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A Comprehensive Review of Human-Centric AI, Regulatory Frameworks, and Their Role in Shaping Industry 5.0 6

A Comprehensive Review of Human-Centric AI, Regulatory Frame...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Misra, Sanjay Barik, Kousik Kvalvik, Petter Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Sciences Universidad de Alcalá Alcalá de Henares Madrid Spain JIS Institute of Advanced Studies and Research JIS University Kolkata India Institute for Energy Technology Halden Norway

This paper examines the core principles and design approaches that define Human-Centric AI (HCAI) and the enhancement of human-machine collaborations with aligning goals with Industry 5.0. Through a comprehensive analysis of existing studies, this paper systematically determines the key challenges of harmonizing HCAI principles with the regulatory demands of Industry 5.0, using a PRISMA chart to select relevant studies from four databases. The study highlighted the major global regulatory frameworks governing AI and how they address the ethical, legal, and social challenges HCAI poses. This work aims to inspire further exploration and development in assessing the potential benefits and challenges of integrating Human-Centric AI into Industry 5.0 by identifying research gaps and proposing future study areas. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Human engineering

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VoiceSense: Designing A Robust Emotion Detection System 7

VoiceSense: Designing A Robust Emotion Detection System

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mishra, Mihir Mishra, Reeta Pandey, Anjali Mintu Ishaan Gopal Department of Computer Science & Engineering Delhi Technical Campus UP Greater Noida India Iilm University School of Computer Science & Engineering UP Greater Noida India Manav Rachna International Institute of Research & studies School of Computer Applications Haryana Faridabad India

ISBN: (纸本)9798350350067

Detecting emotions from speech is an excitingarea of research that can improve how we interact with computers and understand mental health. In our study, we explore how to build a system that can understand different emotions using speech and psychological information. We gather data from various sources like TESS, Crema-D, RAVDESS, SAVEE, and our own custom dataset to create a diverse collection of emotions. To build our system, we use different types of neural networks like CNNs, RNNs, and transformers. After testing our system extensively, we find that it can accurately detect emotions about 77% of the time, which is pretty good. We also look into how our system learns about emotions, which can help us better understand how humans process emotions. Our work not only improves emotion detection technology but also has important implications for making computers more empathetic in how they interact with people. © 2024 IEEE.

关键词： Emotion Recognition

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Crime Prediction on Open Data in India Using Data Mining Techniques

Crime Prediction on Open Data in India Using Data Mining Tec...

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M. Menaka P. Sujatha Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai TamilNadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The growth in crime data collection necessitates a deeper theoretical understanding to support practical crime prevention strategies tailored to specific locations and times. Models for forecasting the frequency of various sorts of crimes under an administrative structure of regions utilized by the Indian police, as well as the frequency of anti-social behavior offenses, are investigated in this study. Four algorithms from several techniques are employed. The information is sourced from the Indian police and encompasses over 200,000 records before preprocessing. Based on predictive performance and processing time, the results indicate that SVM may be utilized to forecast crime ***, an ensemble of data mining classification approaches is employed to anticipate crime. Finally, the optimal forecasting technique for achieving the most stable results is presented. The study yields a model that utilizes implicit and explicit data mining approaches to produce credible crime predictions.

关键词： Support vector machines Time-frequency analysis Law enforcement Computational modeling Predictive models Prediction algorithms Data models

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Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition 7

Self-Paced Neutral Expression-Disentangled Learning for Faci...

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Wu, Zhenqian Li, Xiaoyuan Ren, Yazhou Pu, Xiaorong Zhu, Xiaofeng He, Lifang University of Electronic Science And Technology of China School of Computer Science And Engineering China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Lehigh University Department of Computer Science And Engineering United States

ISBN: (纸本)9798350359145

The accuracy of facial expression recognition is typically influenced by the following factors: high similarities across different expressions, disturbing factors, and the subtle and rapid micro-movements of the face. One potential solution to overcome these obstacles is to leverage the hidden neutral information present in neutral expression images. In this paper, we propose a Self-Paced Neutral Expression-Disentangled Learning (SPNDL) model, which aims to disentangle the neutral information from facial expressions, facilitating the extraction of crucial features and variations. This approach enables the capture of discriminative details within similar expressions and the detection of micro-facial movements. To enhance the learning of these neutral expression-disentangled features (NDFs) and mitigate the challenges posed by non-convex optimization, we propose a self-paced learning (SPL) strategy based on NDFs during the training phase. SPL learns samples from easy to complex by increasing the number of samples selected into the training process, which enables to effectively suppress the negative impacts caused by low-quality samples and inconsistently distributed NDFs. Experiments on three popular databases (i.e., CK+, Oulu-CASIA, and RAF-DB) show the effectiveness of our proposed method. © 2023 IEEE.

