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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Dhinakaran, M. Sundhari, M. Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Galgotias College of Engineering & Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In the age of cloud computing, data stored and processed in the cloud must be secure. This study examines how powerful machine learning can secure cloud computing data. Three trials assessed different machine learning models in this setting. Experiment 1 used a Random Forest model and achieved 95% accuracy, 0.92 precision, 0.96 recall, and 0.94 F1 Score. This shows that the model can accurately categorize security threats with a good balance of true and false positives. A Deep Neural Network (DNN) improved accuracy to 97% in Experiment 2. Precision, recall, and F1 Score values of 0.94, 0.98, and 0.96 demonstrate the DNN's ability to discriminate threats from normal activity. The model captures complicated patterns well, making it a powerful cloud security tool. Security analysis using reinforcement learning, specifically Q-learning, was introduced in Experiment 3. The model's 88% detection rate showed its capacity to identify threats, but its 0.05 false positive rate created a tradeoff between true and false positives. With the 0.12 false negative rate, one can infer improvements in threat detection accuracy. These results indicate that state-of-the-art machine learning is capable of protecting cloud data. The Random Forest and Deep Neural Network models have very high accuracy balanced with reasonable precision-recall trade-offs, whereas reinforcement learning through Qlearning shows promise but needs modification to improve the model's performance in terms of both accuracy and false positive rates. While rising threats need constant adaptation and learning, the model should also fulfill security needs. This study helps formulate a secure cloud computing infrastructure that can stand threats of change. © 2024 IEEE.

关键词： Cloud Computing Data Security Machine learning Q-Learning

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Comparison Between Gramian Angular Fields (GAF) and Markov Transition Field (MTF) Images Data by Using them as Input to the Deep Learning Neural Network Solar Flare Production Platform

Comparison Between Gramian Angular Fields (GAF) and Markov T...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Nagem, Tarek A M Hamad Alshareef, Sohil Mohamed, Abdelhafid Gihedan, Akram Albargathe, Salma Libyan British University Dept. Computer System Design Benghazi Libya University of Benghazi Dept. Software Engineering Benghazi Libya College of Computer Technology Dept. Software Engineering Benghazi Libya Darna University Dept. Computer Science Algoba Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

The prediction of solar storms from real-time satellites data is an essential to protect various aviation, power, and communication infrastructures. For this reason, current research interest is focusing on creating systems that can predict solar fare accurately. This paper combers Gramian Angular Fields (GAF) images and Markov Transition Field (MTF) images that have been generated by converting pre-flare Geostationary Operational Environmental Satellite (GOES) data from 2010 to 2016. After that, the deep learning neural network for the solar flare production platform accepts both MTF and GAF images as input to generate the predictions. Furthermore, this paper;investigated using MTF and GAF images spritely as input to the solar flare production platform. After that, the results of MTF and GAF images were compared using various prediction verification measures. © 2022 IEEE.

关键词： Forecasting

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Disclosure Privacy Information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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Comparative Analysis of RNN Strategies for Image Caption Generation: Evaluating Accuracy and Efficiency Across Feature Extraction Models 1

Comparative Analysis of RNN Strategies for Image Caption Gen...

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1st IEEE International Conference on Advances in Computing, communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Pravinkumar, Savani Dharti Narasiman, V. Khusru Akhtar, Md. Amir Maheshwari, Divya Manjubhashini, M. Chandigarh University Dept. of CSE Chandigarh India Madhav University Dept. Physical Education Pindwara India Madhav University Dept. Mechanical Engineering Pindwara India Usha Martin University Dept. of Computing & IT Jharkhand India Mangalayatan University Dept. of Computer Eng. & Applications Aligarh India Meenakshi A.H. Edu. & Research Dept. of Visual Communication Chennai India

ISBN: (纸本)9798350356816

Researchers in the Artificial Intelligence department have long held a strong interest in image caption generation. This area proves invaluable across fields such as robotic vision and business, where training computers to accurately describe images or environments is essential. Over the years, achieving this task has posed a significant challenge within artificial *** on various RNN strategies and analyzing their influence on phrase production, this study introduces multiple deep neural network models designed for generating image captions. Additionally, the study includes the creation of captions for sample photos and compares several feature extraction and encoder models to determine which achieves the highest accuracy in caption generation. © 2024 IEEE.

关键词： Neural network models

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Multi-Hop Data Delivery in Hybrid Self-Organized Networks for Mountain Rescue 12

Multi-Hop Data Delivery in Hybrid Self-Organized Networks fo...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Huang, Shih-Xiu Lung, An Chen, Jia-You Sung, Wu-Min Chu, Yu-Cheng Tu, Jiun-Yu Hu, Chih-Lin National Central University Dept. Physics Taoyuan City320317 Taiwan National Central University Dept. Communication Engineering Taoyuan City320317 Taiwan Southern Taiwan University of Science and Technology Dept. Computer Science and Information Engineering Tainan710301 Taiwan

ISBN: (纸本)9798350340181

Considering the large-scaled field of mountain rescue, a hybrid self-organized network architecture is developed for multi-hop data delivery against weak network connectivity in challenged areas. UAVs and wireless nodes can self-organize unstructured multi-hop networks to distribute urgent and recuse messages in geographic proximity. To seek lost climbers efficiently, this architecture provides two search methods. (1) In a passive method, climbers send messages via MQTT protocols to neighbor nodes that will further forward messages to the cluster head of a self-organized network. (2) In an active method, UAVs hover over disjoint cluster heads to store, carry, and forward messages, and UAVs are equipped with cameras to perform face recognition of detecting target users. All the messages and camera-initiated video streaming can be passed through self-organized networks and then displayed on the rescue website. © 2023 IEEE.

