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8th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Almodovar, Jacob P. Gloria, Germaine Beatrix A. Paglinawan, Charmaine C. School of Electrical Electronics and Computer Engineering Bachelor of Science in Electronics Engineering Manila Philippines

ISBN: (纸本)9798331531249

This study focuses on the development of a dental problem detection device using the Inception V3 deep learning model and advanced data augmentation techniques. Dental problems such as cavities, impacted wisdom teeth, and crowded teeth pose significant health risks, often requiring advanced imaging for accurate diagnosis. The proposed device utilizes an intraoral camera to capture digital tooth images, which are then processed using the Inception V3 model to identify three dental problems- dental caries, crowded teeth, and partially erupted wisdom teeth. By employing image data augmentation and leveraging datasets from various dental repositories, the study seeks to enhance the accuracy and reliability of dental diagnostics. As a result, the model achieved a total accuracy of 87.2%, in which the model attained a precision of 92%, a recall of 93%, and an Fl-score of 92.5% for caries detection. In detecting crowded teeth, it recorded a precision of 81.1 %, a recall of 90.9%, and an Fl-score of 85.7%. For partially erupted wisdom teeth detection, the precision was 88.8%, the recall 78%, and the Fl-score 83 %. These results indicate that while the model demonstrates strong overall performance, further improvements are necessary to improve the detection of crowded teeth and partially erupted wisdom teeth. This innovative approach endeavors to provide a non-invasive, efficient, and precise tool for dentists, potentially revolutionizing dental healthcare through the integration of advanced AI technologies into routine dental practices. © 2024 IEEE.

关键词： Diagnosis

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Accurate segmentation of COVID-19 infected regions in lung CT scans with deep learning

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Neural Computing and Applications 2024年第35期36卷 22511-22531页

作者： Lenin Marksia, U. Yesubai Rubavathi, C. Department of Electronics and Communication Engineering Francis Xavier Engineering College Tamil Nadu Tirunelveli India Department of Computer Science and Engineering Saveetha College of Engineering Tamil Nadu Chennai India

The widespread impact of coronavirus disease 2019 (COVID-19) has led to a severe health crisis and loss of life affecting billions of people. Detecting COVID-19 early on and distinguishing it from other illnesses is a major challenge in the pandemic. Computed tomography scans are vital for diagnosis, but they are difficult and slow for radiologists to interpret. So, deep learning technology is being used to speed up disease diagnosis, especially for COVID-19. This research employs a multi-scale feature extraction module that captures features at different scales to detect small and large structures in infection areas. The channel attention module enhances feature representation by focusing on informative channels. A bidirectional pyramid module is utilized to acquire fine-grained details and reduce spatial dimensions. The global dependencies and long-range contextual relationships among the images are captured through global and edge paths, resulting in clear and accurate images. Finally, the outputs from both modules are merged in the feature fusion Network to provide an accurate segmentation mask for COVID-19-related areas. The testing is conducted using the COVID-19 lung scan image dataset. Extensive trials are performed using standard metrics, and the output is compared with recently proposed approaches. The results demonstrate that this method achieves 98.6% accuracy, 96.54% precision, 97.52% sensitivity, 98.55% specificity, 98.59% kappa score, and an execution time of 3.2 s. These results suggest that this method performs better than other comparative methods and accurately identifies regions in the lungs affected by COVID-19. © The Author(s) 2024.

关键词： COVID-19

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Maximizing Learning Outcomes while Minimizing: Costs: A Cost-Effective Approach to M-Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第11期18卷 4-14页

作者： Suresh Kumar, B. Hemawathi, M.S. Dhanasekaran, P. Kaliappan, Vishnu Kumar Sivaganesan, D. Department of Electronics and Communication Engineering The Kavery Engineering College Tamil Nadu Salem India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India

