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A Design Thinking Approach of Metaheuristic Empowerment for Energy-Efficient and Optimized Routing protocol in IoT-Enabled Wireless Sensor Networks 3

A Design Thinking Approach of Metaheuristic Empowerment for ...

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3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Princy, D. Kalaivani, D. Vijayaraghavan, T. Dr. S N S Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Dr. S N S Rajalakshmi College of Arts and Science Department of Computer Technology Coimbatore India Sns College of Technology Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370867

This research focuses on energy-efficient routing in WSNs within IoT contexts, comparing established protocols like SEP, HEED, TEEN with the proposed SEP-ACO protocol. By strategically selecting cluster heads based on residual energy, these protocols optimize energy usage, enhancing data collection and transmission. Experimental evaluations highlight SEP-ACO's superior performance, showcasing improved packet delivery ratios and reduced energy consumption compared to existing approaches. This work emphasizes the significance of energy-efficient routing for extending WSNs' operational lifetime in emerging IoT-enabled environments, contributing to IoT and sensor technology advancements. © 2023 IEEE.

关键词： Energy dissipation

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Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions 5

Secure Sentinel Leveraging Machine Learning for Fraud Detect...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 2024 IEEE.

关键词： Adversarial machine learning

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A Robust Graphical Authentication System for the Shoulder Surfing Attack 3rd

A Robust Graphical Authentication System for the Shoulder Su...

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3rd International Conference on Communication, Networks and Computing, CNC 2022

作者： Varshney, Shailja Kumari, Sonam Agarwal, Anannya Pal, Prashant Department of Computer Science and Engineering ITS Engineering College Greater Noida India Department of Computer Science and Engineering Rajkumar Goel Institute of Technology Ghaziabad India Department of Computer Science and Engineering Birla Institute of Technology Mesra India

ISBN: (纸本)9783031431395

The technique of determining whether a user is legitimate or not is known as authentication. It needs a secret field (password) that only the actual user knows. Every security system is created in such a way that it must include at least one authentication technique to safeguard an individual’s identity. There are numerous authentication methods available today, but each method has its own advantages and drawbacks that allow attackers to carry out various exploits including dictionary attacks and shoulder surfing attacks, among others. With some tweaking, graphic password authentication may have fewer drawbacks. A modified version of the graphical password technique is employed in this paper. The user is assigned a three-digit unique number in this that must be kept private. Each digit has a unique value that is determined by a set of computations and algorithms. This makes it difficult for the attacker to decrypt the password and makes it possible to prevent numerous cyber attacks like dictionary attacks and shoulder surfing attacks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Authentication

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SocialChain: A Decentralized Social Media Platform on the Blockchain 1

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2nd EAI International Conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

作者： Prashanth, Mallellu Sai Maheswari, V. Uma Aluvalu, Rajinikanth Kantipudi, M. V. V. Prasad Departmentof Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Departmentof Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Departmentof Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Pune India

ISBN: (数字)9783031660443

ISBN: (纸本)9783031660436

The emergence of blockchain technology has paved the way for innovative solutions in various domains, including social media. This research paper presents the design and implementation of "SocialChain," a decentralized social media platform built on the Ethereum blockchain using the Solidity programming language. SocialChain aims to address issues related to data privacy, content ownership, and censorship prevalent in traditional social media platforms. The proposed system leverages blockchain’s inherent properties such as immutability, transparency, and decentralization to create a trustless and secure environment for social interactions. The platform allows users to create posts, share content, and engage in discussions while maintaining ownership and control over their data. The underlying blockchain ensures that the history of posts and interactions remains tamper-proof and verifiable. The core components of the SocialChain smart contract include data structures for posts and comments, functions for creating posts and adding comments, as well as mechanisms for retrieving post-related information. Each user’s identity is associated with their Ethereum address, eliminating the need for traditional authentication methods and enhancing user pseudonymity. To ensure efficiency, the contract optimizes data storage by using mappings and arrays, minimizing gas costs and improving scalability. The contract also incorporates modifiers to validate post and comment existence, enhancing security and preventing unauthorized access. The results of this research showcase the feasibility of integrating blockchain technology into social media platforms. However, it is important to note that while the implementation provides a foundational framework, practical deployment requires addressing challenges such as user experience, scalability, and incentivization mechanisms. In conclusion, SocialChain exemplifies the potential of blockchain technology to revolutionize the landscape of s

