to accurately recognize sick persons having occult peritoneal metastasis. The proffered paradigm of this chapter identifies the initial phases of occult peritoneal metastasis in GC. The initial phase accompanies metabolomics for inspecting biomarkers. If the sick person undergoes the initial signs of occult peritoneal metastasis in GC, early detection is conducted. Yet, the physical …prognosis of this cancer cannot diagnose it, and so, automated detection of the images by dissecting the preoperational Computed Tomography (CT) images by conditional random fields accompanying Pro-DAE (Post-processing Denoising Autoencoders) and the labeling in the images is rid by denoising strainers; later, the ensued images and the segmented images experience the Graph Convolutional Networks (GCN), and the outcome feature graph information experience the enhanced categorizer (Greywold and Cuckoo Search Naïve Bayes categorizer) procedure that is employed for initial diagnosis of cancer. Diagnosis of cancer at the initial phase certainly lessens the matured phases of cancer. Hence, this medical information is gathered and treated for diagnosing the sickness. Show more
Keywords: Gastric Cancer, MIoT, Greywold and Cuckoo Search Naïve Bayes categorizer, Cuckoo-Grey Wolf search Correlative Naïve Bayes categorizer
DOI: 10.3233/JIFS-233510
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Priya, S. Baghavathi | Rani, P. Sheela | Chokkalingam, S.P. | Prathik, A. | Mohan, M. | Anitha, G. | Thangavel, M. | Suthir, S.
Article Type: Research Article
Abstract: Traditional testimony and electronic endorsements are extremely challenging to uphold and defend, and there is a problem with challenging authentication. The identity of the student is typically not recognized when it comes to requirements for access to a student’s academic credentials that are scattered over numerous sites. This is an issue with cross-domain authentication methods. On the one hand, whenever the volume of cross-domain authentication requests increases dramatically, the response time can become intolerable because of the slow throughput associated with blockchain mechanisms. These systems still do not give enough thought to the cross-domain scenario’s anonymity problem. This research proposes …an effective cross-domain authentication mechanism called XAutn that protects anonymity and integrates seamlessly through the present Certificate Transparency (CT) schemes. XAutn protects privacy and develops a fast response correctness evaluation method that is based on the RSA (Rivest, Shamir, and Adleman) cryptographic accumulator, Zero Knowledge Proof Algorithm, and Proof of Continuous work consensus Algorithm (POCW). We also provide a privacy-aware computation authentication approach to strengthen the integrity of the authentication messages more securely and counteract the discriminatory analysis of malevolent requests. This research is primarily used to validate identities in a blockchain network, which makes it possible to guarantee their authenticity and integrity while also increasing security and privacy. The proposed technique greatly outperformed the current methods in terms of authentication time, period required for storage, space for storage, and overall processing cost. The proposed method exhibits a speed gain of authentication of roughly 9% when compared to traditional blockchain systems. The security investigation and results from experiments demonstrate how the proposed approach is more reliable and trustworthy. Show more
Keywords: Zero Knowledge Proof, RSA accumulator, educational certificates, cross-domain authentication, blockchain
DOI: 10.3233/JIFS-235140
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2023
Authors: Lakshmi Narayanan, K. | Naresh, R.
