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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Langote, Meher Hiwase, Kunal Wande, Radha Tale, Abhay Kalekar, Harsh Gundewar, Swapnil Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Sawangi India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Sawangi India

ISBN: (纸本)9798331522667

Breast cancer forms one of the most common types of cancer among women worldwide;therefore, it highlights the importance of having rapid and timely diagnosis methods. Here, we will survey the use of convolutional neural networks (CNNs) for mutually exclusive cancer detection in ultrasound images using detectors. A dataset of benign and malignant breast tumors visualized via ultrasound is taken as the base for the training and testing of the CNN model. The CNN architecture is arranged to select valuable features from the ultrasound pictures only, but not malignant lesions. The performance and the evaluation of the CNN model are done through metrics like accuracy, precision, recall, and F1 score. The findings confirm the efficiency of using the CNN here, as it shows the extent and solidity of reading textures with excellent precision and quick performance in breast cancer screening. This investigation is the advancement of computer-aided diagnostic tools for breast cancer screening. As medical practice sees these benefits, the efficiency of healthcare delivery and patient outcomes are bound to be optimized. © 2025 IEEE.

关键词： Ultrasonic imaging

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Enhancing Pneumonia Treatment in Children by machine learning Techniques 4

Enhancing Pneumonia Treatment in Children by Machine Learnin...

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

作者： Gajbhiye, Ashwini Gundewar, Swapnil Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Pneumonia is a health challenge that affects many people more so the children, thus leading to high death rates across the world. There is a persuading evidential proof which shows that the techniques in the application of the machine learning (ML) have helped in improving the diagnosis and treatment of pneumonia. This paper discusses a number of ML techniques such as CNNs, SVMs and Random Forest that can be used to enhance the reliability and effectiveness of detecting pneumonia from enhanced chest x-ray images. The classification shows how other methods like Min-Max normalization and a specific type of image pre-processing called Contrast-Limited Adaptive Histogram Equalization (CLAHE) can make image quality even better as well as increase the accuracy of the model. Here, by using the sophisticated dalliance of ML models it is possible to dramatically minimize the incidence of diagnostic errors and enhance the results of pneumonia treatments thus witnessing an important evolutionary leap in the specialty. © 2025 IEEE.

关键词： Convolutional neural networks

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A Review on Web Data Mining to Detect Online Spread of Terrorism Activity 2

A Review on Web Data Mining to Detect Online Spread of Terro...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jha, Saurav Kumar Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine learning Maharashtra Wardha India

ISBN: (纸本)9798331505745

This research study outlines the significant contribution that web data mining can make in discovering and combating the dissemination of online terrorism. With terrorist groups using the internet more and more to deliver propaganda, recruit, and organize, there is a pressing need for efficient monitoring and analysis of web content. This article discusses different data mining methods that can be used to identify and study patterns suggestive of terrorist activity throughout networked digital environments. Specifically, the embedding of machine learning algorithms, i.e., Support Vector machines (SVM), Random Forest and Gradient Boosting, is investigated with regard to its suitability to differentiate between benign and harmful online content. These algorithms can process large volumes of data, where features are extracted, including URL structures, domain ages, and keyword frequencies, to detect anticipate threats. According to the article, feature extraction plays a crucial role in improving detection system accuracy, enabling real-time surveillance of anomalous online activities. In addition, the review discusses the problems that researchers and law enforcement agencies have been encountering in using web data mining for counter-terrorism purposes. Ethical concerns around privacy and civil liberties are addressed and technical challenges, including the quality of data and the dynamic character of online content, are elaborated. Scenarios in which false positives arising in detection systems may occur are also considered, with emphasis on the need for ongoing algorithm and technique refinement. © 2025 IEEE.

