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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Anitha, A. Priya, M. Kamaraj, Balakrishnan Yadav, Niraj Kumar Srivastav, Subhojit School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Madurai Medical College Tamilnadu Madurai India Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Signature is a powerful identity of a person for various legal acts, to authorize a transactions to avoid financial crimes. Now-a-days, we are using touch-sensitive pad for the verification of the signatures. Thus there is high risk of forgery to happen due to replicate the signature patterns. So it is essential to detection and verify the signatures while using public platforms to avoid crime of accessing the personal data. The advancements in image processing and artificial neural network helps in identifying and validating the real signature with the fack ones. This paper proposed a signature validation process using harris corner detection and Siamese neural network. Harris corner detector helps to extract the corners and infer the features of an signature image, whereas Siamese network identifies the pattern of signature by learning with the help of similarity function by taking the probability of the signature images. The proposed model achieves better accuracy of 98% for the new signature identification to the original signatures. © 2024 IEEE.

关键词： Neural networks

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Exploring the Potential of Quantum Computing for Revolutionary Problem-Solving and Computational Advancements

Exploring the Potential of Quantum Computing for Revolutiona...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Guru Vimal Kumar, M. Paramesh, J. Sriram, K.P. Anbalagan, E. Sasikumar, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India Information Technology St. Joseph's Institute of Technology OMR Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

Quantum computing is a model of computing a new approach to perform and solve computational problems and comes up with amazing advantages such as exponential speed-up of specific tasks that lead to a revolution of industries. In this study, Quantum Computing potential is discussed in multiple fields - drug discovery and optimization, and quantum machine learning contrasted with traditional methods based on the running time, accuracy, and scalability of quantum and classical algorithms. Applying several quantum algorithms to molecular simulation, optimization problem, and image classification data sets, quantum algorithms solved problems with at least 70% less number of iterations and at worse the same accuracy as classical methods. For instance, researchers noted that quantum neural networks performed better than classical networks in image recognition. The quantum algorithms in this study were implemented and simulated using IBM Qiskit, D-Wave's Ocean SDK and TensorFlow Quantum. Based on the literature reviewed;it was established that quantum computing has potential to revolutionalise endeavours in drug design, cryptography and artificial intelligent applications. While there are issues in qubit stability quantum decoherence, errors and other related complications, it is predicted that all these problems will be solved by qubit stability and quantum error correction shortly and therefore, realistic, scalable quantum systems are in the next few years. Further research is required on the quantum technology one which may bring a revolution in the way different industries solve their problems. © 2024 IEEE.

关键词： Qubits

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Developing an Adaptive Security Framework for Real-Time Threat Detection and Response in Cloud-Network Systems

Developing an Adaptive Security Framework for Real-Time Thre...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Paramesh, J. Sriram, K.P. Anbalagan, E. Sasikumar, S. Vimal Kumar, M. Guru Saveetha Engineering College Department of Computer Science and Engineering Chennai India Information Technology St.Joseph's Institute of Technology OMR Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha University Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

The study is centred on examining an intelligent security system for real-time threat identification and mitigation in cloud-network systems. The more cloud computing environments are being constructed;conventional models of security do not offer optimal solutions to counteract changing cyber threats. This framework incorporates the use of sophisticated algorithms in machine learning and real-time data analysis to achieve a more proactive form of defence measures. It incorporates anomaly detection, classification algorithms and predictive analysis in the improvement of threat detection and response time. The framework also has active response mechanisms for timely deliberation on anything that may be seen to have a detrimental effect on the cloud. Analyzes reveal high levels of detection, timely responses and scalability proving the suitability of the framework to handle new threats and/or blend with existing cloud components. The findings of this study can be considered as valuable contribution to cloud security since the approach proposed in this paper can be effectively employed to enhance protection against new types of threats in specific organizational contexts. © 2024 IEEE.

关键词： Cloud security

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Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

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computer Systems science & engineering 2023年第7期46卷 835-851页

作者： R.Surendran Youseef Alotaibi Ahmad F.Subahi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,*** utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and ***-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor ***,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the *** this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster *** major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster *** achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence *** addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management *** experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

关键词： Disaster management real-time applications wireless sensor networks clustering bioinspired algorithms

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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

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computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Genomic Insights into the Classification and Identification of Plasmodium Parasites 1

