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Robust Fingerprint Minutiae Extraction and Matching Using Fully Connected Deep Convolutional Neural Network and Improved SIFT 2

Robust Fingerprint Minutiae Extraction and Matching Using Fu...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Husain, Saif O. Reddy, R.Archana Pareek, Piyush Kumar Jagannathan, Srinivasan Jyothi, M. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computer Science and Engineering Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Aiml & Ipr Cell Bengaluru India Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India G.Narayanamma Institute of Technology and Science Department of Information Technology Hyderabad India

ISBN: (纸本)9798350373110

Fingerprint is a most well-known biometric based authentication system that gives a unique identity for each person. In this paper, an authentication framework to enhance the Fingerprint Minutiae Extraction and Matching (FMEM) technique is proposed. A Fully Connected Deep Convolutional Neural Network with Improved Scale-Invariant Feature Transform (FCDCNN-ISIFT) is proposed for feature extraction and feature matching. Initially, the fingerprint images are collected from Fingerprint Verification Competition 2004 (FVC2004) which are first pre-processed using Histogram Equalization (HE) and Normalization. The images are segmented and enhanced by estimating ridge orientation and ridge frequency, and performing Gabor filtering, binarization and thinning. The fingerprint feature extraction and matching test is done using FCDCNN-ISIFT. The FCDCNN is built for detecting minutiae and the ISIFT is applied to the high contrast images to enhance the quality of latent fingerprints. Finally, the loss function is measured to determine the minutiae detection loss. The experimental results show better accuracy of 99.41%, f1-score of 97.73%, and Equal Error Rate (EER) of 1.75% when compared to results of existing approaches like Contrast Limited Adaptive Histogram Equalization - Random Sample Consensus (CLAHE - RANSAC) and Deep Convolutional Neural Network (DCNN). © 2024 IEEE.

关键词： Convolutional neural networks

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Swarm Intelligence-Based Clustering Algorithms for Wireless Sensor Networks

Swarm Intelligence-Based Clustering Algorithms for Wireless ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Nainwal, Ankita Shnain, Ammar Hameed Kumar, Rakesh Boob, Nandini Shirish Verma, Vikas Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This work studies the use of swarm intelligence-based clustering algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) Bee Algorithm (BA), and Firefly Algorithm (FA), to optimize Wireless Sensor Networks. (WSNs). It evaluated their performance by strict simulations on energy consumption level, network lifetime, and clustering quality. It is observed that ACO and PSO consistently showed better results with moderate energy consumption by achieving long network lifetimes as well as precise clustering. However, BA and FA have been competitive but had lower efficiency when looking at certain metrics. The comparative analysis highlights the need for algorithmic selection according to specific demands in WSN deployments. The study complements related research on the application of swarm intelligence in WSN by focusing on routing challenges, energy-aware protocols, and centralized clustering solutions. The results give useful information to practitioners who are looking for the best algorithms, especially in real WSN situations. © 2024 IEEE.

关键词： Wireless sensor networks

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Integrating Embedded Systems with Natural Language Processing: Innovations and applications in Intelligent Systems 4

Integrating Embedded Systems with Natural Language Processin...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shrivastava, Anurag Rawat, Ruchira Al-Fatlawy, Ramy Riad Bansal, Saloni Sehgal, Archana Gupta, Priyanka Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331518981

The amalgamation of Natural Language Processing (NLP) with Embedded Systems signifies a revolutionary progression in the creation of intelligent systems, permitting them to participate in interactions that are akin to those of humans. This study looks into the creative ways that NLP and embedded technology can be used in a variety of industries, such as healthcare, automotive, and smart homes. The paper illustrates how NLP enables embedded devices to comprehend, interpret, and react to human commands, improving user experience and operational efficiency, by looking at the underlying principles. The main obstacles to this integration are thoroughly examined, including resource shortages, the need for real-time processing, and environmental restrictions. The study emphasises how important it is to use low-power, compact NLP models that enable real-time language processing without sacrificing system efficiency. Additionally, the research discusses the fundamental approaches for improving these models' using methods like transfer learning, quantisation, and model reduction. In the end, this work seeks to shed light on the enormous potential of voice-enabled intelligence in embedded systems, opening the door to more interactive, intuitive, and adaptable technologies that can easily adjust to changing human needs. This study advances the continuous development of smart gadgets by bridging the linguistic and technological divide, increasing their usability and accessibility in daily life. © 2024 IEEE.

