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computational Modelling, Simulation and Optimization (ICCMSO), International Conference on

作者： Hussain Suttarwala Ritesh Kumar Madhavi L. Deorankar Dikshendra Daulat Sarpate Pallavi Shingade Surender Kumar Sharma Vrushali More Uday Chandrakant Patkar Deepak K. Sharma Senior Software Engineer American Express Phoenix AZ USA Dept. of AI and Data Science Dr. Akhilesh Das Gupta Institute of Professional Studies New Delhi Delhi India Dept. of Electronics and Telecommunications Marathwada Mitramandal College of Engineering Pune Maharashtra India Dept. of AI and Data Science Zeal College of Engineering and Research Narhe Pune Maharashtra India Dept. of AI and Data Science D Y Patil College of Engineering Akurdi Pune India Dept. of Electrical Engineering Poornima University Jaipur India Dept. of AI and Data Science Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Department of Computer Engineering Bharati Vidyapeeth’s College of Engineering Lavale Pune Dept. of AI and Data Science Savitribai Phule Pune University Pune Maharashtra India

ISBN: (数字)9798350361391

ISBN: (纸本)9798350361407

This study introduces an innovative IoT-enabled wearable device tailored for monitoring and providing feedback on stuttering, leveraging advanced machine learning algorithms to enhance precision and real-time responsiveness. The device integrates an array of hardware and software elements, including sensors, microcontrollers, and communication protocols, to accurately capture speech patterns and movements. Utilizing machine learning techniques such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), the device performs real-time analysis to identify stuttering instances. Detailed discussions cover the criteria for selecting machine learning models, the training and testing procedures, and the metrics used for performance evaluation. The prototype underwent extensive testing and validation, showcasing high accuracy, sensitivity, and specificity in differentiating stuttered from fluent speech. User feedback emphasized the ergonomic design, user-friendly interface, and real-time feedback features, highlighting its potential to significantly enhance stuttering therapy outcomes. Comparative analysis with existing solutions revealed the device’s superior performance, indicating strong potential for clinical application. The study discusses the implications for stuttering therapy, acknowledges the limitations, outlines future research directions, and underscores the device’s contributions to personalized and effective stuttering interventions. This research advances the field of assistive technologies in speech therapy and demonstrates the promise of IoT-based wearable devices integrated with machine learning for improving the quality of life for individuals with stuttering disorders.

关键词： Performance evaluation Machine learning algorithms Accuracy Medical treatment Prototypes Machine learning Real-time systems

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Retrieval-Augmented Generation for Pharmacopoeia: Application and Evaluation

Retrieval-Augmented Generation for Pharmacopoeia: Applicatio...

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Artificial Intelligence and Knowledge engineering (AIKE)

作者： Rizaldi Fahmi Soojin Cheon Yesol Park Joonho Kwon Dept. of Information Convergence Engineering Pusan National University AI Strategy Team Daewoong Dept. of Data Science Pusan National University

ISBN: (数字)9798331517045

ISBN: (纸本)9798331517052

Pharmacopoeia documents are used in pharmaceutical companies as references for ensure safe handling of chemical compounds. However, manual review of these documents can be time-consuming and prone to human error. To assist users in working with these documents, we designed PharmChat, a retrieval-augmented generation (RAG)-based system which enables efficient question-and-answer interactions with multiple short pharmacopoeia documents using natural language. In contrast to other conventional RAG systems, PharmChat converts original pharmacopoeia into separate documents to avoid context mixing during document chunking. PharmChat leverages embedding-based similarity searches between the documents as knowledge-base and user queries using an open-source vector database, ChromaDB. Upon receiving a user query, the most relevant chunks are retrieved and provided as context for a large language model (LLM) to be used as reference to minimize hallucination. Experimental results with real pharmacopoeia short documents demonstrate that our PharmChat system provides quick response times and achieves a BERT-F1 score of 76%.

关键词： Knowledge engineering databases Reviews Large language models Retrieval augmented generation Natural languages Manuals Vectors Time factors Pharmaceuticals

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Moving from Machine Learning to Statistics: the case of Expected Points in American football

arXiv

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arXiv 2024年

作者： Brill, Ryan S. Yee, Ryan Deshpande, Sameer K. Wyner, Abraham J. Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania United States Dept. of Statistics University of Wisconsin–Madison United States Dept. of Statistics and Data Science The Wharton School University of Pennsylvania United States

Expected points is a value function fundamental to player evaluation and strategic in-game decision-making across sports analytics, particularly in American football. To estimate expected points, football analysts use machine learning tools, which are not equipped to handle certain challenges. They suffer from selection bias, display counter-intuitive artifacts of overfitting, do not quantify uncertainty in point estimates, and do not account for the strong dependence structure of observational football data. These issues are not unique to American football or even sports analytics;they are general problems analysts encounter across various statistical applications, particularly when using machine learning in lieu of traditional statistical models. We explore these issues in detail and devise expected points models that account for them. We also introduce a widely applicable novel methodological approach to mitigate overfitting, using a catalytic prior to smooth our machine learning models. © 2024, CC BY.