In the realm of education, the pursuit of effective learning outcomes often faces the challenge of limited resources. This paper explores the intersection of maximizing learning outcomes and minimizing costs through a cost-effective approach to mobile learning (m-learning). Recognizing the ubiquitous presence of mobile devices and their potential to revolutionize learning, this study explores strategies that leverage the capabilities of mobile technology while being mindful of budgetary constraints. By leveraging the flexibility and accessibil- ity offered by m-learning platforms, educators can design engaging and interactive learning experiences that cater to diverse learners needs. The paper investigates various cost-effective methodologies, including utilizing open-source software, repurposing existing resources, and adopting collaborative learning environments. Furthermore, it examines the role of scalable technologies and adaptive learning algorithms in optimizing resource allocation and per- sonalizing learning experiences. Through case studies and empirical analysis, this research illustrates how institutions and educators can achieve significant cost savings without com- promising the quality of educational delivery. Ultimately, the findings highlight the transfor- mative potential of a cost-effective approach to m-learning in enhancing learning outcomes and widening access to education. © 2024 by the authors of this article.

关键词： Contrastive Learning

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Container Based Nomadic Vehicular Cloud Using Cell Transmission Model

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computer Systems science & engineering 2023年第1期44卷 423-440页

作者： Devakirubai Navulkumar Menakadevi Thangavelu Department of Computer Science and Engineering Jayam College of Engineering and TechnologyDharmapuri636813India Department of Electronics and Communication Engineering Adhiyaman College of EngineeringHosur635109India

Nomadic Vehicular Cloud(NVC)is envisaged in this *** predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic *** specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network ***-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network *** speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration *** the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital *** infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled *** simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.

关键词： Vehicular cloud container migration cell transmission model 5G NR V2X PC5 interface

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Analytical Formulation and Numerical Investigation of Electromechanical Dynamics in Circular Biconvex Diaphragm Capacitive Pressure Sensor

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IEEE Sensors Journal 2025年第11期25卷 18867-18875页

作者： Rukshana Bi, Gajula Motwani, Mayank Jindal, Sumit Kumar Vellore Institute of Technology School of Electronics Engineering Vellore632014 India Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India

Capacitive pressure sensors (CPSs) have attracted considerable interest due to their high sensitivity, low energy consumption, and potential for miniaturization, making them suitable for applications in automotive systems, consumer electronics, and industrial automation. The conventional designs of CPSs often face challenges such as limited sensitivity, mechanical instability, and difficulties in achieving increased sensitivity. This study presents a biconvex diaphragm using silicon, featuring a gradual thickness variation, with a maximum thickness at the center. This article offers a mathematical analysis of the sensor, featuring step-by-step derivations for parameters such as the maximum thickness of the diaphragm, working range, capacitance, and capacitive sensitivity. Finally, curves of capacitance and capacitive sensitivity with pressure are obtained in MATLAB, and the results are compared with those of prior works. The proposed sensor shows a more than threefold increase in capacitance and a nearly double improvement in capacitive sensitivity compared to the highest previously reported double touch mode capacitive pressure sensor (DTMCPS). The deflection of the diaphragm is obtained using COMSOL. © 2001-2012 IEEE.

关键词： MATLAB

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A transformer based real-time photo captioning framework for visually impaired people with visual attention

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Multimedia Tools and Applications 2024年第41期83卷 88859-88878页

作者： Muhammed Kunju, Abubeker Kiliyanal Baskar, S. Zafar, Sherin A R, Bushara S, Rinesh A, Shafeena Karim Kerala Kanjirapally India Faculty of Engineering Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer and Electronics Engineering SEST- Jamia Hamdard New Delhi India Department of Electronics and Communication Engineering KMEA College of Engineering Kerala Cochin India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India

In recent years, transformer-based photo captioning frameworks plays a crucial role in improving individuals’ overall well-being, self-reliance, and inclusivity by giving them access to visual content via written and voiced explanations. This research investigates a two-layer transformer architecture to capture extensive relationships in visual data effectively. Using a two-layer transformer design, visual attention mechanism enables the model to autonomously identify relevant regions within a picture while generating a particular word within the anticipated caption. To get the features from the images, a pre-trained Inception V3 model is used, and the feature maps are extracted from the final convolutional block. The dataset used in this research was Flick8k, Flick30k, among bespoke datasets. The model was trained on a TPU platform and implemented on a Raspberry Pi 4B graphics processing unit. To assess the efficacy of the two-layer transformer model for photo captioning, a series of experiments were undertaken using benchmark datasets and with state-of-the-art models. The quality of the generated captions was evaluated using quantitative criteria such as BLEU, BLEUR, ROUGE and F1 score. Furthermore, a series of qualitative assessments were conducted, employing human evaluators, to measure the overall coherence, and fluency of the captions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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Realizing the potential of Internet of Things (IoT) in Industrial applications

