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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pavithra, V. Danisha, B.R. Divyadharshini, V. Koresh, H. James Deva Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331523923

Snow accumulation on solar panels reduces their performance in energy generation regions. Hence certain image processing methods were developed for segmenting snow from the normal areas of solar panels. Graph Cut and Flood Fill are the popular semi-automatic method used for this application where it relies on detecting an efficient region for marking the segmentation process. Similarly, the manual intervention fails to provide consistent results in such methods. Therefore, a customized graph cut methodology is proposed in this work by combining super pixel-based feature extraction and k-means clustering. At first, the input images are preprocessed by dividing the pixels to form super pixels as a group to reduce the computational complexity without losing the meaningful information. Features like intensity, edge density, texture, and color are extracted from these super pixel groups for segmenting the snow-covered regions on a solar panel. Further, the extracted features are normalized by a k-means clustering algorithm for identifying the highest intensity regions to observe the snow-covered regions. A graph cut-based segmentation technique is applied at the end to ensure the precise boundary delineation. The work is also incorporated with several post-processing techniques for improving the segmentation mask quality. This proposed method is evaluated against Graph cut and flood fill and satisfied with an average dice similarity coefficient of 93.09% and 95.11%. © 2025 IEEE.

关键词： Image segmentation

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Application of AI and ML in Wound Healing and Skin Regeneration 4

Application of AI and ML in Wound Healing and Skin Regenerat...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Petkar, Taniya G. Kumar, Praveen Sarate, Kirtiksha U. Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligence-controlled tools improve wound assessment, anticipate healing, and collaborate with regular examinations using cutting-edge dressings and imaging technologies. AI computations work on the management of express data to enhance treatment systems, while advancements in nanomaterials like marine collagen and therapies employing young microorganisms further accelerate skin recovery. Despite encouraging findings, there are still problems, such as potential overfitting and dataset limitations. In general, machine learning is changing the way that injuries are treated by providing proactive, tailored solutions that aim to understand outcomes and lower the cost associated with clinical outcomes. © 2025 IEEE.

关键词： Personalized medicine

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A Deep Learning Based System to Predict the Plant Disease Using Streamlit 4

A Deep Learning Based System to Predict the Plant Disease Us...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Warbhe, Mohan K. Bore, Joy Jordan Chaudari, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering MaharashtraSawangi Wardha442001 India

ISBN: (纸本)9798331523923

The proposed web application for tomato leaf disease detection exemplifies the transformative power of Artificial Intelligence and computer Vision in modern agriculture. Addressing the critical issue of early and accurate disease detection, this solution bridges the gap between advanced technological innovations and the practical needs of farming. The application is designed using Streamlit and Python to combine the usability of a friendly interface with the power of robust analytical capabilities in CNN, offering real-time reliable diagnosis and actionable insights to farmers. This application provides the highest possible accuracy and adaptability through the thorough training and evaluation of models and is a good asset for disease management. Deployment on scalable cloud platforms ensures access, reliability, and global reach. Future improvements, including extending disease detection to other crops, multilingual support, real-time recommendations, and integration with IoT devices, promise to expand its reach and utility. This initiative is a shining example of how AI-based solutions can transform agricultural practices by enhancing productivity, reducing losses, and ensuring sustainability. The application empowers farmers and contributes to a resilient agricultural ecosystem, addressing the pressing need for innovative and sustainable solutions in a rapidly evolving world. © 2025 IEEE.

关键词： Smart agriculture

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MWRS: A MAB-based Worker Recruitment Scheme with Tripartite Stackelberg Game for Reliable Mobile Crowdsensing

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5665-5680页

作者： Ouyang, Yan Zeng, Feng Xiong, Neal N. Liu, Anfeng Pedrycz, Witold School of Computer Science and Engineering Central South University Changsha410083 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istinye University Istanbul Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Mobile Crowdsensing (MCS) has emerged as a compelling paradigm for data sensing and collection, leveraging the widespread adoption of mobile devices and the active participation of numerous users. Despite its potential, MCS faces critical challenges, particularly in recruiting reliable workers and acquiring high-quality sensing data. Most existing approaches assume prior information on worker quality and are vulnerable to collusion attacks, especially having not comprehensively considered workers' reliability and stability. To address these problems, we propose a Multi-Armed Bandit (MAB) based Worker Recruitment Scheme (MWRS) integrated with the Tripartite Stackelberg Game (TSG) for MCS. Specifically, a trust evaluation and truth inference mechanism is introduced to assess the trustworthiness of workers through active truth detection. To enhance recruitment quality, we employ a trust-aware worker selection mechanism that utilizes a modified Upper Confidence Bound (UCB) algorithm, achieving an optimal balance between exploration and exploitation. Furthermore, the interactions among participants are modeled using a TSG framework, which formulates their respective payoffs to determine optimal decision-making strategies, thus achieving mutually beneficial outcomes. Extensive evaluations on real-world datasets demonstrate that our proposed scheme improves total quality by up to 30.8% and reduces regret by up to 80.3% compared to existing methods. © 2025 IEEE.

