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Experimental Comparison of Quantum Support Vector machines and Neural Networks for Classification Tasks on Small Datasets

Experimental Comparison of Quantum Support Vector Machines a...

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research methodologies in knowledge management, Artificial Intelligence and Telecommunication engineering (rmkmATE), International Conference on

作者： Vigilson Prem m Paulraj D Priyanka P. S raagavi A. P Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

In recent years, significant breakthroughs have been made in both artificial intelligence and quantum information science. The synergy between these domains has opened new frontiers for innovation, offering transformative potential across research and industry. A growing body of work has explored quantum-enhanced learning models, with many proposals validated through simulations on classical hardware emulating quantum systems. Yet, the absence of uniform evaluation standards makes it difficult to objectively assess the performance of these emerging techniques. This paper explores the applicability of quantum-augmented learning methods to tangible, albeit small-scale, problem-solving scenarios. Focusing on practical implementation, we empirically evaluate two hybrid approaches: support vector machines with quantum-inspired kernel methods and neural networks adapted for quantum processing. To ensure robust analysis, our experiments leverage five diverse datasets, each processed using multiple quantum state encoding schemes. Through systematic comparison, we aim to clarify the strengths and limitations of current quantum-assisted models in handling real-world data challenges.

关键词： Support vector machines Technological innovation Systematics Quantum entanglement Scalability Neural networks machine learning Hardware kernel Standards

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Covid-19 Forecasting with Deep Learning-based Half-binomial Distribution Cat Swarm Optimization

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computer Systems science & engineering 2023年第1期44卷 629-645页

作者： P.renukadevi A.rajiv kannan Department of Computer Science and Engineering Paavai Engineering CollegeNamakkal637018Tamil NaduIndia Department of Computer Science and Engineering K.S.R.College of EngineeringTiruchengodeNamakkal637215Tamil NaduIndia

About 170 nations have been affected by the COvid VIrus Disease-19(COVID-19)*** governing bodies across the globe,a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive,and they feel challenging to tackle this *** researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these *** the previous works,Long Short-Term memory(LSTm)was used to predict future COVID-19 *** to LSTm network data,the outbreak is expected tofinish by June ***,there is a chance of an over-fitting problem in LSTm and true positive;it may not produce the required *** COVID-19 dataset has lower accuracy and a higher error rate in the existing *** proposed method has been introduced to overcome the above-mentioned *** COVID-19 prediction,a Linear Decreasing Inertia Weight-based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network(LDIWCSO-HBDCNN)approach is *** this suggested research study,the COVID-19 predicting dataset is employed as an input,and the min-max normalization approach is employed to normalize *** features are selected using Linear Decreasing Inertia Weight-based Cat Swarm Optimization(LDIWCSO)algorithm,enhancing the accuracy of *** Cat Swarm Optimization(CSO)algorithm’s convergence is enhanced using inertia weight in the LDIWCSO *** is used to select the essential features using the bestfitness function *** a specified time across India,death and confirmed cases are predicted using the Half Binomial Distribution based Convolutional Neural Network(HBDCNN)technique based on selected *** demonstrated by empirical observations,the proposed system produces significant performance in terms of f-measure,recall,precision,and accuracy.

关键词： Binomial distribution min-max normalization Cat Swarm Optimization(CSO) COVID-19 forecasting

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An optimal detection of android malware using dynamic attention-based LSTm classifier

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Journal of Intelligent and Fuzzy Systems 2023年第1期44卷 1425-1438页

作者： Jebin Bose, S. kalaiselvi, r. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil India Department of Computer Science and Engineering R.M.K College of Engineering and Technology Puduvoyal Gummidipoondi India

In today's world, Android has become the most significant and standard operating system for smartphones. The acceptance of the rapidly growing android system has outcome in a significant enhancement in the number of malware on comparing earlier days. There were several antimalware programs that are designed efficiently for protecting the sensitive data of the user in a mobile system from the occurrence of such attacks. Detection of malware system based on deep learning model along with the use of optimization technique is presented in this work. Initially, android malware dataset input is acquired and the normalization process is done. The feature selection is carried along with the optimization technique recurrent Tuna Swarm Optimization. By this, an optimal selection of features can be attained. © 2023 - IOS Press. All rights reserved.