关键词： Hidden Markov models

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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HealthPredictor: Accurate Health Insights at Your Fingertips 3

HealthPredictor: Accurate Health Insights at Your Fingertips

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sajjan, Mahantesh M. Ammanagi, Nausheen Pujari, Laxmi Kamatar, Vishwanath S Belavatgi, Meenaz K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580 027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi580 027 India

ISBN: (纸本)9798350364569

The "HealthPredictor: Accurate Health Insights at Your Fingertips"system transforms healthcare by predicting diseases based on user symptoms. Utilizing advanced machine learning models such as Decision Tree, SVM, Random Forest, and Logistic Regression, it assesses the likelihood of various illnesses. The system compares symptoms to a comprehensive dataset to provide accurate predictions, aiding in early detection. Users can consult with specialist doctors through an integrated chat feature, ensuring quick access to medical advice. By continuously learning from historical data, the system enhances its predictive capabilities, identifying new health patterns and trends. This proactive approach empowers users with timely information and personalized consultations, leading to better health outcomes. © 2024 IEEE.

关键词： Electronic health record

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Use of Polygram Substitution in Spotting the Swindler in Secret Sharing 11th

Use of Polygram Substitution in Spotting the Swindler in Sec...

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11th International Conference on Intelligent Computing and applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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Efficient Load Balancing Algorithms for Edge Computing in IoT Environments

Efficient Load Balancing Algorithms for Edge Computing in Io...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Nainwal, Ankita Almusawi, Muntather Bansal, Saloni Michaelson, Jacob Kumar, Suresh Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study investigates the accuracy of load balancing calculations in terms of edge computing for IoT. The paper compared four main calculations, namely Round Robin (RR), Weighted Round Robin (WRR), Least Connections (LC) and Ant Colony Optimization ratings in an reproduced edge computing atmosphere. As for the study, it focused on efficiency improvements of asset utilization as well as reducing inactivity and enhancing reasonableness. Weighted Round Robin showed that the predominant execution came about with an astonishing 25% decrease in idleness as compared to a depiction of noncapacity aware task load distribution. The Slightest Associations also showed extraordinary outcomes, achieving 20% decrease in idle period and demonstrated that it could be flexible with workloads. While flexible, Ant Colony Optimization demonstrated an inability to achieve steady variation improvements related with parameter tuning ranging from 15% toward 30%. The discoveries emphasize the significance of calculation choice based on the particular prerequisites of IoT edge situations. © 2024 IEEE.

关键词： Internet of things

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To Detect Spammer from Image-Based Spam E-mail through Adjacent-Duplicate Technique

To Detect Spammer from Image-Based Spam E-mail through Adjac...

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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Patel, Kavita Kumar, Sachin Singh, Vijendra Pratap Singh, Sandeep Kumar Saha, Tapash Kumar Jaiswal, Priya Ashoka Institute of Technology and Management Computer Science and Engineering Department UP Varanasi India Computer Science and Applications Veer Bahadur Singh Purvanchal University UP Jaunpur India Computer Science and Applications Mahatama Gandhi Kashi Vidyapeeth UP Varanasi India Computer Science & Engineering Kashi Institute of Technology UP Varanasi India Computer Science and Applications Kashi Institute of Technology UP Varanasi India

ISBN: (纸本)9781665456371

An Email facility proves to be efficient and more convenient communication way. Now a day, an internet service grows continuously, so information available to user is transformed from text to enriched multimedia. Where, spammer uses a new approach in which text is embedded in images. Widely known image detection algorithm such as optical character recognition (OCR) algorithm is defeated by spam image building techniques where randomization method is used to make sure the entire image is look different to each other. Spam image e-mails are mostly forwarded in a group that have visual similarity but dissimilar due to randomization technique used. The proposed system uses adjacent-duplicate detection technique to identify image-based spam. The system uses conventional spam detection technique to identify image-based spam and then further, use of numerous image-based spam filter to identify all the images which have similarity among them. The system is implemented to gain high identification rate where other side have false positive rate (FPR) is less than 0.001%. © 2022 IEEE.

关键词： Data mining

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Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML 2

Mobile-Based Classification and Detection of Diabetic Retino...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

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Predictive Maintenance of Power Grid Infrastructure using Long Short-Term Memory Networks

Predictive Maintenance of Power Grid Infrastructure using Lo...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mittal, Mudit Alsalami, Zaid Kumar, Rakesh Boob, Nandini Shirish Verma, Vikas Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study examines the application of Long Short-Term Memory (LSTM) networks for the prescient upkeep of a control lattice framework. Leveraging verifiable information comprising sensor readings and support records, the LSTM demonstrate precisely expects potential disappointments or debasement within the control network, empowering proactive support techniques. Through broad experimentation, the LSTM demonstrates accomplishing momentous execution with an exactness of 90%, precision of 88%, review of 92%, F1-score of 90%, and an area beneath the ROC curve (AUC) of 0.95. Comparative examination against standard methods and related works within the writing illustrates the prevalence of the LSTM-based approach in prescient support of control network infrastructure. Optimization of hyperparameters and interpretability examination advance upgrades the model's execution and encourages decision-making in control network administration. This study sheds light on the potential of machine learning strategies to revolutionize control framework upkeep, guaranteeing a more solid, strong, and economic vitality supply. © 2024 IEEE.

关键词： Long short-term memory

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