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Predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework 3rd

Predicting Falls Through Muscle Weakness from a Single Who...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMai 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCai 2024

作者： Zhang, Xia Saleem, Afsah Ilyas, Zaid Suter, David Nadeem, Uzair Prince, Richard L. Zhu, Kun Lewis, Joshua R. Sim, Marc Gilani, Syed Zulqarnain Centre for AI and ML School of Science Edith Cowan University Joondalup Australia Nutrition and Health Innovation Research Institute Edith Cowan University Joondalup Australia Computer Science and Software Engineering The University of Western Australia Perth Australia Medical School The University of Western Australia Perth Australia Department of Endocrinology and Diabetes Sir Charles Gairdner Hospital PerthWA Australia

ISBN: (纸本)9783031820069

Falls are often attributed to poor muscle function, with weak hand grip strength clinically recognized as a major risk factor. However, grip strength is rarely assessed clinically. Low radiation dual-energy X-ray absorptiometry (DXA) whole-body scans, that can be obtained during routine osteoporosis screening, offer a comprehensive overview of body composition, thereby providing valuable information for musculoskeletal health. Here, we propose a machine learning technique, exploiting image and clinical data to classify weak grip strength ( © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Integrating Artificial Intelligence for Sustainable and Adaptable Architecture

Integrating Artificial Intelligence for Sustainable and Adap...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Valapula Lalitha Regidi Suneetha Basi Reddy A Nilesh R. Mate Syeeda Mujeebunnisa Department of CSE(AI&ML) Sai Spurthi Institute of Technology India Department of ECE Sanketika Vidya Parishad Engineering College Visakhapatnam India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India Centre for Distance and Online Education Bharati Vidyapeeth (Deemed to be University) Pune India School of Science and Computer Studies CMR University Bengaluru India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

In nature, flexibility is essential, and ai may enhance it in architecture. The article integrates ai to develop self-learning buildings that can adapt to future obstacles. This research aims to create structures that use sensors to gather environmental data and respond. Phases comprise this study's strategy. In Phase 1, ai in architecture is extensively researched. That study in Phase 1 guided Phase 2's building design. The parametric building design in phase 2 uses machine learning and computational design techniques. Grasshopper scripting and Rhino modelling were used to create an algorithm that would create shapes that would both biomimetic the process of coral growth and be tailored to the project's chosen location. The selection procedure for the produced experimental investigations is shown in Phase 3. To determine which type used the least amount of energy, many evaluations were conducted, including measurements of sunshine, radiation, and shadows. Phase 4 involves developing the shape to improve the building's efficiency. Phase 5 involves conducting performance assessments to demonstrate that the final form is highly adaptable and climatic or ecologically sensitive. This building's radiation exposure, according to the research, is between 200 and 300 kWh/m 2 . According to the shadow study, the building shape casts a shadow for eight hours. According to the analysis, the building's shape makes it more adaptive by reducing its energy use. An ai engine technology is used in the last stage to forecast the building's future growth. By incorporating technology into building design, we may create flexible structures that reduce building energy use and contribute to the creation of a sustainable world

关键词： Phase measurement Shape Architecture Shape measurement Buildings Energy measurement computer architecture Energy efficiency Sensors Artificial intelligence

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Artificial Intelligence-based Diagnostic Analysis for Wireless Capsule Endoscopy in Obscure Bowel Disease Detection: A Potential 22

Artificial Intelligence-based Diagnostic Analysis for Wirele...

