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Emerging Technologies in computer science for Interdisciplinary Applications (ICETCs), International Conference on

作者： Aditi Akarsh Arnav sinha bhavya singh Divyanshu v A sheetal Department of Computer Science and Engineering B.M.S College of Engineering Bangalore Karnataka India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

sign language is an essential tool for communication among individuals with hearing *** recent progress in computer vision and machine learning has significantly advanced the development of systems that recognize and interpret sign language, contributing to their *** systems play a vital role in bridging communication gaps by automatically identifying and interpreting sign language gestures, facilitating seamless and effective communication. This paper reviews and analyzes sign language recognition systems, covering methodologies and potential applications, including data collection, hand and gesture tracking, feature extraction, and classification algorithms. The goal is to promote inclusivity and equal opportunities by fostering communication between sign language users and the broader community.

关键词： Deep learning Training sign language visualization shape Reviews Feature extraction

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“vulnerabilities in smart Contracts: A Detailed survey of Detection and Mitigation Methodologies”

“Vulnerabilities in Smart Contracts: A Detailed Survey of D...

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Emerging Technologies in computer science for Interdisciplinary Applications (ICETCs), International Conference on

作者： Nayantara K Kumar Niranjan v Honnungar M sharwari Prakash J J Lohith Dept. of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

In the ever-changing world of blockchain technology, the emergence of smart contracts has completely transformed the way agreements are executed, offering the potential for automation and trust in decentralized systems. Despite their built-in security features, smart contracts still face persistent vulnerabilities, resulting in significant financial losses. While existing studies often approach smart contract security from specific angles, such as development cycles or vulnerability detection tools, this paper adopts a comprehensive, multidimensional perspective. It delves into the intricacies of smart contract security by examining vulnerability detection mechanisms and defense strategies. The exploration begins by conducting a detailed analysis of the current security challenges and issues surrounding smart contracts. It then delves into established frameworks for classifying vulnerabilities and common security flaws. The paper examines existing methods for detecting, and repairing contract vulnerabilities, evaluating their effectiveness. Additionally, it provides a comprehensive overview of the existing body of knowledge in smart contract security-related research. Through this systematic examination, the paper aims to serve as a valuable reference and provide a comprehensive understanding of the multifaceted landscape of smart contract security.

关键词： surveys systematics Generative AI smart contracts Ecosystems static analysis Maintenance engineering

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Machine Learning Techniques for Prediction and Analysis of benign and Malignant in breast Cancer 12

Machine Learning Techniques for Prediction and Analysis of B...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Divya, P. Rajan, D. Palanivel suguna, R. velliangiri, s. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Tamil Nadu Erode India Cmr Engineering College Department of Computer Science and Engineering Telangana Hyderabad India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narasapur India

ISBN: (纸本)9781665480352

In today's technological world, the medical area has become one of the most popular research topics, and cancer is one of them. One of the major diseases caused for women's life today is breast cancer. breast cancer claims the lives of most women all over the world, not just in India. breast cancer originates due to aberrant cell division, leading to benign or malignant cancer formation. Patients with this disease can only be saved if and when found in the early stages. If it is discovered in the later stages, the chances of survival are less. Early diagnosis will help physicians who will significantly enhance the survival rate of cancer patients. Then only patients can able to avail best possible care by providing better treatment. During the early stage, symptoms must be thoroughly investigated. It is also necessary to develop an automatic prediction system that can classify tumors as benign or malignant. This paper will focus on some Machine Learning techniques for prediction and analyze whether a disease is a malignant or benign stage in breast cancer. © 2022 IEEE.

关键词： Diseases

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solar Powered IoT based smart system for small and Micro Irrigation

Solar Powered IoT Based Smart System for Small and Micro Irr...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOvA 2024

作者： Umachagi, Hamjadali A. bannur, Mukta Kulkarni, Pavankumar Gadad, shweta Ronad, basanagouda F. Patel, saniya M. Department of Electrical and Electronics Engineering BLDEA's V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapura586103 India Karnataka Bagalkote587102 India Department of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi590018 India

ISBN: (纸本)9798331505134

Agriculture is a leading sector and many countries are working towards sustainable agriculture by integrating modern technologies to enhance agricultural production. This paper proposes Internet of Things (IoT) based smart farming technology to support small farmers. The proposed technology involves sensors, weather data acquisition and IoT technology. The methodology includes qualitative design and primary data collection mechanism. Irrigation activities are linked with IoT and automated for efficient and operative irrigation. The proposed system can help farmers to realize their crop growth, reduce the ecological impacts and save water source, energy and manpower while enhancing quality and quantity of the crop yield. The proposed system has potential to revolutionize irrigation by improving crop yields, and increasing overall efficiency of farming activities. © 2024 IEEE.