关键词： Android malware

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Comparative Analysis of CNN models for Braille Character Classification 3

Comparative Analysis of CNN Models for Braille Character Cla...

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3rd International mobile, Intelligent, and Ubiquitous Computing Conference, mIUCC 2023

作者： Adru, Sri Likhita Johnson, Sandra Hemalatha, m. R.M.K. Engineering College Kavaraipettai Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350306231

Braille character classification is a crucial task with applications in accessibility and assistive technology. In this paper, we present a comprehensive study of different Convolutional Neural Network (CNN) models for the classification of Braille characters. We utilize an image dataset containing 1560 images, with each alphabet represented by 60 images. Our study involves evaluating the performance of SqueezeNet, resNet-50, Xception, and a custom CNN model that we designed. By comparing the accuracy and effectiveness of these models, we aim to provide insights into their suitability for Braille character classification tasks. © 2023 IEEE.

关键词： Convolutional neural networks

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AI and Blockchain Based Platform for Empowering Content Creators: Enabling NFT - Driven Content Sharing, Inspiration Generation, and Collaborative Interaction 4

AI and Blockchain Based Platform for Empowering Content Crea...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Adru, Sri Likhita Johnson, Sandra Hemalatha, m. R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai India

ISBN: (纸本)9798350300888

The digital era has revolutionized the way content is created, consumed, and monetized. Content creators face new opportunities and challenges in reaching their audiences and establishing sustainable revenue streams. This research paper explores the potential of an innovative platform that leverages the power of artificial intelligence (AI) and blockchain technology to empower content creators in sharing and selling their work through Non-Fungible Tokens (NFTs). This platform integrates unique features, including text-to-image generation for creative inspiration, a recommendation engine for content discovery, and a collaborative chat room fostering direct interaction among creators and their audiences. The objective of this paper is to provide a thorough examination of the platform's architecture, functionalities, and the potential impact it can have on the content creation ecosystem, thereby offering a comprehensive understanding of its structure and design. © 2023 IEEE.

关键词： Blockchain

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Image Detection for Criminal Investigation using road Traffic Data

Image Detection for Criminal Investigation using Road Traffi...

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research methodologies in knowledge management, Artificial Intelligence and Telecommunication engineering (rmkmATE), International Conference on

作者： Srinivas Suresh kumar Yeswanth Babu JS Guru Sabbarish A. Jasmine Gilda Department of Computer Science and Engineering R.M.K. Engineering College India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

Criminal investigations often rely on traffic data to analyze incidents and identify suspects. This paper explores a machine learning-based image detection system that processes traffic footage to assist in criminal investigations. The proposed system leverages various machine learning algorithms to detect vehicles, license plates, and facial features in traffic data, providing law enforcement with an automated tool for efficient analysis of traffic- related criminal activities.

关键词： Accuracy machine learning algorithms Law enforcement Noise reduction Lighting machine learning robustness road traffic knowledge management License plate recognition

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maintaining Privacy in Contact Tracing Systems for Pandemics Through Edge Computing 1

Maintaining Privacy in Contact Tracing Systems for Pandemics...

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1st International Conference on AImL-Applications for engineering and Technology, ICAET 2025

作者： Tejaswini, Virupakshi Sirisala, Nageswararao K.S.R.M. College of Engineering Department of Computer Science and Engineering A.P Kadapa India

ISBN: (纸本)9798350355611

Data processing and publication are crucial today with the advancements in technologies such as edge computing and IoT(Internet of Things). A vast volume of data is being generated by internet-connected devices, often on a continuous basis. The COVID-19 pandemic data exemplifies this trend of constant data flow, as the global medical emergency has necessitated ongoing data collection. This data must be processed and analyzed, and subsequently published, while ensuring personal privacy is upheld. Edge computing devices can handle various tasks like data preprocessing and analysis in any setting. It is crucial to process data before uploading it to the cloud and sharing it with advisories or media outlets, as improper handling could lead to privacy breaches. To protect personal privacy, measures such as differential privacy techniques are implemented to protect data. Edge computing technologies offer several advantages, including efficient data processing close to the origin to minimize internet data transmission capacity use and enhanced security for confidential information on public cloud infrastructures. © 2025 IEEE.

