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'An Automated social Distance Monitoring Alarm system based on Human structure Using video surveillance in COvID-19 Pandemic by AI Techniques, A Review' 7

'An Automated Social Distance Monitoring Alarm System based ...

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7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021

作者： Kattirnani, suvarna. L. Maidargi, Prasanna bajantri, sandeep C Jahagirdar, Md Wasim. M. bidari, vasant BLDEA'S V.P.DR.P.G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Vijayapur 586101 India

ISBN: (纸本)9781665428491

In the fight against the corona virus (COvID-19), to ease the spread of the disease social distancing is very effective measure. In this pandemic situation people staying at home will flatten the curve. To meet the basic needs of the people, many Customers from manufacturing and pharmaceutical Industrial are going to work every day. In this project, an AI-enabled social Distance detecting tool that can detect it people are keeping a safe distance from each other by analyzing a real time video streams from the camera and also from recorded videos. © 2021 IEEE.

关键词： Python

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security for wireless sensor networks using cryptography

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Measurement: sensors 2023年 29卷

作者： Ramadevi, P. Ayyasamy, s. suryaprakash, Yalla Anilkumar, Chunduru vijayakumar, s. sudha, R. Institute of Aeronautical Engineering Dundigal Hyderabad India School of Computer Science and Engineering VIT University Vellore Campus India Department of Computer Science and Engineering MVGR College of Engineering Andhra Pradesh Vizianagaram India Department of Information Technology GMR Institute of Technology Rajam Andhra Pradesh Vizianagaram India Department of English B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai48 India Department of Computer Science and Engineering Vasavi College of Engineering Telangana Hyderabad India

WsNs (Wireless sensor Networks) are becoming more and more capable with each passing day. Widespread usage of WsNs is helping mitigate many dangers. Protecting these networks from risks need proper security measures. This paper offered a security strategy that makes use of IEKC (Improved Elliptic Key Cryptography). Data encryption and decryption are accomplished via IKEC. A major feature of this technique is that node's source codes maintain Private Keys which protect other keys in non-volatile memories. The suggested technique prevents replay, Dos and sybil attacks. In this node, cluster chiefs are chosen using CsO (Chicken swarm Optimization) and nodes are organised according to network size. According to the findings of the simulation, this security mechanism effectively counters different attacks with fewer resources, and it also significantly strengthens the network's resistance to node capture. Results from experiments show that the suggested model is effective regarding PDR (packet delivery ratio), end-to-end latency, throughputs, and energy consumptions. © 2023

关键词： sensor nodes

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strategic and sustainable ERP Decision Framework: Integrating Multi-Criteria Analysis with Deep-A-CNN-LsTM Models in Corporate strategy

Strategic and Sustainable ERP Decision Framework: Integratin...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： suman Dahiya Nawab Akram K.srinath Yadav T. Manikumar Thulasimani T Neha barodia Universal AI University Maharashtra India Magadh Professional Institute Danapur Patna Bihar India V.S.B Engineering College Karur Tamilnadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Deemed to be University Krishnankoil India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Tamilnadu India Department of Humanities and Social Sciences Graphic Era (Deemed to be) University Dehradun India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Customizing enterprise resource planning (ERP) software to meet a company's unique operational requirements is a crucial consideration throughout implementation. It offers a framework in this post to help management make decisions about customization choices and the capabilities needed to implement them. This structure allows us to identify several openings for tailoring the ERP system and company processes. A preprocessing step, feature extraction, and training the model make up the three phases of the said approach. The preprocessing steps include extracting summary metrics and performing temporal principal component analysis (PCA). One method for extracting features is Kernel Principal Component Analysis, or Kernel PCA. During the training process, a Deep-A-CNN-LsTM was utilized. With an average accuracy of 92.46%, the suggested method outperforms LsTM and CNN.

关键词： Training Measurement Accuracy Feature extraction Enterprise resource planning Convolutional neural networks Kernel Long short term memory Principal component analysis Investment

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Detection of Knee Osteoarthritis from Magnetic Resonance Imaging Using a 3-D Independent Component Analysis Method in Machine Learning 1st

Detection of Knee Osteoarthritis from Magnetic Resonance Ima...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Karve, swagat Dhope, Tanuja satish Kaushal, Rajesh Kumar, Naveen Chippalkatti, Pranav Jadhav, Akshay S. B. Patil College of Engineering Maharashtra Indapur India College of Engineering Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Pune India MIT College of Railway Engineering Maharashtra Barshi India

ISBN: (纸本)9783031860683

Osteoarthritis (OA) is a widespread condition without a currently available treatment, significantly affecting individuals’ quality of life as this is a progressive condition that deteriorates with time, frequently leading to persistent Joint pain and stiffness can become severe enough to make daily tasks difficult. Magnetic resonance (MR) images are commonly employed in OA diagnosis, where medical experts assess changes, especially within the tibio-femoral cartilage compartment for knee OA. This study presents a new diagnostic technique for knee OA that detects the illness from MR images using a support vector Machine (svM) algorithm. Our suggested method employs the Independent Component Analysis (ICA) technique on 3-D MR imaging data from a real-world cohort. The experimental results show that our ICA-svM machine learning model obtained 86% testing accuracy and 72% specificity. and sensitivity of 100%, having been trained on a limited MR image dataset. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： support vector machines

