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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Asati, Siddhi S. Agrawal, Rahul Dhule, Chetan Morris, Nekita Chavan G H Raisoni College of Engineering Department of Data Science Nagpur440016 India Nagpur440016 India

ISBN: (纸本)9798331541217

This study provides a complete overview of the use of deep learning techniques in soil condition analysis and crop se- lection in the agricultural area. Deep learning, a type of machine learning, has emerged as a potent tool for modelling intricate data patterns, making it ideal for assessing complex soil characteristics critical to crop development and yield. Deep learning algorithms, using artificial neural networks, can effectively process a variety of data sources, including satellite imagery, sensor readings, historical records, and weather data, to provide insights into soil moisture content, nutrient levels, soil texture, and other relevant features. Furthermore, deep learning models can assist in crop selection by incorporating characteristics like as seasonality, regional climatic patterns, and the distinction between Kharif and Rabi crops, which are crucial for improving farming techniques and increasing output. This paper examines the most recent research in this topic, focusing on major approaches, problems, and future directions. It also investigates the implications of implementing deep learning in agriculture, including its capacity to enhance food security, transform contemporary agricultural practices, and mitigate environmental damage. © 2024 IEEE.

关键词： Adversarial machine learning

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Multilingual Language Detection and Translation System with Multinomial Naive Bayes 5

Multilingual Language Detection and Translation System with ...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Vuddanti, Sowjanya Ariveni, Dhathri Sri Lekha Kethepalli, Niharika Sri Sandhya Manchimi, Lokesh Yajjavarapu, Jaswanth Bali Reddy College of Engineering Department of Artificial Intelligence and Data Science Lakireddy Andhra Pradesh Mylavaram India

ISBN: (纸本)9798350367171

Smooth communication becomes essential as the world unites different linguistic groupings. The proposed system for language recognition and translation addresses this difficulty. The proposed system uses a Google Translate-powered 'bridge' with a machine learning 'detective' to properly identify text language and translate between any supported combination. The Multinomial Naive Bayes classifier, a machine learning approach, is used by the language detection component to determine the language of input text. As the 'detective,' the Multinomial Naive Bayes classifier correctly determines the language of the input text with a remarkable accuracy rate of 97 %. The model can accurately classify language in a range of languages by preprocessing the text data and generating a representation of words in a bag. To translate text between languages, the text translation component uses the Google Translate API via the Google library. With the flexibility to choose the source and target languages provided by this component, users can translate text from any supported language to the language of their choice. Easy to use, it enables people to promote intercultural understanding and overcome language barriers in social media, business, education, and other domains, making the world more inclusive and connected. © 2024 IEEE.

关键词： Machine translation

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An Efficient Shortest Path Optimized Algorithm for Vehicle Adhoc Networks System 7

An Efficient Shortest Path Optimized Algorithm for Vehicle A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Shalini, K. Reddy, K. Harinath Punati, Kondalarao Kavin, K.S. Veena, P. Pushpalatha, K. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Shamshabad India Annamacharya Institute of Technology and Sciences Department of Electrical and Electronics Engineering Rajampet India Hyderabad Institute of Technology and Management Faculty of Electronics and Communication Engineering Hyderabad500090 India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India N. S. S. Polytechnic College Department of Computer Engineering Pandalam Pathanamthitta India Coimbatore Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore641109 India

ISBN: (纸本)9798350372816

VANET (Vehicular ad-hoc network) is a network of advanced automobiles designed to solve the problem of mobility and, as a result, reduce the number of accidents. Because VANET is a type of MANET (mobile ad hoc network) surrounded by human-driven nodes, the social concept can be used to explain VANET routing decisions. This motivates academics to look into the possibilities of using Social Network Analysis (SNA) to create routing strategies. The most critical problem in any system is parcel drop. Social characteristics-based architectures encounter comparable challenges, resulting in inefficient message delivery. In this research, the dragonfly method is used to the shortest path methodology for fixed line VANET with the purpose of reducing package drop and increasing network reliability. This is made achievable by sculpted system of values centralization analysis, which improves the decision via dragonfly optimization methodology. The behavior of the using nodes capability then be determined by the importance computation. In this study, the sustainability of the optimized shortest path system is measured in terms of End to End delay, Energy Consumption, Packet Loss, and Throughput. The dragonfly algorithm's throughput evaluation is 650 kbps as compared to the traditional way. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Predicting Hand Injury Severity Using Bayesian Networks in Cloud-Based Emergency Medicine 10

