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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Selvaraj, P. Shalma, H. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India Sphoorthy Engineering College Department of Artificial Intelligence and Machine Learning Telengana Hyderabad India

ISBN: (纸本)9798331504403

This work aims to employ machine learning methods and visualization techniques for evaluating water quality by measuring its potability, a numerical indicator of water quality. The work utilizes water quality parameters to determine the stability condition using pH, Temperature, Dissolved Oxygen, Conductivity, Nitrate, Fecal Coliform, and Total Coliform to evaluate the overall water quality. ARIMA is utilized to identify and categorize water potability based on its features. Using Tableau's interactive dashboarding, we created an interactive dashboard to visually simplify water potability metrics in India. The study utilized both real datasets from various locations around the country using government data sourced directly from the Central Pollution Control Board (CPCB). Based on the results of the experiments, the ARIMA was found to be superior to other classifiers in accurately predicting water potability. © 2024 IEEE.

关键词： Water pollution control

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RecipeLens: Revolutionizing Meal Preparation with Image-Based Ingredient Detection and Recipe Suggestions

RecipeLens: Revolutionizing Meal Preparation with Image-Base...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Prajapati, Abhijit Nigam, Muskan Priyanka, R. School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur603203 India

ISBN: (纸本)9798350375237

In today's fast-paced lifestyle, the importance of preparing meals at home has significantly increased due to the high costs and time constraints associated with dining out. However, while home-cooked meals offer numerous benefits, such as cost-effectiveness and convenience, maintaining variety in one's diet is crucial to preventing culinary monotony. Contrary to common belief, creating diverse recipes does not necessarily require an extensive array of ingredients;rather, innovative combinations and techniques can transform familiar ingredients into exciting culinary experiences. This research paper introduces RecipeLens, a novel approach that combines food ingredient detection with recipe suggestions to streamline the cooking process and enhance culinary creativity. Building upon existing studies in food ingredient detection and recipe recommendation, RecipeLens integrates both functionalities into a unified platform, offering users a seamless and efficient way to discover and prepare diverse dishes using available ingredients. To expedite ingredient classification, RecipeLens employs the MobileNet_v2 model, renowned for its accuracy and efficiency in image recognition tasks. Additionally, recipe suggestions are sourced from the Food Recipe API, ensuring the validity and relevance of each recommended dish. Furthermore, RecipeLens accommodates users with limited knowledge of fruit and vegetable names, making it accessible to individuals of varying culinary expertise. Through the integration of ingredient detection and recipe recommendation, RecipeLens empowers users to unleash their culinary creativity, discover new dishes, and enjoy diverse homemade meals with ease and efficiency. © 2024 IEEE.

关键词： Image classification

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Lip Synchronization Model For Sinhala Language Using Machine Learning 4

Lip Synchronization Model For Sinhala Language Using Machine...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Ranaweera, Dilani Weerasinghe, Ruwan Dinalankara, Randima Univeristy of Colombo School of Computing University of Colombo Colombo Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Department of Computer Engineering Colombo Sri Lanka

ISBN: (纸本)9798350384864

Realistic lip-synchronized animations can be produced by the appropriately timed voice and lip motions of the cartoon character. This process is called as 'lip synchronization'. Building a talking face for languages such as English, Korean, and Portuguese has been subject of numerous studies. Compared to other languages, Sinhala is a low-resource language due to less contribution in these researches. This research study focuses to build a frontal view of a synthetic mouth part with smooth lip movements rather than opening and closing while speaking Sinhala sentences. The most difficult challenge is to match the basic sounds, 'phonemes' with the lip movement called 'visemes'. This study has been used static viseme approach by deriving twenty three (23) Sinhala viseme classes and a deep learning model has been developed to map visemes with Sinhala letters. In system implementation, text input is provided first, after which the system produces audio and the deep learning model generates a collection of visemes based on the given text. The system interface then offers three options for playing the visemes at various speeds, including fast, normal, and slow. The user interface was created in Python, and the deep learning model is integrated into the system. This model will be very helpful to use in cartoon industry to build Sinhala speaking cartoon characters and also be used to train deaf people to read lips. © 2024 IEEE.

关键词： Python

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Implementing Blockchain Technology for Accreditation and Degree Verification 22nd

Implementing Blockchain Technology for Accreditation and Deg...

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22nd Asia Simulation Conference, AsiaSim 2023

作者： Azli, Az Mukhlis Iskandar Wahab, Nur Haliza Abdul Zhang, DaYong Kadir, Khairunnisa A. Sunar, Noorhazirah Faculty of Computing Universiti Teknologi Malaysia UTM Johor Johor Bahru Malaysia Faculty of Electrical Engineering Universiti Teknologi Malaysia UTM Johor Johor Bahru Malaysia

ISBN: (纸本)9789819972395

In the evolving academic landscape, the integrity of educational certificates is paramount. These documents serve as formal attestations of one's educational attainment, however, the increasing prevalence of document fraud undermines their credibility. To combat this, we introduce a robust, web-based certificate validation system Universiti Teknologi Malaysia's Blockchain-Based Accreditation and Verification System (UTM-BADVES). Built upon the transparent and immutable infrastructure of Blockchain technology, complemented with a diverse array of advanced technologies, UTM-BADVES offers a secure, efficient, and intuitive solution for real-time validation of academic credentials. The system focuses on data privacy, enabling transcript verification, selective data dissemination, and efficient credential revocation. By doing so, it significantly reduces the potential for academic credential fraud. Consequently, UTM-BADVES safeguards the integrity of educational qualifications, while simultaneously providing immense benefits for educational institutions, employers, and society as a whole. This paper delves into the design and application of UTM-BADVES, elucidating the crucial role it plays in maintaining the sanctity of academic certifications in the digital age. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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The Application of Virtual Reality Using Kinect Sensor in Biomedical and Healthcare Environment: A Review 1