关键词： Crop protection

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Web-Based Smart Healthcare System 2

Web-Based Smart Healthcare System

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Daigavhane, Mansvi Kishorrao Gundewar, Swapnil Gadkari, Ayush Pravin Anpan, Sakshi Prakash Faculty of Engineering and Technology Wardha India Faculty of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Wardha India

ISBN: (纸本)9798331528713

In today's healthcare sector, utilizing information technology is essential for improving hospital operations. This study presents the creation and deployment of a web-based Smart Healthcare system (SHS) aimed at optimizing various administrative and clinical tasks within a healthcare setting. Some of the components are patient portal for signing in, practice calendar, assessment tools, invoicing, and reporting using webbased platform. Web based technologies and keeping a centralized database leads to secure data management, real time information and effective communication between different departments. This paper focuses on the description of the system's layout, structure and basic operations identifying it as a tool for raising organizational productivity, improving data accuracy and promoting patient care benefits. © 2024 IEEE.

关键词： Electronic health record

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics 3

Enhancing Interpretability: The Role of Explainable AI in He...

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

作者： Zade, Nikita Langote, Meher Verma, Prateek Faculty of Engineering & Technology Department of Artificial Intelligence & Data Science Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Artificial Intelligence & Machine Learning Maharashtra Sawangi442001 India

ISBN: (纸本)9798331509675

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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Background initialization in video data using singular value decomposition and robust principal component analysis

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International Journal of Computers and Applications 2023年第9期45卷 600-609页

作者： Gowda, Vishruth B. Gopalakrishna, M.T. Megha, J. Mohankumar, Shilpa Department of Information Science and Engineering SJB Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning SJB Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Bengaluru India

Background initialization is used in video processing applications to extract a scene without the foreground scene. In recent times, the issue of background initialization has gained researchers’ attention in different fields such as video surveillance, video segmentation, computational photography, and so on. The initialization of the background is affected due to different complex dissimilarities such as shadow, intermittent movement, illumination, camera jitter, and clutter. To overcome the aforementioned issues, this paper proposes a decomposition using the combination of the Singular Value Decomposition (SVD) and Robust Principal Component Analysis (RPCA) for Singular Spectrum Analysis (SSA) to perform an effective background initialization. The incorporation of RPCA in SVD is used to overcome the issues related to non-Gaussian noise and it also uses an effective structural knowledge of the video input i.e. sparse and low rank matrix which improves the Peak-Signal-to-Noise-Ratio (PSNR) of the background image. The SBI dataset was used to analyze the performances of SSA-SVDRPCA. The SSA-SVDRPCA is analyzed using MultiScale Structural Similarity Index (MSSSIM), Average gray-level error (AGE), Percentage of clustered error pixels (pCEPS), Percentage of error pixels (pEPs), and PSNR. The existing approaches such as Background Initialization Singular Spectrum Analysis (BISSA) and Quaternion-based Dynamic Mode Decomposition (Q-DMD) are used to compare with the SSA-SVDRPCA method. The PSNR of the SSA-SVDRPCA for Board class is 30.39 dB which is higher than the BISSA and Q-DMD. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Singular value decomposition

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Next-Gen AI in Healthcare: Shaping the Future of Precision and Preventive Medicine 2

Next-Gen AI in Healthcare: Shaping the Future of Precision a...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Tale, Yash Verma, Prateek Pake, Tanushri Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Healthcare professionals benefit from revolutionary changes because of current developments in artificial intelligence technology, which optimise precision medicine and preventive medical practices. artificial intelligence technology transitioned from basic rule-based systems into complex deep learning models that accurately analyse intricate medical data over decades. This paper investigates AI's significant influence on healthcare, including diagnostic and treatment operations, current robotics, and automated patient care systems. AI systems assist medical staff by improving precise diagnoses and simplifying treatments while boosting patient supervision through automated intelligence. The advantages of AI medicine simultaneously trigger crucial legal and moral dilemmas about healthcare data security, automated system bias, and AI-developed judgmental processes. The paper examines these issues regarding AI health care deployment while presenting guidelines to establish responsible and equitable use of AI technology in medical applications. The discussion explores how AI will influence genomic research and robotic surgery while demonstrating its potential to transform healthcare practice globally. This paper examines the complete transformation of contemporary medical care through an analysis of AI effects with identified difficulties and forthcoming potential. © 2025 IEEE.

