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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Yerramreddy, Dhanvanth Reddy Marasani, Jayasurya Gowtham, Ponnuru Sathwik Venkata Harshit, Guduru Anjali Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India Amrita Vishwa Vidyapeetham School of Engineering Dept. of Electronics & Communication Engineering Amritapuri India

ISBN: (纸本)9798350394474

Speech recognition plays a pivotal role in the realm of natural language processing that deals in converting the language into the written text, providing human-computer interaction and enables us to use it widely for applications starting with voice assistants and delving upto the transcription services. Due to the complexity present in performing the task of speech recognition has led to the development of various models to enhance accuracy and efficiency. Our project mainly delves into three prominent speech recognition models that are Whisper, Wav2Vec2 and Speechbrain each of them representing distinct approaches of transcribing spoken language. The significance of the models used lies in their potential to perform better for realtime applications by improving the accuracy and reliability of speech recognition. To evaluate the best model that performs effectively, an array of metrics are used including levenshtein distance and it's similarity percentage, jaccard similarity along with semantic similarity that provides an additional layer of evaluation describing the model's understanding of contextual meaning in spoken language. Out of the three models, Speech Brain model outperformed all the other models through the calculation of Word Error Rate (WER), Character Error rate (CER) and BLEU score. The results have shown the model's efficiency in converting spoken language(audio files) into precise and contextually relevant text. The results shows that these models contribute to the field of speech recognition highlighting the strengths of each approach and among them considering speechbrain as an ideal solution for accurate and meaningful transcription. © 2024 IEEE.

关键词： Speech recognition

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Global Asymptotic Stabilty Analysis of DC-DC Buck Converters 1

Global Asymptotic Stabilty Analysis of DC-DC Buck Converters

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Kannabiran, Kanimozhi Mohamed Rabi, B. Raja Jayapalan, Booma Krismadinata Lakshmanan, Thangapalani Rajalakshmi, K. Npr College of Engineering & Technology Dept. of Electrical and Electronics Engg. Natham India Dept. of Mechanical Engineering Rathinam Technical Campus Coimbatore India Psna College of Engineering and Technology Dept. of Electronics and Communication Engg. Dindigul India Universitas Negeri Centre for Energy and Power Electronics Research Pedang Indonesia Sri Sairam Institute of Technology Dept. of Computer Science and Engineering Chennai India Npr College of Engineering & Technology Dept. of Computer Science and Engineering Natham India

ISBN: (纸本)9798350348637

Stability of power electronic converters is one of the most important problems related to their dynamics. The problems related to global asymptotic stability is discussed here. The design example used is DC-DC buck converter with Adaptive sliding mode control. An averaged small signal model is derived and transfer functions are obtained. Stability is also verified using conventional techniques by MATLAB simulation. Simulation results and mathematical proof shows that closed loop system designed is stable and a fast response is obtained. Simulated results show that the simulated converter system acquires the excellent robustness feature of achieving a stable steady-state and fast transient response under variable operating points. A prototype model has been developed and simulated results are compared with experimental results. The results validate the stability analysis of Dc-Dc buck converter © 2024 IEEE.

关键词： Buck converter

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Innovative Approaches to Dental Age Estimation: A Comprehensive Review of CNN Technologies and Ethical Issue 5

Innovative Approaches to Dental Age Estimation: A Comprehens...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Shyni, I. Domilin Sheeja Herobin Rani, C. Sheeba, I. Rexiline Mahizha, S.I. Dmi Engineering College Dept. Of Computer Science and Engineering Aralvoimozhi India St.Xavier's Catholic College of Engineering Dept. Of Electronics and Communication Engineering Nagercoil India Sathyabama Institute of Technology and Science Dept. Of Electronics and Communication Engineering Chennai India St.Xavier's Catholic College of Engineering Dept. Of Information Technology Nagercoil India

ISBN: (纸本)9798350389609

Estimating dental age is crucial in multiple disciplines, such as forensic science, orthodontics, and pediatric dentistry. Traditional methods, while effective, often involve manual processes and subjective judgment. Recent advancements in Convolutional Neural Networks (CNNs) have introduced innovative approaches that promise greater accuracy and efficiency. This comprehensive review examines the latest CNN technologies applied to dental age estimation, highlighting their methodologies, performance, and advancements. Key aspects covered include the development and optimization of CNN architectures, the role of data preprocessing and augmentation, and the impact of these technologies on predictive accuracy. Additionally, the review addresses critical ethical issues associated with the implementation of CNNs in dental age estimation, such as data privacy, potential biases, and the implications of automated decision-making in sensitive contexts. By integrating insights from current research and practical applications, this review provides a thorough understanding of both the technological innovations and ethical considerations driving progress in determining dental age. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