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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Esha Saxena Manoj Yadav Meenakshi Yadav Preety Shoran Computer Science and Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management Delhi India SMS Medical India Information technology ADGITM NEW DELHI GGSIPU India AI&ML ADGITM India

ISBN: (纸本)9781450399937

Wireless Capsule Endoscopy (WCE) has become one of the most practiced techniques in gastrointestinal (GI) tract disease detection. WCE is expecting to benefit more if examinations are carried out with more advanced ai technologies. Research development in the area of artificial intelligence (ai) for gastrointestinal endoscopy has increased widely to detect multiple lesions, bleeding areas, cancer with more accuracy and detecting the severity of the abnormal area. In this study, we have in view to summarize the importance of ai in Capsule Endoscopy bowel disease detection. Reading capsule endoscopy images and watching its videos is a very time-consuming and error-prone process, ai computerized algorithms if embedded with the device will surely help in detecting every minor problem efficiently. Through this work, we are trying to suggest how useful is ai- based predictive algorithms for WCE in detecting automatic abnormal region classification. A number of studies have shown that an ai-driven method has great potential for investigating various fields of the healthcare sector. This paper gives an outline of the existing position and future potential of ai in Wireless Capsule endoscopy.

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Advanced Hybrid Methodology for Robust Heart Disease Prediction and Feature Optimization 2

Advanced Hybrid Methodology for Robust Heart Disease Predict...

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2nd International Conference on Machine Learning and Autonomous Systems, ICmlAS 2025

作者： Alekhya, G. Sudheer Kumar, C. Bhulakshmi, Ouku Harikrishna, T. V T Ram Pavan Kumar, M. Siva Rama Krishna, Boddu L V Kurnool India Srinivasa Ramanujan Institute of Technology Department of CSE Ananthapuramu India Santhiram Engineering College Department of CSE Nandyal India Annamacharya Institute of Technology and Sciences Department of AI&ML Rajampet India Kakaraparti Bhavanarayana College Department of Computer Science Vijayawada India SRM University Department of CSE AP Mangalagiri India

ISBN: (纸本)9798331505745

This research presents a novel hybrid framework for heart disease prediction, integrating advanced data preprocessing with ensemble learning to enhance diagnostic accuracy. The methodology begins with rigorous data cleansing to ensure reliability, followed by Synthetic Minority Over-sampling Technique (SMOTE) to balance class distribution. Outliers are identified and mitigated using the Z-score method, preserving data integrity. A unique Recursive Hybrid Feature Extraction (RHFE) strategy, combining filter and wrapper techniques, optimizes feature selection by reducing multicollinearity and enhancing model efficiency. Key predictive markers include age, chest pain type, maximum heart rate, ST depression induced by exercise, and major vessel count via fluoroscopy. The refined dataset is used to train an CatBoost -based ensemble model, achieving remarkable performance with 94.2% accuracy, 93.5% precision, 94% recall, and an outstanding ROC AUC score of 0.98. These results highlight the model's robustness and its potential for real-world clinical implementation in early heart disease detection and risk assessment. © 2025 IEEE.

关键词： Risk assessment

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Smart Drug Delivery Systems Using Large Language Models for Real-Time Treatment Personalization

Smart Drug Delivery Systems Using Large Language Models for ...

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Communication & Computing (WCONF), World Conference on

作者： Chinnala Balakrishna Ankit Yadav Jagendra Singh Masarath Saba Shashikant Vineet Shrivastava Department of CSE(AIML & Cyber Security) Guru Nanak Institute of Technology(Autonomous) Hyderabad India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE (AI&ML) CVR College of Engineering JNTUH Hyderabad India Electrical Engineering Department School of Engineering Babu Banarasi Das University Lucknow India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

This research explores the use of large language models, such as BERT and GPT, in developing a smart drug delivery system utilizing real-time personalized treatments. The research aims to utilize large datasets with advanced natural language processing to recommend the appropriate drug for a patient based on their health record with enhanced accuracy and efficiency. The research, which evaluates and compares BERT and GPT, achieves the goal of predicting a drug with high accuracy, and GPT delivers the best results compared to BERT. Specifically, GPT achieved an accuracy of 97.95 % , while BERT's accuracy was 95.50%. Additionally, the research emphasizes the essential aspect of a model's time response since these are real-time clinical decision systems. GPT took 110 milliseconds to predict the drug while the BERT took 120 milliseconds. It is clear from the results of this work that LLM has the potential of changing personalized medicine's approach by recommending drugs in real-time and according to the patient's health record within no time. The proposed system for smart drug delivery is promising to improve healthcare services, patient outcomes, and reduce drug administration errors. Apart from predicting the drug, these research findings can be simulated to the health sectors and integrated with ai technologies to improve decision support systems.

