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Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble and Hybrid CNN-LSTM Models

Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Maharin Afroj Khan Md Sadbin Rifat Md. Saifur Rahman Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Software-Defined Networking (SDN) faces significant challenges in mitigating Attacks known as denial of service (DoS) and distributed DDoS due to its centralized control plane architecture. This centralization makes the control plane an attractive target for attackers, highlighting the need for effective detection and mitigation strategies. In this study, we propose three innovative methodologies to enhance the accuracy of detecting DoS/DDoS attacks in SDN environments. Our first methodology employs an ensemble model with a voting classifier, which achieves a detection accuracy of 97.4%. This approach leverages multiple weak learners to enhance the robustness of detection. The second methodology utilizes an ensemble with a stacking technique, attaining a superior accuracy of 99%, demonstrating the efficacy of integrating multiple base models in a hierarchical fashion. The third approach introduces a novel hybrid model that blends LSTM networks with CNN, achieving a competitive accuracy of 98.51%. A comparative analysis of these methods reveals that, while all three significantly enhance detection rates, the stacking ensemble technique outperforms the others. This finding underscores the potential of advanced ensemble techniques in effectively addressing the dynamic and evolving cyber threats within SDN environments. Our study offers valuable insights into strengthening SDN infrastructures against DoS/DDoS attacks, contributing to the ongoing efforts to secure modern network architectures.

关键词： Logistic regression Accuracy Prevention and mitigation Stacking Network architecture Robustness Real-time systems Ensemble learning Software defined networking Long short term memory

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An Improvised Approach Using YOLOv3 Architecture for Digital Panoramic Teeth Recognition and Classification 46

An Improvised Approach Using YOLOv3 Architecture for Digital...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Krishnappa, Veena Divya Jatti, Anand Rajasree, P.M. Vidya, M.J. Joshi, Revan Kumar Renumadhavi, C.H. Padmaja, K.V. Subramanya, K.N. Krishnamoorthy, Adharsh Rv College of Engineering Dept. of Electronics and Instrumentation Engineering Bengaluru India Rv University School of Computer Science & Engineering Bangalore India Dapm Rv Dental College and Hospital Department of Oral Medicine and Radiology Bengaluru India Rv College of Engineering Department of Industrial Engineering and Management Bengaluru India

ISBN: (纸本)9798350371499

Tooth loss may occur due to a lack of access to diagnostic imaging and other dental radiographs, despite the fact that these images are vital for treating oral health issues. For better teeth recognition and classification networks, a new model based on YOLOv3 is suggested. A smaller convolution layer and architectural deepening for improved feature extraction are two examples of how the model improves upon the YOLOv3 model for better metrics. A reduction in convolution layers allows for fast recognition and the introduction of the network architecture. A validation/test dataset is used to assess the model's performance, with the help of the Radiology department at Bengaluru's DAPM RV Dental College and Ho *** Relevance - When it comes to training artificial intelligence systems, radiologists are indispensable for producing accurate labels. These systems are vital for learning and dependable use in clinical areas. According to the research, artificial intelligence systems may one day be able to detect periodontal issues from digital Panoramic data. © 2024 IEEE.

关键词： Clinical research

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Applications and Approaches of Sentiment Analysis: A Current Review

Applications and Approaches of Sentiment Analysis: A Current...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Sandra G Salaja Silas Elijah Blessing Rajsingh Dept. of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Limitless amount of data is loaded in every moment in social media platforms in the forms of audio, videos, texts, and photos. They are rich sources of information, which has immense value. Hence extraction of information from them is inevitable. Many methods are employed for this and the popular among them to mine the information is sentiment analysis. To materialize the objectives of this study, a systematic review of articles published from 2013 to 2023 was done. Our study reviewed the relevance of sentiment analysis in social media by probing the different approaches followed, the platforms used and its applications, as its main objectives. Its results show that majority of studies applied machine learning approach or hybrid method for executing sentiment analysis.

关键词： Sentiment analysis Social networking (online) Voting Decision making Machine learning Disaster management Fraud Stakeholders Forecasting Meteorology

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Deep Learning Based Segmentation of Weed Images in crop fields using U-Net

Deep Learning Based Segmentation of Weed Images in crop fiel...

