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Enhancing Communication through Speech to Sign Language Converter 2

Enhancing Communication through Speech to Sign Language Conv...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Gayana, M.N. Rumaiz, Mohammed Hussain, Shaz Rasheed Shravya, A. Acharya, Shreenath Parveen, Sana Systems St Joseph Engineering College Dept. of Intelligent Computing & Business Mangaluru India St Joseph Engineering College Dept. of Computer Science and Engineering Mangaluru India

ISBN: (纸本)9798350314045

There are millions of people suffering from speaking and hearing disabilities. Sign language is the only way to communicate for such people to convey their message. The advancement in technology has highly impacted this section of society, hence there is a need to deal with them. This paper describes the Application, "Speech to Sign Language Converter"for communicating with people having hearing and speaking impairments. The primary function of the Application is to convert input sentences into Indian Sign Language (ISL) actions in real time. It takes input in the form of speech or text and produces corresponding sign language using an Avatar which acts based on SiGML code. It can also be used to convert PDF based texts into sign language. In addition, the users can learn Indian Sign Language (ISL) using the tutorials provided within it. The application focuses mainly on converting local languages such as Kannada into ISL. © 2023 IEEE.

关键词： Audition

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Exploring Relationships among Bitcoin's Market Price, Energy Consumption and Carbon Dioxide Emissions: A Machine Learning Approach 1

Exploring Relationships among Bitcoin's Market Price, Energy...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Jamali, Aadil Ali, Najma Imtiaz Brohi, Imtiaz Ali Kanasro, Nadeem Ahmed Murad, Muhammad Umar Jamali, Asad Ali University of Sindh IMCS Jamshoro Pakistan Government College University Dept. of Computer Science Hyderabad Pakistan Istanbul Aydin University Dept. of Software Engineering Istanbul Turkey University of Essex Dept. of Economics Colchester United Kingdom

ISBN: (纸本)9798350373042

The cryptocurrency boom has resulted in a substantial rise in energy usage and carbon emissions. The mining process of Bitcoin is notorious for its energy intensive process because it requires huge computational power. The carbon footprint of Bitcoin mining is a growing concern that's why we have to analyze the number of CO2 emissions resulting from the mining procedure. In this study, we have utilized machine learning techniques to examine the number of CO2 emissions generated by Bitcoin mining, analyzed the correlations between Bitcoin price, electricity consumption, and CO2 emissions, and pinpoint the factors during the process of Bitcoin mining that leads to CO2 emissions. We gathered global data set on Bitcoin electricity consumption, prices, and CO2 emissions. We trained supervised machine learning model (Regression) in Python to analyze and investigate the data. Our research findings have great implications for policymakers and researchers worried about the impact of the latest technologies on the environment, and in future Bitcoin policies and practices;they should prioritize sustainability and reduce the carbon footprint of mining operations. In the end, our research will provide valuable insights into the environmental impact of Bitcoin mining, highlight the factors that cause CO2 emissions and underscored the need for further research and action to reduce the carbon footprint of cryptocurrencies. © 2024 IEEE.

关键词： Carbon footprint

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Application to Enhance the Programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This proposal outlines the development of a comprehensive educational platform aimed at bridging the Information Technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical Information Technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances Information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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A New Configurable SC-based Dual Source Inverter Topology with Improved Reliability

A New Configurable SC-based Dual Source Inverter Topology wi...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Anees, Mohammad Anas Daula Siddique, Marif Mekhilef, Saad Mubin, Marizan Binti Seyedmahmoudian, Mehdi Stojcevski, Alex National University of Singapore Dept. of Electrical and Computer Engineering Singapore University of Malaya Dept. of Electrical Engineering Malaysia Swinburne University of Tech. School of Software and Electrical Engineering. Australia

ISBN: (纸本)9781665471640

This paper presents a novel fifteen-level two-source inverter topology that is reliable against source outages. The topology has two DC sources, a total of ten switches, and four DC-link capacitors. Level-shifted PWM is used to generate the required number of levels. Simulations are performed for normal operation as well as for the source outage conditions. The proposed topology is able to generate multi-level output waveform satisfactorily even at the advent of source outages. The simulation results for the source outages are presented for both static and dynamic conditions along with the THD measurements. © 2023 IEEE.