Predicting Hand Injury Severity Using Bayesian Networks in C...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Yuvaraj, S. Thinakaran, K. Srinivas, S. Porandla Sivakumar, S. Ishwarya, M.V. Thirumaraiselvan, P. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Chennai Tamil Nadu Kattankulathur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Department of Computer Science Tamil Nadu Perambalur India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Predicting the severity of hand injuries in emergency care is the focus of this study, which suggests using cloud-based Bayesian networks. It is crucial to have accurate severity prediction in order to treat emergency hand injuries promptly since they are prevalent. By using the scalability and flexibility of cloud computing, this method constructs a Bayesian severity assessment network with patient data, injury characteristics, and historical medical records. The model's training data comes from a massive database of hand injury cases with different results. Emergency medical professionals may use real-time parameter modifications to strengthen and adapt the Bayesian network. Through the use of the cloud, healthcare providers may simply exchange data and reach consensus. It demonstrates that, compared to other well-established methods of evaluating hand injury severity, the model performs better in making predictions. The suggested method improves severity forecasts and advances cloud-based emergency care. The method for emergency hand injury care uses Bayesian networks in the cloud to improve scalability and accessibility. The findings show that the data-driven and efficient hand injury severity prediction method might transform emergency care by improving patient outcomes. © 2024 IEEE.

关键词： Bayesian networks

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Image Display using FPGA with BRAM and VGA Interface for Multimedia Applications 8

Image Display using FPGA with BRAM and VGA Interface for Mul...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Navaneethan, S. Nath, Srigitha S. Udaya Krishnan, M. Sakthekannan, M.S. Yogavignes, B.M. Lokesh Krishnaa, M. Saveetha Engineering College Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350396638

Using a Field-Programmable Gate Array, this work aims to develop a multimedia system, on-chip Block RAM (BRAM), and a Video Graphics Array (VGA) interface. The system will be centered around a VGA monitor that will display multimedia information. The BRAM will temporarily store the image data while the FPGA converts it to a format compatible with the VGA display. The system can produce 24-bit color at a resolution of 100x100. Verilog will be used to construct the hardware, whilst Xilinx will be used for simulation. To demonstrate how the system operates, it will be tested on an FPGA development board. This work aims to create a low-cost, high-performance approach for rendering and processing pictures for use in multimedia applications. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Survey on: Design, Implementation, and Evaluation of a Secure and Anonymous Communication Platform Utilizing the TOR Network for Enhanced Privacy and data Protection 4

A Survey on: Design, Implementation, and Evaluation of a Sec...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Biradar, Santoshkumar Parsewar, Parth Suresh Mishra, Nilamadhab Galakatu, Ankita Atul Gandewar, Sarthak Santosh G H Raisoni College of Engineering & Management Pune Department Of Cyber Security & Data Science Pune India

ISBN: (纸本)9798331518981

This study presents an advanced anonymous messaging platform that integrates Tor network, VPN, and dynamic IP rotation to provide strong privacy protection for users. Tor network onion routing masks user identities with multilayer encryption between distributed nodes, while a VPN adds an encrypted tunnel, improving security by hiding the user's real IP address. The main innovation is the automatic rotation of the IP address of both the sender and the receiver every 10 seconds, which prevents backtracking and significantly complicates traffic analysis. This dynamic mechanism ensures that even if a user's communication is intercepted, it becomes impractical to trace it back to its original source or destination. By addressing weaknesses such as Tor network's potential exit node tracking and risks of VPN One Content Failure, the platform effectively reduces timing attacks and traffic correlation. The design balances latency and performance, ensuring minimal disruption while maintaining high scalability and resilience against adversaries. The combination of these technologies provides a state of the art solution for secure and private anonymous communication. Keywords: anonymous communication, Tor network, VPN, dynamic IP rotation, onion routing © 2024 IEEE.

关键词： Anonymity

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Flask Powered Heart Health Predictor Using Machine Learning Algorithms 2

Flask Powered Heart Health Predictor Using Machine Learning ...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Bhavani, Dhanunjay Reddy, Kattekommula Farid Kumar Ananthula, Sai Manish Soundararajan, Surendhar Shanmugasundaram, Hariharan Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383652

Heart Health Predictor Using Flask is an innovative web application that integrates Flask as its front end and Python as its back end. Ideal for healthcare professionals and individuals, it forecasts heart disease risk through a seamless fusion of Flask for data input and Python for machine learning. With cardiovascular disease claiming a life every minute, automating prediction becomes crucial. In medical care, information science manages vast data, addressing the complexity of coronary disease prediction. Utilizing a cardiac disease dataset from the UCI AI repository. The research presented here employs AI algorithms - Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests - to predict and categorize the likelihood of coronary sickness. The study, providing a relative report on algorithm performance, reveals Light GBM's exceptional accuracy of 90%, making it a promising solution for enhancing predictive capabilities in heart health. © 2024 IEEE.