关键词： Drugs Training Accuracy Large language models Precision medicine Real-time systems Drug delivery Natural language processing Time factors Diseases

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Enhanced Protection for Information and Network using Intrusion Detection System

Enhanced Protection for Information and Network using Intrus...

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International Conference on Edge Computing and Applications (ICECAA)

作者： S. Faizal Mukthar Hussain R. Karthikeyan S. Ramamoorthi I. Sheik Arafat S. Syed Musthafa Gani Department of Computer Science and Engineering Mohamed Sathak Engineering College Kilakarai India Department of CSE (AI&ML) Vardhaman College of Engineering Hyderabad Telangana India Department of Electronics and Communication Engineering Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Mohamed Sathak Engineering College Kilakarai India

The world of today has been transformed into a little community where everyone may communicate with one another. The widespread use of the internet makes this possible. People now have a huge window of opportunity thanks to the internet to broaden their perspectives. Threats to personal security were another negative, which will result in expenses for both psychological support and money. There is a lot of research being done to preserve the data in such an environment. The results of this survey could improve internetworking environment security and control current assaults like DoS and cyberattacks. As it allows for the interception of private networks, intrusion is a problem that frequently arises in internetwork. Denials of Service Attacks are a significant issue in Internet networks as it can disrupt communication between two or more authorized entities. The entire focus of this research is on examining the approaches used to develop intrusion detection systems.

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An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model

An Innovative Method for Brain Stroke Prediction based on Pa...

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Sustainable Expert Systems (ICSES), International Conference on

作者： K. Kavitha Vanjangi Jyotsna Devi Mohammad Shabbir Alam Sindhu. V Nisarg Gandhewar Gourav Kalra Department of CSE-AI&ML GMR Institute of Technology Razam Andhra Pradesh India Department of Information Technology Aditya Institute of Technology and Management Tekkali Andhra Pradesh India Department of Computer Science College of Engineering and Computer Science Jazan University Jizan Kingdom of Saudi Arabia Department of Computer Science CHRIST University Bangalore Karnataka India Department of AIML Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Mullana Ambala India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%.

关键词： Training Correlation Accuracy Predictive models Brain modeling Feature extraction Prediction algorithms Numerical models Proposals Blood

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Clustering based Ensemble Machine Learning for Integrated Prediction and Classification of Ecological Zones

Clustering based Ensemble Machine Learning for Integrated Pr...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Haider Alabdeli Francis Vijaykumar Anna Reddy Yerrolla Chanti Aruna T M Abhijeet Das Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept of Computer Science and Engineering Malla Reddy Engineering College for Women Secunderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Department of AI&ML Nitte Meenakshi Institute of Technology Bengaluru India Department of Civil Engineering C.V. Raman Global University (CGU) Bhubaneswar India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Accurate classification of ecological zones helps identifying the location of specific ecological features, and analyzing changing conditions. However, existing methods had limitations such as forming different image dimensions and analysing the areas within the images due to high noisy data. To overcome this, the proposed research integrated Density Based Spatial Clustering of Applications with Noise (DBSCAN) with Ensemble Machine Learning (Eml) algorithms using Multilayer Perceptron (mlP), Naïve Bayes (NB), Classification and Regression Tree (CART), and Logistic Regression (LR). To train the model, the Landsat series data which is obtained from Geographical Information System (GIS) is used and then pre-processed using Normalized Difference Vegetation Index (NDVI) to characterize a variety of vegetation properties. The DBSCAN is then used for noisy data to distinguish the densities of data points and to segment data in different fields. The integration of DBSCAN with ml classifiers improved the classification accuracy. The overall experimental results show that the proposed DBSCAN-Eml achieved 98.43% of accuracy and 97.89% of precision when compared with the existing methods Eml based spatial prediction method and Artificial Neural Network Cellular Automata (ANN-CA).