关键词： Decentralized finance

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Innovative Data Encryption Techniques using AI for Wireless Sensor Actuator Network Security 2

Innovative Data Encryption Techniques using AI for Wireless ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Dharmateja, M. Rama, Prabha K.P. Asha, N. Nithya, P. Lalitha, S. Manojkumar, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Sathyamangalam India

ISBN: (纸本)9798350382952

Wireless Sensor Actuator Networks (WSANs) are sensitive to cyber threats due to present static encryption techniques. Adaptive security methods are required in WSAN settings to reduce evolving dangers and maintain data integrity. The paper introduces a novel method for encrypting WSAN data using AI and machine learning. Long Short-Term Memory (LSTM) networks process data, Genetic Algorithms (GAs) optimize encryption, and Isolation Forests detect threats in real-time. The proposed system achieved remarkable performance, with scores of 98% data integrity, 96% error repair, and 95% threat detection accuracy. Encryption rates reach their max at 200 Mbps, with optimal resource usage (30% CPU, 25% RAM) and a 5% false positive rate. The proposed AI-driven security architecture improves data integrity, encryption speed, and overall security efficacy in WSANs, surpassing existing methods. The paper overcomes the limitations of static encryption methods and proposes a proactive protection mechanism against developing cyber threats, making a substantial contribution to WSAN security improvement. © 2024 IEEE.

关键词： Cryptography

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Classification of Downy Mildew Disease in Watermelon Plant Leaf using VGG-16 Convolutional Neural Network 4

Classification of Downy Mildew Disease in Watermelon Plant L...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Khannum, Ayesha Dhanesha, R. Kantharaj, Y. Davangere University Department of Research and studies in Computer Science Davangere India Davangere University Department of studies in Computer Science Davanagere India College of Horticulture Department of Post-harvest Technology Mudigere India

ISBN: (纸本)9798350352931

Downy Mildew, caused by Pseudoperonospora cubensis, poses a serious threat to watermelon (Citrullus lanatus) crops, with the potential to drastically reduce yields and cause substantial economic losses in the agricultural sector. Early and accurate detection is essential for mitigating these impacts and maintaining crop productivity. An automatic detection system was developed using the VGG-16 Convolutional Neural Network (CNN) model, selected for its high accuracy. Trained and validated on a dataset of 585 images, split into 80% for training and 20% for validation, the model achieved 100% accuracy in both phases, with minimal losses of 0.0325 and 0.0098 respectively. These results underscore the model's potential for precise Downy Mildew detection, supporting improved disease management in precision agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploration of Agricultural GDP in Shaoguan City Based on ARIMA Algorithm 24

Exploration of Agricultural GDP in Shaoguan City Based on AR...

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2024 International Conference on Economic Data Analytics and Artificial Intelligence, EDAI 2024

作者： Huang, Jingyi Li, Xiaoqin Qi, Yuhang Jia, Mengyi Luo, Yongbin Zhang, Rong Liu, Lidong Department of Computer Science Guangdong University of Science and Technology Guangdong Dongguan China Department of General Education Guangdong University of Science and Technology Guangdong Dongguan China Art and Design College Department Guangdong University of Science and Technology Guangdong Dongguan China

ISBN: (纸本)9798400713255

ARIMA algorithm is a classic statistical method used for time series prediction, which ensures the stationarity of data by analyzing autoregressive and moving average components in historical data, as well as necessary differential processing, in order to achieve accurate prediction of future values. This article uses ARIMA algorithm to analyze and predict the agricultural GDP of Shaoguan City. By conducting ADF testing on the raw data, the optimal differential data is obtained. Then, the ACF and PACF are analyzed and a model validation table is drawn. Finally, the white noise sequence of the data is checked using ACF and PACF, and the AR residual and MA residual are determined to be white noise sequences that meet the ARIMA model conditions. On this basis, the ARIMA model formula is derived, and the agricultural GDP time prediction series chart is displayed graphically using a computer. The prediction results can provide valuable reference for decision makers. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： ACF

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Handwritten Chinese Character Regularity Assessment Method Based on Improved Googlenet 5

Handwritten Chinese Character Regularity Assessment Method B...

