Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

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: Sugin Lal, G. | Porkodi, R.

Article Type: Research Article

Abstract: The term “educational data mining” refers to a field of study where information from academic environments is predicted using data mining, machine learning, and statistics. Education is the act of giving or receiving knowledge to or from someone who is formally studying and developing a natural talent. Over time, scholars have used data mining techniques to uncover hidden information in educational statistics and other external elements. This study suggests a unique method for analysing academic student performance that is based on data mining and machine learning. Here, the input is gathered as a dataset of student academic performance and is …processed for normalisation and noise reduction. Then, using the Boltzmann deep learning model coupled with linear kernel principal component analysis, this data’s characteristics were retrieved and chosen. Based on weights, information gain, and the Gini index, the characteristics are assessed and optimised. Following the selection of the pertinent data, conditional random field-based probabilistic clustering model is performed using RNN-based training, and the academic performance of the students is then examined using voting classifiers and sparse features. Experimental results are carried out for students academic performance dataset based on subjects in terms of training accuracy, validation accuracy, mean average precision, mean square error and correlation evaluation. Proposed technique attained accuracy of 96%, precision of 95%, Correlation Evaluation of 92% . Show more

Keywords: Student performance analysis, data mining, machine learning, clustering model, academic performance

DOI: 10.3233/JIFS-235350

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Mohan, M. | Tamizhazhagan, V. | Balaji, S.

Article Type: Research Article

Abstract: Cloud computing is a new technology that provides services to customers anywhere, anytime, under varying conditions and managed by a third-party cloud provider. Even though cloud computing has progressed a lot, some attacks still happen. The recent anomalous and signature attacks use clever strategies such as low-rate attacks and attacking as an authenticated user. In this paper, a novel Attack Detection and Prevention (ADAPT) method is proposed to overcome this issue. The proposed system consists of three stages. An Intrusion Detection System is initially used to check whether there is an attack or not by comparing the IP address in …the Blacklist IP Database. If an attack occurs, the IP address will be added to the Blacklist IP database and blocked. The second stage uses Bi-directional LSTM and Bi-directional GRU to check the anomalous and signature attack. In the third stage, classified output is sent to reinforcement learning, if any attack occurs the IP address is added to the blacklist IP database otherwise the packets are forwarded to the user. The proposed ADAPT technique achieves a higher accuracy range than existing techniques. Show more

Keywords: Cloud computing, Bi-directional LSTM, Bi-directional GRU, IP address, and reinforcement learning

DOI: 10.3233/JIFS-236371

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Li, Tao | Zhang, Zhongyu | Tao, Zhigang | Jia, Xinyu | Wang, Xiaolong | Wang, Jian

Article Type: Research Article

Abstract: Rock crack is one of the main factors responsible for rock failure. Uniaxial compression creep tests are performed using acoustic emission techniques, a high-sensitivity, non-radiative, non-destructive testing method to understand the influence of crack number on the precursor characteristics of short-term creep damage in the fractured rock mass. Based on the Grassberger-Procaccia (G-P) algorithm, the calculation step size for the correlation dimension value (D 2 ) of the acoustic emission ringing count rate is consistent with that for the acoustic emission b -value. The influence of the number of pre-cracks on the Acoustic emission precursor characteristics of red sandstone …creep is analyzed. The results show that near the destabilization of the specimen, the Acoustic emission accumulative ringing count surges in a stepwise manner, the Acoustic emission b -value decreases, the D 2 -value increases, the Acoustic emission amplitude shows high intensity and high frequency, and the ringing count increases sharply, all with the characteristics of failure precursors. During the accelerated creep stage of the specimens, with the increase of pre-cracks number, the precursory time points of acoustic emission b -value and D 2 -value advance, and their acoustic emission ringing counts increase sharply. Show more

Keywords: Acoustic emission, b-value, correlation dimension value (D2), precursor information, pre-cracks

DOI: 10.3233/JIFS-238964

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Hou, Xiaoyu | Luo, Chao | Gao, Baozhong

Article Type: Research Article

Abstract: Candlesticks are widely used as an effective technical analysis tool in financial markets. Traditionally, different combinations of candlesticks have formed specific bullish/bearish patterns providing investors with increased opportunities for profitable trades. However, most patterns derived from subjective expertise without quantitative analysis. In this article, combining bullish/bearish patterns with ensemble learning, we present an intelligent system for making stock trading decisions. The Ensemble Classifier through Multimodal Perturbation (ECMP) is designed to generate a diverse set of precise base classifiers to further determine the candlestick patterns. It achieves this by: first, introducing perturbations to the sample space through bootstrap sampling; second, employing …an attribute reduction algorithm based on neighborhood rough set theory to select relevant features; third, perturbing the feature space through random subspace selection. Ultimately, the trading decisions are guided by the classification outcomes of this procedure. To evaluate the proposed model, we apply it to empirical investigations within the context of the Chinese stock market. The results obtained from our experiments clearly demonstrate the effectiveness of the approach. Show more

Keywords: Trading system, ensemble learning, multimodal perturbation method, neighborhood rough set theory

DOI: 10.3233/JIFS-237087

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024

Authors: Zhao, Bin | Cao, Wei | Zhang, Jiqun | Gao, Yilong | Li, Bin | Chen, Fengmei

Article Type: Research Article

Abstract: Aiming at the issue that the current click-through rate prediction methods ignore the varying impacts of different input features on prediction accuracy and exhibit low accuracy when dealing with large-scale data, a click-through rate prediction method (GBIFM) which combines Gradient Boosting Decision Tree (GBDT) and Input-aware Factorization Machine (IFM) is proposed in this paper. The proposed GBIFM method employs GBDT for data processing, which can flexibly handle various types of data without the need for one-hot encoding of discrete features. An Input-aware strategy is introduced to refine the weight vector and embedding vector of each feature for different instances, adaptively …learning the impact of each input vector on feature representation. Furthermore, a fully connected network is incorporated to capture high-order features in a non-linear manner, enhancing the method’s ability to express and generalize complex structured data. A comprehensive experiment is conducted on the Criteo and Avazu datasets, the results show that compared to typical methods such as DeepFM, AFM, and IFM, the proposed method GBIFM can increase the AUC value by 10% –12% and decrease the Logloss value by 6% –20%, effectively improving the accuracy of click-through rate prediction. Show more

Keywords: Click-through rate estimation, GBIFM, GBDT, IFM

DOI: 10.3233/JIFS-234713

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024