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Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

computer Systems science & engineering 2023年第6期45卷 2351-2362页

作者： S.Aanjanadevi S.Aanjankumar K.R.Ramela V.Palanisamy Department of Computer Applications Alagappa UniversityKaraikudi630003TamilnaduIndia Department of Computer Science and Engineering Sri Raaja Raajan College of Engineering and TechnologyAmaravathipudur630301TamilnaduIndia Department of Electrical and Electronics Engineering Ultra College of Engineering and TechnologyMadurai625020TamilnaduIndia

Due to the enormous usage of the internet for transmission of data over a network,security and authenticity become major *** challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database *** describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical(Face,fingerprint,Ear etc.)and behavioural(Gesture,Voice,tying etc.)by means of matching and verification *** this work,biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential *** proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary *** Face Attribute Convolutional Neural Network(FACNN)is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further *** Artificial Intelligence(AI)into the proposed system,automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while *** codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and *** main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify *** system enhances the system performance by 8%matching and verification and minimizes the False Acceptance Rate(FAR),False Rejection Rate(FRR)and Equal Error Rate(EER)by 6 times and increases the data privacy through the biometric cryptosystem by 98.2%while compared to other work.

关键词： FACNN biometric cryptography AI FAR FRR EER

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Optimizing Packet Delivery in Underwater Wireless Sensor Networks: A Comparative Study of Genetic Bee Colony and Ant Colony Algorithms 9

Optimizing Packet Delivery in Underwater Wireless Sensor Net...

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9th International Conference on Communication and electronics Systems, ICCES 2024

作者： Kannappan, A. Kolagani, Nagesh Kandaswamy, Vijay Anand Vishnupriyan, J. Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350377972

Physically Underwater communication adopt the bio inspired genetic bee colony formed network communication structures. Underwater environment condition Communication is established between the Transmitting node and in between several clustering relay nodes to reach receiver nodes. Transmitting nodes are placed on sea bed areas at certain depth and clustering relay nodes are placed in various underwater depth conditions dipping the sensor nodes. Also, underwater battery life is very hard and battery replacement is a very tough task. Hence, an alternate solution of optimization battery capacity utilization is used. This optimization algorithm improves Packet delivery ratio by comparing genetic bee algorithm with Ant colony algorithm. Hence, equivalence of two algorithms implemented in underwater conditions. The genetic bee colony Algorithm produces better output results more than 4 to 6 percentage compared to ant colony parameter output. The genetic bee colony algorithm offers a maximum clustering node technique compared to ant colony algorithm. Its greater support for saving energy consumption extending battery life. © 2024 IEEE.

关键词： Ant colony optimization

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Machine Learning for Wireless Network Optimization: Algorithms and Applications for Adaptive Communication 1

Machine Learning for Wireless Network Optimization: Algorith...

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1st International Conference on Advances in computer science, electrical, electronics, and Communication Technologies, CE2CT 2025

作者： Srinivas, Madhira Surya, S. Thakar, Prachi Deepak Jain, Amit Kumar Department of Computer Science & Engineering Hyderabad501301 India Chennai600116 India KJ college of Engineering & Management Research Department of Computer Engineering Pune India Poornima University Department of Electrical & Electronics Engineering Rajasthan Jaipur303905 India

ISBN: (纸本)9798331518578

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. Machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function. © 2025 IEEE.

关键词： 5G Optimization Adaptive Communication Bandwidth Data Pre-processing EDA Energy Efficiency Latency Machine Learning Network Elasticity Predictive Models Resource Allocation Scalability Signal Strength Wireless Networks

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Web Based Book Recommendation System Using Collaborative Filtering 5

Web Based Book Recommendation System Using Collaborative Fil...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Mankar, Ketaki Pawar, Shruti Agarwal, Harsh Sangale, Tejas Kulkarni, Smita Mit Academy of Engineering Department of Computer Science Engineering Pune India Mit Academy of Engineering Department of Electronics and Electrical Engineering Pune India

ISBN: (纸本)9781665475242

Recommender systems are tools that help end users recommend products and obtain information about their preferences by going online. Today's online bookstores compete with each other in a variety of ways. One of the most powerful ways to efficiently increase revenue by attracting customers is a referral system. This study offers a clear, understandable method for recommending books that aids readers in selecting the best book. The proposed methodology works on training of database and feedback to provide meaningful information that helps users make decisions. In this paper recommendation system is developed by using collaborative filtering method. The machine learning (ML) model KNN is proposed to categorize the books as per user preferences. The overall architecture of the proposed system is introduced and its implementation is demonstrated. © 2023 IEEE.

关键词： Recommender systems

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A Robust Evolutionary Gravity Neocognitron Neural Network Model for Construction Cash Flow Prediction in Complex Project Management

A Robust Evolutionary Gravity Neocognitron Neural Network Mo...

