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Emerging Trends in engineering and Medical sciences (ICETEMS), International Conference on

作者： Govindaraj Ramkumar Hasina Nasrin M Ramya Jayant Giri N P G Bhavani Ahmad Abdelhafiz Ali Samhan Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of CSE (Data Science) MLR Institute of Technology Hyderabad Telengana India Department of ECE S.A. Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagput India Division of Research and Development Lovely Professional University Phagwara India Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Human action recognition is a pivotal area in computer vision with numerous applications, such as video surveillance, healthcare monitoring, sports analysis, and human-computer interaction. This paper presents a robust methodology, Hybrid Neural Learning Strategy, which integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for effective human action recognition. The proposed approach utilizes the UCF101 dataset, which captures 101 diverse human actions with posture-specific information under various lighting, angles, and environmental conditions. The data preprocessing steps include cleaning, feature extraction using OpenPose, and dimensionality reduction through AutoEncoders. A hybrid architecture combining CNN for spatial feature extraction and LSTM for temporal dependency modeling is employed. The model achieves an accuracy of 93.78%, outperforming traditional and hybrid models, such as CNN, RNN, and AutoEncoder-LSTM models. These results demonstrate the model's efficacy in recognizing complex human actions across various scenarios, making it highly suitable for applications in surveillance, healthcare, and human-computer interaction.

关键词： Human computer interaction Autoencoders Medical services Feature extraction Video surveillance Market research Human activity recognition Convolutional neural networks Long short term memory Sports

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The Prediction of Micro Plasma Impacts of Farm Fresh Vegetables Using Machine Learning

The Prediction of Micro Plasma Impacts of Farm Fresh Vegetab...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Maragatham G K. Sundaramoorthy K. Maheswari T. Sajana Mary Shiba C Ashok Kumar Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Information Technology Jerusalem College of Engineering (Autonomous) Chennai Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Department of Computer Science and Business Systems R. M K Engineering College Chennai Tamil Nadu India Department of Computer Science BanasthaliVidyapith Rajasthan India

In general, the innovative foods produced on fruit and vegetable based farms are always high quality and healthy. Indicators of fruit and vegetable consumption include plasma vitamin C and arytenoids, which are plant pigments discovered in blood samples. The researchers decided to utilize blood samples rather than the more common food frequency questionnaire in their investigation. to assess the amount of food consumed in order to forestall measuring errors and to establish dependencies. Because vitamin C and arytenoids may be found in a wide variety of fruits and vegetables, we can use them as objective measures of our consumption of these food groups. The fact that individuals who do not consume a diet that is abundant in fruits and vegetables do not consume significant quantities of vitamin C and arytenoids is reflected in the plasma levels of these individuals. In this paper a smart machine learning algorithm was proposed to predict the micro plasma impacts. This monitors the regular shape and harvesting of different farm fresh products and predicts the impacts of it. This will helpful for farmers to enhance the harvesting.

关键词： Machine learning algorithms Shape Measurement uncertainty Machine learning Pigments Knowledge discovery Plasmas

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Investigating the Big data Challenges of Deep Learning for data science

Investigating the Big Data Challenges of Deep Learning for D...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Prateek Aggarwal Shweta Singh Krishnan Batri N.T. Velusudha Sunil D. Kale Gajendra Shrimal Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

Deep-gaining knowledge is a subset of synthetic intelligence (AI) that employs superior technology, data mining, and system-gaining knowledge to use massive datasets to find insights and styles. It is an area of advanced computing technology that enables computer systems to get the right of entry to analyze and analyze from many statistics. Even as the ability of deep learning is enormous, where its impact remains uncovered, extensive statistics demanding situations lie at the crossroads of its software and effectiveness. Massive records datasets' high quality and shape are essential for successfully implementing deep studying fashions. There may be an increasing need for highly acceptable statistics, including correctly categorized information, for training deep learning fashions. Moreover, deep studying often requires big datasets to provide correct output. It, therefore, calls for records to be amassed from numerous assets, posing the need for more homogeneous, regular, and standardized information. Adequate statistics garage, processing, and analytic solutions must be followed to analyze massive facts to create reachable and usable datasets for successful deep mastering packages. It includes infrastructure capable of securely keeping a large amount of information, in addition to the cloud-based totally systems to efficiently method extensive quantities of statistics. There has been a shift in the direction of using “large records” technologies like Apache Hadoop, Apache Spark, and different associated libraries. Even as deep mastering holds massive capability for uncovering new insights and turning in gigantic value to fields which include healthcare, finance, enterprise control, and more incredible, the associated massive data demanding situations ought to be addressed for those programs to reach their full capability. Despite the present-day challenges, advances in extensive records solutions like Apache Hadoop and Apache Spark, coupled with advanced analytic

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Ensuring the data Security and Integrity over Cloud Computing Environment using Novel Cipher Strategy

Ensuring the Data Security and Integrity over Cloud Computin...

