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

作者： Kavitha, M.N. Saranya, S.S. Prasad, M. Kaviyarasu, S. Ragunath, N. Rahul, P. Kongu Engineering College Department of Computer Science and Design Tamilnadu Erode India PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamilnadu Coimbatore India Controller of Examinations Tamilnadu Namakkal India Kongu Engineering College Bachelor of Science - Information Systems Tamilnadu Erode India

ISBN: (纸本)9798350370249

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems. © 2024 IEEE.

关键词： Intelligent systems

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A Novel Technique of Ranking of Transmission Lines for Maintenance Prioritization Accounting Annual Outage Time and Operational Severities

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Journal of The Institution of Engineers (India): Series B 2023年第6期104卷 1185-1196页

作者： Singh, Pushpendra Arya, Rajesh Titare, L.S. Purey, Pradeep Choube, S.C. Arya, L.D. Department of Electrical Engineering Rajkiya Engineering College Banda AKTU Lucknow UP Banda210201 India Department of Information Technology Acropolis Institute of Technology and Research MP Indore453771 India Department of Electrical Engineering Govt. Engineering College MP Ujjain456010 India Department of Electronic Engineering Maharaja Ranjit Singh College of Professional Sciences MP Indore452001 India Department of Electrical & Elex. UIT MP Bhopal452 003 India Department of Electrical Engineering Shri G S Institute of Technology and Science MP Indore452003 India

This paper describes a novel index for ranking of transmission lines for maintenance purpose. Preventive maintenance of transmission lines may be carried out based on prioritization of lines index. The index is based on severity indices, namely (a) thermal limits, (b) voltage limit violation, (c) voltage stability margin and (d) annual outage time. Thus in maintenance prioritization of lines, annual outage time (reliability index) and operational severity have been considered. The index has been obtained by multiplying the operational severity index with the annual outage time to reflect the true picture for the prioritization of transmission lines for maintenance purposes. It is stressed here that the objective of the paper is to provide an assessment of transmission line outage severity so as to provide a ranking list of lines based on peak load for maintenance prioritization. Lines that have higher index can be selected in priority for maintenance from the list. Maintenance activities involve reduction in failure rate and average repair time thereby giving desired availability. Results have been obtained for determining the maintenance priorities of transmission lines using derived index on three standard test systems. © 2023, The Institution of Engineers (India).

关键词： Electric lines

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Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

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computers, Materials & Continua 2022年第9期72卷 4303-4321页

作者： Saman Riaz Ali Arshad Shahab S.Band Amir Mosavi Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Computer Science Institute of Space TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliu64002YunlinTaiwan Faculty of Civil Engineering Technische Universitat DresdenDresden 01069Germany

The way towards generating a website front end involves a designersettling on an idea for what kind of layout they want the website to have, thenproceeding to plan and implement each aspect one by one until they haveconverted what they initially laid out into its Html front end form, this processcan take a considerable time, especially considering the first draft of the designis traditionally never the final one. This process can take up a large amountof resource real estate, and as we have laid out in this paper, by using a Modelconsisting of various Neural Networks trained on a custom dataset. It can beautomated into assisting designers, allowing them to focus on the other morecomplicated parts of the system they are designing by quickly generating whatwould rather be straightforward busywork. Over the past 20 years, the boomin how much the internet is used and the sheer volume of pages on it demands ahigh level of work and time to create them. For the efficiency of the process, weproposed a multi-model-based architecture on image captioning, consisting ofConvolutional neural network (CNN) and Long short-term memory (LSTM)models. Our proposed approach trained on our custom-made database can beautomated into assisting designers, allowing them to focus on the other morecomplicated part of the system. We trained our model in several batches overa custom-made dataset consisting of over 6300 files and were finally able toachieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50hand-drawn images at 87.86%.

关键词： Deep learning wireframes front-end low fidelity high fidelity design process html computer vision dsl

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A Comparative Analysis of SiC MOSFET Performance in a Traction Inverter 20

A Comparative Analysis of SiC MOSFET Performance in a Tracti...

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20th IEEE Asia Pacific Conference on Circuits and Systems and IEEE Asia Pacific Conference on Postgraduate research in Microelectronics Electronics, APCCAS and PrimeAsia 2024

作者： Wu, Chih-Chiang Rout, Uma Sankar Wang, Yu-Long Lin, Yi-Ling Hu, Jwu-Sheng National Yang Ming Chiao Tung University Department of Electrical Engineering & Computer Science Taiwan Industrial Technology Research Institute Mechanical and Mechatronics Systems Research Laboratories Hsinchu Taiwan

ISBN: (纸本)9798350378771

This study rigorously tests and verifies silicon carbide (SiC) power modules within a developed 800 V electric vehicle traction inverter. SiC traction inverters offer high efficiency, power density, and compact size. The developed SiCbased traction inverter is thoroughly evaluated on the established platform with various switching frequency, achieving a peak drive efficiency exceeding 98% at 6 kHz and a power density of 50 kW/L. © 2024 IEEE.

关键词： Power MOSFET

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Examining the Role of Guardian Communications Software Design for Network Security 15

Examining the Role of Guardian Communications Software Desig...