Genomic Insights into the Classification and Identification ...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Senthil, K. Yuktha Varshika, J. Gopinath, M. Pooja Sri, V. Vishwa, S. Monikaa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Artificial Intelligence and Machine Learning Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Information Technology Chennai602105 India Easwari Engineering College Department of Computer Science and Engineering Chennai602105 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Malaria is a contagious disease caused by plasmodium parasites transmitted to humans through infected female anopheles mosquito bites. The parasites invade Red Blood Cells (RBCs), impairing oxygen transport and potentially leading to respiratory distress and serious complications. Accurate and timely detection of plasmodium-infected cells is crucial for effective treatment and disease management. This study explores the application of Feed Forward Neural Network (FFNN) for the classification of plasmodium parasite-infected cells versus uninfected cells. Our Work Leverages a dataset comprising 27,558 colored images. We preprocess the data using Bilateral filtering, Histogram equalization, and Sobel filters to enhance model robustness. Our FFNN model, characterized by multiple hidden layers, and activation functions, is trained to discern subtle differences between infected and uninfected cells. Gaussian filter is used to highlight and detect the parasitized regions. The model's performance is evaluated using classification rate, precision, Log Loss etc. Initial results demonstrate that our model classifies the parasitized and uninfected cells with a classification rate of 97.5%, precision of 97.62, and log loss of 1.857. © 2024 IEEE.

关键词： Wiener filtering

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Advanced OptiDLCardioNet-Based Cardiac Arrhythmia Detection Model from ECG Signals 5

Advanced OptiDLCardioNet-Based Cardiac Arrhythmia Detection ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Muthukumar, B. Kartheesan, L. Pasupathy, Vijayalakshmi Surendran, R. Rajalakshmi Engineering College Department of Information Technology Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331530013

Heart disease is regarded as one of the most significant health problems dealing by people today. Approximately, cardiovascular disease (CVD) affects around 50 million people. Electrocardiogram (ECG) signals are more vital for identifying and keeping track of individuals with various CVDs. In order to detect different types of arrhythmias, this papers proposed a novel optimization driven deep learning model for cardiac arrhythmia detection, termed as OptiDLCardioNet. To improve and smoothen the ECG signal, a cascaded wavelet augmented Kalman (CWAK) filtering approach is first applied. Next, an Adaptive Position aware Black-winged SqueezeNet (APBWSqueezeNet) model is used for feature extraction. In order to classify the signals for arrhythmia disease diagnosis, the extracted features are input into an Enhanced Dilated Height-Width Axial Attention Convolutional Network (EDilBW-HWAACNet). Moreover, the hyper-parameters of the EDilBW-HWAACNet are adjusted through the application of the Improved Walrus Optimization Algorithm (IWOA). The MIT-BIH arrhythmia database is used to validate the performance of OptiDLCardioNet model. Rendering to the experimental results, the OptiDLCardioNet model is able to achieve high classification accuracy of 99.82%, which is superior to existing methods with fewer significant features. © 2024 IEEE.

关键词： Deep learning

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A Weighted Optimal Scheduling Scheme for Congestion Control in Cloud Data Center Networks

A Weighted Optimal Scheduling Scheme for Congestion Control ...

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作者： Li, Yun Jian, Shi-Jie Hsieh, Sun-Yuan Chung, Wei-Kang Zomaya, Albert Y. National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Institute of Medical Informatics Tainan701 Taiwan The University of Sydney Department of Computer Science and Information Systems CamperdownNSW2006 Australia

Cloud services are experiencing a remarkable increase in the number of users and the resource required over the past few years. Thus, it has become a great challenge for the internet vendors to make a robust framework to serve the customers with low cost and delay. Congestion control is one of the essential topics of routing algorithms in cloud data center networks. In this paper, we propose a weighted optimal scheduling scheme WSPR for congestion control in cloud data center networks which prevents the congestion in advance with the global view so that it can make good use of vacant network resources. We choose BCube as our network model and modify the network topology to fit software-defined networks so as to have a full view of the topology. First, we design the SP graph which contains all shortest paths between a source server and a destination server. Second, we propose WSPR to allocate the most appropriate path to each flow for congestion control. We implement a system to simulate a data center, and evaluate our proposed scheme WSPR by comparing WSPR with other classical methods. The experimental results demonstrate that our proposed scheme WSPR has the best performance in terms of the maximum delay, average delay, and throughput among all compared methods. © 2008-2012 IEEE.

关键词： Optical switches

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A structural variation genotyping algorithm enhanced by CNV quantitative transfer

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Frontiers of computer science 2022年第6期16卷 159-165页

作者： Tian ZHENG Xinyang QIAN Jiayin WANG Department of Computer Science and Technology School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China Institute of Data Science and Information Quality Shaanxi Engineering Research Center of Medical and Health Big DataXi’an Jiaotong UniversityXi’an710049China

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging ***,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of *** characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream *** the allelic copy number increases,the error rate of genotyping increases *** instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the *** by these,we propose a transfer learning-based method to genotype structural variations accurately considering *** method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype *** maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct *** adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be *** applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and *** experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and *** source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

关键词： genotyping copy number variations transfer earning

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