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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Rekha, m.N. Prasad, mudarakola Lakshmi mukherjee, Saptarshi Nikam, Sudam Vasant Sharma, Swati Reddy, Pundru Chandra Shaker Jss Science and Technology University Electrical and Electronics Engineering Sri Jayachamarajendra College of Engineering Karnataka Mysore India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Bharati Vidyapeeth College of Engineering Instrumentation Engineering Navi Mumbai India Presidency University School of Computer Science Engineering and Information Science Karnataka Bangalore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350387933

At present, the speedy growth of natural-language-processing(NLP) has produced considerable evolution for the topic of machine-translation. There have been many deep neural network-based machine-translation(mT) methods that have become increasingly broad. Still, there currently lacks reliable automatic-error-detection(AED) systems for mT outcomes. One important metric for gauging the efficacy of an engineering English translation application is the accuracy of the corresponding deep learning model's translation. This research presents a deep learning-based solution for automatic mistake identification in machine translation results as a means of closing this gap. In this research, we propose a deep generative model to synthesis the training data that will be utilized to train a model to correct grammatical errors in English used in international trade. Next, the learner's corpus is corrected using the grammatical fault alteration approach, and 'error-correct' sentence pairs are created using the exacted aim sentences and by hand interpreted standard sentences and fed back to the error generation technique for retraining. The model's error detection and correction capabilities are enhanced through the establishment of a connection amid the grammatical error recognition and correction model. The suggested error recognition method has been shown to considerably increase the stealthiest of the trigger, guarantee the efficacy of the backdoor assault, and make the trigger resistant to specific data augmentation operations in experiments using datasets like GTRSB. © 2024 IEEE.

关键词： Error detection

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Brain Tumor Classification Using UNet Deep Neural Networks from 3D mRI Images 1

Brain Tumor Classification Using UNet Deep Neural Networks f...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Shaik, mohammad Khaja Kalpana, D. Sesadri, U. mukherjee, Saptarshi Dastagiraiah, C. Reddy, Pundru Chandra Shaker St. Ann's College of Engineering and Technology Electronics and Communication Engineering Andhra Pradesh Chirala India Departmet of CSE-Data Science India Vardhaman College of Engineering Computer Science and Engineering Telanagana Hyderabad India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Anurag University Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798350371802

A patient's therapy options and prognosis are both improved by detecting and diagnosing a brain tumor early. Brain tumors can be located and diagnosed with the help of magnetic resonance imaging (mRI). In real practice, however, medical personnel rely entirely on their own time and expertise when manually identifying brain tumors from a vast number of mRI scans. These days, medical diagnosis and treatment recommendation systems that use computer-aided expertise are all the rage. Brain tumor detection makes use of a plethora of frameworks based on machine-learning(mL) &deep-learning(DL). The purpose of this research is to present a DL-based approach for effective brain tumor segmentation and classification. The research uses convolutional-neural-networks(CNNs) for tumor classification after volumetric segmentation of mRI-images using the 3D-UNet model. To demonstrate the models' validity, loss and precision plots are provided. We compare the outcomes of several methods described in the literature with the measured performance of the models we offer. When compared to the most recent cutting-edge methods, the proposed work comes out on top. © 2024 IEEE.

关键词： magnetic resonance imaging

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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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Utilizing Deep Learning methods for Resource Allocation in 6G Communication Networks 5th

Utilizing Deep Learning Methods for Resource Allocation in 6...

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5th International Conference on Data science, machine Learning and Applications, ICDSmLA 2023

作者： Kavitha, R. Singh, Shweta Devrani, Rekha Kumar, Kakumanu Prabhanjan Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Department of Information Technology and M.Tech Integrated Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

(6G) conversation networks promise extra pace, coverage and capabilities than the current era (5G). To fulfill this promise, deep getting to know techniques can be used to allow better useful resource allocation choices. Deep mastering approaches can gather temporal correlations between node positions and sign characteristics to predict system variables over the years. This statistics may be used to higher control community sources, optimize spectral performance, and decrease latency. Improved resource allocation can also allow smarter visitors routing and selection making, specifically in terms of predicting consumer’s options and wishes. The proposed version uses convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enable seamless community connectivity. To determine resource allocations autonomously, it utilizes converting community topologies, consumer mobility, and a ramification of person site visitor’s profiles. The version optimizes resource allocation and cargo balancing in order to maximize throughput and decrease latency whilst still preserving machine stability. This permits the verbal exchange community to be greater adaptive while also increasing its efficiency. Ultimately, the proposed deep gaining knowledge of-primarily based aid allocation method provides a singular method to improve the overall performance and scalability of 6G verbal exchange networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Detecting Driver Fatigue with Python and OpenCV 10