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

作者： Giri, Rakesh Kumar Chaudhari, Madhuri Harshal Verma, Ashish Dhabliya, Dharmesh Kumar, L.Vinoth Jaison, B. Sunrise University DepartmenT of Computer Science & Engineering Alwar301028 India Dr.D.Y.Patil Science and Computer Science college Department of Computer Science pune Akurdi India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Technology University Department of Information Chennai India R.M.K.Engineering College Department of Computer Science and Engineering Kavarapettai601206 India

ISBN: (纸本)9798350370249

Network protection is a crucial region of generation and PC technology, and numerous equipment and techniques help ensure comfy conversation and records switch. Mum or Dad Communications software program (GCS) is a product suite that gives customers an answer for securing their networks. GCS offers an interactive person interface to facilitate configuration, analysis, and optimization of a community's safety architecture. The suite carries three core components, supplying an excellent and specialized feature. These additives are the mum or dad Firewall, the guardian Host Intrusion Prevention System (HIPS), and the father or mother community protection appliance (NSA). Each component works independently and in concert with each other to provide comprehensive network security. The Guardian Firewall is an application-level firewall that provides a secure edge to the network by controlling all incoming and outgoing traffic and packet communications. It dynamically updates its rules database to recognize and block malicious traffic while allowing access to known secure applications and services. The Guardian HIPS is an internal network monitoring system that can detect and respond to unauthorized intrusions while providing the administrator with real-time alerts and intelligence about the nature of any malicious activity. Finally, the Guardian Network Security Appliance is a hardware-based appliance that can be configured with firewalls and. © 2024 IEEE.

关键词： Communications Dynamically Guardian Security Unauthorized

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Role Identification from Human Activity Videos using Recurrent Neural Networks

Role Identification from Human Activity Videos using Recurre...

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2022 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2022

作者： Arshad, Anam Tiwari, Vivek Lovanshi, Mayank Shrivastava, Rahul Department of Computer Science & Engineering Naya Raipur India Sagar Institute of Science Technology & Research Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350311563

Recognizing the underlying roles in mutual activity is more informative. Role identification has the potential to improve wide range of applications, of activity recognition from safety and security to healthcare. In recent research, for role identification, work is done to identify roles by capturing the knowledge of body parts from an image. This work is complex and not sufficient to take input as English sentences and capture the sequencing and relationship between words. There is a need for simple work which could use recent technologies like Recurrent Neural Networks to capture the recurrent nature of sentences to identify roles. The contribution of this work is proposing a Computational Long Term Memory model where sentences are stored as features and given to a Recurrent Neural Network to identify roles. The appropriate dataset is not available for role identification using sentences. In this view, this work attempt to develop a new custom dataset. The proposed model is tested on accuracy using various Recurrent Neural Networks like Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) etc. The LSTM model gave effective accuracy of 60% on the small custom dataset. © 2022 IEEE.

关键词： Long short-term memory

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Utilizing Machine Learning to Develop an Improved Real-Time Data Analysis System 3

Utilizing Machine Learning to Develop an Improved Real-Time ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khandari, Harsimrat Sahoo, Girija Shankar Raja Praveen, K.N. Shree, Ritu Umamaheswari, B. Yashwant, V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records © 2023 IEEE.

关键词： Statistics

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Fast Erasure Decoding of Reed-Solomon Codes Based on Decomposition of Vandermonde Matrix 16

Fast Erasure Decoding of Reed-Solomon Codes Based on Decompo...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Lin, Zebing Lv, Jingjie Li, Pingping Song, Linqi Liang, Hui Hou, Hanxu School of Computer Science and Technology Dongguan University of Technology China School of Electrical Engineering & Intelligentization Dongguan University of Technology China Shenzhen Institute for Advanced Study University of Electronic Science & Technology of China China City University of Hong Kong Department of Computer Science Hong Kong City University of Hong Kong Shenzhen Research Institute Hong Kong

ISBN: (纸本)9798350390643

Reed-Solomon (RS) codes are widely applied in storage systems. The main bottleneck of RS codes is that the high decoding complexity defined as the number of multiplications and additions. In this paper, we propose a fast decoding algorithm for RS codes that requires fewer number of multiplications. Our fast decoding algorithm is designed by a decomposition of Vandermonde matrices. We show that our decoding algorithm requires fewer number of multiplications than the existing best known algorithm when the number of parities T is large. When the codeword length N=16, our decoding algorithm reduces the number of multiplications by 7% and 29% when T=4 and T=7, respectively, compared with the best known decoding algorithm. © 2024 IEEE.

关键词： Reed-Solomon codes

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Epileptic Seizure Detection using XGBC with SMOTE+ENN: Epileptic Seizure Detection through ML algorithms 6

Epileptic Seizure Detection using XGBC with SMOTE+ENN: Epile...

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6th International Conference on Digital Medicine and Image Processing, DMIP 2023

作者： Nagarajan, Sangeetha Rajangam, Vijayarajan Khan, Faiz Shukla, Mridul Singh, Manas School of Computer Science Engineering Vellore Institute of Technology Chennai India Centre for Healthcare Advancement Innovation and Research Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798400709425

Epilepsy is a disorder of the brain's nervous system in which irregular brain activity results in strange behavior, sensations, and at times loss of awareness. This paper investigates various machine learning (ML) models for the classification of Epileptic Seizure (ES) from electroencephalogram signals. Five classes in the ES dataset are converted into two prominent classes using synthetic minority oversampling with edited nearest neighbor technique and the ML algorithms are experimented on the two classes of the balanced dataset. The performance analysis with the standard metrics reveals that the extreme gradient boosting classifier performs better than other ML models with 98.65% accuracy. © 2023 ACM.

关键词： Machine learning

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DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 690-700页

作者： Deng, Dazhen Wu, Aoyu Qu, Huamin Wu, Yingcai Zhejiang University State Key Lab of CAD&CG China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China

Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to configure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Specifically, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets. © 2022 IEEE.

关键词： Reinforcement learning

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