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

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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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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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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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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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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Prognosticating Liver Debility Using Classification Approaches of Machine Learning

Prognosticating Liver Debility Using Classification Approach...

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International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020

作者： Akshara, Revelly Reddy, Sandhi Kranthi Assistant Professor Department of Computer Science and Engineering Vignan Institute of Technology and Science Yadadri Bhuvanagiri DistrictTelangana India

ISBN: (纸本)9789811572333

In the field of Machine Learning in Healthcare, one of the most compelling factors is the prognosis of illness by examining the characteristics which have the most impact on its recognition. One of the deadliest diseases is a liver disease that accounts for about two million deaths annually worldwide. Prognosticating it in an early stage is helpful to get diagnosed at the right time as this may lead to a complete recovery in some patients. We can effectively prognosticate liver disease using supervised learning techniques specifically classification methods of machine learning. In this paper, different classification methods are applied to the Indian Liver Patient Records Dataset downloaded from *** to prognosticate liver disease. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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Improving Knowledge Retention in a Systems Programming Course Through Confirmatory Multiple-Choice Questions

教育研究与发展期刊

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教育研究与发展期刊 2022年第1期18卷 107-127页

作者：賴建宏鍾斌賢夏延德林聰武 Assistant Professor Department of Information and Computer Engineering Chung Yuan Christian University Professor Department of Information and Computer Engineering Chung Yuan Christian University Professor Department of Computer Science and Information Management Soochow University

Multiple-choice questions (MCQs) are often used in assessments. Although MCQs can be designed to test higher-order cognitive skills such as analysis, synthesis, and evaluation, they are often used to measure recall. This may encourage students to only learn material superficially. To resolve this problem, several variations of MCQs have been proposed to improve learning. We proposed a modified type of MCQ called a ＂confirmatory MCQ＂ (CMCQ). Optional online CMCQ tests were more effective than MCQs and confidence-based marking tests in helping students study material from a systems programming course because they prompted students to thoroughly evaluate each answer.

关键词： ultiple choice knowledge retention assessment evaluation methodology learning strategy

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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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“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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IoT Enabled Wearable Technology Jacket for Tracking Patient Health and Safety System

IoT Enabled Wearable Technology Jacket for Tracking Patient ...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： M. Santhanalakshmi S. Dhanalakshmi M. Radhika G. Kavitha G. Elavel Visuvanathan C. Srinivasan Assistant Professor Department of Computer Science Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women Chennai Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India Assistant Professor Department of Information Technology R.M.D. Engineering College Kavaraipettai Tamil Nadu India Professor Department of Computer Science and Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Adjunct Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Over the years, there have been ongoing efforts to use clothing as a vehicle for distributing digital capabilities. Information technology (IT) is being incorporated into garments to enable people to work more quickly. Because of their increasing availability, accessibility, and, most significantly, cost, wireless body area networks (WBANs) will undoubtedly be used in healthcare to enhance medical data utilization. Whether it has to do with post-operative recovery or Monitoring, applications for their function in the medical field are already being looked into. Wearable technology allows users to complete activities more efficiently by enhancing one or more of their senses with artificial information. The system proposes a method that displays critical information for doctors on a particular cloud for data analysis. In this concept, sensors are attached to someone's coat to collect real-time data. This data is analyzed, uploaded to the cloud using IoT Medium, and notified in the case of an alarming condition. The data access to real-time data, a website built into this module, and an alert notification system to warn the hospital and the family if the numbers are abnormal. A live stream is recorded using the camera, while the GPS module tracks the location. The doctor can take the proper step based on the patient's health.

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