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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management and Research Gwalior India Mits Department of Computer Engineering Gwalior India Sos in Computer Science and Applications Jiwaji University Gwalior India

ISBN: (纸本)9798350346961

One of the major diseases in developing countries is Oral Cancer, caused by alcohol, tobacco product and smoking which creates uncontrolled and abnormal cells in parts of human body. Recent Convolution Neural Network (CNN) has helped in medical industry to used medical images for finding the different types of diseases. The objective of research to build new CNN model which use for analysis the oral cancer images and determine the cancerous and noncancerous image. In this paper, CNN technique and image processing are used to categorize cancer or non-cancer lips and tongue image dataset. Deep Learning approaches is used to develop and check the performance of proposed CNN model. For experiment small Kaggle dataset that contains cancerous and non-cancerous lips and tongue images and apply proposed CNN model (Oral_Cancer_Detection). The result of Oral_Cancer_Detection model is very effective and accurate with low complexity. The Oral_Cancer_Detection model found 94% accuracy in our experiment and achieved better accuracy results in less time.. © 2023 IEEE.

关键词： Neural network models

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Enhancing digital security using Signa-Deep for online signature verification and identity authentication

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第2期8卷 58-69页

作者： Ch, Ravikumar Sridevi, Mulagundla Ramchander, M. Ramesh, Vankudoth Kumar, Vadapally Praveen Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Applications Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Emerging Technologies CVR College of Engineering Hyderabad 500039 India

In the contemporary digital realm, the utilization of online services has surged, facilitated by the seamless integration of deep learning technology, which is paramount in applications demanding precision and efficiency. A pivotal use case in this context is online handwritten signature verification, where the need for exceptional accuracy is indisputable. This paper introduces ‘Signa-Deep, ’ an innovative approach designed to address the challenge of online signature verification and the determination of an individual’s authorization status. The study explores a range of methodologies, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), GoogleNet, and MobileNet, to discern the authenticity of signatures and affirm the identity of the signatory. The results of our proposed method are promising, showcasing its potential to significantly enhance the security of digital transactions and identity verification processes. In summary, ‘Signa-Deep’ harnesses deep learning technology to bolster the accuracy and reliability of online signature verification, thereby contributing to the overall robustness of digital interactions and identity validation processes. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Authorization Status Deep Learning Digital Transactions Security Identity Authentication Online Signature Verification

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Monitoring, Tracking and Fighting Pandemics using Drone-based Artificial Intelligence in IoT 9

Monitoring, Tracking and Fighting Pandemics using Drone-base...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kumar, K. Praveen Sangeetha, S. Kumar, T Rajesh Bhuvaneswari, R. Rasi, D. Mahaveerakannan, R. Kakatiya Institute of Technology and Science Department of Information Technology Warangal India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Computer Science and Applications Vellore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.

关键词： Coronavirus

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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A Comparative Analysis and Prediction of Diabetes Using Machine Learning Approaches 5th

A Comparative Analysis and Prediction of Diabetes Using Mach...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Ansari, Gufran Ahmad Bhat, Salliah Shafi Bhoite, Sachin Department of Computer Science and Engineering and Software Engineering University of Hail Hail Saudi Arabia Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India Pune411 038 India

ISBN: (纸本)9789819763177

Diabetes is a leading cause of death. People with type 1 diabetes (T1D) can face a number of consequences from their illness. Hyperglycemia is a symptom of T1D disease. Hyperglycemia can be symptoms that can be used for predicting diabetes in patients using the machine learning approach (MLA). Learning algorithms play a role in disease detection and prediction of decision-making. The leading cause of diabetes is really a high blood sugar level or a lack of insulin. As a consequence, it is critical to develop a reliable method to predict diabetes. Once it becomes a serious health concern, it will damage different organs such as the lungs, brain, nerves, blood vessels and heart as a result. Several approaches for detecting diabetes have been used in recent decades. Early diabetic diagnosis is currently very important to help the patients’ health, and this can be done by utilizing a variety of procedures. Tenfold cross-validation technique was used to train dataset for classification. In this paper, authors use different classifications of algorithms such as random forest, decision tree, logistic regression and multilayer perceptron for finding T1D patients. The accuracy of MLP is 98.077% which is the highest among other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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Learning Techniques for Depression Detection: A Comparative Studies

Learning Techniques for Depression Detection: A Comparative ...

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Das, Arnab Debbarama, Mrinal Kanti Barik, Rabindra K. National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350399387

Depression is a common mental problem that can fundamentally affect individuals' emotional wellness as well as their everyday lives. After COVID-19 other pandemics and subsequent social isolation this issue is more potent than ever. Numerous research works have been going on searching for methods that effectively recognize depression in order to detect depression. In this regard, a number of studies have been proposed. In this study, it examines a number of previous ones utilizing various Machine Learning (ML) and Artificial Intelligence (AI) methods for depression detection. In addition, various methods for determining an individual's mood and emotion are discussed. This study also discusses how facial expression, voice, gesture can be understood by chatbot and classified it as a depressed person or not. Addition to this, it reviews all the related research works and evaluates their methods to detect depression. © 2023 IEEE.

