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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

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Trend-Forecasting of Covid-19 Cases using Regression Anlaysis and Python 15

Trend-Forecasting of Covid-19 Cases using Regression Anlaysi...

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15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rao, M. Lakshmana Kumar, B. Raghu Kalyanamanohar, V. Gowd, G. Harinath Mechanical Engineering Department Madanapalle Institute of Technology & Science Madanapalle India Mechanical Engineering Department Prasad V Potluri Siddhartha Institute of Technology Vijayawada India Mechanical Engineering Department University College of Engineering JNTU Kakinada India Mechanical Enigeering Department Vemu Institute of Technology India

ISBN: (纸本)9798331300579

The objective of this paper is to compare the registered cases and predicted cases of covid-19 using regression analysis. The effect of covid-19 is more on society. If there is any 4th wave or 5th wave of covid, we would suffer even more. So, to prevent this disaster, Author has attempted in estimating number of covid cases. Here problem taken from the state government. Using forecasting techniques and compared predicted and real values. Then developed a code through python programming. Through this analysis, it has been observed that there is a good agreement between the analytical and programmed trend. Trend forecasting algorithm was developed through python coding. © Grenze Scientific Society, 2024.

关键词： COVID-19

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Real-time Human Action Recognition using Deep Convolutional Neural Networks 15

Real-time Human Action Recognition using Deep Convolutional ...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vignesh, R. Balaraju, J. Poongothai, P. Rajput, Vaishali Rajkumar, N. Pratheep, V.G. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Karunya Nagar Coimbatore India Anuraag University Department of Cse Hyderabad India Department of Computer Science Bangalore India Vishwakarma Institute o Technology Computer engineering Pune India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Cse Thiruvalluvar Tamil Nadu Avadi India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India

ISBN: (纸本)9798350370249

current advances in deep gaining knowledge of have enabled automated systems to understand human moves from movies in actual-time. Deep convolutional neural networks (DCNNs) had been used to allow accurate and efficient real-time movement reputation. with the aid of leveraging the statistics-driven illustration mastering capability of DCNNs, movement popularity has become a powerful device for video analysis applications including robotics, surveillance, entertainment, augmented truth, and healthcare. DCNNs use an aggregate of densely connected convolutional layers and in addition completely linked layers for function extraction from films. The proposed architectures are usually skilled using supervised learning. Convolutional layers are used to understand nearby abstractions in motion pictures, which in turn are used to research worldwide features. diverse techniques together with temporal segmentation, movement-based characteristic extraction, and feature switch gaining knowledge of are also hired by means of DCNNs for achieving higher performance. recent works have investigated exceptional techniques to enhance the accuracy and speed of action reputation the use of DCNNs. This consists of techniques which includes model compression, decoupled inference networks, and efficient convolutional networks. By using combining those strategies, it's far viable to acquire higher popularity accuracy at the same time as at the equal time preserving an excessive computational speed. © 2024 IEEE.

关键词： Computational Equal Excessive Gaining Research Speed

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The Confluence of Stream Data and Quantum Mechanics in Modern Research

The Confluence of Stream Data and Quantum Mechanics in Moder...

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Ramya, S. Ramesh, P. Shrivastava, Anurag Srividya, Mudunuru Nagarajan, Ramavenkateswaran Raj, A. Bheem School of Engineering and Technology Sapthagiri Nps University Bengaluru India Cmr Institute of Technology Department of Eee Bengaluru India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Mlr Institute of Technology Department of Csit Hyderabad India Rv College of Engineering Department of Ece Bengaluru India St. Martin's Engineering College Department of It Secunderabad India

ISBN: (纸本)9798350388800

In the ever-changing field of research, stream data analysis and quantum physics relate in fascinating ways. They process, analyze, and secure data realistically. This article explains the strange links between two apparently unconnected fields. New quantum-based approaches, algorithms, and protocols are also discussed to solve data-flow difficulties. 'Quantum-Enhanced Stream Data Analysis,' recommended for this study, is crucial. It analyses live data using quantum concepts, making it more accurate, quicker, and better. This technology employs secure protocols, feature extraction, grouping, predictive analysis, and quantum data preparation to make stream data safer and more usable. The Quantum k-Means Algorithm groups data in real time, and the QSVM predicts quickly. Quantum security protects data during transmission and processing. These approaches solve digital data stream issues when applied properly. Data science has advanced with stream data and quantum physics. Quantum algorithms let computers make faster, more accurate decisions from large data sets. Quantum security protects sensitive data against unauthorized access. As quantum physics and stream data processing advance, new data organization, storage, and analysis technologies will emerge. © 2024 IEEE.

