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7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Maheswari, M Uma Sudharsanan, R. Arthy, M. Jenefer, Anne Oormila, L. Samuthira Pandi, V. RMD Engineering College Department of Electrical and Electronics Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Information Technology Tamil Nadu Ramapuram India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Agni College of Technology Department of Computer Science Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics Ad Communication Engineering Tamilnadu Ramapuram India

ISBN: (纸本)9798350397253

The conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing;hence, the concept of real-time monitoring is no longer applicable in the modern day. The disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. This research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. The standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. The difference in these two scores is due to the fact that DT's precision is lower. The suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential. © 2023 IEEE.

关键词： Water quality

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Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation 4

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Damaševičius, Robertas Taniar, David Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania Monash University Faculty of Information Technolog MelbourneVIC3800 Australia

ISBN: (纸本)9798350387490

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme. © 2024 IEEE.

关键词： Convolutional neural networks

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User Acceptance of New HCI Technologies 6

User Acceptance of New HCI Technologies

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Katona, Jozsef Kovari, Attila Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Engineering University of Dunaujvaros Department of Natural Sciences Dunaujvaros Budapest Hungary

ISBN: (纸本)9798350328752

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs. © 2023 IEEE.

关键词： Human computer interaction

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Privacy framework for context-aware robot development

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Paladyn 2021年第1期12卷 468-480页

作者： Heuer, Tanja Schiering, Ina Gerndt, Reinhard Department of Computer Science Ostfalia University of Applied Sciences Institute of Information Engineering Wolfenbüttel Germany Department of Computer Science Ostfalia University of Applied Sciences Institute of Distributed Systems Wolfenbüttel Germany

Privacy is an essential topic in (social) robotics and becomes even more important when considering interactive and autonomous robots within the domestic environment. Robots will collect a lot of personal and sensitive information about the users and their environment. Thereby, privacy does consider the topic of (cyber-)security and the protection of information against misuse by involved service providers. So far, the main focus relies on theoretical concepts to propose privacy principles for robots. This article provides a privacy framework as a feasible approach to consider security and privacy issues as a basis. Thereby, the proposed privacy framework is put in the context of a user-centered design approach to highlight the correlation between the design process steps and the steps of the privacy framework. Furthermore, this article introduces feasible privacy methodologies for privacy-enhancing development to simplify the risk assessment and meet the privacy principles. Even though user participation plays an essential role in robot development, this is not the focus of this article. Even though user participation plays an essential role in robot development, this is not the focus of this article. The employed privacy methodologies are showcased in a use case of a robot as an interaction partner contrasting two different use case scenarios to encourage the importance of context awareness. © 2021 Tanja Heuer et al., published by De Gruyter.

关键词： Machine design

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Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification 4

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

ISBN: (纸本)9798350387490

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital. © 2024 IEEE.

关键词： Ophthalmology

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Precision-Adaptive Task Offloading and Resource Allocation for Efficient Positioning and Sensing in Near-Field IoV systems

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IEEE Internet of Things Journal 2025年第13期12卷 22635-22646页

作者： Dai, Cheng Bao, Song Chen, Songlin Garg, Sahil Kaddoum, Georges Sichuan University College of Computer Science Sichuan Chengdu China École de technologie supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India École de technologie supérieure MontréalQCH3C1K3 Canada King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among these, the integration of 6G-enabled extremely large-scale antenna arrays (ELAAs) has extended the range of near-field (NF) communication, enabling their application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing the signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite these advantages, MEC’s limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent performance standards, while lower-accuracy tasks may tolerate reduced precision to save resources. Motivated by this, we propose an accuracy-based classification scheme for IoV positioning and sensing tasks, which dynamically adjusts accuracy requirements to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall quality of service (QoS) metric, and employs an optimization algorithm that leverages gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, signific

关键词： Resource allocation

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Real-Time Monitoring of Glacial Changes Using Artificial Intelligence 4

Real-Time Monitoring of Glacial Changes Using Artificial Int...