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Advancement in Technology (ICONAT), International Conference for

作者： K. Jairam Naik Dept. of Computer Science & Engineering National Institute of Technology Raipur Raipur India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Weeds are problems for crop yields because they exert harmful effects on the yields by competing with cultivated crops for necessary resources like nutrients, sunlight, and soil. As a result, continual attempts are being made to create safer and more effective herbicide formulations. For precision weeding and to reduce pesticide pollution, segmenting weeds in photos is extremely important. However, weeds and crops are comparable in the field setting, making it challenging to precisely segregate weed from intricate field photos. A deep learning-based system for segmenting weeds from photos was proposed in this paper. This algorithm can distinguish between weeds and crops and soil in photos. Utilizing a condensed U-net, this semantic segmentation method was created. An image augmentation strategy was suggested because image tagging for the semantic segmentation of weeds is challenging. The classification algorithms used in the modified U-Net are Support Vector Machine (SVM), Random Forrest (RF) and Decision Tree (DT). These were also used for predicting the weed part in a sample image before their segmentation. The threshold accuracy for segmenting the image using to be around 51.85% with a segmentation time of approximately 01.02 ms whereas our modified U-Netis obtained 91.03% accuracy and the segmentation time of 50.35ms. The results showed the modified U-Net successfully differentiated weeds from images with numerous other plants. This study seeks to demonstrate that the weed-associated image segmentation technique is effectively identifying and separating weeds in intricate field conditions.

关键词： Training Support vector machines Radio frequency Image segmentation Accuracy Pollution Semantic segmentation Training data Soil Classification algorithms

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Anthracnose Disease Detection in Papaya Fruit Using Machine Learning Techniques

Anthracnose Disease Detection in Papaya Fruit Using Machine ...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： P Kumaran Abhishek Tirkey Ujjwal Kumar K Vivek Kumar Dept. of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

The project focuses on predicting whether the papaya fruit is diseased or healthy. The main concern is anthracnose found in papaya fruit. The project was designed to help agriculture. We will use machine learning techniques to predict whether the fruit is sick or healthy. This project was designed to develop and evaluate learning models for predicting diseased and healthy fruits. The proposed method uses various classification algorithms, including decision trees, K-nearest neighbors (KNN), Naive Bayes, logistic regression, random forest, and common methods such as hard choices and soft choices. This study involves the collection of comprehensive information including different images of healthy and diseased fruits. Additionally, the project explores the benefits of integration by combining the results of each algorithm through hard voting and soft voting techniques. The goal of this combination is to increase distribution efficiency overall and power. As a result of the research, a helpful model that can determine if fruit is healthy or unhealthy will be produced. This project will be beneficial in the field of Agriculture and helpful for the farmers.

关键词： Image segmentation Logistic regression Accuracy Refining Predictive models Market research Agriculture

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Opportunistic Migration for Hybrid Memories While Mitigating Aging Effects

Opportunistic Migration for Hybrid Memories While Mitigating...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Aswathy N S Hemangee K. Kapoor Dept. of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Hybrid memory systems composed of Non-volatile memory (NVM) and DRAM to exploit the high density of NVM and low access latency of DRAM. Phase Change Memory (PCM), a type of non-volatile memory, is a viable choice for main memory. High write latency and high voltage requirements for PCM lead to Biased Temperature Instability (BTI) aging and performance degradation. De-stressing the memory circuit at regular intervals controls BTI aging. Memory performance can be enhanced by migrating the highest write count memory pages across memory units. Migration and de-stress halt the service of regular requests and affect the performance of the system. Therefore, it is crucial to control migration and de-stress to enhance hybrid memory performance while mitigating BTI aging. We propose DOPMig, a de-stress-aware page migration technique. The policy migrates write-intensive pages to DRAM at regular intervals but opportunistically parallel to the de-stress operation. This method of background migration helps to reduce the migration overheads and improves performance. DOPMig achieves a performance gain of 22%, and improves memory service rate by 15%, and increases DRAM access by 24%.

关键词： Phase change materials Degradation Nonvolatile memory Memory management Random access memory High-voltage techniques Aging Performance gain Phase change memory Proposals

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Reducing Quantum Measurement Repetitions in Image Classification through Probability Loss

Reducing Quantum Measurement Repetitions in Image Classifica...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Kwangyeul Choi Kyoung Keun Park Taehyun Kim Dept. of Computer Science and Engineering Seoul National University Seoul Republic of Korea

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum computing holds significant promise for solving complex problems more efficiently than classical computers. However, a major challenge lies in the repetitive measurement steps required to obtain reliable results from quantum states. This paper introduces the concept of probability loss to reduce the need for repeated measurements in a hybrid quantum-classical image classification model. For experiment, a hybrid model is introduced which is inspired by the Vision Transformer architecture, our model divides input images into patches and processes them as sequences.