关键词： Data streams

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Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Jagannathan, J. R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India

ISBN: (纸本)9798350388602

The aim of this work is to examine the application of FHE-SMPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SMPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SMPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding. © 2024 IEEE.

关键词： Cloud security

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Predicting Diabetes Risk: A Comparative Analysis of Machine Learning Algorithms 1

Predicting Diabetes Risk: A Comparative Analysis of Machine ...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Thakur, Hardeo Kumar Sahu, Dinesh Prasad Chaturvedi, Rajnish Kumar Jha, Govind Kumar Kumari, Divya School of Computer Science Engineering and Technology Bennett University Greater Noida India Government Engineering College Department of CSE Bihar Munger India USICT GGSIPU Delhi India

ISBN: (纸本)9798331518578

Nowadays, diabetes mellitus is a worldwide health concern that is becoming more common. Therefore, for better early detection techniques (Machine Learning) is need of hour. Six machine learning algorithms are applied in this article to forecast the likelihood of diabetes in three stages: prediabetes, diabetes, and no diabetes. Three metrics i.e., recall, F1 score and precision are applied to evaluate the algorithms performance. Additionally, we have used the Synthetic Minority Oversampling Technique (SMOTE) for improving class imbalance. The results show that different algorithms and stages of diabetes function differently, which emphasizes the necessity of choosing algorithms according to the demands of certain applications. © 2025 IEEE.

关键词： Contrastive Learning

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Enhancing Healthcare Data Security in Cloud Computing Systems through Advanced Homomorphic Encryption Techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Sudha, I. Ramesh, P.S. Ponmalar, A. Jagannathan, J. Pandiarajan, Rajkumar Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India

ISBN: (纸本)9798350388602

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient's data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems. © 2024 IEEE.

关键词： Electronic health record

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Enhancing Medical Image Diagnosis with Convolutional Neural Networks 2

Enhancing Medical Image Diagnosis with Convolutional Neural ...

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2nd IEEE international conference on advances in Information technology, ICAIT 2024

作者： Mahadevan, K. Pushparani, S. SashiRekha, K. Sivagami, V.M. Usharani, R. Department of Computer Science and Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Chennai India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798350383867

This paper is a comparative analysis of medical image diagnosis algorithms with Convolutional Neural Networks (CNNs) and other methods;such as Support Vector Machine (SVM), Random Forest, and k-nearest Neighbors (k-NN) The study performs evaluations on datasets with multimodalities like medical images and diagnostic and various imaging modalities. Results shows that CNNs have the upper hand over traditional, static algorithms across several metrics that assesses the performance of the system such as accuracy, precision, recall, and F1. In particular, CNN precision was 90.5% while the recall was 93.7%, and the accuracy was 92.1% as well as F1-score 92.0%. In addition, the high area under the Receiver Operating Characteristic curve (AUC-ROC) of over 0.95 for the CNN also signed for its excellent discriminatory power. These outcomes only exemplify CNN usefulness in the diagnostic area because it is an exact and accurate method for analyzing medical images. Although the nets CNNs are deployed in clinical cases may help address concerns regarding interpretability, compliance with regulations, and ethical opinions, such issues still exist and require further exploration. Overall, this research takes the first steps in directing algorithmic approach towards medical diagnosis and show the feasibility in deployment of CNNs as inseparable pieces of equipment in modern healthcare. © 2024 IEEE.

关键词： Random forests

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Predicting Reservoir Resource Utilization Using Machine Learning

Predicting Reservoir Resource Utilization Using Machine Lear...

