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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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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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A Review on the Applications of Machine Learning and Deep Learning Algorithms for Image Recognition 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 Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Coronary Heart Disease Prediction Using CNN, RNN, LSTM and Attention DNN Model

Coronary Heart Disease Prediction Using CNN, RNN, LSTM and A...

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

作者： Dhawale, Hrutuja Thakare, Dhanashree Dhule, Chetan Agrawal, Rahul Morris, Nekita Chavan IOT Cyber Security GH Raisoni College of Engineering Department of Data Science Nagpur India

ISBN: (纸本)9798331512965

Coronary Heart Disease popularly referred to as CHD is one of the leading causes of death and illness across the global population making it imperative for the identification of an effective approach to its predictive modeling for early detection. The purpose of this research includes studying a dataset containing a variety of clinical characteristics in order to forecast CHD using sophisticated deep learning structures. Specifically, we implemented and evaluated four distinct models: Convolutional Neural Networks, Long Short-Term Memory networks, Recurrent Neural Networks and Attention Deep Neural Networks (DNN). All models are trained and evaluated on data set and multiple performance measures like accuracy, recall, precision, and F1score were used for the purpose. The experimental outcome showed that the Attention-based DNN had the highest accuracy rate of 91 % followed by the RNN with accuracy rate of 89%. CNN and LSTM models gave moderate accuracy but they did not outperform the best models among options. © 2025 IEEE.

关键词： Convolutional neural networks

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ML-Based College Predictor Telegram Bot Indian engineering Colleges 1

ML-Based College Predictor Telegram Bot Indian Engineering C...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： More, Amruta Patil, Satchal Savale, Aditya Satle, Prafull Tilwane, Krishna Shinde, Sanket Ajeenkya DY Patil School of Engineering Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350355611

This Research aimed at the challenge of finding college for students, but this is complex task as students are unable to find best institutes based on entrance exam scores in India. To bounce on that, we focus on the development of a College Predictor Bot, which uses some key factors notably JEE and CET scores and predicts admission to different Indian colleges. You are trained on years of admissions data from hundreds of colleges, with students of all shapes and sizes and performance levels. In terms of methodology used in the study, a combination of random forest and decision tree models were used for analysing the exam scores and make findings. The ML-model is subject to a lot of statistics and testing, and the analysis results in highly valuable co-relations between performance on entrance exams and admitting results. Our results show that College Predictor Bot accurately predicts admissions, therefore helping students know what colleges are suitable. The larger implications [of this tool] are that this might make the college selection process easier and provide a slightly more transparent and informed way of getting deserved seat in the Eductaion system of Bharat. © 2025 IEEE.

关键词： Students

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Predictive Analysis of Lung Disease using Random Forest and Convolutional Neural Networks Techniques 1

Predictive Analysis of Lung Disease using Random Forest and ...

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

作者： Ninawe, Sujal Sonar, Sunil Dhule, Chetan Agrawal, Rahul Morris, Nekita Chavhan G H Raisoni College of Engineering Department of Data Science Nagpur India Nagpur India

ISBN: (纸本)9798331529246

In the contemporary era, technology is rapidly advancing in various sectors such as IT, banking, e-Commerce, and healthcare. This paper focuses on the healthcare sector, specifically on early detection of lung diseases. Basically, we compared CNN and Random Forest techniques to evaluate their accuracy in detecting lung diseases. The results showed that CNN achieved significantly higher accuracy, outperforming Random Forest due to its advanced feature extraction and learning capabilities. The approach involves analysing gray scale X-ray images to predict lung conditions like lung cancer, tuberculosis, pneumonia, and COVID-19 using Convolutional Neural Networks (CNN's). Additionally, the Random Forest algorithm is applied to evaluate the accuracy of lung disease detection. The CNN model achieved an accuracy of 91% across pneumonia, tuberculosis, COVID-19, and normal datasets, and 96% accuracy for lung cancer detection. The Random Forest algorithm further validated the system's effectiveness through precision, recall, and F1-score metrics. This research highlights the potential of combining CNNs with Random Forest to enhance lung disease detection, supporting early diagnosis and treatment planning. © 2025 IEEE.

关键词： Lung cancer

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Statistical assessment of Indian classical music’s role in diabetes, thyroid, hypertension management

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International Journal of Information Technology (Singapore) 2025年第1期17卷 423-429页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Research Scholar at Department of Computer Engineering Terna Engineering College Assistant Professor at Department of Computer Science and Engineering (Data Science) Vidyavardhinis College of Engineering and Technology University of Mumbai Maharashtra Mumbai India Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India

This multi-center randomized controlled trial explores the therapeutic benefits of Indian classical music, specifically “Raga Therapy,” for managing diabetes, thyroid disorders, and hypertension—prevalent global health issues. The study examines how specific Ragas can enhance traditional treatments: Bhimpalas and Chhanayat for hypertension, Malkauns and Bhupal Todi for diabetes, and Shyam Kalyan and Yaman for thyroid disorders. Results show significant health improvements in patients integrating Raga Therapy into their care, highlighting its holistic benefits. The study underscores the profound connection between music and healing, positioning Indian classical music as a valuable complementary therapy. By addressing a critical gap in the literature, it advocates for further research to understand the mechanisms involved and refine Raga selection for optimal therapeutic outcomes. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Paired t-test Patient satisfaction survey Raga Therapy Wilcoxon signed-rank test

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Harnessing the Power of Attention for Patch-Based Biomedical Image Classification

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IEEE Access 2025年 13卷 36760-36774页

作者： Habib, Gousia Ahmad Malik, Ishfaq Qureshi, Shaima Ra, In-Ho Singh, Saurabh Sharma, Surbhi Department of Electrical Engineering New Delhi110016 India Shoolini University Yogananda School of AI Computer and Data Sciences Solan173229 India Department of Computer Science Engineering Srinagar190006 India Kunsan National University School of Computer Information and Communication Engineering Gunsan54150 Korea Republic of Woosong University Department of AI and Big Data Daejeon34606 Korea Republic of

The analysis of biomedical images, particularly brain MRI scans, is critical in healthcare and medical research. However, conventional approaches such as convolutional neural networks (CNNs) often struggle to capture complex spatial and contextual relationships within medical imaging data, limiting their generalization capabilities. To address these limitations, we propose a novel framework for brain MRI analysis using Vision Transformers (ViTs) augmented with a Shifted Patching Technique (S.P.T.). This approach combines conventional non-overlapping patching with shifted patching to better model intricate spatial dependencies and contextual associations. The proposed method is evaluated on the BRAIN MRI dataset, with standardized image resolution achieved through Lanczos5 interpolation. Further validation is performed on the ChestX-ray14 and Camelyon17 datasets to assess its generalizability across medical imaging modalities. Performance metrics such as accuracy, F1-score, and precision demonstrate that the proposed ViT model consistently outperforms traditional CNN-based methods. Ablation studies further confirm the significant contribution of the S.P.T. and other architectural components to overall performance. These findings highlight the potential of ViT-based architectures with innovative patching techniques as powerful tools for biomedical image analysis. This work offers a robust and scalable solution for enhancing diagnostic accuracy and advancing medical research applications. © 2013 IEEE.

关键词： Diagnosis

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model Machine learning algorithms Remote sensing images

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