关键词： computers computer vision Quantum computing Computational modeling computer architecture Quantum state Transformers Loss measurement Reliability Image classification

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AI-Based Indian Currency Detection for Visually Challenged Users

AI-Based Indian Currency Detection for Visually Challenged U...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： S. Shopika A S Senthil Velan Dharun Prakash SR Pavithra S R Poornima Lakshmi R. Ponnusamy Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

For denomination recognition, this Indian Currency Detection System will use a CNN trained on an extremely large and diverse set of Indian banknotes from $$ 10 to $$ 2000. It possesses an amicable interface through which images can be captured using the camera of the user's smartphone, providing real-time audio feedback to denominate the banknote without requiring any visual assistance. Made with the features of capturing an image, real-time recognition, and user-friendly design, these are made to add a great experience for smooth navigation by a blind person. The novel and innovative features make this Indian Currency Detection System unique. Using CNN, which has been trained on the comprehensive set of Indian banknotes from $$10 to $$2000, the software lets a user capture banknote image directly by the smartphone camera. It allows knowing the denomination through real-time audio feedback without any visual assistance. Such characteristics include image capture, real-time identification, and designing user-friendly user interfaces that make easy navigation of the surroundings possible for the visually impaired. It does more than detect currency. It significantly contributes to the discussion regarding the development and availability of more assistive technologies. It is usable and aims to bring about greater accessibility and inclusivity in promoting financial independence among visually impaired people. Contributing to the ability of cash transactions among the visually impaired to gain confidence in freedom and security, this project aligns with a broader discussion related to assistive technologies.

关键词： Visualization Image recognition Navigation Blindness Assistive technologies User interfaces Cameras Real-time systems Security Currencies

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Prediction of Diabetes Using Machine Learning Approach

Prediction of Diabetes Using Machine Learning Approach

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Niraj Kumar Sinha Meena Pundir Tanu Dhiman Dept. of Computer Science & Engineering Chandigarh University Gharuan Mohali Punjab India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Diabetes is a chronic disease posing a significant global health challenge, with the number of affected individuals projected to nearly double by 2045. This disease, which is characterized by high blood sugar, can cause severe complications such as kidney failure, blindness, and heart problems. To address this pressing concern this study target to enhanced machine learning technology-based system for the early understanding of diabetes with high accuracy. The suggested method makes use of the output of different technique, such as Decision Tree, Random Forest, K-Nearest Neighbor and Naive Bayes, to maximize their benefits and enhance overall prediction performance. The study evaluates the accuracy of each algorithm individually and selects the techniques with the best result as the final predictive system. The results of this study support ongoing initiatives to apply innovative data science methods for diabetes early diagnosis and treatment, ultimately assisting in the avoidance of the disease's severe consequences and enhancing patient outcomes. Based on the Random Forest classifier model, this model generated an accuracy of 96.83%.

关键词： Accuracy Computational modeling Pressing Nearest neighbor methods Prediction algorithms Diabetes Bayes methods Random forests Kidney Diseases

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Using ChatGPT in Undergraduate computer science and Software engineering Courses: A Students' Perspective

Using ChatGPT in Undergraduate Computer Science and Software...

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Frontiers in Education (FIE) Conference

作者： Noah Andersen-Kiel Panagiotis Panos Linos Computer Science and Software Engineering Dept. Butler University Indianapolis Indiana USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice full paper presents an empirical study aimed at evaluating the potential of ChatGPT, an advanced AI-driven chatbot, as a supplementary educational tool in undergraduate computer science and Software engineering (CSSE) courses. The study, initiated in the summer of 2023, focused on assessing ChatGPT's capabilities in generating accurate and complete computer code, identifying and rectifying code defects (bugs), and its scalability in handling larger programs. To achieve this, we conducted a series of experiments with ChatGPT. In one experiment, we introduced bugs into small programs from introductory CSSE courses. ChatGPT was tasked with detecting these defects and providing recommendations for fixing them. We evaluated ChatGPT's effectiveness in bug detection, the quality of its recommendations, and the completeness of the proposed solutions. We sought answers to questions such as whether ChatGPT found all injected defects, provided appropriate recommendations, and delivered high-quality solutions based on criteria like code completeness, size, complexity, and readability. In another experiment, ChatGPT was asked to generate code for assignments from previous CSSE courses, including Intro to computer science and Programming in C++, Intro to Python Programming, and Object-Oriented Programming and Data Structures using Java. We assessed the generated code's correctness and quality in comparison to student-written code. Similarly, in a third experiment, we evaluated ChatGPT's ability to generate larger programs using requirement specifications from an upper-division CSSE course on Agile Software engineering. Analyzing both qualitative and quantitative data from these experiments during the summer, we determined that ChatGPT showed promise as an educational tool. Consequently, we developed a plan to integrate ChatGPT into select CSSE courses for the fall semester of 2023. Specifically, ChatGPT was integrated into two of our introductory CSSE cou

关键词： Surveys Codes Scalability computer bugs Project management Chatbots Object oriented programming Programming profession Software engineering Python

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