关键词： dc source failure fault tolerance level shifted PWM multilevel inverters (MLIs) reliability

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Kubernetes Cluster Disaster Recovery Using AWS 7

Kubernetes Cluster Disaster Recovery Using AWS

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bhavsar, Simran Agrawal, Apoorva Ropalkar, Tanvi Kamdi, Pranjali Hajare, Aparna Deshpande, Shilpa Rathi, Rahul Garg, Devendra Cummins College of Engineering for Women Dept. of Computer Engineering Pune India Veritas Software Technologies India Private Limited Pune India

ISBN: (纸本)9798350304268

As applications in IT have been spanning multiple containers across servers, kubernetes is being used extensively for automating software deployment, scaling and management. Kubernetes eases the container tasks and also other activities like horizontal scaling and canary development. To run cloud-native applications, kubernetes has become the de facto standard for container orchestration, regardless of the underlying platform. This makes it very crucial to have a disaster recovery plan for kubernetes environments. This paper proposes a system for backup and restore of a kubernetes cluster using the snapshot method along with the Public Key Infrastructure (PKI) certificates of a cluster. It also focuses on automating the backup and restore operations using microservices. Furthermore, it makes use of Amazon Web Services (AWS) tools for the reliability, scalability and the automation of backup and restore operations. The user can run the proposed microservices to backup and restore the cluster from any specific timestamp before the disaster. The experimental results demonstrate the effective performance of the proposed method in terms of efficiently restoring the kubernetes cluster in optimal time. © 2023 IEEE.

关键词： Cluster backup Cluster initialization Cluster setup Disaster recovery Kubernetes Master node Restore Worker node

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Harnessing LLMs for VQA: A Prompted Benchmark with Animate/Inanimate Keywords 15

Harnessing LLMs for VQA: A Prompted Benchmark with Animate/I...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Lee, Chanwoo Lee, Hyunjeong Kim, Minsang Kim, Hyun Jang, Haneol Park, Cheoneum Seoul Woman's University Dept. of Software Convergence Seoul Korea Republic of Hanbat National University Dept. of Computer Engineering Daejeon Korea Republic of Korea University Dept. of Computer Science and Engineering Seoul Korea Republic of Electronics and Telecommunications Research Institute Superintelligence Creative Research Lab. Daejeon Korea Republic of

ISBN: (纸本)9798350364637

In the field of NLP, Large Language Models (LLMs) have recently achieved significant advancements, leading to the development of various benchmarks for their evaluation. Along-side NLP, Vision Language Models (VLMs) have also VLM have also significantly progressed, similar to LLMs. However, benchmarks for VLMs are still relatively underdeveloped compared to those for NLP, and their construction is often costly. In this work, we propose an automatically generated benchmark for evaluating VLMs based on LLMs and conduct a visual question answering task to assess this benchmark. The benchmark includes multiple-choice questions that not only distinguish between animate and inanimate objects but also generate these distinctions automatically, along with entity and object information within images. We evaluate the performance of open VLM using the generated multiple-choice questions, demonstrating the model's capabilities and the significance of the automatically generated benchmark. Finally, we discuss the necessity and future directions for benchmark research in this area. © 2024 IEEE.

关键词： Visual languages

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A Deep Learning-Based Spectral Efficiency Maximization in Multiple Users Multiple STAR-RISs Massive MIMO-NOMA Networks 14

A Deep Learning-Based Spectral Efficiency Maximization in Mu...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van An, Beongku Hongik University Dept. of Software and Communications Engineering Graduate School Korea Republic of International University Vietnam National University School of Computer Science and Engineering HCM City Viet Nam Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350335385

In this paper, we investigate a deep learning-based spectral efficiency maximization in multiple users multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks, where multiple STAR-RISs are deployed to assist a base station (BS) in communicating with multiple near and far users simultaneously. To improve spectral efficiency (SE), we formulate a joint optimization of the precoding matrix, the phase shift of reflection and transmission of the STAR-RIS problem subject to the individual quality of services (QoS) of each user, maximum power budget at BS and the discrete phase shift. The formulated problem belongs to the class of mixed-integer non-convex optimization problem, which is difficult to solve optimally. We first relax the formulated problem and then transform it into an equivalent non-convex one but with a more tractable form. We then apply the inner approximation method to approximate the non-convex parts iteratively. Towards real-time optimization, we propose a novel deep learning framework with a convolutional neural network (CNN) model to predict the downlink SE based on users' position information and channel gains. Numerical results show that the proposed approach improves the SE compared to the massive MIMO systems with conventional RIS. Besides, the DL framework for the proposed scheme predicts the optimal SE within a short time compared to the conventional approach. © 2023 IEEE.

关键词： Spectrum efficiency

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Cybersecurity and Privacy Attacks Detection in IoT Networks with Improved Data engineering and Machine Learning Methods 9

Cybersecurity and Privacy Attacks Detection in IoT Networks ...

