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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Nidhi B Kurpad S Dhanyashree V Shivank T A S Surya Shobith S Vinila Jinny Computer Science and Engineering Vellore Institute of Technology Vellore India Electronics and Communication Engineering S J B Institute of Technology Bengaluru India Electrical and Electronics Engineering B N M Institute of Technology Bengaluru India Department of Software Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

The paper introduces a transformative Telemedicine Kiosk designed to enhance healthcare in remote areas by harnessing the synergy of computational intelligence and telemedicine. This advanced kiosk utilizes real-time video conferencing and comprehensive patient monitoring systems, all underpinned by Responsible AI practices that ensure ethical data management and the protection of patient privacy. Equipped with an ESP32 Wi-Fi module, the kiosk provides seamless communication between patients and medical officers, facilitating immediate care and consultation. The system goes beyond diagnostics: it supports healthcare professionals with an integrated medication dispensing mechanism powered by IoT technology. Our investigation delves into the implications of human factors, economics, and technological advances for the future of telemedicine. We present an innovative solution that not only advances healthcare accessibility but also embodies a commitment to responsible and ethical healthcare service provision in underserved regions.

关键词： Ethics Data privacy Patient monitoring Telemedicine Medical services Real-time systems Protection

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation

Multi-Layer Perceptron Regression Model for Material Charact...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Hanima Kannan C H Deepa Gupta Parul Mathur Dhanesh G Kurup Tilesh Sanjay Chaudhari Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Chemical Engineering Creative and Advanced Research Based on Nanomaterials Laboratory Indian Institute of Technology Hyderabad School of Engineering and Applied Science Bennett University Greater Noida U.P. India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an $R^{2}$ of 99.991% and MSE of $6.65 \times 10^{-5}$ , with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions.

关键词： Power engineering Dielectric constant Predictive models Mathematical models Software Scattering parameters Probes Permittivity Standards Load modeling

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Edge AI-Based Dynamic Treatment Strategy Generation Model for Consumer Healthcare Technology

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IEEE Transactions on Consumer Electronics 2024年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Shabaz, Mohammad Ahanger, Tariq Ahamed Keshta, Ismail Saidova, Feruza Soni, Mukesh Karumuri, Srinivasa Rao Inje University Department of AI and Software Gimhae50834 Korea Republic of Al-Maarif University College Department of computer science Al Anbar31001 Iraq Intervention Treatment Institute M.D. Research HoustonTX United States Model Institute of Engineering and Technology J and K Jammu India CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia Tashkent State University of Economics Department of Foreign languages Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India VLSI-Microelectronics Lab Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram Guntur522302 India

Edge AI-based reinforcement learning is less reliant on mathematical models and relies on experience to help with the design and optimization of Consumer Technology models, making it ideal for learning dynamic treatment techniques. However, the current research still has the following problems: 1. The optimality of the learned strategy is considered without taking risks into account, leading to strategies with certain risks. 2. The issue of distribution shift is ignored, resulting in learned strategies that are completely different from those of doctors. 3. The patient's historical observational data and treatments are ignored, making it difficult to accurately assess the patient's state, which in turn leads to the inability to learn the optimal strategy. Based on this, we propose the DOSAC-DTR dynamic treatment strategy model, which integrates Dead-ends and offline monitoring into the Actor-Critic framework. First, we consider the risk of treatment actions recommended by the learned strategy and incorporate the concept of Dead-ends into the Actor-Critic framework. Second, to mitigate the distribution shift problem, we integrate physician monitoring into the Actor-Critic framework. This approach achieves better performance, resulting in lower estimated mortality rates and higher Jaccard index scores. © 1975-2011 IEEE.

关键词： Reinforcement learning

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Process Industry System Modeling Based on Time Series Imputation: A Case Study of Paste Thickener

Process Industry System Modeling Based on Time Series Imputa...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Xiaorui Li Xiaojuan Ban Yizhou Shao Haowei Chen Chao Yao Zhaolin Yuan Guangsheng Liu Lijie Guo Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China School of Artificial Intelligence Hebei University of Technology Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hong Kong China BGRIMM Technology Group Beijing China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

In process CPS systems, highly accurate modeling of the production system is essential for optimal control, fault diagnosis, and flexible manufacturing. Traditional data-driven modeling of industrial processes focuses on predicting future trends based on variable periodicity and trends, often neglecting the impact of control variables. This paper proposes a new approach to improve CPS accuracy, particularly for systems heavily influenced by controlled variables. Firstly, CPS modeling is redefined as a time series imputation problem, introducing exogenous variables via a sequence modeling algorithm based on a state space model to enhance forecasting accuracy. Secondly, sequence inputs are encoded in patches, improving the model's ability to process local sequence information. The proposed method is evaluated on a real deep cone thickener underflow concentration forecasting task and compared with state-of-the-art time series forecasting methods, demonstrating superior performance. This study not only provides a more accurate model for CPS but also offers a framework for integrating control variables into predictive modeling.