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4th International Conference on Electronics, Biomedical engineering, and Health Informatics, ICEBEHI 2023

作者： Candra, Henry Yuniati, Umi Chai, Rifai Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

ISBN: (数字)9789819714636

ISBN: (纸本)9789819714629

Virtual Reality (VR) and the Kinect sensor, as promising tools in biomedical research, offers diverse applications in medical education, rehabilitation, diagnostics, and health research. The problem statement highlights the demand for innovative solutions and introduces VR and Kinect as potential transformative technologies. This review analyzes the importance of these technologies, their contributions, and future potential. It stands out by evaluating various Kinect-based systems in medical settings. By highlighting distinct features, advancements, and limitations, it provides guidance for future research. Relevant literature was gathered from databases such as Google Scholar, IEEE Xplore, and PubMed. The results showcase a wide range of applications, including patient autonomy, stroke rehabilitation, diagnostics, and monitoring. Despite challenges in accurate movement tracking, integration into clinical settings, and limited generalizability of findings due to small sample sizes, VR and Kinect show potential for revolutionizing healthcare delivery and improving patient outcomes. Their adaptability, affordability, and immersive nature of these technologies offer promising avenues for personalized interventions, remote healthcare, training, and enhanced patient engagement. As these technologies evolve, continued research and development are crucial to optimize their impact in shaping the future of healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual reality

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Advancement in Image Restoration Through GAN-based Approach 15

Advancement in Image Restoration Through GAN-based Approach

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rama, P. Pandiaraj, A. Angayarkanni, V. Prakash, Y. Jagadeesh, S. SRM Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Nagar TN Kattankulathur603203 India SRM Institute of Science and Technology Computing Technologies College of Engineering and Technology SRM Nagar TN Kattankulathur603203 India

ISBN: (纸本)9798350370249

This paper introduces IR-GAN (Image Restoration Generative Adversarial Network), a groundbreaking tool accessible on GitHub, designed to elevate image quality and rejuvenate damaged or low-resolution photographs. Leveraging the power of generative facial priors, IR-GAN adeptly transforms aged images, effectively reducing blurriness and revitalizing historical snapshots with remarkable precision. At the heart of IR-GAN lies its pre-trained Generative Adversarial Network (GAN) model, meticulously crafted to intelligently fill in missing or damaged areas within images by discerning intricate patterns present in the dataset. This innovative approach enables IR-GAN to seamlessly replace distorted or blurred segments with visually coherent elements, preserving the authenticity and essence of the original photographs. Through the integration of advanced image processing techniques and state-of-the-art deep learning methodologies, IR-GAN emerges as a promising solution for digital restoration and preservation endeavours. Its ability to breathe new life into old photographs while maintaining their historical significance holds immense value for archivists, historians, and enthusiasts alike. Experimental results presented in this paper underscore IR-GAN's efficacy in the transformative process of old photographs into visually appealing images. The showcased capabilities of IR-GAN align with the burgeoning demand for sophisticated image enhancement tools within the realm of image processing, paving the way for future advancements in digital restoration and preservation technologies. © 2024 IEEE.

关键词： Generative adversarial networks

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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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Multi-lingual news stack based ensemble classification for deep news feed analysis

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Neural computing and Applications 2025年 1-12页

作者： Sakthivel, Rakesh Kumar Nagasubramanian, Gayathri Sankayya, Muthuramalingam Kadry, Seifedine Rocha, Alvaro Karnataka Bengaluru India Department of Information Technology Thiagarajar College of Engineering Madurai India Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Alvaro Rocha ISEG University of Lisbon Lisbon Portugal

Analyzing and extracting effective words of news feeds are critical tasks. News making sources may feed their news articles in different manners based on their own perspective. In this situation, finding likelihood samples from the vast news feeds is an essential task. Moreover, acquiring optimal word deviations, news varieties, sentiment metrics, and multi-lingual analysis are major tasks of this proposed system. To achieve this massive news feed analysis task, the proposed system uses specially designed multi-classifier units and news stack manipulation procedures. The proposed system integrates different classifiers and dynamic news stacks to handle datasets of multi-lingual attributes. The results show that the proposed method performs with better accuracy rate than existing techniques. This gives 10 to 15% higher accuracy rate than existing works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Data accuracy

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Broker-Less OPC UA PubSub Communication Model Performance Analysis 30

Broker-Less OPC UA PubSub Communication Model Performance An...

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30th IEEE International Conference on Telecommunications, ICT 2024

作者： Homay, Aydin Chrysoulas, Christos Watt, Tess Wollschlaege, Martin De Sousa, Mario Maglaras, Leandros Technische Universität Dresden Faculty of Computer Science Dresden Germany School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom University of Porto Faculty of Engineering Porto Portugal

ISBN: (纸本)9798350356694

The advent of Industrial Internet of Things (IIoT) has accelerated the adoption of OPC UA (Object Linking and Embedding for Process Control Unified Architecture) PubSub communication models, enabling efficient data exchange in industrial automation systems. This study delves into the performance analysis of a broker-less OPC UA PubSub communication model, a paradigm gaining prominence for its potential to streamline communication in distributed systems. Our methodology involves the establishment of a representative test environment, configuration of OPC UA PubSub components without an intermediary broker, and the design of diverse test scenarios. The results of this performance analysis not only reveal the inherent capabilities of the broker-less OPC UA PubSub communication model but also identify potential bottlenecks and areas for optimization. This research advances our understanding of broker-less OPC UA PubSub communication models, offering empirical evidence of their performance in diverse scenarios. © 2024 IEEE.

关键词： Electronic data interchange

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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