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2024 International Conference on Science, Technology, engineering and Management, ICSTEM 2024

作者： Karthick, K. Talari, Praveen Nishadevi, V. Krishna Prakash, R. Asokan, R. Velusamy, A. Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Telangana Hyderabad India Dept. of Computer Science and Engineering Mahendra College of Engineering Tamilnadu Salem India Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798350376913

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Traffic congestion

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Cryptosight: Decrypting Future Trends with LSTM, PyCaret and Jinja 2.0 1

Cryptosight: Decrypting Future Trends with LSTM, PyCaret and...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Mhatre, Soham Motiram Mishra, Brajesh Kumar Raja, Ishan Kumar Goswami, Bigit Krishna Agrawal, Sneha Upadhyay, Pranjal National Institute of Technology Dept. of Electronics and Communication Engineering Nagaland Dimapur India National Institute of Technology Dept. of Mechanical Engineering Nagaland Dimapur India National Institute of Technology Dept. of Electrical & Electronics Engineering Nagaland Dimapur India National Institute of Technology Dept. of Computer Science and Engineering Nagaland Dimapur India

ISBN: (纸本)9798350377002

The primary objective of this research is to develop and evaluate combined cryptocurrency price prediction models using Long Short-Term Memory (LSTM) neural networks along with traditional time series models and machine learning techniques like Random Forest and Linear Regression. The LSTM models will capture long-term dependencies in sequential cryptocurrency price data, while the other models provide complementary predictive capabilities. This multi-model approach aims to leverage the strengths of different techniques to improve overall prediction accuracy. Additionally, this research seeks to explore the practical application of these predictions in real-world cryptocurrency trading scenarios. The study will collect historical price data and relevant news for major cryptocurrencies from reputable sources. Data preprocessing, feature engineering, model training, hyper-parameter tuning, and evaluation using metrics like RMSE, MAE, and R-squared will be performed. The predictive performance of the LSTM-based models will be compared against baseline models, with statistical significance testing. Interpretability analysis will provide insights into the factors driving price movements. Furthermore, the research will investigate integrating the predictions into trading algorithms and evaluate their efficacy through back-testing on historical data. Limitations, challenges, and future research directions in cryptocurrency price prediction will also be discussed. © 2024 IEEE.

关键词： Prediction models

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IoT ML Driven Holistic Health Monitoring and Fitness Assessment Empowering Proactive Wellbeing Management

IoT ML Driven Holistic Health Monitoring and Fitness Assessm...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Mishra, Sachin Singh, Anurag Dept. of Computer Science and Engineering Government Engineering College Raipur Chhattisgarh Raipur India International Institute of Information Technology Dept. of Electronics & Communication Engineering Chhattisgarh Naya Raipur India

ISBN: (纸本)9798350374711

In recent years, significant advancements in the area of non-invasive and wearable technology have paved the way for widespread applications of personalized and remote health monitoring. Health monitoring enables proactive management of one's well-being, and enhances overall health outcomes. With the advent of such technologies there are lots of platforms developed. But such platforms are not successful in providing the followings: 1) the ability to monitor all major health vitals in one unified application, & 2) the provision of a holistic health score derived from these non-invasive vitals for fitness measurement. In response, this paper presents a novel solution featuring: 1) An IoT-ML enabled platform for self-monitoring of important health vitals, and 2) A fitness assessment score derived through ML algorithm using the health vitals of a person. By integrating data on various health parameters including heart rate, blood oxygen levels, and sleep patterns, advanced machine learning models are developed to analyze this data and predict the fitness score that accurately reflects an individual's overall health and fitness level. The performance of the proposed estimation model is evaluated using R2 and mean square error (MSE) and the best value is achieved using the XGBoost regressor of values 0.967 and 0.0033. This solution empowers individuals to take control of their well-being and improve their overall quality of life. Overall, the integration of non-invasive methods and IoT sensors for health vitals monitoring and fitness assessment presents a novel approach with significant potential to revolutionize health care. By empowering individuals with real-time insights and a comprehensive fitness score, this solution facilitates informed decision-making and enables individuals to proactively manage their health and well-being, ultimately enhancing their overall quality of life. © 2024 IEEE.

