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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pathan, Shabana Sharma, Sanjeev Kumar Department of Computer Science & Engineering Oriental University M.P Indore India Department of Computer Science and Engineering Oriental Institute of Science and Technology M.P Bhopal India

ISBN: (纸本)9798331300579

Data mining has applications for a wide range of enterprises, including those in the banking, telecommunications, energy, medical, marketing, and finance industries. The real-world situations involve noisy, unpredictable, ambiguous, and untrustworthy data, which further muddies the waters. During the information-gathering process, uncertainty arises in various application domains and is comprised of measurement errors, outdated information (data staleness), and repeated entries of the same type. This could lead to the prediction model performing poorly. The suggested method uses Deep Learning to control the uncertainty in the dataset before building Decision Tree models for categorization. This study compares two proposed approaches for decision tree optimization with the state-of-the-art approaches based on F1 score, accuracy, precision, recall, and evaluation parameters. © Grenze Scientific Society, 2024.

关键词： Prediction models

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A Hybrid U-Net with Active Contours for Plant Leaf Disease Segmentation and Classification 3

A Hybrid U-Net with Active Contours for Plant Leaf Disease S...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Sivamurugan, J. Naveen, G. Rajalakshmi, D. Reddy, P. Venkata Subba Taher, Shaik Deepak, Sannapu Madha Institute of Engineering and Technology Department of Computer Science and Engineering Chennai India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Thiruvallur India

ISBN: (纸本)9798331509675

The economic growth of a nation entirely depends upon the agriculture and agricultural products. In developing countries like India, agriculture is the primary source of income and its contributing 17% to the total GDP. There are plenty of factors lead to plant disease which impacts the quality and yield of plants. Though manual method of detection is time consuming and it may have chance for errors. This method is not enough to identify and limit the spread of plant disease. To establish an automated plant disease detection in farms will reduce the risk of plant disease and promotes real-time monitoring of crops in a daily basis. Artificial Intelligence (AI) took part in supporting farmers to get instant solution in selection of fertilizers, classifying the quality of agricultural products, weather prediction and soil nutrient level detection. In this study, we proposes a novel segmentation approach namely Hybrid U-Net with active contours to segment the disease affected portion on leaf which support farmers to identify disease at early stage. This study provides a comprehensive analysis of plant disease segmentation by proposed method with conventional approaches. The publically available dataset is chosen for this analysis and performance of conventional studies was compared. This study presented current trends of plant disease segmentation and several other image classification techniques. Experimental results evaluating that the proposed study improved segmentation better than conventional methods. © 2025 IEEE.

关键词： Wiener filtering

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of Information Technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of Technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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A rehabilitation framework based on motor imagery induced wheelchair movement using fuzzy vector quantization

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3025-3036页

作者： Ghosh, Ahona Saha, Sriparna Ghosh, Lidia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal India Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India

Stroke and many other neural disorders affecting motor functions lead to a significant degradation in the quality of life. The solution to the problem can be found in integrating brain commands with a mind driven wheelchair system for the disabled persons leading to motor rehabilitation, for achieving efficient control in different environments. But, the performance issues like the lack of robustness and reliability of the existing prototypes motivated us to design a multimodal rehabilitation framework using brain-computer interfacing for people with movement complications with the help of an electroencephalogram and a mobile robot. The current framework utilizes Fuzzy Vector Quantization as a motor imagery-based movement classifier after channel selection from electroencephalographic signals and feature extraction from that. The proposed model of robot navigation based on movement classification achieves 96.45% accuracy and it outperforms existing works by a remarkable margin. After getting successful results, a mobile robot-based prototype of a wheelchair (here, using TurtleBot3) is designed and presented with real-time implementation proving its suitability in day-to-day rehabilitative platforms. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Electroencephalogram Fuzzy vector quantization Motor imagery Rehabilitation TurtleBot3 Wheelchair

