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Risk Assessment of Global Temperature Anomalies Using Intelligence Frameworks: Evaluating the Impact of Socioeconomic Factors and with a Case Study on Heatstroke Deaths in India

Risk Assessment of Global Temperature Anomalies Using Intell...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Alok Kumar Pati Jemmut Patnaik Sruti Dash Tuheena Mohanty Alok Ranjan Tripathy Department of Computer Science Ravenshaw University Cuttack India Department of Computer Application Siksha ’O’ Anusandhan Deemed to be University Bhubaneswar India Department of Computer Application Kalinga Institution of Industrial Technology Bhubaneswar India Department of Computer Science & Engg IMIT Cuttack India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Rising global temperatures due to climate change have significantly increased the risk of heat-related illnesses, particularly heatstroke, in various regions. As global warming continues, it is crucial to understand patterns in heatstroke occurrences to predict and address future impacts. This study uses Machine Learning (ML) techniques to explore the connection between increasing temperatures and heatstroke incidents. ML models analyze extensive datasets containing meteorological data, health records, and geographical variables to uncover correlations between temperature spikes and heatstroke prevalence. The research incorporates supervised learning models such as linear Regression, lasso regression, ridge regression and voting regressor to predict high-risk periods and regions vulnerable to heatstroke. Additionally, time-series analysis and Prophet model is used to forecast future heatstroke cases by identifying climate data trends. These models are trained and validated using historical temperature and heatstroke fatality data from different regions. Preliminary findings indicate that extreme heatwaves, along with humidity and urbanization factors, significantly contribute to heatstroke occurrences. This study, leveraging ML, aims to improve the ability to predict and prevent heat-related health crises, offering valuable insights for public health planning, early-warning systems, and climate adaptation strategies to protect populations from the adverse effects of global warming.

关键词： Heating systems Temperature distribution Analytical models Temperature dependence Correlation Urban areas Machine learning Predictive models Data models Global warming Climate change Data analysis

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

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Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models 3

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kumar, Gaurav Devayani, G. Saraswathi, C. Melba Kani, R. Dongre, Sandeep Mallick, M. S. Mohamed School of Computer Application Lovely Professional University Phagwara India M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Mar Ephraem college of Engineering and Technology Kanyakumari India Nagpur India Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331508456

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences. © 2025 IEEE.

关键词： Marketplaces

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Breast Cancer Prediction Using Machine Learning 5

Breast Cancer Prediction Using Machine Learning

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dhondiyal, Shiv Ashish Luthra, Pooja Sharma, Bhupender Rajni, Rajni Uniyal, Ishita Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Coer University Department of Computer Science and Application Uttarakhand Roorkee India Guru Jambheshwar University of Science and Technology Department Of Computer Science & Engineering Haryana Hisar India Graphic Era Hill University Department of Electrical Engineering India

ISBN: (纸本)9798331522100

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer. © 2025 IEEE.

关键词： Image segmentation

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Ai-Driven Sustainability: Exploring Historical Developments, applications, and Future Horizons

Ai-Driven Sustainability: Exploring Historical Developments,...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Prashant Johri Abhay Chauhan Deepak Deepak Kumar Kumud School of Computer Application and Technology Galgotias University Department of Computer Applications Raja Mahendra Pratap Singh State University Aligarh

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Artificial Intelligence has now emerged as one of the most powerful tools to tackle various sustainability issues from climate change and resource management to environmental degradation. The historical development, applications, and future horizons of AI-driven sustainability are researched in this paper, focusing on some of the significant contributions made in sectors ranging from energy and agriculture to urban planning. Industries will be able to optimize resource use, enhance energy efficiency, and introduce sustainable practices with the help of AI technologies such as machine learning, deep learning, and big data analytics. Several other relevant issues regarding the integration of AI include data privacy, biases, limitation of infrastructure, and regulatory uncertainties. The latest innovations in AI, such as quantum computing and bio-inspired algorithms, show promise in pursuit of sustainability goals. The paper reiterates the need for cross-sectoral collaboration, responsible development, and strategic governance in unleashing the maximum positive impact of AI on global sustainability.

关键词： Deep learning Technological innovation Ethics Data privacy Quantum computing Energy efficiency Resource management Artificial intelligence Sustainable development Optimization

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Risk Assessment of Global Temperature Anomalies Using Intelligence Frameworks: Evaluating the Impact of Socioeconomic Factors and with a Case Study on Heatstroke Deaths in India

Risk Assessment of Global Temperature Anomalies Using Intell...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Pati, Alok Kumar Patnaik, Jemmut Dash, Sruti Mohanty, Tuheena Tripathy, Alok Ranjan Ravenshaw University Department of Computer Science Cuttack03 India Siksha 'O' Anusandhan Deemed to be University Department of Computer Application Bhubaneswar30 India Kalinga Institution of Industrial Technology Department of Computer Application Bhubaneswar24 India Imit Department of Computer Science & Engg Cuttack India

ISBN: (纸本)9798331530259

关键词： Supervised learning

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Energy-Proficient Cluster Enrichment in Wireless Sensor Networks via Categorized Fuzzy Clustering and Multi-Hop Routing Optimization

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SN computer Science 2025年第1期6卷 1-12页

作者： Shakya, Harish Kumar Chandanan, Amit Kumar Subbalakshmi, Chatti Khambra, Geetanjli Ansari, Mohd Shahnawaz Kushwaha, Ajay Roy, Vandana Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya (A Central University) Chhattisgarh Bilaspur India Department of Computer Science and Engineerings Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of Computer Science and Application The Bhopal School of Social Sciences (BSSS) College M.P. Bhopal India Department of Computer Science and Engineering Eklavya University Damoh MP Damoh India Computer Science and Engineering Rungta College of Engineering and Technology Bhilai India Department of Electronics Communication Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