关键词： Accuracy Time series analysis Process control Predictive models systems modeling Prediction algorithms Market research Imputation Manufacturing Forecasting

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Price Estimation of Used Cars Using Machine Learning Algorithms 3rd

Price Estimation of Used Cars Using Machine Learning Algorit...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Santhi, K. Chellatamilan, T. Kavitha, A. Raahil, Armaan Padmavathy, N. Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031289743

In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precise car prices. Despite the fact that there are websites offering this service, they could not employ the most precise prediction system. It is also possible to predict a used car's true market value using a variety of models and techniques. It's important to understand their genuine market value before buying or selling. Both buyers and sellers will be benefitted from these accurate predictions. Support Vector regression, Random Forest regression, and CatBoost regression techniques are used in the proposed system. In the existing method [13], mean absolute error for decision tree regression was 0.6711, which was the least among other algorithms like Linear regression, Lasso regression, Ridge regression, Bayesian Ridge regression, and etc., they used. In the proposed system, mean absolute error (MAE) for Support Vector regression, CatBoost regression and Random Forest regression techniques are 0.1459, 0.1371 and 0.1284 respectively. The prices of second hand/used cars are predicted using the CatBoost regression, Support Vector regression, and Random Forest regression techniques. The accuracy of these algorithms are 86.28%, 85.40% and 87.16%. Among these three algorithms, Random Forest regression gives the least MAE of 0.1317 and the highest accuracy of 87.16%. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Forecasting

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Contextual Multi-Armed Bandit based Beam Allocation in mmWave V2X communication under Blockage

Contextual Multi-Armed Bandit based Beam Allocation in mmWav...

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IEEE Conference on Vehicular Technology (VTC)

作者： Arturo Medina Cassillas Abdulkadir Kose Haeyoung Lee Chuan Heng Foh Chee Yen Leow Department of Engineering King’s College London London UK Department of Computer Engineering Abdullah Gul University Kayseri Turkey School of Physics Engineering and Computer Science University of Hertfordshire Hatfield UK 5GIC & 6GIC Institute for Communication Systems (ICS) University of Surrey Guildford UK Wireless Communication Centre Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia

Due to its low latency and high data rates support, mmWave communication has been an important player for vehicular communication. However, this carries some disadvantages such as lower transmission distances and inability to transmit through obstacles. This work presents a Contextual Multi-Armed Bandit Algorithm based beam selection to improve connection stability in next generation communications for vehicular networks. The algorithm, through machine learning (ML), learns about the mobility contexts of the vehicles (location and route) and helps the base station make decisions on which of its beam sectors will provide connection to a vehicle. In addition, the proposed algorithm also smartly extends, via relay vehicles, beam coverage to outage vehicles which are either in NLOS condition due to blockages or not served any available beam. Through a set of experiments on the city map, the effectiveness of the algorithm is demonstrated, and the best possible solution is presented.

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Iot Based Smart Assistant Cane For The Visually Impaired

Iot Based Smart Assistant Cane For The Visually Impaired

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International Conference on communication, Computing and Internet of Things (IC3IoT)

作者： A Harini C Buvana M Harshini S Keerthana D Prabhu S K Umamaheswaran Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai Tamil Nadu Department of Information Technology Sri Sairam Engineering College Chennai Tamil Nadu Department of Artificial intelligence and Data Science Sri Sairam Engineering College Chennai Tamil Nadu Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai Tamil Nadu Department of Mathematics Sri Sairam Engineering College Chennai Tamil Nadu

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

This paper presents the design and implementation of a Smart Cane for the visually impaired individuals, incorporating several sensors and features to improve their mobility and safety. The device includes an ultrasonic sensor for obstacle detection, an MPU-6050 device for detecting falls and tilts, a GPS module for navigation, an RTC module for medicine reminders, and a fitness tracking module for monitoring health parameters. Additionally, a Bluetooth headset with a microphone is provided to communicate with the voice assistant in noisy areas. The Smart Cane triggers a buzzer for help and sends a notification to the caretaker in case of a fall or crash. The device is powered by a LIPO battery and is designed to improve the quality of life of visually impaired individuals by providing them with enhanced mobility and safety features.