关键词： Electronic health record

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Utilizing Deep Neural Networks to Improve Intrusion Detection System(IDS)

Utilizing Deep Neural Networks to Improve Intrusion Detectio...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Saad, Ahmed Mahmoud Meekay, Mohammed Awad El Sayed, Mostafa Samy Mansoura University Mansour Electronics and Computer and Systems Engineering Dept. Cairo Egypt Mansoura University Mansour Electronics and Communications Dept. Cairo Egypt

ISBN: (纸本)9798350351408

Internet services are now accessible everywhere in the world due to the rapid advancement of networks and technology in recent decades. The development of information security solutions that can recognize novel threats is now critical due to the surge in piracy and the compromising of many modern systems. An intrusion detection system (IDS) is one of the most important information security technologies. It uses machine learning and deep learning algorithms to locate anomalies in the network. The main goal of this paper is to identify the unknown assault package by utilizing a novel intrusion detection method that offers excellent network performance. In this model, two techniques-binary classification and multiclass classification-that make use of a deep neural network algorithm are used to detect attacks. The suggested's outstanding accuracy. © 2024 IEEE.

关键词： Network intrusion

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification 15

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Kyritsis, Konstantinos Liapis, Charalampos M. Spatiotis, Nikolaos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical & Computer Engineering Dept. Patras26334 Greece University of Patras Mathematics Dept. Patras26504 Greece Computer Technology Institute & Press 'Diophantus' Patras26504 Greece University of Patras Computer Engineering & Informatics Dept. Patras26504 Greece

ISBN: (纸本)9798350368833

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification. © 2024 IEEE.

关键词： Zero-shot learning

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A Study on the Efficient ESS Charging/Discharging Operation Algorithm in LVDC Microgrid Environment

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Transactions of the Korean Institute of Electrical Engineers 2024年第6期73卷 1063-1073页

作者： Jung, Sang-Woo An, Yoon-Young Kim, Ki-Il Dept. of Computer Science and Engineering Chungnam National University Korea Republic of ICT Convergence Standards Research Division Electronics and Telecommunications Research Institute Korea Republic of

Modern power systems are developing in a sustainable and energy-efficient direction, and research on intelligent power grids that accommodate new and renewable energy is being actively conducted. Since new and renewable energy-based distributed power generation systems and ESS are output as DC power, the DC microgrid reduces the power conversion step and improves the stability and efficienc of the power distribution network. The core of this study is to compare and analyze the performance of the three charge/discharge operation algorithms based on AI prediction in the LVDC microgrid environment. The AI server learns real-time data through interworking with the EMS to predict future load usage and solar power generation, and performs the charge/discharge operation algorithm of ESS based on the collected ESS state data. This paper presents the configuration of the LVDC microgrid testbed, the integration method between EMS and electrical facilities, and analyzes the performance of the developed ESS charge/discharge operation algorithm. It also evaluates the effectiveness of each operation algorithm through actual operation data and simulation, analyzes how these strategies contribute to system load reduction and energy efficiency improvement, and provides guidelines for selecting the optimal ESS charge/discharge operation algorithm under specific conditions. © The Korean Institute of Electrical Engineers.

关键词： Solar energy

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Narrow Beamforming in Non-Terrestrial 5G/6G Networks: Is it Possible?

Narrow Beamforming in Non-Terrestrial 5G/6G Networks: Is it ...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Alsaeedy, Alaa A. R. Chong, Edwin K. P. Colorado State University Dept. of Electrical and Computer Engineering Fort Collins United States University of Al-Qadisiyah Dept. of Electronics and Communication Engineering Diwaniyah Iraq

ISBN: (纸本)9798350376715

This article explores beamforming in 5G/6G radio coverage, with a focus on satellite-based non-terrestrial networks (NTNs). It discusses the challenges of applying phased-array antenna technologies, which are vital for achieving narrow beamforming in both 5G/6G terrestrial networks (TNs) and NTNs. We highlight advancements in material technologies that enable the creation of compact and powerful massive Multi-Input Multi-Output (mMIMO) antennas, introducing nano-antenna arrays (e.g., 1024 × 1024 elements) that occupy 1mm2 at terahertz frequencies (1-10THz). We also detail how applying mMIMO antenna arrays in space offers new potential for achieving narrow beamforming in satellite-based 5G/6G NTNs. We investigate the ongoing efforts in deploying such mMIMO in space to replicate the cellular radio coverage of TNs. © 2024 IEEE.

关键词： Nanoantennas

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