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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Kajendran, K. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This research attempts to identify stock market bubbles using technical indicators combined with machine-learning processes. If not detected early on, stock bubbles arising from overvaluation and speculation can incur huge losses. The study used Technical Indicators such as the Relative Strength Index(RSI), Moving Average Convergence Divergence(MACD), Bollinger Bands, and Volume Indicators to catch the market trends. These features were analyzed using Support Vector Machine, Logistic Regression, and Random Forest models with a 99.84% test accuracy and AUC equal to 1.0 obtained with Random Forest, which proved to be, in this research, the most reliable for bubble detection. The study compares, in detail, the results, including Confusion Matrices and Receiver Operating Characteristic curves, validating the findings. The approach here would be a robust and data-driven framework for investors and analysts to recognize bubbles, facilitating better decision-making by risk minimization. The results also contribute to further developments in the role of machine learning in equity markets. © 2025 IEEE.

关键词： Marketplaces

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

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computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic Butterfly Optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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APF-GAN:Exploring asymmetric pre-training and fine-tuning strategy for conditional generative adversarial network

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Computational Visual Media 2024年第1期10卷 187-192页

作者： Yuxuan Li Lingfeng Yang Xiang Li College of Computer Science Nankai UniversityTianjinChina School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina

The use of generative adversarial network(GAN)-based models for the conditional generation of image semantic segmentation has shown promising results in recent ***,there are still some limitations,including limited diversity of image style,distortion of detailed texture,unbalanced color tone,and lengthy training *** address these issues,we propose an asymmetric pre-training and fine-tuning(APF)-GAN model.

关键词： tuning network asymmetric

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

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computers, Materials & Continua 2025年第4期83卷 1275-1290页

作者： Yasser Alharbi Sarwar Shah Khan College of Computer Science and Engineering University of HailHail55436Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan

Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关键词： Sentiment analysis stochastic gradient descent reviews English IMDb dataset Arabic dataset

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Interval Valued Secondary k-Range Symmetric Fuzzy Matrices with Generalized Inverses

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2051-2066页

作者： Prathab, H. Ramalingam, N. Janaki, E. Bobin, A. Kamalakannan, V. Anandhkumar, M. Saveetha Engineering College Tamilnadu Chennai602105 India Faculty of Science and Humanities SRM Institute of Science and Technology Tamilnadu Ramapuram600089 India Panimalar Engineering College Tamilnadu Chennai600123 India Tamilnadu Villupuram605108 India Panimalar Engineering College Tamilnadu Chennai600 123 India Tamilnadu Villupuram605108 India

This research examines an interval-valued secondary k-Range symmetric fuzzy matrix. It discusses the relationships between different types of matrices, specifically interval-valued s-k Range symmetric, interval-valued k-Range symmetric, and interval-valued Range symmetric matrices. The study establishes the necessary and sufficient criteria for an interval-valued s-k Range symmetric fuzzy matrix. It is demonstrated that s-symmetry implies s-Range symmetric and the reverse is necessarily true. Also, we illustrate a graphical representation of Kernel symmetric, Column symmetric, and Range symmetric adjacency and incidence fuzzy matrices. Every adjacency fuzzy matrix is symmetric, Range symmetric, Column symmetric, and Kernel symmetric, but the incidence matrix satisfies only the KS conditions. Similarly, every Range symmetric adjacency fuzzy matrix is a Kernel symmetric adjacency fuzzy matrix, but a Kernel symmetric adjacency fuzzy matrix need not be a Range symmetric fuzzy matrix. Also, in every isomorphic graph, its adjacency fuzzy matrix is a Kernel symmetric, Range symmetric, and Column symmetric adjacency fuzzy matrix, but the converse need not be true. Additionally, equivalent criteria for various g-inverses of an interval-valued s-k Range symmetric fuzzy matrix being interval-valued s-k Range symmetric matrices are also established. The generalized inverses of an interval-valued s-k Range symmetric matrix corresponding to the sets A{1, 2}, A{1, 2, 3} and A{1, 2, 4} are *** this paper, we present an application of soft graphs in decision-making through the use of the adjacency matrix of a soft graph. We have developed an algorithm for this purpose and provide an example to demonstrate its application. © (2024), (International Association of Engineers). All rights reserved.