Article Type: Research Article
Abstract: Vehicular Ad-Hoc Network (VANET) Technology is advancing due to the convergence of VANET and cloud computing technologies, Vehicular Ad-Hoc Network (VANET) entities can benefit from the cloud service provider’s favourable storage and computing capabilities. Cloud computing, the processing and storage capabilities provided by various cloud service providers, would be available to all VANET enterprises. Digital Twin helps in creating a digital view of the Vehicle. It focuses on the physical behaviour of the Vehicle as well as the software it alerts when it finds issues with the performance. The representation of the Vehicle is created using intelligent sensors, which are …in OBU of VANET that help collect info from the product. The author introduces the Cloud-based three-layer key management for VANET in this study. Because VANET connections can abruptly change, critical negotiation verification must be completed quickly and with minimal bandwidth. When the Vehicles are in movement, we confront the difficulty in timely methods, network stability, and routing concerns like reliability and scalability. We must additionally address issues such as fair network access, inappropriate behaviour identification, cancellation, the authentication process, confidentiality, and vehicle trustworthiness verification. The proposed All-Wheel Control (AWC) method in this study may improve the safety and efficiency of VANETs. This technology would also benefit future intelligent transportation systems. The Rivest–Shamir–Adleman (RSA) algorithm and Chinese Remainder Theorem algorithms generate keys at the group, subgroup, and node levels. The proposed method produces better results than the previous methods. Show more
Keywords: Cloud computing, VANET, RSA, CRT, AWC
DOI: 10.3233/JIFS-233527
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023
Authors: Zhao, Liang | Wang, Jiawei | Liu, Shipeng | Yang, Xiaoyan
Article Type: Research Article
Abstract: Tunnels water leakage detection in complex environments is difficult to detect the edge information due to the structural similarity between the region of water seepage and wet stains. In order to address the issue, this study proposes a model comprising a multilevel transformer encoder and an adaptive multitask decoder. The multilevel transformer encoder is a layered transformer to extract the multilevel characteristics of water leakage information, and the adaptive multitask decoder comprises the adaptive network branches. The adaptive network branches generate the ground truths of wet stains and water seepage through the threshold value and transmit them to the network …for training. The converged network, the U-net, fuses coarse images from the adaptive multitask decoder, and the fusion images are the final segmentation results of water leakage in tunnels. The experimental results indicate that the proposed model achieves 95.1% Dice and 90.4% MIOU, respectively. This proposed model demonstrates a superior level of precision and generalization when compared to other related models. Show more
Keywords: Water leakage, multilevel transformer encoder, adaptive multitask decoder, adaptive network branches, converged network
DOI: 10.3233/JIFS-224315
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Zhou, Xiao-Guang | Chen, Ya-Nan | Ji, Jia-Xi
Article Type: Research Article
Abstract: The multi-attribute decision-making (MADM) methods can deeply mine hidden information in data and make a more reliable decision with actual needs and human cognition. For this reason, this paper proposes the bipolar N -soft PROMETHEE (preference ranking organization method for enrichment of evaluation) method. The method fully embodies the advantages of the PROMETHEE method, which can limit the unconditional compensation between attribute values and effectively reflect the priority between attribute values. Further, by introducing an attribute threshold to filter research objects, the proposed method not only dramatically reduces the amount of computation but also considers the impact of the size …of the attribute value itself on decision-making. Secondly, the paper proposes the concepts of attribute praise, attribute popularity, total praise, and total popularity for the first time, fully mining information from bipolar N -soft sets, which can effectively handle situations where attribute values have different orders of magnitude. In addition, this paper presents the decision-making process of the new method, closely integrating theoretical models with real life. Finally, this paper analyses and compares the proposed method with the existing ones, further verifying the effectiveness and flexibility of the proposed method. Show more
Keywords: PROMETHEE method, bipolar N-soft set, attribute praise, attribute popularity, multi-attribute decision-making
DOI: 10.3233/JIFS-236404
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Wang, Jinxin | Wu, Zhanwen | Yang, Longzhi | Hu, Wei | Song, Chaojun | Zhu, Zhaolong | Guo, Xiaolei | Cao, Pingxiang
Article Type: Research Article
Abstract: Distributed flexible flowshop scheduling is getting more important in the large-scale panel furniture industry. It is vital for a higher manufacturing efficiency and economic profit. The distributed scheduling problem with lot-streaming in a flexible flow shop environment is investigated in this work. Furthermore, the actual constraints of packaging collaborative and machine setup times are considered in the proposed approach. The average order waiting time for packaging and average order delay rate is used as objectives. Non-dominated sorting method is used to handle this bi-objective optimization problem. An improved encoding method was proposed to address the large-scale orders that need to …be divided into sub-lots based on genetic algorithm. The proposed approach is firstly validated by benchmark with other multi-objectives evolutionary algorithms. The results found that the proposed approach had a good convergence and diversity. Besides, the influence of the proportion of large-scale orders priority level and sub-lot size was investigated in a panel furniture manufacturing scenario. The results can be concluded that the enterprise could obtain shorter order average waiting time and delay rate when the sub-lot sizes were set as two and the order priority level was allocated in the proportion of 1:2:3:4:5. Show more