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4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Babu, K. Vijayalakshmi, G. Umadevi, P. Patel, Neeraj Gopalakrishnan, S. Srm Institute of Science & Technology Kattankulathur India Prince Shri Venkateshwara Padmavathy Engineering College Ece Chennai India Kpr Institute of Engineering and Technology Computer Science and Business Systems India Ies Institute of Technology and Management Ies University Bhopal India Department of Information Technology Kelambakkam India

ISBN: (纸本)9798350393248

Because of the fast changes that climate change is bringing about in glacial environments, real-time monitoring is essential to comprehending and reducing its consequences. In order to observe glacial change in real time, this research study provides a novel method that makes use of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). We start by providing a brief overview of the problem's urgency and the shortcomings of the conventional monitoring techniques. The study then goes on to talk about the main elements of our AI and ML-based system, which include data integration from IoT devices and distant sensors, machine learning algorithms, and analysis of satellite images. Researchers can more accurately understand the intricate dynamics of glacier behaviour thanks to the automated detection of glacial movement, changes in ice thickness, and patterns of meltwater made possible by AI and ML models. As we dig further, the article looks at creating an approachable interface that would make real-time glacier data accessible to scholars, decision-makers, and the general public. It also emphasises how early warning systems may help reduce the risks associated with glaciers and provide guidance for plans aimed at adapting to climate change. This multi-faceted approach, from the simplicity of AI and ML implementation to its intricate data analysis capabilities, in conjunction with IoT, represents a significant advancement in our ability to monitor and respond to glacial changes in real-time. © 2023 IEEE.

关键词： Global warming

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The Empirical Evaluation of Artificial Intelligence-based Techniques for Improving Cyber Security 2

The Empirical Evaluation of Artificial Intelligence-based Te...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Nayak, Chhaya Rao, Chintala Lakshmana Alam, Tanweer Singh, Shalini Islam, Shaziya Maginmani, Umme Habiba Pimpri Chinchwad College of Engineering Pune India School of Law Visakhapatnam India Islamic University of Madinah Faculty of Computer and Information Systems Department of Computer Science Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Ece Uttarakhand Dehradun248002 India Rungta College of Engineering and Technology Bhilai India Secab Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapur India

ISBN: (纸本)9798350346640

Artificial intelligence (AI) technologies have been recently developed, and their advantages can be observed in a wide range of domains, from image processing to face detection. AI-based approaches can assist opponents to enhance their attack strategies while also improving cyber protection technologies. The efficiency of AI techniques against cyber security issues are evaluated. Here, the primary data and a quantitative study design approach is used. The information is then obtained from software industry professionals. The sample size for this study is 549 people, and it included confirmatory factor analysis, discriminant validity testing, model fundamental analysis, and hypothesis evaluation. All variables' P-values were found to be statistical, with the exception of intelligent agents, which had no meaningful relationship to AI or cyber security. The main issues were availability, geographical locations, study population, and other related variables. © 2023 IEEE.

关键词： Factor analysis

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Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces 38

Taming "data-hungry" reinforcement learning? Stability in co...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Duan, Yaqi Wainwright, Martin J. Department of Technology Operations and Statistics Stern School of Business New York University New YorkNY10012 United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering and Computer Science Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States

We introduce a novel framework for analyzing reinforcement learning (RL) in continuous state-action spaces, and use it to prove fast rates of convergence in both off-line and on-line settings. Our analysis highlights two key stability properties, relating to how changes in value functions and/or policies affect the Bellman operator and occupation measures. We argue that these properties are satisfied in many continuous state-action Markov decision processes. Our analysis also offers fresh perspectives on the roles of pessimism and optimism in off-line and on-line RL. © 2024 Neural information processing systems foundation. All rights reserved.

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Utilizing Motor-Imagery Brain-computer Interfaces for the Assessment of Developmental Coordination Disorder in Children 46

Utilizing Motor-Imagery Brain-Computer Interfaces for the As...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Lee, Kuan-Yi Chang, Kong-Yi Hsu, Hao-Che Tseng, Yu-Ting Wei, Chun-Shu Lin, Shih-Syun Chuang, Chun-Hsiang National Taiwan Ocean University Department of Computer Science and Engineering Keelung Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Computer Science and Engineering Hsinchu Taiwan National Tsing Hua University Department of Kinesiology Hsinchu Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan National Tsing Hua University Research Center for Education and Mind Sciences Hsinchu Taiwan

ISBN: (纸本)9798350371499

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder characterized by significant motor difficulties that affect daily life. Current assessment methods primarily focus on behavioral analysis, lacking in neuroscientific metrics for a comprehensive evaluation. This study introduced an electroencephalography-based motor imagery brain-computer interface classification system for evaluating children with DCD. A key of this system was the implementation of entropy-based data screening, which markedly enhanced classification performance. Notably, using mu band power in a support vector machine achieved an accuracy rate of 79.0%. These findings pave the way for developing a tool that could assist professionals in identifying children potentially affected by DCD. © 2024 IEEE.

关键词： Electroencephalography

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