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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： Surjeet Kumar Md Shamsher Alam Zeeshan Khursheed Shadan Bashar Neha Kalam Computer Science & Engineering Assistant Professor in Government Engineering College Sheikhpura Bihar India Computer Science and Engineering Associate Professor in R.V.S College of Engineering & Technology Jamshedpur India DGM Jio Platform Limited Mumbai India Master of Computer Application Assistant Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science and Engineering Student R.V.S College of Engineering & Technology Jamshedpur India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

In the contemporary digital era, data has evolved into the cornerstone of myriad sectors, encompassing academia, commerce, and medical research. Yet, the acquisition of insights from this expansive reservoir of information has historically demanded a level of proficiency in formal languages such as SQL, thereby imposing a substantial barrier for individuals lacking technical expertise. In response to this pervasive challenge, the advent of Natural Language Interface to Databases (NLIDB) has emerged as a promising and transformative solution. NLIDB offers users the capability to engage with databases through the utilization of their natural language, whether expressed in textual or verbal form. This paradigm shift empowers both seasoned experts and neophytes to effortlessly extract valuable information from databases without the prerequisite of specialized technical skills. In this research paper, we propose an innovative system that capitalizes on an intermediary SQL generation process, facilitating the seamless bridge between natural language input and subsequent database retrieval. This novel approach eliminates the need for users to possess awareness of the intricate underlying complexities of the database management process. By simplifying the interaction between users and databases, our system endeavours to democratize data access and promote the universal utilization of this invaluable resource across diverse domains.

关键词： Structured Query Language Databases Natural languages Semantics Relational databases Manuals Reservoirs

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Designing an AI driven intelligent Tutorial System

Designing an AI driven intelligent Tutorial System

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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： Md Shamsher Alam Surjeet Kumar Zeeshan Khursheed Hemant Kumar Mahato Shadan Bashar Anjali Suman Computer Science and Engineering Associate Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science and Engineering Assistant Professor in Government Engineering College Sheikhpura Bihar India DGM Jio Platform Limited Mumbai India Master of Computer Application Assistant Professor in NSHM Durgapur India Master of Computer Application Assistant Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science & Engineering Student R.V.S College of Engineering & Technology Jamshedpur India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. This research paper explores the design and implementation of an AI-driven Intelligent Tutorial System (ITS) aimed at enhancing the personalized learning experience. The system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. The paper delves into the key components of the proposed ITS, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. The paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. Through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITS on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. The findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： Ethics Data privacy Machine learning algorithms Image recognition Decision making Tutorials Market research

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A GAN-Based Model of Deepfake Detection in Social Media

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Procedia computer science 2023年 218卷 2153-2162页

作者： Preeti Manoj Kumar Hitesh Kumar Sharma Research Scholar School of Computer Science University of Petroleum and Energy Studies (UPES)Dehradun 248007 India Associate Professor Engineering and Information Sciences University of Wollongong Dubai Knowledge Park Dubai UAE Associate Professor School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun 248007 India

DeepFake uses Generative + Adversarial Network for successfully switching the identities of two people. Large public databases and deep learning methods are now rapidly available because of the proliferation of easily accessible tools online. It has resulted in the emergence of very real appealing fake content that produced a bad impact and challenges for society to deal. Pre-trained generative adversarial networks (GANs) that can flawlessly substitute one person's face in a video or image for that other are proving supportive for implementing deepfake. This paper primarily presented a study of methods used to implement deepfake. Also, discuss the main deepfake's manipulation and detection techniques, and the implementation and detection of deepfake using Deep Convolution-based GAN models. A study of Comparative analyses of proposed GAN with other exiting GAN models using parameters Inception Score “IS” and Fréchet Inception Distance “FID” is also embedded. Along with the abovementioned, the paper discusses open issues and future trends that should be considered to advance in the field.

关键词： Digital Forensics Image Vision Deep Learning Generative adversarial network Deep Fakes Media Forensics Face Manipulation Face Recognition

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A Deep Neural Network for Image Classification Using Mixed Analog and Digital Infrastructure 3rd

A Deep Neural Network for Image Classification Using Mixed A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Kala, R. Punitha, M. Poomani Alias Banupriya, P.G. Veerasamy, B. Bharathi, B. Alzubi, Jafar Ahmad Abed Associate professor Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Information Technology Sethu Institute of Technology Kariapatti Puloor India Department of Computer Science and Engineering Karpagam Institute of Technology Tamil Nadu Coimbatore641032 India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Information Technology Coimbatore Institute of Engineering and Technology Tamil Nadu Coimbatore641109 India School of Engineering Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789819986453