关键词： Network architecture

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Automated Disease Detection in Plant Images using Convolution Neural Network 1

Automated Disease Detection in Plant Images using Convolutio...

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1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Deepkiran Singh, Laxman Pandey, Mrinal Lakra, Sachin Manav Rachna University Dept. of Computer Science and Engineering Faridabad India Noida Institute of Engineering and Technology Dept. of Electronics and Communication. Engineering Greater Noida India NorthCap University Dept. of Computer Science and Engineering Gurugram India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

In this work, authors developed detection system for plant-disease using convolution neural network (CNN) model. The developed system was trained using healthy and unhealthy plants leaves on the dataset of 20,639 images taken in both lab and real-world environment from the open fields. The dataset used in this work includes 3 plant species with 15 dissimilar classes including some healthy leaves of plants. Performance of developed model was compared against most prominent machine learning algorithms such as LDA, KNN, and Gaussian Naïve Bayes classifier etc. On comparing the results, convolution neural network model was found comparatively more competent than other existing machine learning using CNN achieving the peak classification accuracy of 97% and then followed by LDA and KNN with accuracy of 95.7% and 94.4%, respectively. The results reveals that the developed system can effectively be utilized for disease detection and real-Time monitoring of plant crops in the open fields. © 2022 IEEE.

关键词： Neural network models

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Hardware Implementations of Dehazing Algorithms

Hardware Implementations of Dehazing Algorithms

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： E Akash S R Ashokkumar N Banupriya K Subash Thangam N Sanjay Kumar M Jananeshwaran Dept. Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore India

Haze, a phenomenon arising from the scattering of light by atmospheric particles, imparts undesirable visual artifacts and degradation to images, posing challenges across various applications. This review paper delves into the realm of dehazing algorithms, with a specific focus on their hardware implementations. The introductory section lays the groundwork by elucidating the nature of haze, its diverse types, and the detrimental impacts it inflicts on image quality through light scattering mechanisms. In response to the pressing need for real-time dehazing solutions, the paper navigates through the landscape of hardware-based dehazing algorithms This review addresses the intricate hurdles encountered in translating dehazing algorithms into hardware architectures. These challenges span architectural design considerations, power efficiency optimizations, memory bandwidth management, and algorithmic adaptation for parallel processing. To benchmark the advancements achieved thus far, the paper delves into a comparative analysis of diverse hardware implementations and their corresponding results. Notable architectural choices, from FPGA (Field-Programmable Gate Array) to specialized ASICs (Application-Specific Integrated Circuits) are dissected in terms of their performance metrics, energy efficiency, and suitability for real-time applications. This assessment also encompasses an examination of trade-offs between accuracy and computational speed in various hardware contexts. Drawing insights from the reviewed literature, this paper offers a comprehensive overview of the strides taken in the field of hardware-based dehazing algorithms. The synthesis of challenges, architectures, and comparative results provides a roadmap for future research directions, offering insights into potential areas of improvement and innovation.

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Sign Language Recognition by using Spatio-Temporal Features

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Procedia computer Science 2024年 233卷 353-362页

作者： Renjith S Manazhy Rashmi Sumi Suresh M S Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri 690525 India Dept. of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amritapuri 690525 India Dept. of Computer Science and Engineering University at Bufalo New York City USA

The recognition of sign language has significant importance as an application that addresses the communication challenges faced by those within the Deaf and hard-of-hearing populations. This study introduces a methodology for the identification of sign language using spatio-temporal attributes, specifically concentrating on two distinct sign language datasets: the CSL (Chinese Sign Language) alphabet and the ArSL (Arabic Sign Language) alphabet. The study's objective is to create a powerful recognition system capable of reliably identifying sign language motions. A combination of spatio-temporal characteristics collected from video sequences of sign language motions is used in the suggested technique. These characteristics capture the spatial layout as well as the temporal dynamics of the motions, allowing the model to recognise signals with more precision. Experiments were carried out on the CSL and ArSL alphabet datasets to validate the method's efficacy. The findings of the trials are encouraging. The suggested system recognized CSL alphabet signs with an accuracy of 90.87% and ArSL alphabet signs with an accuracy of 89.46%. These high accuracy rates show the power of the spatiotemporal feature-based method for sign language identification. The system's success on two different sign languages implies that it is adaptable and useful in a wide variety of sign language applications. This study contributes to the evolution of assistive technology, making sign language recognition more accessible and efficient for those who communicate using sign language. Furthermore, the findings pave the way for additional research into spatiotemporal feature-based approaches in sign language recognition, with potential applications in real-world settings such as sign language interpretation and communication support.

关键词： Sign Language Classification Spatio-temporal features Time Distributed CNN

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, Science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Prior Knowledge based Inverse Parametric Modeling of UWB Band-Notched Antennas

Prior Knowledge based Inverse Parametric Modeling of UWB Ban...

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2022 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2022

作者： Sarkar, Debanjali Khan, Taimoor Talukdar, Fazal Ahmed Rengarajan, Sembiam R. Dept. of Electronics and Communication Engineering Nit Silchar Silchar India California State University Dept. of Electrical and Computer Engineering Northridge United States

ISBN: (数字)9781946815156

ISBN: (纸本)9781946815156

We have developed a prior knowledge input-based artificial neural network model. The prior knowledge has been incorporated into the model using trained multilayer perceptron network. The proposed model has been validated by designing an f-shaped UWB antenna loaded with electromagnetic bandgap structure and rectangular split ring resonator. © 2022 USNC-URSI.

关键词： Inverse problems

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