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Evaluation of Biometric Classification and Authentication Using Machine Learning Techniques

Evaluation of Biometric Classification and Authentication Us...

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Umasankari, N. Muthukumar, B. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of Cse Sathyabama Institute of Science and Technology Chennai India Department of Information Technology Dmi College of Engineering Chennai India

ISBN: (纸本)9798350334357

This research article proposed the performance measurement of biometric image with computational methodology. This research adopts the following procedures: pre-processing, Feature extraction and Classification. Designing and building the algorithm and simulation programs have been done in a MATLAB environment. My SQL has been utilized for the maintenance of overall dataset. By employing the classification technique comparative analysis was carried out by inspecting three data mining techniques which were: Random Tree, The multilayer Perceptron neural network (MPNN), and the C4.5 decision tree (DT) algorithms. As per the final conclusion, the Random Forest Classifier algorithm exhibits greater performance in contrast to the other techniques. It has been found that the 93.5 % accuracy exhibited by Random Forest Classifier is much greater and enhanced. © 2023 IEEE.

关键词： Classification (of information)

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Examining the Effects of Applying AI to Enhance Big Data Retrieval Efficiency

Examining the Effects of Applying AI to Enhance Big Data Ret...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sharma, Nakul S. Yadav, Dhananjay Shrivastava, Manish Mandal, Sanjeev Kumar Ravivarman, G. Jana, D Little Femilin Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India

ISBN: (纸本)9798350329773

This studies paper explores how synthetic Intelligence (AI) technologies may be used to improve the efficiency of information retrieval from huge databases, as applied in a diffusion of industries. The paper will discuss the concept of AI as a tool to reduce information complexity, at the same time as inspecting current studies projects inside the subject and unique programs for the enterprise and patron worlds. It'll then discover the demanding situations that get up from applying AI and massive records technology, and description a suggested framework for first-rate practices. The paper will recall how AI may be utilized to enhance information seek outcomes in real-time, and provide an explanation for the diverse ways wherein this might be carried out. Subsequently, the paper will discuss the capability implications of the usage of AI for large statistics Retrieval efficiency, as well as its destiny applications. © 2023 IEEE.

关键词： Computational complexity

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A Secure NFT-Based System for Eliminating Counterfeit Certificates 5

A Secure NFT-Based System for Eliminating Counterfeit Certif...

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5th IEEE PhD Colloquium on Emerging Domain Innovation and technology for Society, PhD EDITS 2023

作者： Aparna, N. Kesavamoorthy, R. Cmr Institute of Technology Department of Information Science & Engineering Bengaluru India Visvesveraya Technological University Belagavi590018 India Cmr Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350316278

In recent times, the proliferation of fake certificates has become a growing concern, undermining the credibility of educational and professional qualifications. This paper presents a system based on blockchain designed to avoid the issuance and circulation of counterfeit certificates using Non-Fungible Tokens (NFTs). Using the unchangeable and see-through nature of blockchain technology, the proposed system ensures secure verification and authentication of certificates. By employing NFTs, each certificate is tokenized, making it unique, indivisible, and tamper-resistant. The system provides a robust and trustworthy platform for institutions, employers, and individuals to eradicate the menace of fake certificates, promoting trust and integrity in the certification ecosystem. © 2023 IEEE.

关键词： Smart contract

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Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

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Journal of Harbin Institute of technology(New Series) 2024年第1期31卷 90-98页

作者： K.Rama Devi V.Srinivasan G.Clara Barathi Priyadharshini J.Gokulapriya Department of Information Technology Panimalar Engineering CollegeChennaiTamil Nadu 600029India Department of Computer Applications Dayananda Sagar College of EngineeringBengaluru 560078KarnatakaIndia Department of Computer Appliations Karpagam Academy of Higher EducationCoimbatoreTamil Nadu 641021India Department of Computer Science Rathinam College of Arts and ScienceCoimbatoreTamil Nadu 641021India

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

关键词： electricity consumption predictive model data analytics smart metering machine learning

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Exploring the Benefits of AI for Content Retrieval 3

Exploring the Benefits of AI for Content Retrieval

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Begum, Ahmadi Karthikeyan, Kavitha Takale, Dattatray G. Bhambu, Pawan Yadav, Dhananjay Das, Sidhant Faculty of Engineering and Technology Department of Basic Science Karnataka Ramnagar India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The latest emergence of synthetic Intelligence (AI) and its packages in content retrieval have made it an increasing hobby for educational and technical networks. AI has made the information and utilization of content material retrieval gear less complicated and less complicated, offering a more advantageous consumer revel. This newsletter explores the advantages of AI for content retrieval and affords examples of standard AI-based content material retrieval packages. First, AI algorithms help improve the accuracy of content retrieval, allowing more excellent relevant content material to be retrieved from the web. Furthermore, AI can be used to perceive and advocate content that can be of hobby to the person primarily based on the consumer's possibilities and behaviors. AI can examine textual content files, organizing them into topics and sub-topics to facilitate green content material retrieval. Moreover, online entities such as engines like Google or content recommendation systems can leverage AI-primarily based predictive analytics to tailor content material hints to character users, allowing more powerful content retrieval. In conclusion, AI can help streamline and enhance content retrieval by enhancing the accuracy of seeking outcomes, offering more excellent applicable content pointers, and imparting better organization of documents and topics. © 2023 IEEE.