关键词： Adversarial machine learning

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A Survey on Fish Size Measurement in Aquatic Environments

A Survey on Fish Size Measurement in Aquatic Environments

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Bangalore Humanitarian Technology Conference (B-HTC), IEEE

作者： Ankitha K Venugopala P S Takeshi Kumaki Dept. of Artificial Intelligence and Data Science NMAMIT Nitte India Dept. of Electronic and Computer Engineering Ritsumeikan University Kusatsu Shiga Japan

ISBN: (数字)9798350344509

ISBN: (纸本)9798350344516

Fish size measurement in aquatic environments plays a pivotal role in understanding fish biology, ecology, and fisheries management. This survey explores various methodologies employed for fish size measurement, emphasizing their significance in aquaculture, fisheries, Ocean and ecological research. Techniques encompass both contact and non-contact methods, with advancements in imaging technologies allowing for non-invasive measurements. These approaches involve the use of cameras, image processing software, and advanced machinery to estimate fish size without causing stress or harm to the fish. While these methods offer promising avenues for precise measurements, challenges exist, including accuracy concerns and environmental limitations. Consequently, there's ongoing research to refine these techniques and ensure their applicability across different fish species and aquatic conditions. Overall, this survey highlights the evolving background of fish size measurement techniques and their importance in various domains within aquatic sciences.

关键词： Surveys Accuracy Sea measurements Size measurement Fisheries Software Real-time systems

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Transforming Text to Video: Leveraging Advanced Generative AI Techniques

Transforming Text to Video: Leveraging Advanced Generative A...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： P Indira Priya M Manju R Anitha R Harsavardhini S Sri Harini Dept. of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Dept. of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

This comprehensive evaluation paper delves into the realm of textual content-to-video generation using artificial intelligence. The authors present an inclusive analysis of key reference papers: VGEN - Text to Video Generation Using AI, MAGVIT, STF, DirecT2V, Text2Video-Zero, Tune-A-Video, and four supplementary papers. These manuscripts collectively address various challenges and propose groundbreaking methodologies for producing high-quality videos from textual prompts. VGEN aims to synthesize videos from textual descriptions using pioneering approaches inspired by the strategic tokenization and embedding techniques employed in MAGVIT. STF introduces a cutting-edge 3D generative model specifically designed for text-to-video generation, while DirecT2V focuses on developing lightweight video diffusion models that operate within a low-dimensional latent space. Text2Video-Zero explores zero-shot video generation using diffusion models, and Tune-A-Video introduces a one-shot tuning method that leverages image diffusion models to generate videos. The additional papers contribute to advancements in latent diffusion models, video LDMs, and spatio-temporal attention mechanisms. By incorporating these techniques, the project aims to generate high-fidelity, long-duration videos with content consistency and motion coherence. Code and models are provided for reproducibility, encouraging collaboration in the field. Extensive qualitative and quantitative evaluations demonstrate the effectiveness and versatility of the approaches in producing realistic and diverse videos aligned with textual descriptions. This comprehensive review paper serves as a guide to current strategies in text-to-video technology, offering insights and directions for future research in this exciting field.

关键词： Translation Three-dimensional displays Statistical analysis Streaming media Diffusion models Aerodynamics Reproducibility of results Tokenization Text to video Tuning

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Advancements in EEG-Based Emotion and Consciousness Recognition: Techniques, Challenges, and Innovations

Advancements in EEG-Based Emotion and Consciousness Recognit...

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Innovations in Communications, Electrical and Computer engineering (ICICEC), International Conference on

作者： Ranjana B Jadekar Poornima B Gowramma B H Puneeth S P Dept. of Computer Science and Engineering Data Science Bapuji Institute of Engineering and Technology Visvesvaraya Technological University Belavavi Karnataka India Dept. of Information Science and Engineering Bapuji Institute of Engineering and Technology Visvesvaraya Technological University Belavavi Karnataka India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

This study delves into the application of Electroencephalogram (EEG)-based techniques for emotion and consciousness recognition, focusing on disorders of consciousness (DOC). By evaluating existing approaches in terms of techniques, methodologies, and accuracies, the research highlights the efficacy of both machine learning and deep learning models. The key challenges were identified such as noise removal due to artifacts and variability in patient responses. The findings underscore the potential of advanced computational methods in enhancing the reliability and precision of EEG-based diagnostic tools, paving the way for better clinical interventions and deeper understanding of human emotions. Also, the findings show that using deep learning, the current works have achieved better results for identification of emotion.