Recently, Deep Learning (DL) methods are a well-known solution for image and object detection in computer vision applications. The software-based implementation of DL algorithms requires huge resources and consumes more power. To overcome these challenges, a hardware implementation of Deep Neural Network (DNN) using the integrated analog–digital architecture was proposed in this article. The Very Large-Scale Integration (VLSI)-based DNN implementation utilizes Pulse-Width Modulation (PWM), and analog–digital circuits. The integration of analog and digital architecture integrates the benefits of both circuits, which reduces power consumption and enhances processing speed and classification accuracy. The fabrication of the VLSI chip includes the DNN neuron circuit, PWM circuit, digital adders-subtracters, and SRAM memory. The experimental result verifies that the presented DNN VLSI architecture reduces the power consumption of the neural system, and enhances the image classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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When Computing Follows Vehicles: Decentralized Mobility-Aware Resource Allocation for Edge-to-Cloud Continuum

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IEEE Internet of Things Journal 2025年

作者： Nezami, Zeinab Chaniotakis, Emmanouil Pournaras, Evangelos University of Leeds School of Electronic and Electrical Engineering Leeds United Kingdom University College London Associate Professor with the Ucl Energy Institute London United Kingdom University of Leeds Professor at the School of Computer Science Leeds United Kingdom

The transformation of smart mobility is unprecedented-Autonomous, shared and electric connected vehicles, along with the urgent need to meet ambitious net-zero targets by shifting to low-carbon transport modalities result in new traffic patterns and requirements for real-time computation at large-scale, for instance, self-driving cars and augmented reality applications. The cloud computing paradigm can neither respond to such low-latency requirements nor adapt resource allocation to such dynamic spatio-temporal service requests. This paper addresses this grand challenge by introducing a novel decentralized optimization framework for mobility-aware edge-to-cloud resource allocation, service offloading, provisioning and load-balancing. In contrast to related work, this framework comes with superior efficiency and cost-effectiveness under evaluation in real-world traffic settings and mobility datasets. This breakthrough capability of 'computing follows vehicles' proves to be superior to balance resource utilization in a highly cost-effective way, while preventing service deadline violations by 14%-34%. © 2014 IEEE.

关键词： Distributed Optimization Dynamic Resource Allocation Edge-to-Cloud Computing Multi-agent System Smart Mobility

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A new algorithm for detection of nodes failures and enhancement of network coverage and energy usage in wireless sensor networks

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Materials Today: Proceedings 2023年 80卷 1717-1722页

作者： Satyanarayana, P. Mahalakshmi, T. Sivakami, R. Alahmari, Saad Ali Rajeyyagari, Sivaram Asadi, Srinivasulu ECE V. R. Siddhartha Engineering College Vijayawada India Department of ECE Prasad V Potluri Siddhartha Institute of Technology India Associate Professor CSE Sona College of Technology TN Salem 5 India Department of Computer Science Shaqra University Saudi Arabia Data Analytics Research Laboratory BlueCrest University College Monrovia Liberia

Present natural lives shows rising attention in various applications of the wireless sensor networks (WSNs). Significant applications are isolated and stiff regions where human involvement is hazardous or unfeasible. Some examples are space investigation, battlefield observation, costal, and edge security;nowadays, such networks are needful in several manufacturing and customer applications. A WSN is a device system, indicated as nodes;these nodes intellect the atmosphere and transfer the details collected from the ground through a wireless connection. Statistics are sent, probably via many hops, to a target that can use it nearby or is linked to various networks using the gateway. The sensor nodes might be stationary or changeable. Every node has clear data about the neighborhood or not. Communication over WSN over ecological hazards is a key issue. These constraints might impact the performance of the sensors/direction finding protocols and resource utilization;thus, they may show the way to the node crash situation, i.e., software/hardware breakdown, safety threats, extreme power utilization, etc. It is essential to examine the collision of breakdown over system performance. In the projected method, relay nodes are used as secure nodes for the positioned sensor nodes. The projected method is separated into two phases: the intra segregation stage and inter segregation stage. In the intra segregation stage, the unnecessary nodes reach nearer to the segregation border, and dispersion slowly enlarges the coverage. The projected model is examined through simulations and compares the results with existing methods. © 2021

关键词： Sensor nodes

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Mitigating Credit Card Fraud: A Machine Learning Perspective

Mitigating Credit Card Fraud: A Machine Learning Perspective

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： Shivam Bhushan Indrajeet Gupta Angayarkanni S A Balakiruthiga B Pandimurugan V Rajaram V Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Associate Professor Computer Science and engineering Acharya Institute of Technology Bangalore India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