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Discover Internet of Things 2025年第1期5卷 1-16页

作者： Rathi, Bharat Thapaswi, S. Kambhampati, Meghana Jain, Vineet Akshay, P. Pandey, Trilok Nath Pradhan, Sunil Kumar School of Electronics Engineering Vellore Institute of Technology Chennai600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The Internet of Things (IoT), which enables seamless connectivity and effective data exchange between physical items and digital systems, has completely changed the way we interact with our surroundings. This study evaluates IoT applications in healthcare, transportation, agriculture, and smart cities, highlighting advancements, opportunities, and challenges. Using a systematic literature review and case study analysis, it identifies essential IoT components, including sensors, communication protocols, data analytics, and security methods. Key findings demonstrate IoT's potential to enhance healthcare through real-time monitoring and cost reduction, optimize transportation systems for resource efficiency, improve agricultural productivity via precision farming, and enable sustainable urban development in smart cities. However, challenges like data privacy, interoperability, and scalability remain critical barriers to widespread adoption. This study underscores the need for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to address these challenges and unlock IoT's full potential. It provides actionable insights and future directions for realizing a connected, efficient, and sustainable IoT-driven future. © The Author(s) 2025.

关键词： Precision agriculture

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Analysis of interferometric configuration for optical devices

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Journal of Optical Communications 2024年第4期45卷 737-740页

作者： Sharma, Himanshu Kumar, Arun Department of Computer Science & Engineering Jecrc University Jaipur India Department of Electrical & Electronics Engineering Jecrc University Jaipur India

With the exponential growth in information related applications and the continuous increase in voice over IP (VoIP) applications, the carriers are expanding their networks to provide improved services to their end users. In such cases, the synchronous optical network/synchronous digital hierarchy model is not suitable. In this work, we are going to investigate the electro optic switch with the help of a VB script to simulate the device for various parameters. Further, we will then scan the central electrode voltages and take account of the overlap integral taking place at the output port with the help of waveguide mode. The best switching for switches at different voltages so as to have negligible coupling loss for different input signals is investigated. We aim to evaluate the MZI switches with the Li-Tn channel profile wave guiding structure and confirm their performance as compared to the existing MZI switches. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Chemical bonds

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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

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computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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A Deep Learning Framework for Hourly Bitcoin Price Prediction Using Bi-LSTM and Sentiment Analysis of Twitter Data

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SN computer science 2024年第6期5卷 767页

作者： Patel, Raj Chauhan, Jaya Tiwari, Naveen Kumar Upaddhyay, Vipin Bajpai, Abhishek Computer Science and Engineering Rajkiya Engineering College Kannauj Electronics Engineering Harcourt Butler Technical University Kanpur

Cryptocurrencies like bitcoin predictions hold significant importance due to their impact on various stakeholders and aspects of the financial landscape. Investors heavily rely on the predictions to make effective and efficient conclusions about buying, selling, or holding Bitcoin as part of their investment portfolios. Accurate forecasts enable effective risk management, allowing individuals and institutions to confidently navigate the volatile cryptocurrency market. In this paradigm, our emphasis is on the prediction of the next hour’s bitcoin price. In most of the model, we give the bitcoin price as input but here along with the bitcoin price we are giving coin price and sentiments as input and it predicts the next hour’s output price. In this work, we have used bidirectional LSTM with preprocessed data. We have performed data cleansing and sentiment analysis as part of the data pretreatment step. Recent 90-days data have been selected for the prediction which is then fed to a bidirectional LSTM model. It is a specialized version of recurrent neural network (RNN) architecture that is often used in sequence-to-sequence tasks, including time series prediction. It can be applied to Bitcoin price prediction by leveraging its ability to capture dependencies in both past and future data. The source code of the project is available at: https://***/rajpatel4835/Forecasting-Bitcoin-Price. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bidirectional LSTM Bitcoin (BTC) Recurrent neural network (RNN) Sentiment analysis

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