关键词： Decision making

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Lung sound disease detection using attention over pre-trained efficientnet architecture

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Multimedia Tools and Applications 2024年 1-35页

作者： Nair, Anuja Vadher, Himanshu Patel, Pal Vyas, Tarjni Bhatt, Chintan Bruno, Alessandro Department of Computer Science & amp Engineering School of Technology Nirma University Gujarat Ahmedabad382481 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382007 India Department of Business Law Economics Consumer Behaviour ’Carlo A. Ricciardi’ Faculty of Communication IULM University Milan20143 Italy

Air pollution has been on the rise for quite a while now and with it is the increasing number of cases involving respiratory diseases. These respiratory diseases range from the mild ones to the most severe ones. Therefore, we need to detect these diseases. The most efficient way of checking the health of the lung is through the use of lung sounds. But the lung sounds carry with them the minute variations that a doctor or any human sometimes misses. To tackle the same, this paper proposes a deep learning model that detects a person’s lung disease by processing lung sounds. The ICBHI 2017 dataset comprising the lung sound audios and their corresponding labels is used to train the proposed model. The dataset consists of lung sounds and some additional background noise has been removed using the bandpass Butterworth filter, whose frequency range has been set to 250Hz - 2000Hz. Later, the amount of data is multiplied through data augmentation for audio signals. The augmented data is later converted to mel spectrograms, which are, in turn, fed into the deep learning model. The model that gives the best accuracy is EfficientNet-B0 + attention with a training accuracy of 99.72%, validation accuracy of 99.82%, precision, recall and f1-scores of 99.82% as well. Furthermore, a comparison of the pre-trained model’s training and inference times has been performed. Although the proposed model takes a significant amount of training time, it takes the least amount of inference time compared to the other pre-trained models that have been tested. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Butterworth filters

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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Towards Non-Invasive Swallowing Assessment: An AI-Powered Interface for Swallowing Kinematic Analysis using High-Resolution Cervical Auscultation 46

Towards Non-Invasive Swallowing Assessment: An AI-Powered In...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Luo, Yuewen Anwar, Ayman Ren, Siyi Coyle, James L. Sejdic, Ervin University of Toronto Division of Engineering Science Faculty of Applied Science & Engineering TorontoON Canada University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing is a pivotal physiological function for human sustenance and hydration. Dysfunctions, termed dysphagia, necessitate prompt and precise diagnosis. Videofluoroscopic swallowing studies (VFSS) remain the gold standard for swallowing assessment but pose accessibility and radiation exposure concerns. High-resolution cervical auscultation (HRCA) presents a non-invasive alternative with comparable accuracy. Yet, existing studies have only assessed HRCA in tandem with VFSS, without applying it to clinical settings. We propose an AI-enhanced PC software deploying HRCA for real-time bedside swallowing screening. This software proficiently detects abnormalities and precisely measures key kinematic events, including upper esophageal sphincter opening and laryngeal vestibule closure duration. The system was evaluated through two studies, encompassing a VFSS comparative analysis and a usability study. Our findings underscored the software's superior operational efficiency and diagnostic accuracy, positioning our HRCA-based system as a refined and pragmatic alternative for dysphagia assessment. © 2024 IEEE.

关键词： User centered design

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Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

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SN computer science 2024年第6期5卷 1-17页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

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Black Hole Attack Detection Using Dynamic Threshold Values and Residual Energy in VANETs

Black Hole Attack Detection Using Dynamic Threshold Values a...

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2025 IEEE International Conference on Computational, communication and Information Technology, ICCCIT 2025

作者： Shalom Priscilla, I. Senthil Kumar, C. Buvaneswari, B. Shiva Kumar, M. Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India SRM Madurai College for Engineering and Technology Department of Electronics and Communication Engineering Madurai India

ISBN: (纸本)9798331512965

In black hole attack, a hostile vehicle or node within the VANET pretends to have the optimal route to the destination, convincing other nodes to route their data packets through it as they head towards the destination, trailing behind the malicious node, receives the data, it either discards the packets completely or forwards them selectively, causing disruptions in communication. To overcome this dilemma the research discusses a novel approach for categorizing nodes in AODV (Adhoc On demand vector Routing Protocol) networks according to their residual energy (RE) and forward rate (FR). With the goal to identify nodes that are considered energy-constrained, the proposed approach defines a residual energy threshold (RET) and includes dynamic threshold values (MinDTV and MaxDTV) for the forward rate. Three categories are applied to nodes: 1. (Trusted), 2. (Intermediate), and 3. (Untrusted or Energy-Constrained). This methodology results in a more trustworthy and energy-efficient network by dynamically updating node selection based on present forwarding behavior and levels of energy. The proposed approach boosts the overall strength, longevity as well, and integrity of RPL networks through the elimination of energy-depleted and unstable nodes;hence, it is especially suitable for applications in scenarios with scarce resources and malicious activity. © 2025 IEEE.

关键词： Black holes

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