关键词： Accuracy Normalized difference vegetation index Machine learning algorithms Vegetation mapping Landsat Machine learning Data models Classification algorithms Noise measurement Geographic information systems

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The Role of ai-Enhanced Financial Literacy and HR Marketing in Personal Investment Decisions A Quantitative Analysis of Urban and Rural Households

The Role of AI-Enhanced Financial Literacy and HR Marketing ...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Sunita Pachar B V Srinivasulu Bodhana Dharani P. Vijaya Kumar Reddy Monika Gupta Siva Koteswara Rao Katta IBM Department GLA University CSE (AI&ML) Vignan Institute of Technology and Science Hyderabad Department of MBA Malla Reddy Engineering College (Autonomous) Hyderabad Aditya University Business School Chitkara University Punjab Department of Mechanical KIETW Kakinada

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Policymakers around the world have begun to pay more attention to financial inclusion because of the strong foundation it is believed to provide for human growth. National monetary systems as they have always been cannot appeal to every part of society. Members of society who are already well-off find it difficult to participate in the financial industry due to the legacy system's many obstacles. Theoretically, this research aims to shed light on how Artificial Intelligence (ai) can encourage financial inclusion. In order to back up its claims, the report cites actual instances of governments using ai to break down obstacles to financial inclusion. With the right infrastructure and regulations in place, artificial intelligence (ai) can help expand access to financial services. By learning about different investment options and how to diversify their portfolios, people with financial literacy are better able to handle their own money. Investors sought to strike a better income-expenditure balance by taking advantage of new options to invest in firms' stocks, bonds, shares, and other financial instruments through traditional and nontraditional investment channels, as well as stock exchanges. The present economic climate encourages individual investors to trade stocks due to factors such as low stock prices, capital gains, financial interest, and stock market rumours. The reason being that these indexes offer more equitable investment opportunities than traditional forms of investment, such as bank savings. On the other hand, financial literacy (FL) provides a pathway for investors to make wise use of resources by way of investing in solid investments.

关键词： Technological innovation Statistical analysis Government Decision making Big Data Solids Risk management Artificial intelligence Stock markets Investment

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Biometric-Based Access Control Systems with Robust Facial Recognition in IoT Environments 3

Biometric-Based Access Control Systems with Robust Facial Re...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Satish Babu, B.V. Manoranjini, J. Changala, Ravindra Aarif, Mohd Mary, S. Suma Christal Raj, I Infant Prasad v Potluri Siddhartha Institute of Technology Department of It Vijayawada India Chaitanya Bharathi Institute of Technology Department of Ai & Ml Hyderabad India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Associate Editor-Publishing Global Research Network Noida India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

The integration of biometric-based access control systems with robust facial recognition technology within Internet of Things (IoT) environments holds paramount significance in addressing contemporary security challenges. The novelty of the research endeavours to contribute to this critical area by investigating and implementing a novel approach, employing Convolutional Neural Networks (CNN) for facial classification within the biometric framework. The significance of this study lies in its potential to enhance security measures in IoT environments, ensuring reliable and efficient access control. Previous attempts at integrating facial recognition into IoT have encountered notable challenges, including suboptimal accuracy, vulnerability to adversarial attacks, and scalability issues. This research builds upon these prior issues by introducing CNN for facial classification, leveraging its capabilities in image processing and pattern recognition to achieve enhanced accuracy and resilience against potential security threats. The novelty of the study is underscored by the application of CNN within the biometric context of IoT, offering a cutting-edge solution tailored to the intricacies of interconnected devices. Experiments conducted on benchmark datasets showcase the promising capabilities of the proposed system, with face recognition accuracies reaching an impressive 98%. These results underscore the potential of this research to significantly advance the state-of-the-art in biometric-based access control, particularly within the context of IoT environments. The utilization of CNN introduces a pioneering element, contributing to the evolution of secure and efficient access control solutions in the era of IoT. © 2024 IEEE.

关键词： Biometrics

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