关键词： Diseases

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Speech Difficulties and Clarification: A Systematic Review 11

Speech Difficulties and Clarification: A Systematic Review

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Dixit, Ashutosh Sethi, Preeti Garg, Puneet Manoj Pruthi, Juhi J. C. Bose University of Science and Technology Ymca Department of Computer Engineering Haryana Faridabad India Abes Engineering College Department of Computer Science and Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9781665487320

Natural language processing helps the computer to process human languages. In general, human language is presented either in the form of text or speech utterances. Speech based computer interaction system helps the computer to understand human language and perform various tasks for verbal instructions and to make intelligent assistive system. Background noise is the most common factor that causes degradation of the quality and intelligibility of speech. The term background noise refers to any unwanted signal that is added to the desired speech signal. In fact, language description is the main motivation behind widely used machine learning (ML) techniques such as Hidden Markov Models, Discriminant Learning, Structured Sequential Learning, Bayesian Learning, and Adaptive Learning. In addition, speech clarification is being used by machine learning as a large-scale, realistic application to assess a particular method's performance and to address new challenges arising due to the naturally sequential and dynamic character of speech. At the other side, although speech clarification occurs for some applications, it remains largely an unexplained issue for most of the applications. This overview article provides readers an insight about modern ML techniques as they are currently used and relevant to future speech explanation systems. Articles are organized according to the most important ML paradigms that are already popular and can contribute significantly to voice recognition. © 2022 IEEE.

关键词： Machine learning

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Reinforcement Learning Based Prediction and Classification of Sleep Disorder 2

Reinforcement Learning Based Prediction and Classification o...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Maragatham, T. Vinodhini, B. Abineshwar, M.K. Kongu Engineerning College Department of Computer Technology-PG Erode India SNS College of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

The application of machine learning techniques for the classification of sleep stages based on electroencephalogram (EEG) data has experienced substantial advancements in recent years (SSC). Nonetheless, the efficacy of these models is largely contingent upon the availability of extensive labeled datasets for training, which limits their applicability in real-world scenarios. In such contexts, sleep research facilities can generate significant volumes of data;however, the process of labeling this data can be both resource-intensive and time-consuming. A prominent strategy to mitigate the challenge of insufficient labels is the adoption of the Reinforcement Learning (RIL) paradigm, which has seen a surge in interest recently. This research aims to evaluate the extent to which RIL can enhance the performance of existing SSC models within a limited-label environment. Conduct a comprehensive analysis across three SSC datasets and find that competitive results can be attained by optimizing pretrained SSC models with merely 5% of labeled data, in contrast to traditional supervised training utilizing complete labeling. Furthermore, self-supervised pretraining enhances the robustness of SSC models against challenges associated with domain shifts and data imbalance. In the context of limited labeled data, this study explores the potential of self-supervised pretraining to improve the performance of contemporary sleep stage classification models. © 2024 IEEE.

关键词： Self-supervised learning

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Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

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computers, Materials & Continua 2022年第3期70卷 5107-5132页

作者： Jung-Hwa Kim Jin-Woo Jeong Department of Computer Engineering Kumoh National Institute of TechnologyGumi39177Korea Department of Data Science Seoul National University of Science and TechnologySeoul01811Kore

Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction ***,previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye *** makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics:1)users’continuous movements,2)various lighting conditions,and 3)a limited amount of available *** address these issues,we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze *** proposed system captures the user using multiple cameras with depth and infrared modalities to train more robust gaze estimators under the aforementioned *** this end,we implemented a deep learning pipeline that can handle different types and combinations of *** proposed system was evaluated using the data collected from 10 volunteer participants to analyze how the use of single/multiple cameras and modalities affect the performance of head-gaze *** various experiments,we found that 1)an infrared-modality provides more useful features than a depth-modality,2)multi-view multi-modal approaches provide better accuracy than singleview single-modal approaches,and 3)the proposed estimators achieve a high inference efficiency that can be used in real-time applications.

关键词： Human-computer interaction deep learning head-gaze estimation indoor monitoring

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