关键词： smart agriculture

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An Accurate Prediction and Diagnosis of Alzheimer's Disease using Deep Learning 2

An Accurate Prediction and Diagnosis of Alzheimer's Disease ...

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

作者： baskar, s. Prasad, Mudarakola Lakshmi sharma, Nipun Nandhini, I. Katale, Tejaswini Reddy, Pundru Chandra shaker Dr. Mgr Educational and Research Institute Electrical & Electronics Engineering Tamil Nadu Chennai India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Presidency University Electronics and Communication Engineering Karnataka Bangalore India V. S. B Engineering College Department of Information Technology Tamil Nadu Karur India Vishwakarma Institute of Technology Department of Computer Engineering Maharastra Pune India Manipal University Jaipur School of Computer Science and Engineering Rajasthan Jaipur303007 India

ISBN: (纸本)9798350314045

Recent years have seen an increase in the amount of focus placed on the study of Alzheimer's disease (AD) categorization and prediction. Historically, one would handle such an issue by employing a combination of several types of machine learning algorithms, including regular machine learning algorithms and deep learning algorithms. However, academics are making ongoing efforts to seek for the possibilities of just employing the deep learning method. In order to accurately diagnose this disease, early diagnosis and detection are required. Due to the vast number of people who present themselves with the disease, manual diagnosis by medical professionals is fraught with the possibility of error and is extremely time consuming. The analysis and classification of AD have been approached using a variety of approaches;nonetheless, there is a pressing want for early analysis solutions that are more accurate. The model that was developed as a result of this research provides a deep-learning (DL)-based clarification for the analysis and classification of AD. This solution would make use of the DenseNet-169 &ResNet-50 CNN structures. The AD is categorized in the proposed model as not having dementia, having very mild-dementia, having mild-dementia, and having moderate-dementia. During both the train and the testing stages, the DenseNet-169 demonstrated superior performance. The accuracy values for train &test for DenseNet-169 are respectively 98.7% and 87.36%. In comparison, the accuracy for ResNet-50 were respectively 93.70% and 85.8%. The analysis and classification of AD can be done in real time using the model that was proposed. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Detection of Emergency vehicles in Traffic and Assign Traffic Free Path Using Deep Learning 4

Detection of Emergency Vehicles in Traffic and Assign Traffi...

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4th International Conference on sentiment Analysis and Deep Learning, ICsADL 2025

作者： vikruthi, sriharsha singasani, Tejesh Reddy Ram Pavan Kumar, v.T.M. Nagendrudu, P.v.v.s.D. Raghavendra, Ch sahith, R. B v Raju Institute of Technology Dept of Cse Telangana Medak Narsapur502313 India University of Cumberlands LouisvilleKY United States Kakaraparti Bhavanarayana College Dept of Computer Science Andhrapradesh Vijayawada India Aditya Univeristy Department of Artificial Intelligence and Machine Learning Suramplaem India Andhra Pradesh Guntur India Cvr College of Engineering Dept of Computer Science and Engineering Telangana Hyderabad501510 India

ISBN: (纸本)9798331523923

Too much of Traffic jams are in a highly populated country. sometimes emergency vehicles such as fire-fighter and ambulance get stuck in traffic, putting lives at threat in many cases. Priority should be given to special vehicles and their way should be cleared. However, traffic police find it difficult or sometimes impossible to handle with it. Emergency vehicles have visual and audio warning devices help them to overtake traffic by demanding as little space as possible from other vehicles. Many death happen due to delay in doctors, ambulance and fire engine vehicles while reaching their destinations. The traditional systems didn't provide path free route accurately. Therefore, by using detection of emergency vehicles in traffic and assign traffic free path using deep learning system, emergency vehicles can get free path or fast route by observing the traffic cameras. so, those emergency vehicles can reach the location on time by using RNN system. Therefore, the main objective is to improve this system for better results interms of accuracy, detection rate and precision. © 2025 IEEE.