关键词： Matrix algebra

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Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

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computers, Materials & Continua 2024年第4期79卷 735-759页

作者： Wael M.S.Yafooz Abdullah Alsaeedi Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user ***, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

关键词： Mobile apps issues play store user comments deep learning LSTM bidirectional LSTM

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Tri-M2MT:Multi-modalities based effective acute bilirubin encephalopathy diagnosis through multi-transformer using neonatal Magnetic Resonance Imaging

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CAAI Transactions on Intelligence Technology 2025年第2期10卷 434-449页

作者： Kumar Perumal Rakesh Kumar Mahendran Arfat Ahmad Khan Seifedine Kadry Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhon KaenThailand Noroff University College and Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

Acute Bilirubin Encephalopathy(ABE)is a significant threat to neonates and it leads to disability and high mortality *** and treating ABE promptly is important to prevent further complications and long-term *** studies have explored ABE ***,they often face limitations in classification due to reliance on a single modality of Magnetic Resonance Imaging(MRI).To tackle this problem,the authors propose a Tri-M2MT model for precise ABE detection by using tri-modality MRI *** scans include T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),and apparent diffusion coefficient maps to get indepth ***,the tri-modality MRI scans are collected and preprocessesed by using an Advanced Gaussian Filter for noise reduction and Z-score normalisation for data *** Advanced Capsule Network was utilised to extract relevant features by using Snake Optimization Algorithm to select optimal features based on feature correlation with the aim of minimising complexity and enhancing detection ***,a multi-transformer approach was used for feature fusion and identify feature correlations ***,accurate ABE diagnosis is achieved through the utilisation of a SoftMax *** performance of the proposed Tri-M2MT model is evaluated across various metrics,including accuracy,specificity,sensitivity,F1-score,and ROC curve analysis,and the proposed methodology provides better performance compared to existing methodologies.

关键词： Acute Bilirubin Encephalopathy(ABE)Diagnosis feature extraction MRI multi-modality multi-transformer neonatal

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Smart traffic control: machine learning for dynamic road traffic management in urban environments

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Multimedia Tools and Applications 2025年第12期84卷 10321-10345页

作者： Khan, Hameed Thakur, Jitendra Singh Deptt of Computer Science & Engineering Jabalpur Engineering College Madhya Pradesh Jabalpur India

Roadside and outside environmental elements contribute to the road traffic setting's highly dynamic and turbulent nature. The human factor, primarily disregarded in the present research, is an essential element that contributes to the traffic context in addition to infrastructure-related elements like traffic signals, road infrastructure, and other road networks. Timing the green light and tracing the object that makes the incorrect turn using real-time visual information for traffic monitoring are still challenging tasks for the conventional traffic control system. We describe a self-adaptive real-time algorithm based on real-time traffic flow and monitoring. Combining image processing with AI-powered, self-adaptive machine learning for controlling traffic clearance at intersections is a forward-thinking approach with great potential. The suggested system uses the You Only Look Once v3 (YOLOv3) model and single image processing using a neural network to determine traffic clearance at the signal. YOLOv3 method to recognize objects from video frames. Subsequently, the centroid object tracking technique is used to monitor the movement of each vehicle within a proposed framework. We implemented algorithms to identify vehicles traveling in the incorrect direction based on their trajectories. This integrated approach enhances accurate object recognition, real-time vehicle tracking, and the detection of traffic violations, enhancing overall road safety measures. The experimental findings are quite promising, achieving an exclusive comparison between expected and actual vehicle numbers is crucial for any traffic monitoring system. The average object detection accuracy of 88.43% is impressive, and the exceptional 90.45% accuracy in tracking vehicles engaging in wrong turns or reckless driving behaviors is particularly noteworthy—it provides the system's ability to address safety concerns effectively. Integrating a Convolutional Neural Network (CNN) into the algorithm to all

关键词： Machine learning

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