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Distance Matrix and Markov Chain Based Sensor Localization in WSN

computers, Materials & Continua 2022年第5期71卷 4051-4068页

作者： Omaima Bamasaq Daniyal Alghazzawi Surbhi Bhatia Pankaj Dadheech Farrukh Arslan Sudhakar Sengan Syed Hamid Hassan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthanIndia University of Engineering and Technology LahorePakistan Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamilnaduIndia

Applications based on Wireless Sensor Networks(WSN)have shown to be quite useful in monitoring a particular geographic area of *** geometries of the surrounding environment are essential to establish a successful WSN *** it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes(SN)in a WSN is always a challenging *** this research paper,Distance Matrix and Markov Chain(DM-MC)model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the *** method further employs a Markov Chain Model(MCM)for energy optimization and interference *** are performed against two well-known models,and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing *** probability is used in the Markova chain to sustain higher energy ***,the proposed Distance Matrix and Markov Chain model decrease energy use by 31%and 25%,respectively,compared to the existing DV-Hop and CSA *** experimental results were performed against two proven models,Distance VectorHop Algorithm(DV-HopA)and Crow Search Algorithm(CSA),showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption(EC).These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework.

关键词： Wireless sensor network resource optimization routing distance matrix Markov chain

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DNA Cryptography for Secure Multilingual Cloud Messaging 6th

DNA Cryptography for Secure Multilingual Cloud Messaging

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6th International Conference on Smart computing and Informatics, SCI 2024

作者： Digumarthi, Suseela Singidi, Sugunasri Paladugu, Triveni Mattaparthi, Ramakrishna Chandra Sekhar Reddy, N. Department of Computer Science and Engineering Godavari Institute of Engineering and Technology Andhra Pradesh Rajahmundry India Department of Computer Science and Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Tamil Nadu Chennai India

ISBN: (纸本)9789819619801

Cloud computing stands at the forefront of contemporary advancements in distributed computing, providing a range of data storage, including on-demand services, platform services, and network capabilities. Despite the compelling advantages, many enterprises exhibit hesitancy in embracing cloud services, primarily stemming from apprehensions about data security, given that the data are housed on servers belonging to the cloud service provider. Addressing this concern has prompted researchers worldwide to explore various avenues for fortifying and ensuring the security of data stored in cloud computing. Among these approaches, the bi-directional DNA encryption algorithm is gaining prominence as a notable contender. However, a notable limitation of the current methodology lies in its exclusive focus on the American Standard Code for Information Interchange character set, neglecting the broader scope of non-English users engaging with cloud computing. Consequently, this proposed endeavor seeks to augment the BDEA to accommodate Unicode characters, ensuring a more inclusive and robust security framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed cloud

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AI Innovator: Text to Image Generation using GAN

AI Innovator: Text to Image Generation using GAN

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Nimbarte, Mrudula Hussain, Alfiya Budhlani, Kanak Bansod, Chirag Ramdham, Mayur Kanhe, Om Khullar, Amit S.B. Jain Institute of Technology Management and Research Department of Computer Science and Engineering MS Nagpur India

ISBN: (纸本)9798350348460

This review provides a comprehensive review of the latest approaches and advances in text-to-image processing using artificial neural networks (GANs). The work under review uses GAN architecture, where generators and classifiers work together to extract corresponding images from descriptions. This research includes a comprehensive review of the GAN framework, delving into the complexities of the generator and discrimination during training. The project solves problems related to converting text input into visual images using a combination of natural language processing and computer vision. This article reviews the main concepts of the architecture as well as the use of advanced neural networks for text understanding and image synthesis. Particular attention is paid to the optimization process the generator uses to improve the quality and integrity of the resulting images. Additionally, the review highlights the evolution of different labels and discusses their implications for real-world applications. © 2024 IEEE.