关键词： COVID-19

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computer Vision-Hybrid Learning based on Multi Scale Dilated Convolution Module Mechanism Implemented for Object Detection 1

Computer Vision-Hybrid Learning based on Multi Scale Dilated...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Kalpanadevi, D. Kartheeban, K. Mayilvaganan, M. Bamaruckmani, P. Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Koil India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Computer Science PSG College of Arts and Science Coimbatore India Department of Computer Science and Computer Applications Ayya Nadar Janaki Ammal College Sivakasi India

ISBN: (纸本)9781665460842

For smart traffic management and highway regulation, vehicle detection and statistics in highway surveillance video scenes are crucial. The massive installation of traffic surveillance cameras resulted in the collection of a sizable database of traffic video footage for analysis. In order to achieve the best detection from prediction, YOLOV3 (You Only Look Once) can be achieved in this study by including a multi-scale augmentation convolutional module technique. The first camera reads the licence plates of approaching vehicles before blockchain technology is used to store predictions in the cloud. It can display available parking spaces and be embedded in parking spaces. If there aren't any open spots, they are shown similarly. To make sure the automobile stays in the parking spot after it leaves the lot, a second camera scans the licence plate once again and matches it to the time of arrival. The size of vehicle objects varies substantially when viewed from this angle, and it is harder to spot small items that are off the road. It is crucial to successfully address the aforementioned issues and further apply them while dealing with complicated camera scenarios. This article concentrates on the aforementioned issue and offers a workable method so that the outcomes of vehicle detection can be used for tracking numerous objects and vehicle counts. The convolutional network's takes an input must be a fixed size, and its more complex structure necessitates lengthy training sessions and substantial resources. It extracts the feature points that need to be identified, compares them with the ORB algorithm, establishes road detection lines, and counts the number of moving vehicles and the volume of traffic. © 2022 IEEE

关键词： Convolution

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4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Roselinkiruba, R. Jude Moses Anto Devakanth, J. Sharmila, L. Vasumathy, M. Misba, M. Saranya Jothi, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India Madanapalle Institute of Technology and Science Department of Computer Applications Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore India

ISBN: (纸本)9798331515720

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. The aim of this study is to increase the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly Optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three important criteria such as edge, entropy and correlation. Finally, the data is embedded in the LSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. Then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time. © 2024 IEEE.

关键词： Image quality

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pFedCal: Lightweight Personalized Federated Learning with Adaptive Calibration Strategy

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IEEE Transactions on Services Computing 2025年第3期18卷 1627-1640页

作者： Deng, Dongshang Wu, Xuangou Zhang, Tao Xiang, Chaocan Zhao, Wei Xu, Minrui Kang, Jiawen Han, Zhu Niyato, Dusit Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243002 China Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Provincial Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Chongqing University College of Computer Science Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Guangdong University of Technology School of Automation Guangzhou510062 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Federated learning (FL) is a promising artificial intelligence framework that enables clients to collectively train models with data privacy. However, in real-world scenarios, to construct practical FL frameworks, several challenges have to be addressed, including statistical heterogeneity, constrained resources, and fairness. Therefore, we first investigate an aggregation gap caused by statistical heterogeneity during local model initialization, which not only causes additional computational overhead for clients but also leads to the degradation of fairness. To bridge this gap, we propose pFedCal, a novel personalized federated learning with lightweight adaptive calibration strategy that performs calibration compensation through the prior knowledge of clients. Specifically, we introduce compensation for each client at the model initialization, with the compensation derived from the global gradient and the latest gradient bias. To enhance the calibration effect, we introduce a smoothing-based calibration strategy, and we design an adaptive calibration strategy. A representative example demonstrates that the proposed calibration and smoothing strategies improve fairness for clients. The theoretical analysis indicates that with an appropriate learning rate, pFedCal converges to a first-order stationary point for non-convex loss functions. Comprehensive experimental results show that pFedCal achieves faster convergence, higher accuracy, and improved fairness than the state-of-the-art methods. © 2008-2012 IEEE.

关键词： Federated learning

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Secured Reversible Data Hiding Scheme with NMI Interpolation and Arnold Transformation

Secured Reversible Data Hiding Scheme with NMI Interpolatio...

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International Conference on Network Security and Blockchain Technology, ICNSBT 2023

作者： Jana, Manasi Jana, Biswapati Joardar, Shubhankar Jana, Sharmistha Lu, Tzu Chuen Department of Computer Applications Haldia Institute of Technology West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal West Midnapore India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India West Bengal Midnapore India Department of Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9789819944323

A novel and efficient 2-level secure reversible data hiding (RDH) method has been proposed based on image interpolation and Arnold transformation. An input image is scaled-down to original image and then scaled up to cover image using NMI interpolation method. The gray values of pixels are classified into a set of ranges. A difference value is calculated from adjacent pixels and it is used to find out the lower bound and upper bound of the range interval to which it belongs to. Before embedding, the secret data is scrambled using Arnold transformation implementing the 1st level security of proposed method. The scrambled secret data again updated to modified secret message to implement 2nd level security of proposed scheme. The input image is taken as reference during data embedding phase to raise the quality of the stego image. The experimental analysis shows that the proposed method has relatively higher imperceptibility and data embedding capacity than other schemes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Interpolation

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