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

作者： Wankhede, Vidhi Verma, Prateek Gahane, Shailesh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Sciences and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The examination of human gait is essential for comprehending motor function, the evolution of diseases, and the effectiveness of rehabilitation in different clinical and research environments. artificial intelligence (AI) advancements have recently improved gait analysis methodologies, resulting in increased accuracy, efficiency, and therapeutic effectiveness. This review article compiles the existing literature on AI-based gait analysis. The review emphasizes the role of AI in gait analysis, namely via the use of vision-based approaches, wearable sensor technologies, markerless motion capture systems, and inertial sensor-based methodologies. AI has shown potential in identifying musculoskeletal problems including osteopenia and sarcopenia, forecasting the start of Parkinson's disease, assessing the effects of post-stroke rehabilitation, and diagnosing neurological conditions. In addition, the integration of AI with new technologies such as augmented reality (AR), smart sensors, and wearable devices presents intriguing opportunities to improve gait rehabilitation, enable individualized therapies, and facilitate remote patient monitoring. Although there have been significant breakthroughs, there are still obstacles in creating AI models that can be easily understood for clinical decision-making, establishing standardized procedures for collecting data, and verifying AI algorithms across various populations. This study underlines the transformational potential of AI-based gait analysis in customized healthcare, rehabilitation techniques, and clinical results. In order to fully harness the potential of AI in enhancing human health and well-being, it is imperative to do more research and foster multidisciplinary cooperation. © 2024 IEEE.

关键词： Clinical research

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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A Comprehensive Exploration of Real-Time 3-D View Reconstruction Methods

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第12期5卷 5915-5927页

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati781039 India Indian Institute of Technology Kanpur Department of Electrical Engineering 208016 India

Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This article thoroughly examines and tests contemporary methodologies addressing challenges in 3-D view reconstruction. The methods being explored in this article are categorized into volumetric and mesh, generative adversarial network based, and open source library based methods. The exploration of these methods undergoes detailed discussions, encompassing methods, advantages, limitations, and empirical results. The real-time testing of each method is done on benchmarked datasets, including ShapeNet, Pascal 3D+, Pix3D, etc. The narrative highlights the crucial role of 3-D view reconstruction in domains such as robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The article also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms. © 2020 IEEE.

关键词： Generative adversarial networks

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AI-Driven Approaches to Enhancing Mental Wellbeing and Stress Relief

AI-Driven Approaches to Enhancing Mental Wellbeing and Stres...

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

作者： Revathi, K. Priyanka, S. Priyadharshini, S. Niranjan, N.N. Visalachi, N. Sumathi, P. Sns College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Sns College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Sns College of Engineering Department of Information Technology & Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798331509828

Mental health issues affected millions of peoples in worldwide, who often struggle with stigma, high costs, and low access to real life care. Conventional therapy methods faced lot of challenges to identify the issues. These challenges need to highlight for more accessible, affordable, and effective mental health solutions. Traditional models of care often fall short of providing adequate support to all those in need, leading to gaps in care and disparities in mental health outcomes. So, need for Innovation in Mental Health Support for effective mental health solutions to address these challenges. Introduction to 'InnerPeaceAI' Aims to provide personalized, on-demand therapy using advanced AI technology, offering a confidential and user-friendly platform to improve mental health care accessibility. InnerPeaceAI aims to bridge the gap between the need for mental health care and its accessibility by leveraging advanced AI technology to deliver therapy sessions tailored to individual needs. The platform uses natural language processing and machine learning algorithms to create personalized treatment plans, identify mental health issues, and provide real-time support to users. This innovative approach allows for immediate access to therapy, without the constraints of time, location, or cost. © 2025 IEEE.

关键词： Electronic health record

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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The Impact of 5G technology on the Functionality and Performance of Internet of Things (IoT) Devices 8

The Impact of 5G Technology on the Functionality and Perform...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Yesankar, Prajyot Gourshettiwar, Palash Gote, Pradnyawant Jiet, Moses Makuei Gadkari, Ayush Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350376425

The design of wireless mobile devices of the next generation 5G promises to address the demands of complex IOT designs in terms of connectivity technologies. This The study illuminates the architecture, benefits, and needs that5G can satisfy. drawbacks of the 5G network. Numerous sites throughout the world have conducted extensive analyses of IOT devices with 5G capabilities and research on this kind of network. A new era of communication has begun with the introduction of 5G technology, which promises unheard of velocity, low latency, and enormous capacity. The significant effects of 5G on the operation and efficiency of connected Internet of Things (IoT) gadgets are examined in this research. IoT devices stand to gain from improved communication capabilities as 5G networks proliferate, opening the door for creative applications in a variety of industries. The first section of the article gives a summary of the main characteristics of 5G technology, highlighting how it has the potential to completely change how Internet of Things devices interact and communicate. Real-time data transfer is made possible by 5G networks far lower latency and higher capacity, which open up possibilities for faster and smooth interactions between IoT *** report also explores how 5G may affect how much electricity Internet of Things devices use. 5G has the ability to solve a long-standing issue in the IoT ecosystem by extending the life of the batteries of IoT devices with the adoption of cost-effective communication protocols and increased network efficiency. A solution that can efficiently handle enormous volumes of information being transferred at very high capacity is required as the Internet of Things gains popularity One of the main demands or goals that need to be reached in the near future, or the next generation of IOT devices, is more capacity, a faster data transfer speed and less delay. © 2024 IEEE.

关键词： 5G mobile communication systems

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A Comprehensive Review of Supervised Learning Algorithms in Healthcare Applications 2

A Comprehensive Review of Supervised Learning Algorithms in ...