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IEEE International Conference on engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Praveen Kumar E B. Yasotha Narmadha PG P. Chandrakala C. Ushapriya G Chamundeeswari Department of CSE (IoT) Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Data science and Business systems SRMIST kattankulathur Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning St. Martin's Engineering College Telangana India Department of Electronics and Communication Engineering Saveetha Engineering college Chennai Tamil Nadu India

The advent of cloud computing has revolutionized the Internet. Users may effortlessly collaborate, back up, and access their information from any location thanks to cloud computing. When it comes to providing IT enabled services via the internet, cloud computing is the clear winner. Cloud computing allows a large number of people to share resources like computers, programs, and servers. Users of cloud services often have no idea where their data actually resides or where it came from. Users of cloud computing services are understandably concerned about the safety of their data once it has been uploaded to the cloud with respect to service providers. Privilege control, comprehensive encryption algorithms, management of keys, provenance instances and global consistency verification are only some of the numerous data privacy and security issues that occur as a result. This study introduces a novel cloud security strategy, Cloud based Novel Cipher Strategy (CNCS), and analyzes issues related to the safety and integrity of data stored in the cloud. We're looking into many data safety related factors here. In this article, we'll look closely at a selection of data protection strategies now in use throughout the globe to guarantee the highest level of security possible. Later, the study delves into the various methods of cloud based data security. To prevent unwanted access, alteration, or deletion of dynamic as well as static information, a procedure for validating their integrity is outlined. The proposed system CNCS efficiency is evaluated in the resulting section through the following metrics such as: integrity level, data storage efficiency and security level. All these metrics are evaluated based on the size of data, in which it is tested with respect to number of days the proposed scheme is tested over the industrial environment to prove the performance and efficiency of the proposed approach.

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Enhancing data Visualization in Real-Time with Machine Learning

Enhancing Data Visualization in Real-Time with Machine Learn...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sripriya T Renu Kachhoria Sudhanshu Dev Mahesh TR Nivesh Kalyan Acharjya Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

Statistics visualization is an effective tool for information and communicating insights from massive facts sets. However, giving complicated records in a correct and complete style becomes challenging, mainly when the statistics are dynamic and change rapidly in real time. Device learning may be leveraged to enhance records visualization, making it more particular and comprehensive. This paper discusses the integration of machine learning techniques to enhance data visualization in real-time. The exponential growth of data in recent years has made it challenging for traditional techniques to effectively visualize and interpret data in real-time. Machine learning algorithms offer the ability to analyze and classify data in real-time, providing a more accurate and efficient way to visualize complex datasets. By leveraging machine learning, data visualization can be enhanced by automatically generating visual representations that are tailored to the data and user preferences. This paper presents an overview of the different machine learning techniques that can be applied to data visualization, along with their advantages and limitations. Additionally, a case study is presented to demonstrate the effectiveness of using machine learning for real-time data visualization. Through this study, it is shown that the integration of machine learning can significantly improve the speed, accuracy, and usability of data visualization in real-time scenarios. By using including the strength of machine learning to statistics visualization, such methods can permit quicker and deeper insights into records, taking into consideration higher selection making within the modern-day international wherein facts modifications swiftly.

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Expression of Concern for: Discovering Innovative Applications for Real-Time data Analysis with Machine Learning

Expression of Concern for: Discovering Innovative Applicatio...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Prranjali Jadhav Raja Praveen K N Prateek Aggarwal Sandeep Gautam Preeti Naval D. Nirmala Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This paper investigates the capability for discovering modern programs for actual-time information analysis the use of system learning. We will take a look at the technology and strategies used in system getting to know and talk their adaptive potential to interpret and analyze dynamically changing facts inputs. Especially, we can examine existing technological methods to understand the capacity of AI to discover creative solutions to complicated challenges. We will also explore capability techniques to contain gadget gaining knowledge of into present real-time records analysis platforms and architectures. Finally, we are able to speak the programs of machine studying in real-time records evaluation and its potential to allow fast and correct decision making. This paper gives a theoretical framework to apprehend and harness the power of device gaining knowledge of to investigate and interpret real-time facts in modern approaches.

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Exploration of Contour Coding and Its Application in Digital Image Processing

Exploration of Contour Coding and Its Application in Digital...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Little femilin Jana D Prema S. Kadam Neeraj Sharma Avni Garg Kalyan Acharjya Venkadeshwaran K Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Electrical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India

Photograph processing is a discipline of laptop science that concentrates on manipulating digital photos and using algorithms to recognize and beautify the photo's quality. Contour coding is a powerful approach used in virtual picture processing that allows one to increase, shop, and examine virtual snapshots from a combination of strategies like aspect detection, morphological processing, and segmentation. Contour coding aims to reduce complex virtual pix to a fixed of fundamental symbolic features that may be created, stored, and manipulated without loss in quality or element. Applications of contour coding are specially used for item reputation, segmentation, spatial pattern reputation, and scientific photograph processing. This newsletter offers the theory and fundamentals of contour coding and explores its software in actual-world virtual picture processing scenarios. We speak about the significance of contour coding and present a short evaluation of existing algorithms and improvement techniques used to achieve virtual photograph processing desires. We additionally gift the advances and challenges of contour coding and how it could form the future of digital picture processing.

