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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Kiranagi, Soumya S Patil, Savitha Sharnbasva University Department of Artificial Intelligence and Data Science Kalaburagi India Sharnbasva University Department of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350372892

there are hundreds of kinds of plants on Earth, and many of them have medicinal or curative properties. Approximately 80% of the global population continues to rely on traditional medicine. In Ayurveda, the use of herbs as medicine dates back to Vedic times. Recognizable identification of the medicinal plants is crucial for the efficacy of ayurvedic, social, and homoeopathic treatments. For the treatment of skin conditions, lung infections, asthma, dyspepsia, and other ailments, many people throughout the globe rely on medicinal plants. One of the most important steps in making an Ayurvedic medicine is finding the right medicinal leaf. Misclassification or incorrect identification of medicinal leaves could affect Ayurveda's efficacy. Form, colour, and texture of leaves are the most basic characteristics that must be present to identify a medicinal plant. While the form and venation pattern of individual leaves might vary greatly, members of the same class of leaves tend to have commonalities. In light of this need, here is a model that makes use of an SVM Machine Learning Algorithm to reliably identify and categories medicinal leaves. © 2024 IEEE.

关键词： Plant diseases

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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AI-Powered Mental Health Screening and Support for Homeless Children

AI-Powered Mental Health Screening and Support for Homeless ...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Raga Madhuri, C.H. Bandaru, Jaya Sankar Krishna Srinu, Medisetti Vardhan, Gangadhari Midhun Anand Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798331536336

Mental health illness is a significant global public health threat exacerbated by the lack of effective early identification and intervention measures. This project aims to address these challenges by focusing on mental health recognition among homeless children. Individuals who are particularly vulnerable due to the loss of their parents. We created a model that screens mental health status by analyzing the responses to screening pediatric symptoms checklist (PSC) questions using an SVM algorithm. This model achieved 90% accuracy in predicting the mental health status of children. This approach makes it more accessible and effective for caretakers in orphan homes. The idea of providing some clear guidelines from the obtained results would make sure these children are well taken care of at the right time, which ultimately improves the mental health status of this group of people. © 2025 IEEE.

关键词： Pediatrics

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A Review on the Applications of Machine Learning and Deep Learning Techniques for Skin Cancer Detection 4

A Review on the Applications of Machine Learning and Deep Le...

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

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through Machine Learning (ML) and Deep Learning (DL) models. This review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector Machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. This article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 IEEE.

关键词： Adversarial machine learning

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Research on Semantic Matching of Thesis Themes Based on Comparative Learning and Adversarial Training 7

Research on Semantic Matching of Thesis Themes Based on Comp...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Zhang, Xu Shao, Zengzhen Li, Zhuangzhuang Han, Shuai Sun, Yajing Shandong Normal University Department Of Information Science And Engineering Jinan China Shandong Women's College Department Of Data Science And Computer Science Jinan China

ISBN: (纸本)9798350361445

At the doctoral level in the field of computer science, the domain of literature plagiarism detection encompasses numerous plagiarism detection algorithms, but relatively fewer have focused on methods specifically designed for topic-based plagiarism detection. To address this research gap, this paper introduces a text semantic matching algorithm based on contrastive learning and adversarial training, which we have named 'Comparative Adversarial Training (CAT).' Specifically, during the fine-tuning process, we generate affirmative and negative sentences using paper titles and keywords and optimize the model's performance through contrastive learning. Simultaneously, we generate adversarial samples by perturbing the word embedding matrix of the model and then employ these adversarial samples for adversarial training. Through experiments conducted on our proprietary internal dataset, the fine-tuned BERT model exhibited an average performance improvement of 3.71% compared to the baseline BERT model. Similarly, the fine-tuned RoBERTa model demonstrated a performance enhancement of 3.18% relative to the RoBERTa baseline model. Based on these experimental results, our algorithm's effectiveness has been thoroughly validated. © 2024 IEEE.