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Optimizing Forecast Accuracy in Cryptocurrency Markets:Evaluating Feature Selection Techniques for Technical Indicators

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computers, Materials & Continua 2025年第5期83卷 3411-3433页

作者： Ahmed El Youssefi Abdelaaziz Hessane Imad Zeroual Yousef Farhaoui IMIA Laboratory Faculty of Sciences and TechniquesMoulay Ismail University of MeknesErrachidia52003Morocco Department of Computer Science Faculty of Science of MeknesMoulay Ismail University of MeknesMeknes50000Morocco

This study provides a systematic investigation into the influence of feature selection methods on cryptocurrency price forecasting models employing technical *** this work,over 130 technical indicators—covering momentum,volatility,volume,and trend-related technical indicators—are subjected to three distinct feature selection ***,mutual information(MI),recursive feature elimination(RFE),and random forest importance(RFI).By extracting an optimal set of 20 predictors,the proposed framework aims to mitigate redundancy and overfitting while enhancing *** feature subsets are integrated into support vector regression(SVR),Huber regressors,and k-nearest neighbors(KNN)models to forecast the prices of three leading cryptocurrencies—Bitcoin(BTC/USDT),Ethereum(ETH/USDT),and Binance Coin(BNB/USDT)—across horizons ranging from 1 to 20 *** evaluation employs the coefficient of determination(R2)and the root mean squared logarithmic error(RMSLE),alongside a walk-forward validation scheme to approximate real-world trading *** results indicate that incorporating momentum and volatility measures substantially improves predictive accuracy,with particularly pronounced effects observed at longer forecast ***,indicators related to volume and trend provide incremental benefits in select market ***,an 80%–85% reduction in the original feature set frequently maintains or enhances model performance relative to the complete indicator *** findings highlight the critical role of targeted feature selection in addressing high-dimensional financial data challenges while preserving model *** research advances the field of cryptocurrency forecasting by offering a rigorous comparison of feature selection methods and their effects on multiple digital assets and prediction *** outcomes highlight the importance of dimension-reduction strategies in developing more efficient and resi

关键词： Cryptocurrency forecasting technical indicator feature selection walk-forward volatility momentum trend

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VSNet: classification of pulmonary nodules in 3D using vision transformer and sequence spatial attention mechanism

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Multimedia Tools and Applications 2025年第14期84卷 13885-13903页

作者： Tang, Dongfang Xiao, Ting Yang, Fan Zhang, Conghao Wang, Zhe Gao, Wen Department of Thoracic Surgery Huadong Hospital Affiliated to Fudan University Shanghai200040 China Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China

Accurate classification of benign and malignant nodules in Computed Tomography (CT) scans is crucial for the early detection of lung cancer and computer-Aided Diagnosis (CAD) systems. Despite significant advancements, challenges such as the interpretability of the reasoning process and the lack of fine-grained representations persist. To address these challenges, we propose a 3D VSNet. Our approach incorporates a Sequence Spatial Attention Module (SSAM) that automatically locates the sequence of the encoder’s output and the receptive field region required, achieving the acquisition of key features of pulmonary nodules. We leverage the characteristics of shallow and deep features based on 3D Vision Transformer (ViT) and Convolutional Neural Networks (CNNs) to obtain fine-grained representations and improve nodule classification. Additionally, we implement a new training strategy using Supervised Contrastive (SC) loss and Proxy-Anchor (PA) loss to optimize the embedding feature of similar samples and the direct cosine distance, and enhance the convergence speed of tuple sampling. Our experiments on the LIDC-IDRI dataset demonstrate the effectiveness of our proposed technique, achieving an accuracy of 90.28% and a precision of 91.63%. Furthermore, we conduct ablation experiments to analyze the contribution and influence of each component of our method. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Glaucoma Classification Using Improved Pretrained Model 12th