The WSN's lifetime can be extended by practicing energy saving. By using the appropriate routing mechanism when executing clustering, it can be done successfully. For improved energy conservation, clustering, and routing work together. Clustering lowers duplicate data transmission, while superior routing enables energy-saving travel along the quickest route to the destination. Nodes can be grouped via clustering, and each group will have CH. CH provides data to the BS either directly or via the intermediary nodes. By reducing the workload of resource-limited nodes, clustering chooses CH from resource-rich nodes. The entire network needs to undergo re-clustering in the traditional WSN clustering approach. The suggested protocol creates an energy-efficient cluster head selection approach applying FIS in hierarchical WSNs, which in turn increases sensing network lifetime, power consumption rate, and network communication performance. The proposed FBUCA and Modified minimum spanning tree with fuzzy logic can efficiently route data packets. The load-balancing among clusters and decreased energy usage demonstrated by the proposed model in MATLAB R2016 simulations are key to increasing throughput and extending the network's lifetime. The proposed approach takes into account six of the aforementioned use scenarios. The experimental results confirmed the adaptability and sturdiness of the proposed method, showing that it works well with a large number of WSNs. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CH FBUCA Fuzzy logic WSN

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A Multimodal Fusion and Ensemble Approach for Robust Fake News Detection through Deep Learning, Reinforcement Learning, and Using Blockchain to Enhance Security and Authorization

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2025年第2期18卷

作者： Kumar, Vivek Satveer Ahmad, Waseem Kumar, Satendra Department of Computer Application Quantum University U.K Roorkee India Department of Computer Science & Engineering Meerut Insitute of Engineering and Technology U.P Meerut India

Background: computer science significantly influences modern culture, especially with the rapid breakthroughs and technology in social media networking. Social media platforms have become significant channels for sharing and exchanging daily news and information on many issues in the current digital environment, which is known for its massive data collection and transmission capabilities. While there are many benefits to this environment, there are also many false reports and information that deceive readers and users into believing they are receiving correct information. Objective: Nowadays, all users use social media to obtain news content, but sometimes some malicious users tamper with real news and then spread fake news, which may reduce the reputation of social media. Therefore, many existing models have been introduced to detect fake news, but these models are based on traditional machine learning algorithms, such as decision tree (DT), multilayer perceptron (MLP), random forest (RF), etc. These models Lack of performance, security, and authorization. Our proposed model can solve existing model problems using reinforcement learning and blockchain technology. Methods: In this research paper, we explain a new way to identify fake news. This new approach, combined with policy-based heuristic reinforcement learning (PHRL), where the model dynamically adjusts through iterative learning, is the key innovation and gradually improves classification accuracy. The same as our smart contract authorization method, which enhances the authenticity of content posted safely by authorized users and improves the transparency and accountability of information. Results: Our model was tested on real-time information collected from various sources with 70% accuracy and valid authentication. Conclusion: Our proposed model produced better results with a Mean Absolute Error (MAE) of 0.0811 and Root Mean Squared Error (RMSE) of 0.2847, both significantly lower values. Our proposed mode

关键词： Deep reinforcement learning

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions 2

The Symphony of Style: An Extensive Examination of Determina...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Guru Prasad, M.S. Bhosagi, Ravatappa A. Madhusudhana, K. Prithviraj Chandrappa, S. Kumar, Vikash Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India NMAMIT Mangalore India Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (纸本)9798331530389

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends. © 2025 IEEE.

关键词： Convolutional neural networks

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Estimating Fetal Gestational Age from Ultrasound Images Using Deep Learning Techniques

Estimating Fetal Gestational Age from Ultrasound Images Usin...

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

作者： Suganthi, N. Mohana Subhashni, R. Chitra, A. Rajakumari, S. Priya, T. Anitha, P. Caroline Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India St.Peter's Institute of Higher Education and Research Department of Computer Applications Avadi Chennai54 India EastPoint College of Higher Education Department of Computer Application Jnana Prabha East Point Campus Karnataka Bengaluru560049 India

ISBN: (纸本)9798331509828

Monitoring fetal development and guaranteeing mother health depend on correct estimate of fetal gestational age. Conventional approaches of gestational age estimate mostly depend on manual estimations from ultrasonic scans, which may be laborintensive and prone to human mistake. Variability in operators, picture quality, and fetal position might potentially affect current methods and cause possible errors and inconsistent outcomes. We offer the Ultrasound Images Using Deep Learning Techniques (UI-DLT) framework, a strong, automated method for fetal gestational age estimate, to handle these problems. UI-DL T analyzes ultrasonic pictures and enhances the accuracy and consistency of gestational age estimations by using deep learning-more especially, convolutional neural networks (CNNs). UI-DLT reduces human error and standards outcomes across several clinical environments by automating the analytic process. CNNs are used in the proposed system to categorize and examine ultrasonic pictures, therefore spotting important information on fetal development and growth. By extracting deep picture elements that match developmental phases, our method guarantees accurate and consistent gestational age estimations, thereby beyond the capacity of conventional manual techniques. Experimental data on ultrasonic image datasets shows significant increases in estimating accuracy of the UI-DLT framework. This approach not only improves the accuracy of gestational age prediction but also helps to progress clinical decision-making and mother-fetal health care. © 2025 IEEE.

关键词： Ultrasonic imaging

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