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2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Joseph, X.Feix Selvaraj, G. Babu, M. Arumuga Banu, A. Shajitha Bhuvanesh, A. Keerthanadevi, R. Loyola Institute of Technology and Science Department of Electrical and Electronics Engineering Kanyakumari India Ramco Institute of Technology Department of Mathematics Rajapalayam India Christ the King Engineering College Department of Electrical and Electronics Engineering Coimbatore India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

In order to keep expenses in check while building projects are being carried out, accurate cash flow forecasting is crucial. The project's unique features and risk considerations make it challenging, despite extensive study, to develop a reliable model for predicting future cash flows. Furthermore, both sequence and non-sequence elements have a significant influence on the building projects' cash flow. As a result, the authors of this research suggested a new inference model based on artificial intelligence (AI) called Evolutionary Gravity Neocognitron Neural Network (EG-NN). This model takes project complexity into account and attempts to handle non-sequence elements in time series issues. By attaining the highest values of 2.35%, MAPE of 5.65%, MAE of 2.03%, and coefficient of determination of 0.99, the experimental findings on thirteen construction projects have validated the EG-NN as the top model for cash flow forecasting. When compared to other AI-based inference models, the statistical result shows that cash flow forecasting can be improved by at least 14.1 percent in terms of root-mean-square error (RMSE) and 15.2 percent in terms of mean absolute error (MAE). Managers may benefit from the EG-NN network model's ability to predict and manage construction projects' cash flow. © 2024 IEEE.

关键词： Project management

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A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

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Digital Communications and Networks 2023年第5期9卷 1113-1122页

作者： Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Department of Computer Science and Information Systems BITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Systems and Computer Engineering Carleton UniversityOttawaONK1S 5B6Canada

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of

关键词： Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation Systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT)

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Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection

Comparative Study between KNN and CNN's Techniques for Kidne...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Tbenawey, Fahdah M. Alotaibi, Shoayee Salem, Marwa Mir, Shamaila Mir, Zahida Computer Science and Engineering College University of Ha'il Department of Computer Science and Information Hail Saudi Arabia Faculty of Engineering Department of Electrical Communication and Electronics Systems Engineering Cairo Egypt

ISBN: (纸本)9798350347050

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB. © 2023 IEEE.

关键词： MATLAB

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A Hierarchical Framework for Fake News Detection Using Semantic Analysis and Source Reliability 2

A Hierarchical Framework for Fake News Detection Using Seman...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Ashokkumar, S.R. School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350371055

Fake news has become a significant problem in modern society, with the rapid dissemination of false information through social media and online platforms. This has led to an urgent need for effective methods to detect and combat fake news. In this paper, we propose a hierarchical framework for fake news detection using semantic analysis and source reliability. The framework comprises two main tiers: semantic analysis and source reliability. In the first stage, the text of the news article is analyzed using natural language processing techniques to extract features such as keywords, sentiment, and tone. Similarly, the credibility of the source is determined based on factors such as the website's domain authority and previous track record of spreading fake news. In the second tier, a hierarchical classifier combines the results from the semantic analysis and source reliability levels to make the final prediction. The hierarchical classifier initially employs a binary classifier to determine if the article is fake or not. If it is classified as fake, it is then passed on to another classifier to determine the severity of the fake news. Our framework aims to provide a more accurate and robust approach for detecting fake news by considering both the content of the news article and the credibility of its source. Experimental results on a real-world dataset demonstrate the effectiveness of our proposed framework compared to traditional fake news detection techniques. © 2024 IEEE.

关键词： Fake News Detection Machine Learning NLP

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6172-6185页

作者： Liu, Jiaxu Yi, Xinping Huang, Xiaowei The University of Liverpool School of Electrical Engineering Electronics and Computer Science LiverpoolL69 3GJ United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

Hyperbolic graph convolutional networks (HGCNs) have demonstrated significant potential in extracting information from hierarchical graphs. However, existing HGCNs are limited to shallow architectures due to the computational expense of hyperbolic operations and the issue of oversmoothing as depth increases. Although treatments have been applied to alleviate oversmoothing in graph convolutional networks (GCNs), developing a hyperbolic solution presents distinct challenges since operations must be carefully designed to fit the hyperbolic nature. Addressing these challenges, we propose DeepHGCN, the first deep multilayer HGCN architecture with dramatically improved computational efficiency and substantially reduced oversmoothing. DeepHGCN features two key innovations: 1) a novel hyperbolic feature transformation layer that enables fast and accurate linear mappings;and 2) techniques such as hyperbolic residual connections and regularization for both weights and features, facilitated by an efficient hyperbolic midpoint method. Extensive experiments demonstrate that DeepHGCN achieves significant improvements in link prediction (LP) and node classification (NC) tasks compared to both Euclidean and shallow hyperbolic GCN variants. © 2024 IEEE.

关键词： Network architecture

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