关键词： Adversarial Training computer Vision Generative Adversarial Networks (GANs) Image Synthesis Multimodal Learning Natural Language Processing (NLP) Neural Network Architecture Text to Image Generation

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ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

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2023 IEEE International Conference on research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

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Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Advancing Mars Exploration: Development of a Large-Scale Gravity Compensation System for Landers during Descent

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6697-6710页

作者： Sun, Haining Tang, Xiaoqiang Ge, Shuzhi Sam Agency for Science Technology and Research Singapore Institute of Manufacturing Technology 138634 Singapore Tsinghua University State Key Laboratory of Tribology Department of Mechanical Engineering Beijing100084 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

As space exploration advances, an increasing number of planets are becoming targets for landing missions. Before officially launching a lander, it is essential to conduct landing tests on Earth to verify the functionality of the lander. One key challenge is to simulate the target planet's gravity environment during the lander's descent. In this study, a large-scale gravity compensation system is developed to physically simulate the Martian gravity for the landing tests of a Mars lander. The system integrates a constant force mechanism and electronic control systems, working closely to ensure smooth transitions between working states during the landing tests. Based on the principle of minimum potential energy, an expression for the output force of the constant force mechanism is established, and the influence of factors such as mechanical parameters on the output force is analyzed. Experimental results show that the system can provide gravity compensation for a 720-kg Mars lander. It maintains a nearly constant output force even when the lander touches the ground and during the subsequent period, demonstrating its adaptability to transient collisions and sudden state changes. © 1965-2011 IEEE.

关键词： Space research

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Simulation and Analysis of a MEMS based Micromirror System for Deployment in Optical Beam Steering Systems Used in Experimental Quantum computing 4

Simulation and Analysis of a MEMS based Micromirror System f...

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4th International Conference for Emerging Technology, INCET 2023

作者： Abani, Rita Indian Institute of Science Education and Research Bhopal Department of Electrical Engineering and Computer Science Bhopal India

ISBN: (纸本)9798350335750

The aim of this study is to highlight the utility and applicability of Microelectromechanical systems (MEMS) technology in addressing the existing challenges of precision control of beams in performing manipulation of two-dimensional atomic systems or states of a qubit in Quantum Optics setups. This study specifically focuses on the utility of a controllable MEMS based Micromirror that maneuvers a laser source to access multiple positions of a qubit in a two-dimensional trapped lattice;an application of pertinent interest to Experimental Quantum Computation. This paper focuses on simulating a MEMS based micromirror system proposed by (Knoernschild et al. [1]) in COMSOL and analyzing its architecture. © 2023 IEEE.

关键词： MEMS

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Automating Machine Learning Model Development: An OperationalML Approach with PyCARET and Streamlit

Automating Machine Learning Model Development: An Operationa...

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2023 Innovations in Power and Advanced computing Technologies, i-PACT 2023

作者： Annamalai, R. Deena, S. Venkatakrishnan, R. Shankar, Harrieni Sree Harshitha, Y. Harini, K. Nikhil Reddy, M. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Sri Ramachandra Institute of Higher Education and Research Porur Chennai India

ISBN: (纸本)9798350325188

OperationalML (Automatic Machine Learning)refers to tools and techniques that automate various machine-learning model development aspects, including data preparation, feature engineering, model selection, hyperparameter optimiza-tion, and evaluation. OperationalML can significantly reduce the time and expertise required for building high-quality machine- learning models while making the process more transparent and scalable. In this study, we present an implementation of OperationalML using two open-source libraries: PyCARET and Streamlit. Our work focuses on evaluating the performance and usability of our implementation across classification tasks from publicly available datasets. Results show that our OperationalML system outperforms manual approaches, especially in speed and ease of use. Additionally, we conduct sensitivity analysis to assess the effectiveness of each component within the system and offer insights into future improvements that could enhance the overall quality of the generated models. This work contributes to the growing body of literature on Machine learning in Production, serving as a valuable resource for practitioners, researchers, and educators who want to quickly develop effective predictive models without spending extensive time on training machine learning. © 2023 IEEE.

关键词： Automatic Machine Learn- ing Data Preparation Feature engineering Hyperparameter Optimization Model Selection PyCaret Streamlit

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