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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Rakhi Bhardwaj Ayush Billade Srinivas Chenna Atharva Deshpande Vaishnavi Arthamwar Assistant Professor Department of Computer Engineering Vishwakarma Institute of Technology Pune India Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Cyberbullying in group chat applications has become an increasingly pressing issue in today's digital society, affecting users across various platforms. This review aims to explore and analyze the current techniques, methodologies, and challenges in detecting cyberbullying in group chat environments. By examining existing literature and approaches, we focus on how NLP, ML, and DL techniques are applied to identify harmful behaviors in real-time conversations. The review also highlights the complexities of analyzing unstructured text data, the role of sentiment analysis, and the need for context-aware models to detect subtle forms of harassment. Additionally, we investigate privacy concerns, ethical considerations, and the limitations of existing models in multilingual and culturally diverse groups. The paper emphasizes the importance of developing more robust, scalable, and adaptive detection systems to create safer online environments. This review concludes with future research directions, suggesting the integration of advanced AI techniques and collaborative efforts between researchers, developers, and policymakers to mitigate the growing cyberbullying problem in group chat applications.

关键词： Adaptation models Sentiment analysis Ethics Reviews Cyberbullying Oral communication Detectors Multilingual Complexity theory Context modeling

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Design and Development of a Novel Algorithm to Predict Fake Logo using Learning based Digital Image Analysis Methodology

Design and Development of a Novel Algorithm to Predict Fake ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： N. Nagajothi Shabana Memon G. Mohan Parashuram Shankar Vadar Anita Soni S. Prince Sahaya AP/ Mathematics Nandha Engineering College (Autonomous) Erode- 52 Assistant Professor Bharati Vidyapeeth (Deemed to be University) Institute of Management Kolhapur Associate Professor Department of Mathematics K.S.Rangasamy College of Technology Tiruchengode TamilNadu Assistant Professor Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India Department of Computer Science and Engineering IES University Bhopal Assistant Professor (Sl.G) Department of Computer Science Engineering Sri Ramkrishna Engineering College Coimbatore

In the realm of fake logo detection, the continuous surge in digital content manipulation necessitates advanced and efficient methodologies for safeguarding brand integrity. This study explores the application of diverse machine learning models, ranging from traditional algorithms like K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to sophisticated deep learning (DL) architectures such as VGG19, Inception, and Convolutional Neural Networks (CNN). The proposed methodology combines feature extraction using VGG19 with classification using an Inception model, presenting a novel approach to enhance detection accuracy. Evaluation metrics, including accuracy, precision, specificity, and sensitivity, which enhance the performance of each model. Computational efficiency is also analyzed, providing a comprehensive understanding of the trade-offs between accuracy and processing speed. The proposed methodology emerges as a promising solution, achieving a remarkable accuracy of 97% while maintaining efficiency. This study contributes to the evolving landscape of fake logo detection, offering valuable insights for practical implementation in scenarios demanding precision, speed, and reliability.

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Cloud-Driven Wearables using Naive Bayes Algorithm and Advanced Sensors in Smart Fabrics

Cloud-Driven Wearables using Naive Bayes Algorithm and Advan...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： M.D. Anto Praveena Nukala Sujata Gupta V. Bini Marin J Gnana Jeslin R. Meenakshi M. Muthulekshmi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Assistant Professor Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Assistant Professor Faculty of Management Studies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Professor Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Adjunct Professor Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

In the realm of fashion technology, the introduction of smart textiles and wearable sensors may be attributed to the fast progression of technology and the rising need for customized, interactive, and intelligent apparel. This study investigates the interface of electronics and textiles to develop cutting-edge, multifunctional, and flexible apparel. This research digs into the fascinating world of smart textiles. It investigates the many different kinds of sensors, such as temperature sensors, heart rate monitors, motion detectors, and chemical sensors that may be easily incorporated into the fabric. These sensors make a wide variety of applications possible, such as improved user experiences, monitoring of health conditions, and data collecting on the surrounding environment. In addition, to assure the practicability and usage of smart textiles, it addresses the problems and possibilities connected with various power sources, connection choices, and data processing units. The purpose of this study is to contribute to the continuous development of intelligent clothing, which has the potential to revolutionize the fashion industry by producing clothes that not only represent an individual's sense of style but also give useful insights, functionality, and connectedness to the digital world.