In this day and age of online and digital transactions, credit cards have come up as a popular option of payment. This has led to the increase in the fraudulent credit card transactions and as a result the banks and the customers have to bear the loss. It is very vital for the credit card companies to identify and stop the fraud transactions. The main aim of this project is to create a model that can distinguish fraudulent credit card transactions from real ones. The proposed model uses the transaction patterns of previous credit card transaction data which contains both genuine and fraud data to train itself using suitable ML algorithms and identify the fraudulent transactions and nip it in the bud. We will be using using SMOTE to process the data considering the imbalance between legitimate and fraudulent transactions, hyperparameter tuning the model for increased effectiveness and using random forest classifier and decision tree classifier to compare results. By using the above mentioned techniques we saw an increase in accuracy and recall. These models offer a scaleable solution to the problem of real time fraud detection in an ever growing and vast financial landscape

关键词： Machine learning algorithms Accuracy Credit cards Data models Real-time systems Fraud Microelectronics Random forests Tuning Testing

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A Review on Big Data Analytics in Internet of Things (IoT) and Its Roles, Applications and Challenges 2nd

A Review on Big Data Analytics in Internet of Things (IoT) a...

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2nd International Conference on Data science, Machine Learning and Applications, ICDSMLA 2020

作者： Mothe, Rajesh Tharun Reddy, S. Vijay Kumar, B. Rajeshwar Rao, A. Chythanya, Kanegonda Ravi Research Scholar School of Computer Science and Artificial Intelligence S R University Warangal India Department of CSE SR Engineering College Warangal India Warangal India Associate Professor School of Computer Science and Artificial Intelligence S R University Warangal India

ISBN: (纸本)9789811636899

Big Data Analytics is a popular term all around utilized for a huge arrangement of data and it's analytics, it manages a large volume of heterogeneous information as organized, semi-organized and unstructured. We are having some knowledge on how information is pulled back from various sources and get converged for information preparing. In this paper, our major concentration on basic importance of the Big Data Analytics and IoT (Internet of things) and the multi-faced motivation behind Big data Analytics in IoT like its roles, applications, and challenges. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Utilizing CNN-Based architecture for Automated Differentiation between Gram-Positive and Gram-Negative Bacteria

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Procedia computer science 2025年 258卷 3900-3909页

作者： Debashree Borthakur Smriti Priya Medhi Ranbir Chowdhury Subrata Sinha Bipin Kumar Sharma Jayanta Aich Research Scholar Dept of Microbiology Tripura University Tripura India Assistant Professor (Senior) Dept of Bio Sciences Assam Don Bosco University Assam India Assistant Professor (Senior) Dept of Computer Science and Engineering Assam Don Bosco University Assam India Undergraduate student Dept of Computer Science and Engineering Assam Don Bosco University Assam India Associate Professor Dept of Computational Sciences Brainware University Kolkata India Centre for Multidisciplinary Research and Innovations Brainware University Barasat Kolkata Assistant Professor (Stage III) Dept of Microbiology Tripura University Tripura India Associate Professor Department of Computational Sciences Brainware University Kolkata India

Objectives: This study explores the integration of advanced machine learning methods, specifically convolutional neural networks (CNN), with bacterial image classification to enhance the reliability and efficiency of bacterial classification systems. We present a deep learning approach for the automatic classification of Gram-stained bacterial images into Gram-positive and Gram-negative categories. Methods: Gram staining is a widely used microbiological technique that distinguishes bacteria based on cell wall composition, classifying them as either Gram-positive or Gram-negative. We propose a CNN-based architecture designed to automate this classification process. The model is trained on a large, annotated dataset of Gram-stained bacterial images, leveraging the power of deep learning to achieve state-of-the-art performance in terms of both accuracy and processing speed. Results: Our results show that deep learning can significantly improve the accuracy and efficiency of Gram-stained bacterial image classification, paving the way for automated microbiological analysis in clinical and research settings. Our proposed architecture has classified the gram-positive and gram-negative images of bacterial cells, achieving an accuracy of 95.74% and precision of 96.97%. Novelty: Historically, Gram-stained bacterial image classification has been performed manually by microbiologists. This research pioneers the use of deep learning techniques for the automated classification of these images, achieving substantial improvements in both accuracy and speed. This innovation paves the way for the integration of automated image analysis into microbiological diagnostics and research.

关键词： Gram staining Bacterial Image Classification Deep Learning Convolutional neural network (CNN) Microbiology Gram-positive Gram-negative Automated analysis

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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

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computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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