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international conference on advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Selvamarilakshmi, D. Vani, R. Aravinth, S. Thenappan, S. Begam, R. Benazir Gangarani, N. Agni Coll Technol Dept Mech Chennai Tamil Nadu India Dr SNS Rajalakshmi Coll Arts & Sci Autonomous Dept Comp Sci Coimbatore Tamil Nadu India St Josephs Coll Engn Chennai Dept MBA Tami Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Commun Engn Chennai Tamil Nadu India Rajalakshmi Engn Coll Dept ECE Chennai Tamil Nadu India Paavai Engn Coll Dept ECE Namakkal Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Retinopathy of Prematurity (ROP) is a significant threat to premature babies' vision, needing early diagnosis and prognostic assessment. Existing systems frequently need more subjectivity and high accuracy, making prompt intervention difficult. A machine learning (ML)-based method for ROP diagnosis and prognosis is provided to address these challenges. The system uses big datasets to train ML algorithms, automating and standardizing the diagnosis process. The system enables ROP early detection and accurate prognosis by combining supervised and unsupervised learning techniques, feature selection, and dimensionality reduction. When compared to existing systems, it outperforms them in terms of accuracy (0.92 vs. 0.86), sensitivity (0.89 vs. 0.75), specificity (0.88 vs. 0.74), and AUC-ROC (0.85 vs. 0.76). Notably, the proposed system performs well across a variety of machine learning models, including Random Forest (0.92), Gradient Boosting (0.91), and Support Vector machine (0.89). These results highlight the system's potential to transform ROP care, resulting in improved visual outcomes for premature babies.

关键词： Retinopathy of Prematurity premature infants machine learning early diagnosis prognostic assessment medical imaging clinical features

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A machine learning based Consumer Power Management System using Smart Grid

A Machine Learning based Consumer Power Management System us...

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2023 international conference on Recent advances in Science and engineering Technology, ICRASET 2023

作者： Jadagerimath, Anasuya N Mallikarjunaswamy, S. Mahesh Kumar, D. Sheela, S. Prakash, S. Tevaramani, Saleem S Bengaluru India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India Global Academy of Technology Dept of Computer Science and Engineering Bengaluru India East Point Group of Institution Dept of Computer Science and Engineering Bengaluru India Ks Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306927

In recent years, the electricity crisis has increased day by day due to the huge number of electrical appliances being used by the public in their daily lives. Due to this, consumer demand is a very important parameter in power management systems. Hence, to overcome the above drawbacks, a proposed machine learning-based consumer power management system (MLCPMS) in the smart grid is used to analyse electricity consumption and bill generation with respect to the daily basis. As per simulation analysis, the proposed consumer power management (CPM) algorithm showed better performance as compared to the conventional electrical digital metre with respect to electricity consumption and bill analysis. © 2023 IEEE.

关键词： machine learning

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AI-Driven Clinical Decision Support systems: Revolutionizing Healthcare with Predictive Models

AI-Driven Clinical Decision Support Systems: Revolutionizing...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Shrivastava, Anurag Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Lakhanpal, Sorabh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331512965

The integration of artificial intelligence (AI) into Clinical Decision Support systems (CDSS) is changing the face of healthcare by allowing AI powered predictive, data driven insights, enabling medical practitioners to prolong their diagnosis and treatment planning. To make better and faster clinical judgements, CDSS based on AI using machine learning techniques such as ensemble methods and deep learning is discussed in this paper. He said most of the focus is on models that can predict future events, diagnose plagues and deliver tailored treatment programs sifting through mountains of health care data, such as genetics, medical imaging and EHRs. Some of the main problems we work with as we implement AI driven CDSS is its data privacy, model interpretability and integration to current healthcare processes. In addition this study summarizes recent advances in artificial intelligence (AI), enabling real time analysis and making real time decisions to improve patient outcomes and reduce workloads of healthcare personnel. Case examples that employ AI-CDSS to identify diseases, optimize treatments and predict risk are provided to show how they can change the face of clinical practice. Finally, we explore where AI healthcare may be going from here with a focus on the importance of deploying these systems with patient well being in mind and with full compliance to the relevant regulations. © 2025 IEEE.

关键词： AI-Driven CDSS Clinical Decision Support Healthcare machine learning Predictive Models

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Predicting Consumer Complaint Disputes in Finance using machine learning (AIOPS) 2

Predicting Consumer Complaint Disputes in Finance using Mach...