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Relay-Assisted Connectivity Management for Underwater High Speed Communication Networks

Relay-Assisted Connectivity Management for Underwater High S...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Girija Shankar Sahoo G. Renuka Swapnil K. Shinde Krishnan Batri Zeeshan Ahmad Lone Ayaan Faiz Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Relay-assisted connectivity control for underwater high-pace verbal exchange networks improves verbal exchange velocity and reliability by connecting Wi-Fi networks to relays. By allowing communication among unique networks, it enables higher insurance, higher air interface, higher interference immunity, and higher network resilience. With underwater excessive pace verbal exchange networks, relays are used to ensure reliable operation to increase the networks' transmission range. Such relay-assisted verbal exchange networks can offer higher records costs than a single factor-to-factor link should provide. They can help to reduce the effect of interference that affects the speed of conversation over lengthy distances. This generation offers some benefits: extended latency, stepped forward bandwidth usage, accelerated community topology, improved fault tolerance and robustness, better statistics synchronization, and quicker handover in multi-radio networks. Additionally, with the help of relay-assisted verbal exchange networks, such networks' deployment, and operation costs can be decreased substantially.

关键词： Costs Interference Spread spectrum communication Routing Robustness Topology Synchronization

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New Approach for Efficient Malicious Multiparty Private Set Intersection

SSRN

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SSRN 2023年

作者： Lv, Siyi Wei, Yu Jia, Jingyu Li, Xinhao Liu, Zheli Chen, Xiaofeng Guo, Liang College of Cyber Science The College of Computer Science Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin China Department of Computer Science Purdue University United States School of Cyber Engineering Xidian University Xi’an China Huawei Technology Co. Ltd Shenzhen China

The Multiparty Private Set Intersection (MPSI) protocol allows untrusted parties to compute the intersection of their sets without revealing any additional information. However, existing solutions face challenges in terms of security and efficiency. Some schemes are secure only against semi-honest adversaries and cannot effectively defend against malicious adversaries. Other schemes, while capable of withstanding malicious adversaries, exhibit efficiency problems, especially as the number of corrupt participants increases. A practical solution must maintain high security and efficiency, even when handling large-scale data and a majority of corrupted *** paper introduces a novel scheme named TH-PSI, built upon a new star-shaped topological network communication framework based on the trusted execution environment. TH-PSI has been rigorously proven to be secure within the standard UC framework against malicious adversaries. To demonstrate the efficiency of TH-PSI, we implement the TH-PSI scheme and tested it in both LAN and WAN settings, involving up to 30 participants and datasets as large as $2^{22}$. We compare TH-PSI with state-of-the-art schemes, ENOC21 and NTY21. Our experiments show that TH-PSI is an efficient solution capable of delivering results within a matter of seconds, even when dealing with data at the million-level and a majority of corrupted participants. © 2023, The Authors. All rights reserved.

关键词： Efficiency

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An Investigation into the Effectiveness of DNS-Based Authentication for Wireless Networks

An Investigation into the Effectiveness of DNS-Based Authent...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Ashu Katyal Rajesh Pandian Ritu Sharma T. Surulivel Rajan Nakul S. Sharma Vaishali Singh Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

the security of Wi-Fi networks is turning into an increasing number, significant with the growth of Wi-Fi technology. To prevent malicious entities from connecting to the network, getting admission to manipulate mechanisms was advanced, which might be based totally on authentication. Conventional authentication mechanisms like passwords are prone to brute-pressure attacks. Numerous options, such as dynamic DNS-primarily based authentication, have been proposed to provide stronger authentication. This research paper investigates the effectiveness of Domain Name system (DNS)-based authentication for wireless networks. The DNS is a critical component of the internet infrastructure and is used to translate domain names into IP addresses. With the rapid growth of wireless networks and the increasing popularity of devices that connect to the internet, securing these networks has become a major concern. Traditional methods of authentication, such as username and password, have proven to be vulnerable to various attacks. This study aims to explore the potential of DNS-based authentication in addressing these security concerns. Through a literature review and analysis of existing authentication protocols, this paper evaluates the strengths and weaknesses of DNS-based authentication. The research also includes a survey of wireless network users to gather their insights and experiences with current authentication methods. The results of this study will provide valuable insights into the effectiveness of DNS-based authentication and its potential as a secure method for wireless network authentication. The effects imply that DNS-primarily based authentication has several wonderful blessings over traditional approaches and may be a powerful mechanism for securing wireless networks.

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