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2024 IEEE international conference on Recent advances in science and engineering technology, ICRASET 2024

作者： Bhavya, B.M. Ravikiran, V. Pes College of Engineering Mandya Sri Siddhartha Academy of Higher Education Department of Mca Tumakru572107 India Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Department of Cse Tumakru572107 India

ISBN: (纸本)9798350388602

The key issues in Reservoir Usage are the Computation of Reservoir Storage and Prediction of Water Levels using Machine Learning. The research integrates complex underground water techniques with AI to forecast water level trends in related reservoirs. and the application of Artificial Intelligence (AI) and Machine Learning (ML) algorithms, including Bayesian Networks (BN), Decision Trees (DT), k-nearest Neighbors (kNN), and Support Vector Machines (SVM), Artificial Neural Networks (ANN), Rule-Based optimization, Long Short-Term Memory networks (LSTM) for handling heterogeneous data to provide solutions. Quantifying reservoir operations is an essential process for the efficient management of water resources. Water level records are often the only data available for reservoir operation analysis, which causes an ill-posed problem in water resource system planning. This study proposes a framework to estimate reservoir operations using machine learning models and reasoning carefully derived from expert knowledge and soft data. © 2024 IEEE.

关键词： Long short-term memory

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Plant Care AI an AI Based Solution for Plant Disease Diagnosis and Assistance -A Comprehensive Survey

Plant Care AI an AI Based Solution for Plant Disease Diagnos...

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2024 international conference on advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Kulkarni, Abhijit Verma, Seema Shingane, Tanish Ghogare, Snehal Dhupe, Shivkanya JSPM's Rajarshi Shahu College of Engineering Computer Science and Business Systems Pune India

ISBN: (纸本)9798350388800

The field of plant care has seen significant advancements with the increasing application of artificial intelligence (AI) and machine learning (ML) techniques across various domains. This review explores the integration of deep learning and machine learning with image recognition technologies for plant disease identification and treatment. The aim of the proposed project is to develop a comprehensive web application tailored for plant care, featuring automated plant disease detection, treatment recommendations based on identified conditions, suggestions for nearby nurseries to acquire necessary materials, and chatbot support for user inquiries. By leveraging advanced image recognition methods, the proposed system seeks to enhance the efficiency and accessibility of plant maintenance practices. Additionally, the inclusion of Google Maps facilitates seamless navigation to local nurseries, improving the user experience and promoting proactive plant care management. This study provides an overview of current methods, discusses potential opportunities and challenges, and proposes a framework for developing an intelligent plant care system to address the evolving needs of horticulturists and farmers. © 2024 IEEE.

关键词： Deep learning

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Lunar Exploration Robot Mobility and Stability: A Comprehensive Analysis and Simulation Study

Lunar Exploration Robot Mobility and Stability: A Comprehens...

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2024 IEEE international conference on Control science and Systems engineering, ICCSSE 2024

作者： Xu, Yansong Li, Junlin Luo, Haitao Dong, Yuming Wang, Yaowei Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China University of Chinese Academy of Sciences Shenyang China School of Mechanical Engineering Shenyang University of Technology Shenyang China

ISBN: (纸本)9798331517199

This paper conducts an in-depth study on the mobility and stability of lunar exploration robots in the complex lunar environment. By defining the robot's primary geometric parameters and two typical motion configurations, the paper analyzes the distribution of wheel loads, mobility performance, and stability performance. Specifically, considering the characteristics of the soft lunar soil, a contact dynamics model between the wheels and the soft lunar soil is established. The discrete element method (DEM) was used to simulate the lunar soil surface, and multi-body dynamics and DEM coupled simulation experiments were conducted. The experimental results indicate that the robot's mobility and stability are significantly affected by the height of the center of mass and ground clearance. The analysis of traction torque, sinkage, and slip ratio further verifies the accuracy and practicality of the model. This research provides theoretical foundations and technical support for the design and operation of lunar exploration robots. © 2024 IEEE.

关键词： Wheels

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