关键词： Surgery

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Predicting Agricultural Crop Damage Caused by Unexpected Rainfall Using Deep learning

Predicting Agricultural Crop Damage Caused by Unexpected Rai...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Fulkar, Bhushan Patil, Pawan Srivastav, Gaurav Mahale, Promod Maharashtra Wardha India Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350375237

Efficient allocation of resources and timely agricultural interventions depend on the precise identification of crop loss at the field parcel level. Using recent data from 2018 to 2023, this study investigates the integration of deep learning techniques with real-time field photography to classify agricultural field parcels into those experiencing crop loss and those not. We build and assess deep learning models specifically for crop loss classification using a dataset from the Finnish Food Authority (FFA) that combines on-the-ground photos taken during field visits with comprehensive field parcel information. In this work, we use convolutional neural networks (CNNs) to examine the visual characteristics that we have extracted from field photos in order to identify the subtleties that may indicate crop health or possible loss. Our goal is to improve the model's generalization capabilities over a variety of crop types and agricultural landscapes by training it on a combination of field parcel data and corresponding photographs. © 2024 IEEE.

关键词： Image segmentation

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Identification of Potholes and Speed Bumps Using SSD and YOLO 15th

Identification of Potholes and Speed Bumps Using SSD and YOL...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Chandak, Payal Chaurasia, Shivamkumar Patil, Megharani Department of Artificial Intelligence and Machine Learning Thakur College of Engineering and Technology Mumbai India

ISBN: (纸本)9789819786305

Using SSD and YOLO to efficiently detect potholes and speed bumps is the main goal of this project, which aims to improve road safety and construction quality. Hazard priority is emphasized and the limitations of the manual method are addressed. YOLO and SSD architectures are used, trained, and optimized on a variety of datasets with different road appearances and conditions. To improve model capabilities, data augmentation approaches are used. The study explores techniques for processing data quickly through optimization. Accuracy, recall, and efficacy are measured by gathering large amounts of data, training models, and assessing results on different test sets. Extensive analysis between YOLOv4 and SSD-MobileNet demonstrates clear differences in performance. YOLOv4 achieves an overall improved object detection proficiency of 85.48, a superior mAP@0.5 score. By comparison, SSD-MobileNet demonstrates unique metric strengths in precision (0.42), recall (0.81), and F1 score (0.82). In particular, SSD-MobileNet significantly outperforms YOLOv4 (52.51 ms) in inference time (7 ms), demonstrating its effectiveness in real-time applications. The study suggests using SSD-MobileNet because of its excellent F1 score, repeatability, and precision for real-time applications. Because of its quick inference time, this model is a great fit for high-stakes situations where accurate and timely object identification is necessary, such as security systems, drones, and autonomous cars. It also balances performance measurements with practicality. Novelty: By utilizing cutting-edge computer vision techniques, this study creatively combines SSD and YOLO to produce a safer and more effective method of infrastructure management and road danger monitoring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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artificial intelligence and machine learning in Telemedicine: Transforming Remote Healthcare 2

Artificial Intelligence and Machine Learning in Telemedicine...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Khadse, Shrikant Verma, Prateek Lande, Vivek Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Deemed to be University Faculty of Engineering and Technology Wardha India Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Deemed to be University Wardha India

ISBN: (纸本)9798331505745

The concept of telemedicine developed before the upsurge of popularisation of telehealth services, particularly with the support of artificial intelligence and machine learning. This review aims to deeply analyse how these technologies integrate and change telemedicine, improve the diagnostics of diseases, help in the treatment process, and provide more accessible healthcare. Predictive analytics, natural language processing, and tele-therapy monitoring are examples of the ML and A.I. technologies used in patient care management. This is the time of more intelligent and more effective remote care services with the help of several innovative technologies and phenomena like A.I. health chatbots and intelligent treatment algorithms. However, several factors limit its development, including data privacy, legal issues, and users' access to technologies. Directions for further development and continuous healthcare improvement include integrating advanced 5G communication systems and wearables and exploiting blockchain technology for data interchange. These developments create prospects to eliminate current impediments and enhance the provision of healthcare services worldwide. This scope consists of mechanisms for the best present data, emphasizing growth areas of new technologies, especially artificial intelligence and machine learning, that transform healthcare systems. © 2025 IEEE.

关键词： Surgery

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