关键词： Navigation Personal voice assistants Sensor phenomena and characterization Safety Telecommunication computing Noise measurement Intelligent sensors

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities

The Reliability of Information Sources: Student Perceptions ...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources; social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape.

关键词： Economics Power engineering Accuracy Social networking (online) Engineering profession Education Reliability engineering Information filters Information age Reliability

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Enhancing Monkeypox diagnosis and explanation through modified transfer learning, vision transformers, and federated learning

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Informatics in Medicine Unlocked 2024年 45卷

作者： Ahsan, Md Manjurul Alam, Tasfiq E. Haque, Mohd Ariful Ali, Md Shahin Rifat, Rakib Hossain Nafi, Abdullah Al Nomaan Hossain, Md Maruf Islam, Md Khairul Department of Industrial and Systems Engineering University of Oklahoma Norman 73019 OK United States Department of Cyber-Physical Systems Clark Atlanta University Atlanta 30314 GA United States Department of Biomedical Engineering Islamic University Kushtia 7003 Bangladesh Department of Computer Science and Engineering BRAC University Dhaka 1212 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh

The Monkeypox outbreak has emerged as a pressing global health challenge, evidenced by rising cases across nations. Individuals afflicted exhibit diverse dermatological symptoms that risk further transmission via contamination. Our study assessed the efficacy of three modified transfer learning models (M-VGG16, M-ResNet50, M-ResNet101) alongside vision transformers (ViT) across four investigations. We achieved high accuracy in discriminating Monkeypox cases, with M-VGG16 achieving 88%, 76%, and 77% accuracy in Studies One, Two and Four and M-ResNet50 achieving 89% in Study Three. To comprehend triggers for Monkeypox onset, we utilized Local Interpretable Model-Agnostic Explanations (LIME) to explain predictions visually. LIME alignments underscored our models' high accuracy, correlating with segmented identification of infected regions. Further, we implemented Federated Learning on decentralized data to evaluate generalization capabilities. Blending established deep learning with emerging decentralized learning and explanation techniques is vital in improving predictive accuracy and elucidating Monkeypox intricacies amid the persisting global outbreak. Our study emphasizes the continued relevance of pioneering techniques while introducing new approaches to address this major health challenge. © 2024 The Authors

关键词： Disease diagnosis Image segmentation LIME Transfer learning Vision transformer

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Design of a Sensor Measuring Station for Pasture Parameters Remote Monitoring 4

Design of a Sensor Measuring Station for Pasture Parameters ...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Valov, Nikolay Evstaiev, Boris Mladenova, Tsvetelina Valova, Irena Kadirova, Seher Markov, Nikolay Stoycheva, Svetoslava Atanasova, Tatiana Varlyakov, Ivan University of Ruse Angel Kanchev Department of Automatics and Mechatronics Ruse Bulgaria University of Ruse Angel Kanchev Department of Electronics Ruse Bulgaria University of Ruse Department of Computer Systems and Technologies Ruse Bulgaria Research Institute of Mountain Stockbreeding and Agriculture Agricultural Academy Troyan Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Science Modeling and Optimization Department Sofia Bulgaria Trakia University Department of Morphology Physiology and Nutrition of Animals Stara Zagora Bulgaria

ISBN: (纸本)9781665468350

This paper presents an approach for designing a specific meteo measurement station for remote and fully automatic monitoring of pastures and meadows. The station measurement soil parameters: temperature, moisture, electrical conductivity, pH, content of nitrogen, phosphorus, potassium and air parameters - temperature, humidity, atmospheric pressure. These parameters together with timestamp are sending to data server using LoRaWAN interface. With the help of proposed system and artificial intelligence it is possible to refine the necessary actions to take care of the terrain, predict the amount and quality of the produce by analysing the different parameters of the field, monitoring the atmospheric conditions etc. © 2022 IEEE.

关键词： Microcontrollers

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