关键词： Data Sharing Differential Privacy (DP) Edge Computing Individual Privacy

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Optimized Extreme Learning machine Framework for Efficient ransomware Detection Using Seagull Optimization-Based Feature Selection 2

Optimized Extreme Learning Machine Framework for Efficient R...

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2nd IEEE International Conference on Emerging research in Computational science, ICErCS 2024

作者： rajululi, G. Govinda Geetha, r. Bebitha, J. Basha, Sk mahaboob Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India R.M.K. Engineering College Department of Computer Science & Engineering Chennai India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Computer Science & Engineering Chennai India TKR College of Engineering and Technology Department of Computer Science & Engineering Telangana India

ISBN: (纸本)9798331534950

ransomware is becoming a more serious threat to cybersecurity, and conventional detection techniques frequently fail to maintain up with its changing strategies. To overcome this difficulty, this research proposes an extreme learning machine (ELm) framework enhances the system the efficiency in threat identification. The Static and dynamic feature of files is identified using ELm for analyzing their behavior patterns. The Seagull Optimization Algorithm (SGO) is employed for feature selection refining the dataset by eliminating redundant characteristics, to improve detection process. The system model trained with a Support Vector machine (SVm) to determine which files are malicious and which are safe. The ELm-SGO-SVm framework is evaluated using performance metrics for identify ransomware threats and it has efficacy in early ransomware detection and mitigation by showing that it greatly outperforms existing techniques. However, this proposed method strengthens the cybersecurity defenses against ransomware threat and ensures better protection for important digital threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Effective Segmentation of Brain Tumors through the GOA Algorithm using Deep Learning 2

Effective Segmentation of Brain Tumors through the GOA Algor...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Haritha, V. Babu, J.Jagan Saranya, r. Yogaraja, C.A. rajalakshmi, S. manimegalai, L. R.M.K Engineering College Computer Science and Engineering India R.M.D Engineering College Kavaraipettai India Rmk College of Engineering and Technology India Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering India R.M.K. College of Engineering and Technology Electronics and Communication Engineering India

ISBN: (纸本)9781665456302

Image segmentation is used to isolate aberrant tumor regions inside the brain, which is a necessary step in the diagnostic procedure. The tissue of the brain is required for the magnetic resonant Image (mrI) for segmenting the brain tumor. The primary objective of this proposed work is to develop a non-invasive approach to estimate the grade of a Brain Tumor (BT) using mrI image. Tumor area was recovered from postprocessed image employing the fuzzy C means methodology. The texture, local binary configuration (LBP) and fractal characteristics were recovered when the characteristics were extracted. The settings for three alternative classification algorithms, including random Forest (rF), k-Nearest Neighbor (kNN) and Support Vector machine (SVm), were then accustomed using the Grasshopper Optimization Algorithm (GOA). Finally, the performance of three graders was assessed before and after optimizing. © 2023 IEEE.

关键词： Support vector machines

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multi-objective load balancing based on adaptive osprey optimization algorithm

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3871-3878页

作者： Panneerselvam, karthick Nayudu, P. Prathap Banu, m. Sheerin rekha, P.m. School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Rajeev Gandhi Memorial College of Engineering and Technology Andhra Pradesh Nandyal India Department of Information Technology R.M.K. Engineering College Tamil Nadu Kavaraipettai India Department of Information Science and Engineering JSS Academy of Technical Education Karnataka Bangalore India

With the increasing complexity of distributed systems, achieving an optimal distribution of tasks across resources is paramount for enhancing system performance. Therefore, in this study, a novel multi-objective load balancing approach based on adaptive osprey optimization algorithm (AO²) within computing environments is proposed. Our proposed method aims to simultaneously optimize multiple objectives, such as minimizing energy consumption, cost and time, through the application of advanced optimization algorithms. The proposed system initially predicts the load of each virtual machine (Vms). After the prediction process, the tasks are assigned to the Vms. The research involves a comprehensive evaluation comparing the proposed approach with existing load balancing techniques, showcasing its effectiveness in achieving superior results. The findings demonstrate the potential of the multi-objective optimization algorithm to enhance load balancing efficiency in diverse computing scenarios, providing a valuable contribution to the field of distributed systems and resource management. © Bharati Vidyapeeth's Institute of computer Applications and management 2024.

关键词： Adaptive osprey optimization algorithm Load balancing multi-objective and energy consumption Virtual machine

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