关键词： Face recognition

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Modelling and Performance Analysis of Physical and Electrical Properties of BaZrS3 Chalcogenide Perovskite Solar Cells 24

Modelling and Performance Analysis of Physical and Electrica...

引用

24th IEEE International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2024

作者： Gahlawat, Devansh Kaur, Jaspinder Basu, Rikmantra Sharma, Ajay Kumar Rani, Uma National Institute of Technology Delhi Department of Electronics and Communication Engineering New Delhi India National Institute of Technology Delhi Department of Computer Science and Engineering New Delhi India Dr. Akhilesh Das Gupta Institute of Professional Studies Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350390537

This study explores the application and utility of BaZrS3-based perovskite solar cells (PSCs) for thin-film photovoltaic applications. The proposed device exhibited stable current up to 1.1 eV and maintained over 65% quantum efficiency across a broad spectrum. The Mott-Schottky analysis indicated a bandgap of approximately 1.7 eV. Nyquist and Bode plots confirmed robust impedance behaviour. The device displayed V oc=1.33 V, Jsc = 16.56 mA/cm2, FF = 83.97%, and Efficiency = 18.58%. BaZrS3's exceptional optical and electrical properties, coupled with environmental stability, position it as a promising material for advanced PSCs. © 2024 IEEE.

关键词： Perovskite solar cells

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Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbreak: A Comprehensive study in Healthcare

Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbrea...

引用

MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)

作者： Pritam Ramesh Ahire K. Ulaga Priya Computer Science & Engineering Department Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

A comprehensive health monitoring & analyzing system that provides personalized recommendations, goal setting, and tracking based on relevant factors related to BMI & COVID-19. System aims to analyze attack obesity through prediction, prevention and ongoing monitoring through appropriate predictive analytical system. The system's Risk Assessment component categorizes individuals by their risk levels using factors like age, gender, medical history, and behavior related to pre & post covid-19 related to BMI, then relates appropriate connections in human body. The wellness solution includes features such as fitness tracking, data integration, personalized metrics, and predictive analytics to create a holistic approach for predictive analytics health management. The system healthcare monitoring and analyzing system is a groundbreaking solution that leverages predictive analytics to promote a healthy lifestyle, combat obesity, and enhance overall well-being. By considering a range of factors and employing cutting-edge technology, we empower individuals to take control of their health, achieve their fitness objectives, and mitigate health risks.

关键词： COVID-19 Obesity Pandemics Urban areas Sociology Medical services Mental health

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Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Preetha P A. Packialatha Department of Computer Science Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics.

关键词： Measurement Heart Filtering Data preprocessing Data acquisition Predictive models Feature extraction Sensors Monitoring Diseases

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication

Advancements in Speech Recognition: Exploring Feature Extrac...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： R. Bhavani Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy.

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Empowered Artificial Intelligence Approach using Intuitionistic Fuzzy based Deep Neural Network for Chronic Diseases Prediction

Empowered Artificial Intelligence Approach using Intuitionis...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： D. Karthik S. Arun Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Managing the increasing prevalence of chronic illnesses is a major worldwide health concern. It's important to understand the medical problem, its stages, types, and available treatments. The most important component in the early diagnosis of chronic diseases might enhance the diagnostic procedure more successfully. Medical professionals can diagnose a process with the help of numerous artificial intelligence tools. However, ambiguity, irrelevance, and inconsistency in the disease dataset have an impact on the classification accuracy. Conventional machine learning techniques are inadequate for managing scenarios of uncertainty that arise in datasets related to chronic illnesses. To anticipate chronic diseases, each characteristic in the suggested work is represented with a hesitation index. In order to compute the hesitation degree as a more informative procedure in determining the association among features, the belongingness and non-belongingness aspects of the intuitionistic fuzzy concept are induced in the prediction process. This work effectively manages the large chronic illness dataset by developing an intuitionistic fuzzy hesitation index-based similarity measure to weed out unimportant and strongly associated characteristics and enhance the classification model. Utilizing the scored feature information, the deep neural network modifies its hyper parameter to address the overfitting issues that frequently arise in DNNs. The performance of the empowered intuitionistic fuzzy-based deep neural network (EIF-DNN) is analyzed using five distinct chronic conditions, including hypertension, diabetes, hepatitis, cancer, and stroke. Comparing the proposed EIF-DNN against the current versions of multi-layered networks, the simulation results show that it produces promising results in predicting chronic diseases at an early stage.

关键词： Training Uncertainty Accuracy Artificial neural networks Stroke (medical condition) Prediction algorithms Indexes

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