关键词： Semantics

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A Robust data Security Model Development to Maintain the Integrity of Information Using Artificial Intelligence and Cyber Laws

A Robust Data Security Model Development to Maintain the Int...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Kumar, G. Muthu Hemanand, D. Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The field of artificial intelligence (AI) has seen significant advancements in recent years. These days, artificial intelligence (AI) tools are being utilized by organizations in both the public and commercial sectors all over the world. Individuals, organizations, and society as a whole will reap broad and significant advantages as a result of the capabilities of artificial intelligence (AI) both today and in the near future. Nevertheless, these very same technical advancements give rise to significant concerns, such as the question of how to ensure that artificial intelligence technology is built and implemented in a manner that is in accordance with the applicable data privacy laws and standards. The fast development of artificial intelligence presents substantial hurdles in terms of protecting customers' privacy and the confidentiality of their data. The purpose of this essay is to suggest an all-encompassing strategy for the development of a framework to solve these concerns. First, an overview of prior research on security and privacy in artificial intelligence is presented, with an emphasis on both the progress that has been made and the limits that still remain. In the same vein, open research topics and gaps that need to be addressed in order to improve existing frameworks are recognized. Regarding the development of the framework, the topic of data protection in artificial intelligence is discussed. This includes elaborating on the significance of protecting the data that is utilized in artificial intelligence models, as well as elaborating on the policies and practices that are in place to ensure the data's safety and the methods that are utilized to maintain the data's integrity. Additionally, the security of artificial intelligence is investigated, which includes an analysis of the vulnerabilities and dangers that are present in artificial intelligence systems, as well as the presentation of instances of potential assaults and malevolent manipulations,

关键词： Risk assessment

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A multilayer network diffusion-based model for reviewer recommendation

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Chinese Physics B 2024年第3期33卷 700-717页

作者：黄羿炜徐舒琪蔡世民吕琳媛 Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Dataspace Hefei Comprehensive National Science CenterHefei 230088China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Cyber Science and Technology University of Science and Technology of ChinaHefei 230026China

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy *** systems are powerful tools developed in computer science and information science to deal with this ***,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform *** this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual *** network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among *** results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking *** work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

关键词： reviewer recommendation multilayer network network diffusion model recommender systems complex networks

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Advancements in Breast Cancer Detection: A Holistic Evaluation of Deep Learning Models with Histology and Thermal Imaging datasets 7

Advancements in Breast Cancer Detection: A Holistic Evaluati...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Chandra, Sanskriti Baghel, Pallavi Lakra, Nancy Saxena, Shivam Kumar, Santosh IIIT-Naya Raipur Data Science And Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Computer Science And Engineering Chhattisgarh India

ISBN: (纸本)9798350359299

This research addresses the pressing issue of breast cancer detection, emphasizing the development and evaluation of deep learning models using two distinct datasets. The first dataset involves histology images, where Convolutional Neural Networks (CNN), ResNet50, and a novel Concatenated Model are employed and rigorously evaluated. The second dataset comprises thermal images, providing an alternative perspective for breast cancer detection. The study reveals promising outcomes, with the Concatenated Model demonstrating the highest overall performance on histology images, achieving an accuracy of 87%, precision of 77%, recall of 77%, and an F1-Score of 77%. For thermal images, CNN achieves an accuracy of 80%, albeit with a lower recall of 51%, while ResNet50 attains the highest recall at 67%. These findings underscore the efficacy of deep learning models for breast cancer detection and emphasize the importance of considering diverse datasets and modalities for a comprehensive evaluation, contributing valuable insights to developing robust breast cancer screening systems to enhance early detection rates and save lives. © 2024 IEEE.

关键词： Infrared imaging

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Classification of Tuberculosis Bacilli from Microscopic Sputum Smear Images Using Swin Transformer

Classification of Tuberculosis Bacilli from Microscopic Sput...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Vallabhaneni, Praneeth Satapathy, Ashutosh Indugula, Manisha Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331509828

Tuberculosis (TB) has been a great challenge in the health world, and proper treatment requires proper diagnosis at the right time. This paper has classified the bacilli in sputum samples into single/simple and clump formations using deep-learning transformer techniques. The method starts with semantic segmentation, where TransUNet model has been used on sputum images to detect regions containing the bacilli. The superior visual task performer, the Swin Transformer, further classifies the regions identified. Accuracy, precision, recall, F1-score, and AUC-ROC are the metrics used to evaluate the model's performance. This classification contributes to TB diagnosis by quantifying bacterial load and categorizing infection severity. This type of differentiation may guide clinicians in developing more precise treatment strategies as the variability of infection severity may be observed in patients suffering from TB. Integrating the recent transformer models, such as Swin Transformer and TransUNet, into the microbiological diagnosis process will improve the speed of TB diagnosis, and automated bacterial classification will occur through an organized process. This study will help diagnose TB by improving the capacity of diagnosis, which subsequently helps in better decision-making regarding treatment and management, independent of human interpretation. The successful classification of TB bacilli resulted in an average accuracy of 93%, average precision of 93%, average recall of 92.5%, and average F1-score of 92.5%. © 2025 IEEE.

关键词： Semantic Segmentation

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Accelerating local SGD for non-IID data using variance reduction

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Frontiers of computer science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

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