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2nd IEEE international conference on advances in Information Technology, ICAIT 2024

作者： Jondhale, Rohidas Patil, Shlok Shinde, Akash Ajalkar, Deepika Biradar, Santoshkumar Pune India

ISBN: (纸本)9798350383867

Consumer complaints in the financial sector present challenges including reputations damage and financial losses. This paper proposes a novel approach for predicting disputed complaints using a scalable machine-learning pipeline. We leverage historical complaint data, employing feature engineering techniques to extract insights from structured and unstructured complaint text. Our model, built using PySpark ML, achieves high accuracy in classifying newly registered complaints as likely to be disputed or not. Automated retraining using Airflow ensures continued effectiveness. Real-world dataset evaluation demonstrates the performance of traditional methods, enabling proactive intervention and mitigating risks. This study underscores the value of machine learning in enhancing complaint management processes in finance, with potential applicability across industries. © 2024 IEEE.

关键词： Adversarial machine learning

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A Meta-learning Framework with Model Stacking for Depression Detection Through Sentiment Classification

A Meta-Learning Framework with Model Stacking for Depression...

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2024 international conference on advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Remya, K. Ranjana, P. Hindustan Institute Of Technology And Science Department Of Information Technology Chennai India Hindustan Institute Of Technology And Science Department Of Cse Chennai India

ISBN: (纸本)9798350364828

In the proposed system, named AdaptiveSentinel, introduces a cutting-edge approach for depression detection depends on sentiment classification. Leveraging the power of meta-learning frameworks and model stacking, AdaptiveSentinel which aims to automate the intricate process of selecting the most effective machine learning-Driven models, preprocessing techniques, and featured representations for accurate sentiment analysis in the context of depression disorder. The system will begins by preparing a dataset with labeled textual samples, by emphasizing sentiment distinctions get related to depression severity. It then employs a meta-learning driven framework to autonomously explore a diverse range of machine learning-Driven models and configurations. Proposed system intelligently will navigates through options such as Gradient supported Boosting simultaneously optimizes hyper parameters through cross-validation. By Following model optimization, the system may engages in feature engineering, extracting of valuable information from the textual data and incorporating meta features such as user demographics and posting patterns. The Core part of AdaptiveSentinel lies in its model stacking mechanism, where it formatted a powerful ensemble by combining of predictions from the selected models. This stacked ensemble not only captures patterns in the data, also mitigates over fitting, resulting in a more robust and accurate depression detection system. The Evaluation metrics may includes accuracy, precision, and recall, validates the effectiveness of the ensemble model. Once get validated, the system can be deployed for real-world applications, continuously get monitored, with periodically retrained to adapt to evolving patterns in sentiment. It will be done by providing a dynamic and reliable tool for depression detection. AdaptiveSentinel also represents a significant stride towards automated, efficient, and precise sentiment-depending depression identification, holding prom

关键词： Sentiment analysis

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The Future of Intelligence: Advancements and Challenges in AI and machine learning 15

The Future of Intelligence: Advancements and Challenges in A...

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15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pushpa, R. Rekha, H. Akshaya Institute of Technology Department of computer Science and Engineering Tumakuru India Shridevi Institute of Engineering and Technology Department of Information Science and Engineering Tumakuru India

ISBN: (纸本)9798331300579

The incorporation of machine learning (ML) and Artificial Intelligence (AI) technologies across diverse industries presents considerable potential for both economic expansion and societal ***, which includes both Narrow and General forms, in conjunction with ML, enables data-driven decision making and automation in numerous industries, including healthcare, finance, and ***, the extensive implementation of these technologies is beset by obstacles such as ambiguity in algorithms, ethical concerns, and *** paper studies the developments and obstacles in Al and ML, as well as their practical implementations in nanotechnology, medicine, governance, and *** the potential for economic benefits and societal advancements is apparent, it is imperative to prioritize responsible and equitable implementation of Al by addressing organizational shortcomings, personnel issues, and economic *** is imperative that governments, organizations, and academia work together in concert to surmount these challenges and fully exploit the capabilities of Al and ML for the betterment of *** paper emphasizes the significance of conducting a thorough analysis of the ethical, societal, and economic consequences in order to effectively achieve the potential of Al and ML. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Unveiling the Vision: A Comprehensive Review of Computer Vision in AI and ML

Unveiling the Vision: A Comprehensive Review of Computer Vis...

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2024 international conference on advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Laad, Meena Maurya, Ratan Saiyed, Najeeb Symbiosis Institute Of Technology Dept. Of Applied Science Maharashtra Pune India Symbiosis Institute Of Technology Dept. Of Artificial Intelligence And Machine Learning Maharashtra Pune India

ISBN: (纸本)9798350364828

In the era of rapid technological advancement, Computer Vision has emerged as a transformative force, reshaping the landscape of Artificial Intelligence (AI) and machine learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer Vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer Vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of Computer Vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern Computer Vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. Applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of Computer Vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where Computer Vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring. © 2024 IEEE.

关键词： Computer vision

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machine learning in Weather Forecasting: A Comparative Approach with Emphasis on Neural Networks and Optimizer

Machine Learning in Weather Forecasting: A Comparative Appro...