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5th International Conference on Frontiers technology of information and computer, ICFTIC 2023

作者： Kang, Zhennan Yang, Yong Qu, Fuheng Li, Jinzhuang Changchun University of Science and Technology College of Computer Science and Technology Changchun China College of Software Jilin Electronic Information Vocational and Technical College Jilin China

ISBN: (纸本)9798350309034

The standardization assessment of handwritten Chinese characters currently often relies on manual feature extraction, which excessively depends on subjective experience and lacks feature extraction capability. To address this, this article proposes an improved GoogleNet network for the standardization assessment of handwritten Chinese characters. First, it replaces the standard convolution in the original Inception block with dilated convolution to increase the receptive field and capture more detailed features. Second, it replaces the activation function with Leaky ReLU to reduce feature loss during backpropagation. Next, an attention mechanism is introduced to focus on the key features of Chinese characters. Finally, transpose convolution is introduced at intermediate layers of the network, incorporating shallow-level features to enhance the network's ability to learn Chinese character features. Experimental results demonstrate that the accuracy of this method is improved by 4.87% compared to the original network, reaching 83.96%. © 2023 IEEE.

关键词： attention mechanism chinese character standardization dilated convolution feature fusion googlenet

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Optimal Deep Learning Enabled Communication System for Unmanned Aerial Vehicles

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computer Systems science & Engineering 2023年第4期45卷 955-969页

作者： Anwer Mustafa Hilal Jaber S.Alzahrani Dalia H.Elkamchouchi Majdy M.Eltahir Ahmed S.Almasoud Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia

Recently,unmanned aerial vehicles(UAV)or drones are widely employed for several application areas such as surveillance,disaster management,*** UAVs are limited to energy,efficient coordination between them becomes essential to optimally utilize the resources and effective communication among them and base station(BS).Therefore,clustering can be employed as an effective way of accomplishing smart communication systems among multiple *** this aspect,this paper presents a group teaching optimization algorithm with deep learning enabled smart communication system(GTOADL-SCS)technique for UAV *** proposed GTOADL-SCS model encompasses a two stage process namely clustering and *** the initial stage,the GTOADL-SCS model includes a GTOA based clustering scheme to elect cluster heads(CHs)and organize ***,the GTOADL-SCS model develops a fitness function containing three input parameters as residual energy of UAVs,average neighoring distance,and UAV *** classification process,the GTOADLSCS model applies pre-trained densely connected network(DenseNet201)feature extractor with gated recurrent unit(GRU)*** ensuring the enhanced performance of the GTOADL-SCS model,a widespread simulation analysis is performed and the comparative study reported the significant outcomes over the existing approaches with maximum packet delivery ratio(PDR)of 92.60%.

关键词： Unmanned aerial vehicles energy efficiency smart communication system deep learning

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Person Re-Identification Algorithm Based On Identity information Matrix 5

Person Re-Identification Algorithm Based On Identity Informa...

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5th International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Yan, Yue Yao, Junxiang Zhu, Hao Luo, Yuhui Li, Xiaopan Shen, Yunchen Zhang, Jing Liu, Yinxu Li, Xiangkui College of Physics and Information Engineering Zhaotong University Zhaotong China College of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Computer Science and Technology Harbin University of Science and Technology Harbin China

ISBN: (纸本)9781839539848

In order to overcome the domain shift problem in person re-identifacation algorithms, the paper proposes a person re-identifacation algorithm oriented towards identity information matrix. Firstly, the algorithm trained two dictionaries through low-rank decomposition. One dictionary represents interference information other than person in the image, and the other dictionary represents person feature information after removing interference information. Then, the person coding coefficients obtained through person feature information training are combined with identity information to train the classifier. At the same time, the consistency matrix and uncorrelation matrix of identity information are introduced to improve the effectiveness of identity information. Finally, the algorithm is tested based on a classifier. The experiment shows that the rank1 value of the algorithm is within PRID2011, VIPeR, i_LIDS and CHUK01 datasets achieved 41.20%, 50.32%, 82.27%, and 75.41% respectively, which is superior to other newer person re-identifacation methods. The paper introduces an identity information matrix to overcome the domain shift problem caused by changes in background, lighting, and other factors, which can improve the person re-identifacation rate to a certain extent. © The Institution of Engineering & technology 2023.

关键词： Classification (of information)

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