Keywords: Distributed flexible flow shop scheduling, Panel furniture manufacturing, Lot-streaming, Packaging collaborative, Setup time
DOI: 10.3233/JIFS-237378
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Ding, Huafeng | Shang, Junyan | Zhou, Guohua
Article Type: Research Article
Abstract: Emotional state recognition is an important part of emotional research. Compared to non-physiological signals, the electroencephalogram (EEG) signals can truly and objectively reflect a person’s emotional state. To explore the multi-frequency band emotional information and address the noise problem of EEG signals, this paper proposes a robust multi-frequency band joint dictionary learning with low-rank representation (RMBDLL). Based on the dictionary learning, the technologies of sparse and low-rank representation are jointly integrated to reveal the intrinsic connections and discriminative information of EEG multi-frequency band. RMBDLL consists of robust dictionary learning and intra-class/inter-class local constraint learning. In robust dictionary learning part, RMBDLL …separates complex noise in EEG signals and establishes clean sub-dictionaries on each frequency band to improve the robustness of the model. In this case, different frequency data obtains the same encoding coefficients according to the consistency of emotional state recognition. In intra-class/inter-class local constraint learning part, RMBDLL introduces a regularization term composed of intra-class and inter-class local constraints, which are constructed from the local structural information of dictionary atoms, resulting in intra-class similarity and inter-class difference of EEG multi-frequency bands. The effectiveness of RMBDLL is verified on the SEED dataset with different noises. The experimental results show that the RMBDLL algorithm can maintain the discriminative local structure in the training samples and achieve good recognition performance on noisy EEG emotion datasets. Show more
Keywords: Multi-frequency band, dictionary learning, electroencephalogram, noise data, low-rank representation
DOI: 10.3233/JIFS-233753
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Wei, Jiaxin | Yang, Jin | Liu, Xinyang
Article Type: Research Article
Abstract: Due to intensified off-balance sheet disclosure by regulatory authorities, financial reports now contain a substantial amount of information beyond the financial statements. Consequently, the length of footnotes in financial reports exceeds that of the financial statements. This poses a novel challenge for regulators and users of financial reports in efficiently managing this information. Financial reports, with their clear structure, encompass abundant structured information applicable to information extraction, automatic summarization, and information retrieval. Extracting headings and paragraph content from financial reports enables the acquisition of the annual report text’s framework. This paper focuses on extracting the structural framework of annual report …texts and introduces an OpenCV-based method for text framework extraction using computer vision. The proposed method employs morphological image dilation to distinguish headings from the main body of the text. Moreover, this paper combines the proposed method with a traditional, rule-based extraction method that exploits the characteristic features of numbers and symbols at the beginning of headings. This combination results in an optimized framework extraction method, producing a more concise text framework. Show more
Keywords: OpenCV, dilation operation, text structure extraction
DOI: 10.3233/JIFS-234170
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Yu, Peng | Song, Huxiong | Liu, Hui
Article Type: Research Article
Abstract: How to expand the variable domain and monotonicity of aggregation functions to generate new aggregation functions is an important research content in aggregation functions. In this work, the concept of interval-valued pre-(quasi-)grouping functions is given by relaxing the interval monotonicity of interval-valued (quasi-)grouping functions to interval directional monotonicity. Then, some basic properties of interval-valued pre-(quasi-)grouping functions and the relationship between interval-valued pre-(quasi-)grouping functions and pre-(quasi-)grouping functions are presented. Accordingly, several construction methods of interval-valued pre-(quasi-)grouping functions are proposed. Finally, the concept of ( I G , IN ) -interval-valued directional monotonic fuzzy implications and QL …-interval-valued directional monotonic operations are introduced on the basis of interval-valued pre-(quasi-)grouping functions I G , interval-valued overlap functions IO and interval-valued fuzzy negations IN . In addition, related studies were conducted on the basic properties of ( I G , IN ) -interval-valued directional monotonic fuzzy implications and QL -interval-valued directional monotonic operations. Show more
Keywords: Interval mathematics, Aggregation functions, Pre-(quasi-)grouping functions, Interval-valued directional monotonic fuzzy implications
DOI: 10.3233/JIFS-233318
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-28, 2024
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