Glaucoma Classification Using Improved Pretrained Model

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9789819731794

The most common cause of blindness in the world is glaucoma. It can lead to a decline in eyesight and quality of life if not addressed within the stipulated time. In computer vision, convolutional neural networks (CNNs) have achieved a viable solution. The most standard glaucoma screening method is fundus imaging due to the compromises it makes between mobility, size, and price. In this paper, we utilized the RIM-1 DL dataset in two different versions: RIM-1 Hospital and RIM-1 Randomly. To enhance the image quality and increase accuracy, we used median filtering pre-processing techniques in this work. We also used fundus images to automatically diagnose glaucoma using six different Image-net-trained models (DenseNet121, DenseNet169, VGG19, InceptionV3, ResNet50, and MobileNetV2). The six CNN architectures mentioned above are modified using normalization, inception, and global pooling layers in the model with the pre-trained classification models. Results indicate that the DenseNet-121 is the most accurate model in this work, improving accuracy in the RIM-1 Hospital dataset from 70.68 to 80.13 and in the RIM-1 randomly generated dataset from 79.31 to 87.67 after the models were fine-tuned. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural network models

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Fake News Detection Using Machine Learning 1st

Fake News Detection Using Machine Learning

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1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Samad, Abdul Dhanda, Namrata Verma, Rajat Department of Computer Science and Engineering Amity University Uttar Pradesh Lucknow Campus Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (纸本)9783031487804

The news which is specially created to misguide as well as mislead the readers is termed fake news. Fake news can cause potential harm to both the individual and society. The problem of fake news has increased far more quickly in recent years. Social networks have significantly changed the scope and effect of their overall influence. Daily, a huge amount of information is released in print and online media, but it can be difficult to determine if the information is accurate or not. A significant amount of study has been done in this area in recent years with positive outcomes. In this modern era, machine learning is the answer to every issue. It can provide answers that people cannot readily think of and is applied in practically all disciplines, domains, and research. Many different algorithms in machine learning and artificial intelligence can help us in identifying as well as eliminating fake or false news. In this article, supervised learning is used. To determine the false news, the authors have used four machine learning algorithms in this study: Decision Tree (DT) classification, Logistic Regression (LR), Random forest (RF) and Passive Aggressive classifier (PAC). After comparing the output of each method, the one with the best accuracy is selected. The PAC outperforms the other classifiers and produces the best result. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision trees

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Silver nano-particles' size prediction using deep learning model with green method

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Materials Today: Proceedings 2022年 69卷 1193-1199页

作者： Mathumathi, M. Vetriselvi, T. Matheswaran, P. Nandhini, R. Selvi, A. Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India

When it comes to casting technologies and other production processes such as welding, DL models minimise the need for costly experimental inquiry. One of the most important criteria in nanotechnology is the measurement of particle size. They've received a lot of interest for their chemical, physical, and clinical uses because of their outstanding qualities. By adopting a green chemical reduction process, the nano-silver crystals are generated in the biopolymer interface without any agglomeration. Nano-metal production relies on precise control of particle size, and this approach provides that. For the prediction size of Ag-NPs via the green approach, a hybrid ideal of Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and model is presented in this research. Temperature, starch stabiliser, and AgNO3 concentration are among the experimental variables fed into the CNN-LSTM model. The size of a nano particle is the output of the CNN-LSTM model. Making three subsets of the experimental data for training, validation, and testing was one of the initial steps in creating an adapting model. The silver nanoparticles were identified using X-ray diffraction and transmission electron microscopy. Ag-NPs' crystallographic airplanes were found to be face-centered cubic, according to XRD analysis (fcc).When comparing with other existing classifiers, DNN achieved high performance on precision, i.e. 89.87%, and this is due to the presence of simple structure. The CNN and auto encoder achieved nearly 82% and 80% of accuracy, where LSTM achieved 77.25% of accuracy and proposed CNN-LSTM achieved 83.50% of recall. © 2022

关键词： Long short-term memory

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