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From Clicks to Conversions: Analysis of Traffic sources in E-Commerce

arXiv

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arXiv 2024年

作者： Muralidhar, Amrutha Lakkanna, Yathindra Dept. of Computer Science and Engineering B.M.S. College of Engineering Bengaluru India Dept. of Fashion and Lifestyle Accessory Design National Institute of Fashion Technology Bengaluru India

Over the past years, e-commerce platforms have expanded substantially, providing customers with convenient shopping experiences. To enhance e-commerce websites, it's essential to grasp user engagement and factors affecting conversion rates. This optimization is achieved by aligning platforms with user expectations, thereby fostering successful online shopping experiences, and contributing to sustained growth in the dynamic digital marketplace. In this paper, we conduct a comprehensive analysis focusing on user interactions, conversion metrics, and the entire user journey within an e-commerce platform. Our exploration spans exit rates and sessions across different devices and browsers, conversion rates for various traffic sources, and the user journey from product details to checkout. Findings suggest a need for targeted improvements in mobile optimization and browser compatibility, indicated by higher exit rates on mobile devices and varying rates on different browsers. The conversion rate analysis emphasizes the varying effectiveness of traffic sources, highlighting the potential of advertised mediums, while identifying areas for improvement in referral and affiliate traffic. Examining the user journey showed potential bottlenecks in the conversion process, the identified gap was between user interest and completed transactions. Our study suggests improving the checkout process strategically to streamline user transactions. These insights provide actionable guidance for businesses seeking to refine their platforms and optimize performance in the ever-evolving landscape of e-commerce. © 2024, CC bY.

关键词： behavioral research

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Twitter sentiment Classification with Deep Learning LsTM for Airline Tweets 8

Twitter Sentiment Classification with Deep Learning LSTM for...

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8th International Conference on Advanced Computing and Communication systems, ICACCs 2022

作者： Lakshmanarao, A. srisaila, A. Kiran, T. srinivasa Ravi Department of Information Technology Aditya Engineering College Surampalem India Jawaharlal Nehru Technological University Kakinada East Godavari District Kakinada India V.R Siddhartha Engineering College Department of Information Technology A.P Vijayawada India P.B. Siddhartha College of Arts & Science Department of Computer Science A.P Vijayawada India

ISBN: (纸本)9781665408165

As a result of increase in internet usage, there is a massive amount of information available to web users, as well as a massive amount of new information being created daily. To facilitate internet pick-up, trading ideas, and disseminating assessments, the internet has evolved into a stage of large volumes of data. Facebook, and Twitter generate a lot of data every day. As a result, text handling is crucial in making decisions. sentiment analysis has surfaced as a method for analyzing Twitter data. In this paper, we collected a Kaggle dataset with airline tweets. It contains three variants of tweets: neutral, positive, negative. First, we used NLP methods to clean the text data. Later, we applied RNN, LsTM, stacked LsTM, bidirectional LsTM, and GRU techniques for classifying tweets in three different ways: positive vs negative sentiment analysis, neutral vs positive sentiment analysis, and neutral vs negative sentiment analysis. We achieved an accuracy of 93% for the classification of positive and negative airline reviews. We achieved accuracy values of 84.5%,83.8% for neutral vs positive and neutral vs negative tweets. The results show that, the proposed RNN/LsTM/GRU model performed well for sentiment classification. Keywords-sentiment analysis, Tweets, RNN, LsTM, GRU. © 2022 IEEE.

关键词： sentiment analysis

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Machine Learning and Deep Learning framework with Feature selection for Intrusion Detection

Machine Learning and Deep Learning framework with Feature Se...

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2022 International Conference on Communication, Computing and Internet of Things, IC3IoT 2022

作者： Lakshmanarao, A. srisaila, A. Ravi Kiran, T. srinivasa Aditya Engineering College Department of It Surampalem India Jawaharlal Nehru Technological University Kakinada Department of It East Godavari District Kakinada India Department of Information Technology V.R Siddhartha Engineering College A.P. Vijayawada India Department of Computer Science P.B. Siddhartha College of Arts & Science A.P. Vijayawada India

ISBN: (纸本)9781665479950

Increases in the size of the network and associated data have been a direct effect of technological breakthroughs in the technology and communication areas. As a result, new types of assaults have emerged, making it more difficult for network security systems to identify potential threats. An intrusion Detection is a critical cyber security method that keeps track of the progress of the network's software or hardware. In order to keep up with the ever-increasing rate and diversity of cyber threats, researchers have turned to machine learning approaches to build intrusion detection systems (IDs). Using machine learning algorithms, it is possible to identify with high precision the major differences between normal and abnormal data. In this paper, we proposed three feature selection techniques followed by machine learning and deep learning for IDs. We collected two different datasets and used the ANOvA F-value based method, impurity-based feature selection, and mutual information-based techniques for identifying the best features. Later, we applied three ML algorithms K-NN, Decision Trees, Logistic Regression, and Deep Learning Feed Forward Neural Networks on two datasets and achieved an accuracy of 88%, 99.9% with feed forward neural networks. The results shown that our model performed well compared to conventional methods. © 2022 IEEE.