关键词： Deep learning Technological innovation Emotion recognition Accuracy computational modeling Noise Noise reduction Brain modeling Electroencephalography Reliability

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Patient-Adaptive and Learned Mri data Undersampling Using Neighborhood Clustering

Patient-Adaptive and Learned Mri Data Undersampling Using Ne...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Siddhant Gautam Angqi Li Saiprasad Ravishankar Dept. of Computational Mathematics Science and Engineering Michigan State University East Lansing MI USA Dept. of Biomedical Engineering Michigan State University East Lansing MI USA

There has been much recent interest in adapting undersampled trajectories in MRI based on training data. In this work, we propose a novel patient-adaptive MRI sampling algorithm based on grouping scans within a training set. Scan-adaptive sampling patterns are optimized together with an image reconstruction network for the training scans. The training optimization alternates between determining the best sampling pattern for each scan (based on a greedy search or iterative coordinate descent (ICD)) and training a reconstructor across the dataset. The eventual scan-adaptive sampling patterns on the training set are used as labels to predict sampling design using nearest neighbor search at test time. The proposed algorithm is applied to the fastMRI knee multicoil dataset and demonstrates improved performance over several baselines.

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DeepCensored: Deep-Learning Based Probabilistic Forecasting Framework for Censored data

DeepCensored: Deep-Learning Based Probabilistic Forecasting ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jiahao Tian Michael D. Porter School of Data Science University of Virginia Charlottesville VA USA Dept. of Systems and Information Engineering School of Data Science University of Virginia Charlottesville VA USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with partially observed (censored) data, remains challenging. This paper introduces DeepCensored, a novel deep learning-based framework that combines the Expectation-Maximization (EM) algorithm with deep neural networks to deliver robust probabilistic forecasts. DeepCensored naturally handles interval-censored event data, where exact event times are unknown but fall within a specific interval. Through extensive simulations and real-world crime data analysis, our method significantly outperforms traditional forecasting approaches, reducing the Mean Absolute Error (MAE) by 50% and better detection of emerging crime trends. These results highlight DeepCensored's potential to provide actionable insights for law enforcement and public safety by predicting crime intensity and enabling resource-efficient policing strategies.

关键词： Time series analysis Predictive models Probabilistic logic Market research data models Time measurement Public security Resource management Forecasting Synthetic data

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The Utilization of Google Cloud in the Healthcare Industry

The Utilization of Google Cloud in the Healthcare Industry

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Rishikesh Kapse Palash Gourshettiwar Dept. of Artificial Intelligence and Data Science Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra Dept. of Computer Science And Medical Engineering Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, “artificial intelligence,” and “machine learning” applications in diagnostics, diagnostics, and personalized medicine. data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare.

关键词： Industries Cloud computing data privacy Technological innovation Medical services Machine learning Teamwork Security Predictive analytics Medical diagnostic imaging

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Indoor Real-Time Location System for Efficient Location Tracking Using IoT

Indoor Real-Time Location System for Efficient Location Trac...

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Verma, Sunny Raj, Tej Joshi, Kapil Raturi, Preeti Anandaram, Harishchander Gupta, Ashulekha Dev Bhoomi Uttarakhand University Dept. of Electronics & Communication Engineering Dehradun India Uttaranchal University Dept. of Computer Science & Engineering Dehradun India Tula's Institute of Technology Dept. of Computer Science & Engineering Dehradun India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore India Graphic Era University Dept. of Management Studies Dehradun India

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Researchers have broadened the focus of RFID technology development because to the growing need for low-cost edge devices to bridge the physical-digital gap. In addition to item identification, researchers have explored the use of RFID tags for low-power wireless sensing, localization, and activity inference. A security system utilized today is not powerful enough to provide a real-time alert after identifying a problem. Movement detection is a technology for detecting a change in the environment relative to an object. Sensor-based applications can be used to watch activity and receive alerts when movement is detected, which solves the issue and saves time and money. RFID technology is used to track the location of people or things in an interior setting in real time. A reader and several tags, each of which can house a number of sensors, make up an RFID system. In this research, we present the iLocate framework for the IoT, a recently developed real-time locating framework using dynamic RFID for resource the board in indoor settings. iLocate used ubidots to assist with a broad range RFID organization. Our exploratory findings and the actual project have both shown the prevalence. Location tracking indoors will be more effective using this iLocate framework. © 2022 IEEE.

关键词： Internet of things

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