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An Automated Methodology for Detecting Cercospora Infestations in Eggplant using YOLOv10N

An Automated Methodology for Detecting Cercospora Infestatio...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S. Deepan Ms. M. Revathy Department of Computer Science and Engineering KIT - Kalaignarkarunanidhi Institute of technology Coimbatore India Assistant Professor / CSE KIT - Kalaignarkarunanidhi Institute of technology Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Cercospora leaf spot, a damaging fungal disease in eggplants, threatens agricultural productivity and farmer livelihoods. This study introduces an automated system for early detection of Cercospora infestations and affected percentages using advanced image processing and the YOLOvlON algorithm. A diverse dataset of annotated eggplant leaf images highlights healthy and infected regions. Preprocessing steps, including noise reduction and feature enhancement, ensure accurate detection. Supervised data augmentation enhances the model's adaptability to real-world conditions. The proposed system achieves high precision with competitive metrics, such as mean Average Precision (mAP), precision, and recall. Its lightweight design ensures compatibility with various devices, promising accessible solutions for farmers. Future developments aim to integrate mobile platforms, advancing sustainable and scalable plant disease management in agriculture.

关键词： Productivity Precision agriculture Performance evaluation Plant diseases Sentiment analysis Noise reduction Object detection Data augmentation Real-time systems Pragmatics

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Dynamic Defense Model against Eclipse Attacks in Proof-of-Work Blockchain Systems

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Procedia computer science 2024年 235卷 1202-1212页

作者： Surendra Reddy Vinta Sunil A. Patel Aws Zuhair Sameen Mukesh Soni Dr Ihtiram Raza Khan Hayder Mahmood Salman Associate Professor School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Assistant Professor L. D. College of Engineering Ahmedabad College of Medical Techniques Al-Farahidi University Baghdad Iraq Assistant Professor Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab 140413 India Academician Jamia hamdard Delhi Computer Science Department Al-Turath Universiy College Baghdad Iraq

An emerging threat within blockchain systems is the malicious tactic known as eclipse attacks, wherein attackers aim to deceive and gain control over targeted nodes. Despite the increasing popularity of blockchain technology, effective defence mechanisms against eclipse attacks have been lacking. A novel and dynamic defence model designed explicitly for Proof-of-Work (PoW) consensus blockchain systems is proposed to address this critical issue. The model relies on a node-to-node evaluation mechanism to ensure robust protection. By leveraging mutual evaluations conducted between clients and incorporating the renowned Kademlia algorithm, a ranking value is assigned to each node within the defence model. These rankings empower nodes to prudently select trustworthy peers as neighbours based on a threshold comparison, effectively mitigating the risk posed by malicious nodes. Notably, the proposed approach significantly enhances the security of blockchain systems without necessitating modifications to existing protocols or network structures. The effectiveness of the proposed defence model in countering eclipse attacks is extensively validated through a series of experiments. Furthermore, the approach surpasses previous defence strategies, offering a higher probability of successful defence while maintaining minimal overhead. With its straightforward implementation and operational efficiency, the dynamic defence model is an ideal solution for real-world deployment scenarios. This research presents a compelling and robust defence model against eclipse attacks in PoW blockchain systems, addressing the existing gap in defence mechanisms. Furthermore, this advancement paves the way for the wider adoption of blockchain applications across various domains by providing enhanced security, reliability, and trustworthiness.

关键词： Blockchain Eclipse Attacks Dynamic Defence Model Proof-Of-Work (Pow) Consensus Network Structures Targeted Nodes

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Emotion Recognition in Real Time for Personalized Healthcare Using Digital Twin Approach

Emotion Recognition in Real Time for Personalized Healthcare...

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International Conference on Advances in Information Technology (ICAIT)

作者： B. Karthikeyani D. Yuvaraj T. Parimalam P. Parthasarathi Nandha Arts and Science College & Assistant Professor @ Shree Venkateshwara Arts and Science (Co Education) CollegeGobi Department of Computer Science and Design Kongu Engineering College Perundurai Department of Computer Science Nandha Arts and Science College Perundurai Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Facial Emotional Recognition (FER) in healthcare can help to monitoring the patient by analyzing the emotional state of the patient through various emotional states. In medical industry it can help to trace the emotional state changes and diagnosis the pattern which are mapped respective to the patient's history. Digital twin technology is transforming healthcare systems and improving patient care, clinical operations, predictive analytics, and training and simulation by integrating virtual simulations and emerging analytic mechanism through ML and DL techniques. By allocating resources, monitoring workflow, and providing individualized patient care, these algorithms reduce healthcare operations and assist in determining an individual's risk. Since FER has lot of technical challenges like illumination, obstruction issues also high implementation cost. To solve these issues, the FER system will embed with the digital twin technology to provide a better result from real time sensing cameras also analyzing the result and guiding for a better treatment of an individual. We propose a system of end to end framework which produces best results in minimum amount of time with maximum accuracy. This real time implementation will help all the healthcare centers/industry to study an individual's health status as well as early prediction of diseases with effective treatment steps.