Detecting Driver Fatigue with Python and OpenCV

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Pandi, C. Anish, T.P. Selvanayaki, S. Jagadeesan, P. Vedaraj, m. Sanjay, R. Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Computer Science and Engineering Avadi India R.M.K College of Engineering and Technology Computer Science and Engineering Tamil nadu India Saveetha Engineering College Artificial Intelligence and Data Science Tamil nadu Thandalam India R.M.D Engineering College Computer Science and Engineering Tamil nadu India

ISBN: (纸本)9798350353068

Utilizing eye aspect ratio analysis and facial landmarks, the sleep detection tool is intended to detect indicators of sleepiness or drowsiness in people. By leveraging the face recognition library, the program detects and extracts facial landmarks, with a particular focus on locating the positions of the eyes. The software analyzes the eye aspect ratio (EAR), which takes into account the relative distances between particular eye landmarks, to ascertain if an individual has fully or partially closed their *** video frame capture is performed from a chosen video source, typically a webcam, allowing real-time monitoring of the user. Each frame is processed to detect and analyze the eye regions, computing the EAR values for both the left and right eyes. If the EAR falls below a predefined threshold for a consecutive number of frames, it indicates that the person's eyes are closed for an extended duration, suggesting potential drowsiness or *** enhance user safety, an alarm sound is played when the program detects drowsiness, providing an immediate alert to the individual. The alarm sound serves as an auditory cue, helping the person regain alertness and avoid potential accidents or undesired consequences. Additionally, the program displays the computed EAR on the video feed, allowing for visual feedback and monitoring of the eye aspect ratio values for debugging purposes and fine-tuning the system's *** employing this sleep detection program, various applications can benefit from its capabilities. For instance, it can be utilized in the context of drowsy driving detection systems, alerting drivers when they exhibit signs of fatigue and helping prevent accidents due to impaired driving. moreover, the program holds potential for use in other scenarios where monitoring individuals' alertness and detecting sleep-related patterns is essential, such as workplace safety or medical *** summary, the sleep detection program employs faci

关键词： Video signal processing

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A Crucial Review on Ai Use In Hybrid Grids: A Ultra Smart Way Towards micro Grid

A Crucial Review on Ai Use In Hybrid Grids: A Ultra Smart Wa...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Gill, Amandeep Khalid, Abdul murugan, R. Vivekananda Global University Department of R&d Cell Jaipur India Noida Institute of Engineering & Technology Department of It and M.Tech Integrated Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India

ISBN: (纸本)9798350357769

The smart grid seamlessly integrates modern control, communication, and metering technologies, revolutionising the capture of large-scale, multi-type, high-dimensional data about electric power grid operations. Even with these developments, traditional approaches to modelling, optimisation, and control still face challenges when handling large amounts of data. As such, the noticeable uptick in the use of methods based on AI in the context of smart grids is significant. A thorough analysis is conducted in this study, providing an organised overview of common AI approaches used for important tasks including load estimation, power grid stability evaluation, defect detection, and security issues in smart grids and electrical infrastructure. In addition, the poll sheds light on upcoming research difficulties and imagines how AI technologies will be used to create genuinely intelligent electric grids. Additionally, the study pinpoints chances to use AI to solve smart grid problems. In the end, the article highlights how AI approaches may significantly improve the resilience and dependability of smart grid mechanisms, therefore bringing in an era for flexibility and effectiveness in the field of electric power distribution. © 2023 IEEE.

关键词： Artificial Intelligence Cloud Computing Consumer Behavior Prediction Data Privacy Faults Detection Load Forecasting Power Grid Stability Assessment Security Issues Smart Grid Survey

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Delving into the Intricacies and Nuances of modern Nested Network Systems 7th

Delving into the Intricacies and Nuances of Modern Nested Ne...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： mishra, Amba Saikia, Ankur Pan Krishnamoorthy, Ramkumar Patel, Warish Department of IT and M.Tech Integrated Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Technology Assam Down Town University Assam Guwahati India Tiergartenstr. 17 Heidelberg69121 Germany Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9789819753369

The paper "Delving into the Intricacies and Nuances of modern Nested Network Systems" investigates a novel approach to solving the problems inherent in today’s nested network systems. The suggested technique is an all-encompassing strategy for dealing with such complex systems that makes use of Breadth-First Search (BFS), Genetic Algorithms (GAs), and Deep Neural Networks (DNNs). The purpose of this research is to compare the suggested method to six current practices. The scalability, latency, security, load balancing, fault tolerance, energy efficiency, and quality of service of the network, among others, will all be evaluated as part of this strategy. Using these measures, the suggested technique is compared to the state of the art, with numerical results demonstrating its superiority. The use of four distinct kinds of diagrams to display the comparisons further emphasizes the better performance of the suggested approach. The suggested approach is the best option for dealing with the complexity of contemporary multilayer network topologies, as it enhances performance in a broad range of ways. In order to meet the ever-evolving problems presented by such networks, the results highlight the need of adaptive optimization, automation, and cutting-edge learning methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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Invoice Processing Automation Using UI Path Studio 5