关键词： documents organizing recommendation streamline understanding

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Sustainable AI Systems for Monitoring and Predicting Wildfires in Vulnerable Forest Regions

Sustainable AI Systems for Monitoring and Predicting Wildfir...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Raj, T. Selvin Retna Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vikkiramapandian, D. Jothi, J. Nirmal Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331509828

Wildfires pose significant threats to ecological systems, human safety, and economic stability, especially in vulnerable forest regions. To address this critical issue, a sustainable AI-driven framework, WiSEFire (Wildfire Surveillance and Estimation), is proposed for real-time monitoring and prediction of wildfires. The framework integrates a hybrid approach, utilizing Random Forest for feature analysis and Gated Recurrent Units (GRU) for temporal fire prediction. The system processes multi-source data, including satellite imagery, IoT sensors, and meteorological information, to enhance detection accuracy and prediction reliability. Simulation experiments were conducted using benchmark datasets to evaluate WiSEFire against state-of-the-art algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Performance metrics such as accuracy, latency, and detection efficiency were analyzed. The results demonstrated that WiSE Fire outperformed existing methods, achieving superior detection precision and reduced latency while maintaining energy efficiency. This research highlights the potential of sustainable AI frameworks for mitigating wildfire risks and improving emergency response capabilities © 2025 IEEE.

关键词： Convolutional neural networks

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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An Enhanced Support Vector Machine Based Pattern Classification Method for Text Classification in English Texts 7

An Enhanced Support Vector Machine Based Pattern Classificat...

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7th IEEE International Conference on Computational Systems and information technology for Sustainable Solutions, CSITSS 2023

作者： Thatha, Venkata Nagaraju Veerasekharreddy, B. Venugopal, G. Ashok, K. Maddu, Sundarababu Srilakshmi, V. Mlr Institute of Technology Department of Information Technology Hyderabad India Kl Education Foundation Department of Computer Science and Engineering Vaddeswaram India Hyderabad India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India Medak Narsapur India

ISBN: (纸本)9798350343144

Text mining is a growing in a unique field that attempts to convey information from natural language text. It is too strength be uncertainty characterized as the development of analysing text as well as to take out the information that is constructive for particular purposes. Compared with other kind of data stored in databases, text is unstructured, shapeless as well as difficult to deal with algorithmically. Traditionally the patterns of the text are analyzed by using PTM and FTM algorithms but this algorithms produces less accuracy. To improve the performance of pattern classification, in this paper an Enhanced Support Vector Machine based Pattern classification method(ESVMPCM) is proposed. Then ESVMPCM is applied to Reuters dataset and measure the performance using different metrics as precision, recall, F-measure and accuracy. © 2023 IEEE.

关键词： Support vector machines

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Automated Leukaemia Prediction and Classification Using Deep Learning Techniques 2

Automated Leukaemia Prediction and Classification Using Deep...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Narendran, M. Amarendra, K. Revathy, D. Divyapushpalakshmi, M. Sharma, Dilip Kumar Upreti, Kamal Kcg College of Technology Department of Information Technology Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India Karpaga Vinayaga College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chengalpet India Gitam University Department of Computer Science and Engineering Bengaluru561203 India Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India Department of Computer Science Delhi -NCR Ghaziabad India

ISBN: (纸本)9798350369960

Leukemia is typically diagnosed based on an abnormal blood count, frequently an elevated White Blood Cell (WBC) count. The diagnosis is established through bone marrow, replaced by neoplastic cells. Acute Lymphoblastic Leukemia (ALL) is a type of leukaemia that affects the blood and bone marrow. Leukaemia primarily affects children and adults around the world. Early leukaemia detection is critical for appropriately treating patients, especially children. This research aims to present a diagnostic method that uses computational intelligence and image processing algorithms to identify blast cells from ALL images. The medical image is prepared initially using the preprocessing and segmentation technique for efficient classification. In this research, the type is accomplished using Bidirectional Associative Memory Neural Networks (BAMNN), where the accuracy is 96.87%, the highest classification rate and outperforms the existing technique. © 2023 IEEE.

关键词： accuracy activation function and associative memory bone marrow classification DL Leukaemia neural network neurons

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of information and Communication technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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