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

作者： Barhate, Aditya Tale, Abhay Jikar, Nayan Verma, Prateek Kumar, Praveen Yesankar, Prajyot Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331528713

Supervised learning has revolutionized the concept of personalization in treatment with the development of Precision Medicine. This review aims to provide a systematic analysis of the utilization of supervised learning in healthcare and its functions in diagnostics and prognoses, recommendations for treatment, and patient surveillance. Classification algorithms such as Logistic Regression, Support Vector Machines, and Decision Trees. Regression techniques such as Linear Regression, Polynomial Regression, and Neural Networks. Ensemble methods such as Bagging, Boosting, and Stacking. Deep learning techniques such as Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory are discussed for their efficiency and applicability in various healthcare applications. There are strengths highlighted by the review including enhanced diagnostic capabilities and patient-targeted therapy alongside weaknesses such as quality of data, interpretability as well as compatibility with existing healthcare systems. This review gives a more generalized approach to how supervised learning algorithms help to enhance healthcare results and identifies the likely topic that requires more research in the future. © 2024 IEEE.

关键词： Self-supervised learning

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Streamlining Deep Learning Based Alzheimers Disease Classification: A Simplified Approach with ADASYN

Streamlining Deep Learning Based Alzheimers Disease Classifi...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational intelligence, RAEEUCCI 2024

作者： Saravanan, V. Sidharth, T. Vijay, S. Sankar Ganesh, S. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore641407 India

ISBN: (纸本)9798350354539

Consuming and complex, while existing deep learning (DL) methods may suffer from complexity and overfitting issues. This study proposes an integrated approach that combines Convolutional Neural Networks (CNNs) with the Adaptive Synthetic Sampling (ADASYN) algorithm to address class imbalance and enhance AD classification accuracy. This model architecture prioritizes simplicity and efficiency, utilizing an optimal number of layers to achieve effectiveness. Experimental results on a dataset of brain MRI images demonstrate the efficacy of this approach, with a classification accuracy of 98%. By integrating machine learning techniques with advanced sampling strategies, this research contributes to advancing early detection and diagnosis of Alzheimer's Disease, fostering strides towards improved patient care and management. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Predicting Pedestrian Movement in Unsignalized Crossings: A Contextual Cue-Based Approach 16

Predicting Pedestrian Movement in Unsignalized Crossings: A ...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Muduli, Kaliprasana Sahu, Vikas Ghosh, Indrajit Indian Institute of Technology Roorkee Civil Engineering Department Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Civil Engineering Department & Joint Faculty Roorkee India

ISBN: (纸本)9798350383119

To ensure safe and secure coexistence between pedestrians and autonomous vehicles (AVs), AVs must be able to anticipate pedestrian behavior and respond to it. This research gathers video data from real traffic scenes to predict pedestrian crossing intentions at unsignalized crossings. Computer vision techniques such as YOLOv4, Deep SORT, and perspective transformation are employed for road user detection, tracking, and mapping image coordinates to world coordinates to prepare trajectory datasets. Using trajectory data, several features influencing pedestrian intention like walking speed, location in the road environment, count and direction of approaching traffic, speed and type of closest approaching vehicle upstream, etc., are extracted. The dataset for this study was obtained by analyzing 1,411 pedestrians, resulting in 223,136 samples. To predict pedestrian crossing intentions, LSTM and Bi-LSTM with an attention mechanism model were built and trained to anticipate the pedestrian crossing intention at unsignalized crossing. The proposed model successfully combined the characteristics and surrounding dynamics of pedestrians to produce accurate predictions, Bi-LSTM with an attention mechanism outperformed LSTM, achieving an AUC of 95.3%, 91.1%, 89.2%, 87.5%, and 84% on the testing dataset at unsignalized crossing on the 0.6 sec, 1.2 s, 1.8 s, 2.4 s, and 3 s time horizons. These outcomes can be used to improve Connected and Autonomous Vehicle (CAV) technologies, infrastructure-to-vehicle (I2V) connectivity, and driver assistance systems to enhance pedestrian safety while navigating through pedestrian crosswalks. © 2024 IEEE.

关键词： Attention Computer vision Pedestrian Crossing Intention Prediction Unsignalized Crossings

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A Modified White Shark Optimizer for Gene Selection Optimization Problem 2

A Modified White Shark Optimizer for Gene Selection Optimiza...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Makhadmeh, Sharif Naser Fakhouri, Hussam N. Al-Betar, Mohammed Azmi University Of Petra Data Science And Artificial Intelligence Department Amman Jordan University Of Petra Faculty Of Information Technology Data Science And Artificial Intelligence Department Amman11196 Jordan Ajman University Ajman United Arab Emirates

ISBN: (纸本)9798350394962

This paper investigates the application of the White Shark Optimizer (WSO) to gene selection, a fundamental task in machine learning to analyze complex, high-dimensional data such as gene expressions derived from DNA microarray studies. The problem of gene selection is complicated by the presence of redundant and misleading genes that can reduce the effectiveness of machine learning models. We divided gene selection techniques into two categories: candidate-based and envelope-based, focusing on prominent methods such as Kullback-Leibler and Chi-square to measure the difference between the probability distributions. We focus on the novel WSO, which draws inspiration from the predatory strategies of white sharks, famous for their adaptability and efficiency in complex scenarios. The paper presents a modified version of WSO, tailored to the binary nature of gene selection, and evaluates its effectiveness against particle swarm optimization. The results highlight the superior accuracy and robustness of WSO, where the proposed WSO outperformed the compared method in obtaining the accuracy and fitness values of the three datasets. © 2024 IEEE.

关键词： Gene expression

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