Invoice Processing Automation Using UI Path Studio

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5th IEEE International Conference on Computing, Power, and Communication technologies, IC2PCT 2024

作者： Ramyadevi, R. Anbuselvan, A. Julian, Anitha Selvi, S. murali Dhar, m.S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India R.M.K Engineering College Department of Computer Science and Engineering 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)9798350383522

Invoices are the fundamental document that play a vital role in encapsulating the details of the products and the business transactions between the seller and the buyer. We cannot check our invoices at any time for emergencies. Substituting the case, by storing the basic details of multiple invoices in an excel sheet might be used for future purposes. This paper provides the advancement of data extraction through automation technologies. We begin this research by the technique Optical Character Recognition (OCR) in automation of data extraction from invoices. This reduces the manual errors during extraction as the entire process works with the automation and provides an accuracy of 95%. The paper discusses the design of the automation that has to be processed for the data extraction including the invoice details, line items and relevant metadata and stored in a structured and accessible format. This storage allows the customer to get through all their product details in a document. Furthermore, the study investigates the integration of extracted data from the invoice to the customer. This integration acts as the strategic role in transforming invoice data into a dynamic asset, enabling customers to glean insights. The paper incorporates real-world examples and case studies to illustrate implementations of scrapping techniques using automation. By addressing challenges such as technological limitations, integration complexities, and data security concerns, the paper provides practical understanding for customers in processing the automation. © 2024 IEEE.

关键词： Accuracy Automation technologies Data Extractions Data Storage Invoice Processing Optical Character Recognition (OCR)

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Analysis and Estimation of Discounts on Product Prices in E-Commerce Sites 5

Analysis and Estimation of Discounts on Product Prices in E-...

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5th IEEE International Conference on Computing, Power, and Communication technologies, IC2PCT 2024

作者： Julian, Anitha maheswar, Challa Ramyadevi, R. murali Dhar, m.S. Selvi, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.K Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Sales on e-commerce platforms are significantly influenced by discounts. When a discount is offered, 80% of buyers who initially show disinterest end up making a purchase, per a Retail me-not survey. This increases revenue and draws in new clients who might not have otherwise thought about making a purchase. Another survey by Accenture reveals that 75% of consumers are more inclined to choose a brand that provides personalized discounts. To retain customers, e-commerce websites can implement tailored discount strategies based on customers' past purchases or preferences. The decision-making process for discounts involves various factors. Pricing strategies and discounts, as identified in the study, are pivotal in influencing consumer behavior and encouraging purchases, particularly among new customers. This research delves into the complexities of discounting practices and their impact on the online retail ecosystem. Employing a comprehensive methodology that combines quantitative and qualitative techniques, the study investigates the frequency, trends, and effectiveness of discounts across different e-commerce platforms. Through extensive data collection, including details on discount percentages, durations, and consumer feedback, the research aims to reveal insights into the strategic implementation of discounts. The study's main objective is to analyze discounts that are given on a variety of products by gathering information from an online store and using the characteristics, reviews, and ratings of the products as factors to calculate the discount value. © 2024 IEEE.

关键词： data analysis discount analysis EDA estimation techniques feedback and reviews predictive modelling

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Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6601-6616页

作者： Takiddin, Abdulrahman Ismail, muhammad Atat, Rachad Serpedin, Erchin Florida State University Department of Electrical and Computer Engineering FAMU-FSU College of Engineering TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States Lebanese American University Department of Computer Science and Mathematics Beirut1102-2801 Lebanon Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States

Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversaries, with different levels of knowledge about the system, inject adversarial samples to circumvent the grid's attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this article answers three research questions. 1) What is the impact of utilizing spatio-temporal features to craft adversarial samples and how to select attack nodes? 2) How can adversaries generate surrogate spatio-temporal data when they lack knowledge about the system topology? 3) What are the required model characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we examine the robustness of several detectors against five attack cases and conclude the following: 1) Attack generation with full knowledge using spatio-temporal features leads to 5%-26% and 2%-5% higher degradation in detection rate (DR) compared to traditional FDIAs and using temporal features, respectively, whereas centrality analysis-based attack node selection leads to 3%-11% higher degradation in DR compared to a random selection;2) Stochastic geometry-based graph generation to create surrogate adversarial topologies and samples leads to 3%-13% higher degradation in DR compared to traditional FDIAs;and 3) Adopting an unsupervised spatio-temporal graph autoencoder (STGAE)-based detector enhances the DR by 5-53% compared to benchmark detectors against FDIEAs. © 2020 IEEE.

关键词： Cyber attacks

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