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2024 international conference on advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Sulochana, Balla Charishma Pragada, Bhavya Sri Kiran, Boya Chaitanya Reddy, Gaddam Anvith Murali, K. Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350371567

Our initiative addresses the complex issues of fore- casting and analysis in a world where weather patterns remain a mystery. Defying convention, we utilise eight different machine learning technologies that are rigorously examined utilising measures such as Confusion Matrix, F1Score, Accuracy, Precision, and Recall. Our ability to analyse weather is strengthened by this method, which helps those who make decisions about transportation, agriculture, and disaster relief. We provide three neural network players-LSTM, Feed Forward Neural Network, and Recurrent Neural Network-to increase forecast precision. We compare various models based on criteria such as Activation Functions, Optimizers, Loss, and Accuracy in an effort to identify the optimal neural architecture. Combining machine learning with neural networks creates a comprehensive approach that addresses long-term climate trend analysis, provides a dependable tool for short-term forecasts, and closes knowledge gaps in complicated weather patterns. The project's potential to improve decision-making in the face of weather uncertainty makes it significant. By combining the flexibility of machine learning with the temporal awareness of neural networks, this method advances weather forecasting. It guarantees a more precise and knowledgeable reaction to the difficulties brought up by dynamic atmospheric circumstances. © 2024 IEEE.

关键词： Weather forecasting

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A review of recent advances and strategies in transfer learning

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international JOURNAL OF SYSTEM ASSURANCE engineering AND MANAGEMENT 2025年第3期16卷 1123-1162页

作者： Gholizade, Masoume Soltanizadeh, Hadi Rahmanimanesh, Mohammad Sana, Shib Sankar Semnan Univ Fac Elect & Comp Engn Semnan Iran Kishore Bharati Bhagini Nivedita Coll Dept Math Kolkata 700060 India

While machine learning has achieved remarkable success across various applications, this success often relies on the assumption that training and testing data originate from the same domain with a shared distribution and feature space. This assumption is frequently violated in real-world scenarios where training data may derive from different distributions, often due to limited access to labeled samples or data collection challenges. This situation necessitates the development of robust methodologies encapsulated in the framework of transfer learning, which aims to enhance learner performance in different domains by leveraging knowledge from a source domain to improve outcomes in a target domain. This review provides a comprehensive assessment of the evolving landscape of transfer learning, highlighting the need for a detailed literature survey on current approaches and their implications. By categorizing theoretical transfer learning algorithms based on the number of source domains and the handling of incomplete multi-source data, we offer a distinctive perspective that elucidates the practical utility of these methodologies. Our exploration spans various pivotal categories, including single-source/single-target and multi-source/single-target frameworks, while systematically addressing the complexities of incomplete transfer learning and the integration of fuzzy systems to manage uncertainty. Additionally, we introduce commonly used public datasets in transfer learning and present the outcomes of several key algorithms discussed in this review on these datasets. This empirical perspective illustrates the practical effectiveness of the methodologies and provides insights into their performance under diverse conditions. This extensive analysis summarizes key findings from diverse studies, delineates their interconnections, and highlights their applicability to real-world problems, thereby serving as a valuable resource for researchers navigating the intricate dynamic

关键词： Transfer learning machine learning Multi-source learning Single-source learning Inductive learning Transductive learning Fuzzy systems Incomplete data Literature review

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A comprehensive review of recent advances and challenges in artificial intelligence and machine learning 4th

A comprehensive review of recent advances and challenges in ...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Singla, Ishdeep Hans, Aarti Smiksha Diya Swami, Harsh Kumar Ranjan, Rahul Computer Science and Engineering Chandigarh University Mohali Gharuan India

ISBN: (纸本)9781032911571

Modern advances in Artificial Intelligence (AI) and machine learning (ML) have transformed many businesses and elements of everyday life. machine learning and deep learning support disease diagnosis, drug development, and patient risk assessment. This study examines how healthcare organizations evaluate enormous data sets using machine learning. This paper examines pros and downsides of current methods and research obstacles. Our goal is to educate healthcare professionals and government organizations on machine learning-based big data analytics in intelligent healthcare. AIML-powered chatbots enable governments educate and serve residents. They assist people get permits, answer questions, and use government services. Assistants help students make appointments, answer inquiries, and offer course material. Lead generation, product recommendations, and marketing efforts using AIML chatbots may boost revenue. AIML helps companies make data-driven decisions, find patterns, and recognize trends using data analysis and predictive analytics. machine learning studies how computers learn and improve without programming. machine learning (ML) is a very captivating technology that has garnered significant interest over the course of technological advancements. As indicated by its nomenclature, this attribute endows the computer with a capacity that enhances its resemblance to human behaviour: the ability to acquire knowledge. © 2025 the Author(s).

关键词： Adversarial machine learning

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