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9th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Elmazi, Donald Karras, Dimitrios A. Alkholidi, Abdulsalam Capari, Klea Faculty of Engineering Dept. of Electronics and Telecommunications Tirana Albania Dept. S/W Engineering Tirana Albania Dept. General School of Science Athens Greece Tirana Albania Faculty of Engineering Sana'a University Yemen University of Rijeka Faculty of Engineering Dept. of Computer Engineering Croatia Faculty of Engineering Cit Dept. of Software Engineering Tirana Albania

ISBN: (纸本)9798350333794

Presently, the global proliferation of Internet of Things (IoT) devices and applications has accelerated owing to their advantages in enhancing the business and industrial environment, as well as the daily lives of individuals. Nevertheless, IoT devices are vulnerable to malevolent network traffic, which can lead to undesirable outcomes and disrupt the operation of IoT devices. Consequently, it is imperative to devise a screening method for network traffic to identify and categorize malicious activity and reduce its harmful effects. In this regard, the present study proposes a framework and thoroughly compares single and ensemble machine learning models that are using predictive analytics to detect and classify network activity in an IoT network. Specifically, our framework and models distinguish between normal and anomalous network activity. Furthermore, network traffic is classified into eight categories, normal, denial of service (DoS) attack, Scan attack, Data Type Probing, Malicious Operation, Malicious Control, Spying attack and Wrong Setup based attack. The special focus of this paper is in identifying privacy related attacks in comparison with main cybersecurity related ones. Several supervised machine learning models have been herein implemented to characterize their performance in detecting and classifying network activities for IoT Networks based on the ensemble learning framework as well as on the single learning machines framework. Moreover, classifiers based on the statistical learning theory framework are herein involved too, including hybrid Bayesian theory models, decision trees (DT) and the ordinal Learning Modelling approach. The statistical pattern recognition based models as well as all supervised learning machines models have been evaluated on a broad benchmark dataset for IoT attacks. Besides, an improved data engineering feature processing model has been applied to the dataset under consideration to improve the accuracy of all models under co

关键词： Internet of things

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Comparison Between Gramian Angular Fields (GAF) and Markov Transition Field (MTF) Images Data by Using them as Input to the Deep Learning Neural Network Solar Flare Production Platform

Comparison Between Gramian Angular Fields (GAF) and Markov T...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Nagem, Tarek A M Hamad Alshareef, Sohil Mohamed, Abdelhafid Gihedan, Akram Albargathe, Salma Libyan British University Dept. Computer System Design Benghazi Libya University of Benghazi Dept. Software Engineering Benghazi Libya College of Computer Technology Dept. Software Engineering Benghazi Libya Darna University Dept. Computer Science Algoba Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

The prediction of solar storms from real-time satellites data is an essential to protect various aviation, power, and communication infrastructures. For this reason, current research interest is focusing on creating systems that can predict solar fare accurately. This paper combers Gramian Angular Fields (GAF) images and Markov Transition Field (MTF) images that have been generated by converting pre-flare Geostationary Operational Environmental Satellite (GOES) data from 2010 to 2016. After that, the deep learning neural network for the solar flare production platform accepts both MTF and GAF images as input to generate the predictions. Furthermore, this paper;investigated using MTF and GAF images spritely as input to the solar flare production platform. After that, the results of MTF and GAF images were compared using various prediction verification measures. © 2022 IEEE.

关键词： Forecasting

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Online Learning and Assessment of Theoretical Knowledge in Electrical engineering Courses for Part-Time Education 15

Online Learning and Assessment of Theoretical Knowledge in E...

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2023 15th Electrical engineering Faculty Conference, BulEF 2023

作者： Ivanova, Yoana Filipova-Petrakieva, Simona Technical University of Sofia Dept. Computer Systems Sofia Bulgaria Technical University of Sofia Dept. Fundamentals of Electrical Engineering Sofia Bulgaria

ISBN: (纸本)9798350326536

Nowadays our normal life is unavailable without the Internet. Through the web, the same information is accessed from different places in the world. Both desktop computers and various types of mobile devices, including phones, can be used. Recently, e-learning as a model for knowledge learning has become more popular. This became a necessity after the global COVID pandemic. In some aspects, online learning is even more effective than in person learning as it allows students to study at their own speed. In the present paper, a Web-based application for learning theoretical knowledge in a science subject field is developed. Lectures and sample quiz questions to assess the knowledge are implemented therein. In case of an incorrect answer to the test questions, the application refers the learner to the place in the theory corresponding to the question. Two roles are implemented in the application - administrator and user (learner). The following technologies - HTML, CSS, JavaScript, ***, MongoDB, Bootstrap, jQuery И PUG, are used to develop the application. Desktop and mobile versions of the application are proposed. It is directed only at learning and testing theoretical knowledge, in particular in the different courses in the field of Electrical engineering. It can be used to simplify some of the part-time learning education for students (lectures, tutorials, course assessments, and projects). The respective labs will be performed on-site - in laboratories. The final assessment of the studied subject can also be done online. It will save transportation time for both learners and teachers. And, time is money. © 2023 IEEE.

关键词： Students

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