关键词： Recurrent neural networks

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Wind Driven Optimization-based Medical Image Encryption for blockchain-Enabled Internet of Things Environment

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computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.s.s.Anupama Raed Alsini N.supriya E.Laxmi Lydia seifedine Kadry sang-soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PsNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PsNR of 60.7036 db.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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User-Centric Drink Customization with Cloud-Powered Collaborative Filtering with Raspberry Pi Integration 11

User-Centric Drink Customization with Cloud-Powered Collabor...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Gowriswari, s. Fathima, s shafla Joseph Rajan, D. Antony Mohankumar, N. sasirekha, v. Rajmohan, M. Sri Sairam Engineering College Department of Instrumentation and Communication Engineering Tamil Nadu Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350357

This paper proposes an approach for user-centric drink customization by combining Raspberry Pi technology with cloud-powered collaborative filtering. by using collaborative filtering algorithms housed in cloud architecture, the proposed solution seeks to improve the beverage customization experience. A specialized Raspberry Pi interface allows users to input their preferences and comments, which are automatically included in the collaborative filtering mechanism. With the help of this cloud-based system, user data can be processed and analyzed in real time, enabling personalized drink suggestions. Adding Raspberry Pi devices to a system makes it more responsive and makes data processing more localized, which is great for users. The combination of Raspberry Pi with cloud-powered collaborative filtering shows that it can provide a highly customized and adaptable drink experience. This study adds to the growing body of knowledge in cloud-based collaborative filtering and Internet of Things (IoT) applications, and it provides a potential way to improve customer happiness in the beverage business. © 2024 IEEE.

关键词： Collaborative filtering

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Implementing AI-Powered Chatbots in Agriculture for Optimization and Efficiency 2

Implementing AI-Powered Chatbots in Agriculture for Optimiza...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： bhuvaneswari, C. Pokhariya, Hemant singh Yarde, Pallavi vekariya, vipul Patil, Harshal Natrayan P.S.V.College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Mittapalli635108 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Balaji Institute of Technology and Management Sri Balaji University Department: Technology and Management Maharashtra Pune411 033 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat Waghodia391760 India Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350327533

Agriculture is critical to the global economy, therefore the introduction of automation in this field is now an essential and rising topic internationally. Demands for food and labor are rising at an exponential rate, outpacing the capabilities of conventional farming practices. A new age of revolutionary change has been caused by the incorporation of Artificial Intelligence (AI) into agriculture. This study focuses on the development of an AI-powered Chatbot that is customized for the agricultural sector, offering assistance with crop recommendation and disease detection. The two machine learning models used to handle crop suggestions are Gaussian Naive bayes (GNb) and support vector Machine (svM). Using the vGG-16 transfer learning model, disease predictions are made. The models are evaluated thoroughly, and as a result, GNb and vGG-16 are chosen to be a part of the Chatbot's architecture. Without the need for professional knowledge, the Chatbot can help farmers select crops that are suited for cultivation and evaluate the health of their crops. The designed Chatbot is confirmed to be reliable and effective after extensive testing of its performance through various queries. by utilizing AI, we made a smart tool for effective crop management and harvesting. © 2024 IEEE.

关键词： Agriculture Chatbot Crop suggestion Gaussian naive bayes Plant Disease vGG-16

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Multivariate Time series Forecasting for Cloud Machine Utilization

Multivariate Time Series Forecasting for Cloud Machine Utili...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Archana Naik Kavitha sooda Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The cloud machine utilization prediction using a multivariate time series model is explored in this study. The cloud machine usage data set samples are selected with varied patterns. The long sequence forecasting model Informer is investigated for the forecasting. The model results are benchmarked against LsTM, GRU, Prophet, and XGboost across the diverse datasets. The performance is evaluated using RMsE. Experimental results show that the Prophet model performs best overall due to its ability to capture both seasonal and irregular patterns accurately. The RMsE value for Informer is 0.13, Prophet is 0.097, XGboost is 0.096, LsTM is 0.1725, and GRU is 0.1547.

关键词： Cloud computing Accuracy scalability Time series analysis Predictive models benchmark testing Data models Internet of Things Forecasting Long short term memory

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