关键词： Feedforward neural networks

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sDN enabled role based shared secret scheme for virtual machine security in cloud environment

Cyber Security and Applications

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Cyber security and Applications 2024年 2卷

作者： Anitha, H.M. Jayarekha, P. sivaraman, Audithan Mehta, Ashima v, Nalina Department of Information Science and Engineering B.M.S College of Engineering Bengaluru India Department of Information Technology Netaji Subhas University of Technology New Delhi Jafferpur India Department of Computer Science and Engineering Dronacharya College of Engineering Gurugram India

Cloud computing has given a new direction towards the usage of resources based on the demand without depending on the location. Even though there are many advantages with cloud computing there are challenges and security being one among them. Especially at Infrastructure as a service (Iaas) level, where the actual resources are shared, security has given more importance. Robust access control mechanisms are to be applied to safeguard the cloud environment. In this paper, software Defined Network (sDN) enabled role-based access control along with trust-based model is proposed. This model considers the roles of the users and provides the finely grained access to the virtual machines in the cloud. secret shares are shared fairly among the users based on two parameters namely trust and roles assigned to the participants. secret has to be reconstructed to access the virtual Machine (vM) and to reconstruct, the secret shares are taken from different trusted users at different levels. Cloud service provider will be unaware of the secret shares which are distributed among the participant users. sDN Controller is responsible for taking care of share generation, distribution of shares among the user participants and reconstruction of secret. In order to avoid malicious user participants, trust evaluator periodically checks the trust value. The security analysis prove that scheme is more secure and efficient in comparison with other approaches. © 2024

关键词： Cloud computing Role based access control secret sharing software defined network Trust evaluation module

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Enhancing Fake Review Detection: A Hierarchical Graph Attention Network Approach Using Text and Ratings

Enhancing Fake Review Detection: A Hierarchical Graph Attent...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： T. Anuprathibha Rvs Praveen Jayanth H P sukumar G. suganthi T Ravichandran Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Digital Engineering and Assurance Hyderabad Telangana India Department of Commerce and Management St. Claret College Autonomous Bengaluru Karnataka India Department of Computer Science and Engineering Nandha Engineering College (Autonomous) Erode Tamilnadu India Department of Mathematics Sona College of Technology Salem Tamil Nadu India Department of Artificial Intelligence and Data Science Akshaya College of Engineering and Technology Coimbatore India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The establishment of trusting connections between buyers and sellers is essential in electronic commerce (EC) since it paves the way for mutual benefit. Users can research a business's reputation before deciding to do business with them. There are some aspects of the reputation system that are unflattering to both vendors and clients. For instance, as a consequence of their dishonesty or lack of effort while posting reviews, some users may create phantom feedback, which seems as fabricated assessments meant to boost their *** proposed approach incorporates stage preprocessing, feature extraction, and model training. In order to prepare the data for the sA job, the pre-processing phase incorporates initial procedures that aid in data transformation. One way to improve the model's accuracy is to use features extraction to remove irrelevant data points. We kept using the HGAN model throughout training. This state-of-the-art method outperforms svM and LsTM with an average accuracy of 91.26%.

关键词： Training support vector machines Accuracy Reviews Phantoms Feature extraction Data models Electronic commerce Long short term memory business

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Cross-Layer Optimization for Wireless systems Using computer vision Methods

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Indira, D.N.v.s.L.s. sobhana, M. Ram, M. sitha Ganiya, Rajendra Kumar Rao, J. Nageswara berhanu, Afework Aemro Department of Information Technology Seshadri Rao Gudlavalleru Engineering College AP Gudlavalleru521356 India Computer Science and Engineering V.R. Siddhartha Engineering College Kanuru Vijayawada India India Department of Information Technology Vignan's Institute of Information Technology AP Visakhaptnam530049 India Department of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering AP 521230 India Department of Environmental Engineering College of Biological and Chemical Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

Ad hoc network nodes are aggregate data packet from different environment;there is multiple path communication causing the sudden energy depletion in network. This type of energy loss can lead to failure of connectivity between the two intermediate nodes. If link gets failure, then it has frequent loss of data packets. Less energy nodes do not classify data from the network structure. It reduces packet delivery ratio and increases the energy consumption. The proposed cross-layer method for data agglomeration (CLA) is designed to organize the data packet frequently among the various communication routes;the nodes in the path can able to proceed packet organization for the support of cross-layer scheme. Magnificent path discovery algorithm is constructed to offer the better packet collection route to target node. This process uses multisource node with multiple path for packet transmission in network. It minimizes the energy consumption and increases the packet delivery ratio. The simulation parameters are delay, detection efficiency, energy consumption, network lifetime, and packet delivery ratio. © 2023 D. N. v. s. L. s. Indira et al.

关键词： Energy utilization

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