关键词： Training Emotion recognition Face recognition Medical services Speech recognition Cameras Real-time systems Digital twins Medical diagnostic imaging Monitoring

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Improving the state of materials in cybersecurity attack detection in 5G wireless systems using machine learning

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Materials Today: Proceedings 2023年 81卷 700-707页

作者： S. Sumathy M. Revathy R. Manikandan Assistant Professor & Head Department of Computer Science Sir Theagaraya College(Shift II) Chennai India Assistant Professor Department of Computer Science Sir Theagaraya College(Shift II) Chennai India Academic Co-Ordinator & Head Dept. of Computer Science The Quaide Milleth College Chennai India

The utilization of information from the radio channel is useful in detecting the spoofing attacksin 5G wireless communications. This concept has been used for a wide range of uses for the Internet of Things (IoT) environment by users and their IoT devices. But how these tasks can minimise the effects of cyber threats in genuinely complex networks has not yet been sufficiently addressed. These are seriously exposed, as is the case with 5G broadband networks due to a wide variety of technology at various abstract stages. As 5G IoT is the 5G environment, including the IoT background, in this article, we are an artificial intelligence (AI) intended to minimise the impact of 5G IoT threats, when extended to the participants involved on a number of *** paper uses Support Vector Machine (SVM) based PHY-layer authentication algorithm to detect the possible security attacks in 5G wireless communication at physical layer. It is utilized in increasing the rate of authentication with test features. The detection rate is improved further with test statistic features. The model is implemented on multiple-input multiple-output (MIMO) channel. The simulation results shows that the proposed method yield the high detection rate on all attacks.

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“Machine Learning-Based Food Edibility Prediction Using Neural Networks”

“Machine Learning-Based Food Edibility Prediction Using Neu...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Narendra Kumar S Ravi KM Arahanth M G Satheesh Pragathi Basavaraj Department of Biotechnology R V College of Engineering BE students Department of Computer Science and Engineering R V College of Engineering Scientific officer cum Assistant Professor R V College of Engineering

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Safety and the edibility of products is a must in the food industry. This research paper introduces a machine learning method for predicting whether food products can be eaten or not. A neural network model was created using various characteristics of the dataset including shelf life, manufacture date, optimum storage temperature and product type (solid, liquid or gas) to predict the edibility of a given item. The performance of the model was measured using accuracy, Mean Squared Error (MSE), and R-squared (R2). These findings demonstrate that neural networks are effective in classification tasks as they offer dependable means of forecasting if food is unsafe to eat.[1] Traditional methods have received a significant boost by this approach since it allows faster scalable automated predictions that could be integrated into larger food safety systems

关键词： Temperature measurement Solid modeling Accuracy Temperature Computational modeling Scalability Neural networks Predictive models Solids Food safety

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Collaborative Defense Strategies: AI, ML, and Cybersecurity: A Comprehensive Study 12th

Collaborative Defense Strategies: AI, ML, and Cybersecurity:...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Borah, Samarjeet Dhabliya, Dharmesh Dhage, Prashant Shukla, Jyoti Rai, Sumit Kumar Bhattacharya, Saurabh Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pimpri Pune India Assistant Professor SCSE Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819601462

In the fast-changing field of cybersecurity, the rise of advanced threats requires new and creative defense strategies. This study thoroughly examines collaborative defense strategies, focusing on the utilization of artificial intelligence (AI) and machine learning (ML) techniques. The study aims to evaluate the effectiveness of five prominent machine learning algorithms (random forest, support vector machine (SVM), gradient boosting, isolation forest, and one-class SVM) in the field of cybersecurity. The assessment is carried out utilizing the NSL-KDD dataset, which is a standard in the industry. The study commences by investigating the origins of collaborative defense, analyzing its historical development and fundamental principles. The text explains the importance of AI in collaborative defense, highlighting machine learning algorithms and natural language processing as essential elements. The study explores the uses of artificial intelligence, specifically in identifying, averting, and reacting to threats. A substantial part of the research focuses on evaluating the effectiveness of the five chosen machine learning algorithms. The evaluation includes predictive analysis to forecast cyber threats and proactive defense measures. The study examines how machine learning improves the sharing of threat intelligence by facilitating real-time information exchange between collaborating entities. The challenges and considerations related to incorporating AI and ML into cybersecurity are examined, emphasizing ethical implications, bias, fairness, transparency, and accountability. Case studies and success stories from real-world scenarios showcase examples of successful collaborative defense strategies applied in various industries and sectors. The study evaluates and contrasts the random forest, SVM, gradient boosting, isolation forest, and one-class SVM algorithms based on performance metrics like accuracy, precision, recall, and F1 score, utilizing the NSL-KDD dataset. T

关键词： Adversarial machine learning

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Enhancing Relational Database Interaction through Open AI and Stanford Core NLP-Based on Natural Language Interface

Enhancing Relational Database Interaction through Open AI an...

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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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