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A Reinforcement-Learning Self-Healing Approach for Virtual Network Function Placement 36

A Reinforcement-Learning Self-Healing Approach for Virtual N...

引用

36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Carleton University Dept. of Systems and Computer Eng. Ottawa Canada Dept. of Software Engineering and It Montréal Canada

ISBN: (纸本)9781665477161

Modern networking paradigms like Service Function Chaining (SFC) allow for services to be broken down to a series of ordered and interconnected Virtualized Network Functions (VNFs) that can be hosted in generic servers in EdgeCloud datacenters. Nonetheless, a critical issue arises, when a hardware or software failure occurs and the VNFs of an SFC need to be repositioned, allowing to autonomously bring the system back to its normal operation, a process called self-healing. In this paper, a distributed methodology is proposed that aims to address this challenge, considering the requirements of all involved actors. Specifically, a Reinforcement Learning (RL) based algorithm is proposed that allows to iteratively optimize and determine an SFC healing solution upon a datacenter failure. As a second stage, a revenue-driven resource allocation mechanism is integrated, to resolve the contention for resources in an already functional datacenter that potentially occurs due to the repositioning. Various simulation scenarios prove the efficiency of our proposed resilient healing mechanism. © 2023 IEEE.

关键词： Resource allocation

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Factoid Question Answering System using Knowledge Graph 7

Factoid Question Answering System using Knowledge Graph

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Gaikwad, Vikas Patil, Archana College of Engineering Dept. of Computer Engineering and It Pune India

ISBN: (纸本)9798350304268

Here we discusses various topics related to natural language processing and question answering systems. It explains the difference between reading comprehension QA and Knowledge Graph Question Answering (KGQA) and highlights the importance of world knowledge for dialogue systems. The text then goes on to describe a simple question answering system and the limitations of such systems in handling complex questions. Finally, the concept of knowledge graphs is introduced and their role in NLP applications is discussed. Overall, the text provides an overview of key concepts and techniques used in question answering systems and their potential applications in various fields. © 2023 IEEE.

关键词： Dialogue systems Knowledge Graph Natural Language Processing Question Answering System

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Advancement in Image Plagiarism Detection using Feature Extraction and Deep Learning Approaches 5

Advancement in Image Plagiarism Detection using Feature Extr...

引用

5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Bhandari, Sonam Chaudhary, Sumit Malhotra, Sonal Joshi, Kapil Kirola, Madhu Rajkumar Uttaranchal Institute Of Technology Uttaranchal University Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Dev Bhoomi Uttarakhand University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350367171

With the widespread use of digital media, amount of visual content on the internet and the incidence of image plagiarism has also increased. In various domains including academia, journalism and creative industries, unauthorized use or reproduction of images without proper attribution has become significant concern. Scaling, rotation, cropping, filtering due to these diverse range of transformations detecting image plagiarism is a challenging task now. This study investigates the use of deep learning approach to image plagiarism detection that combines methods for similarity measurement, clustering and feature extraction. A comprehensive solution is being proposed by combining Scale Invariant Feature Transform (SIFT) for extracting features, Bag of Visual Words (BoVW) model and Convolutional Neural Networks (CNNs) for image analysis. The effectiveness of the proposed approach through extensive experiments is demonstrated, outperforming the baseline methods and showcasing superior performance metrics. © 2024 IEEE.

关键词： Contrastive Learning

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Advancements in Animal Tracking: Assessing Deep Learning Algorithms 15

Advancements in Animal Tracking: Assessing Deep Learning Alg...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Majumder, Avishek Mahmud, Tanjim Habib, Israt Binteh Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

In the rapidly advancing field of computer vision, object detection has become crucial for various applications, including animal tracking, face detection, and surveillance systems. This study investigates the efficacy of contemporary object detection methodologies by evaluating the performance of the You Only Look Once (YOLO) models and TensorFlow Model Zoo architectures for animal tracking. YOLO models, known for their ability to process entire images in real-time and predict bounding boxes and class probabilities simultaneously, offer significant advantages over traditional methods such as Convolutional Neural Networks (CNNs) and Fast R-CNNs. This paper compares the performance of YOLOv5 and YOLOv7, alongside TensorFlow-based models like Faster R-CNN ResNetv152 and SSD ResNet101, using a dataset of animal images. Our findings reveal that YOLOv5 outperforms other models with a mean average precision (mAP) of 9 7.5%, demonstrating superior accuracy and efficiency in object detection tasks. YOLOv7 also shows strong performance with an mAP of 96.7%, while TensorFlow Model Zoo's Faster R-CNN and SSD models lag behind with mAPs of 81.9% and 81.6%, respectively. The results highlight the significant advancements in deep learning and object detection algorithms, particularly the advantages of YOLO's architecture in handling complex detection tasks in real-world scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-Terrorist Attacks 14

Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-T...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Samaraweera, S.A.R.P. Siriwardana, S.M.D.N. Fernando, W.D.V. Rajakarunaratne, D.N. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350335095

Maritime cyber-terrorist attacks have become a major concern to the entire world in recent decades. Everyone should be more aware of marine strategies to prevent cyberterrorist attacks, both locally and globally. This study investigates how Sri Lanka and the Sri Lanka Navy (SLN) may successfully address maritime security concerns by utilizing current resources and developing a marine strategy to counter such maritime cyber-terrorism assaults. The literature review in this thesis primarily evaluates the level of understanding in Sri Lanka concerning when marine cyber terrorism strikes may occur. Furthermore, this thesis investigates if Sri Lanka has any maritime rules and regulations. The use of such laws and regulations will concentrate on how they can increase their expertise. © 2023 IEEE.

关键词： Cybersecurity

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Classification of Skin Lesions using Squeeze and Excitation Attention based Hybrid Model of DenseNet and EfficientNet

Classification of Skin Lesions using Squeeze and Excitation ...

引用

2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Armaǧan, Senanur Gündoǧan, Esra Kaya, Mehmet Firat University Dept. of Computer Engineering Elaziǧ Turkey Firat University Dept. of Software Engineering Elaziǧ Turkey

ISBN: (纸本)9798350369106

Skin cancer, like all other types of cancer, is a health problem where early diagnosis saves the patient's life. Successful detection of skin lesions in dermoscopic images enables this process to be managed more quickly. Deep learning architectures have advanced significantly in recent years, particularly in the classification of medical images. For the multi-class classification of skin lesions, a hybrid model was developed in this study utilizing the convolutional neural network architectures of DenseNet and EfficientNet. In addition, SE (Squeeze and Excitation) attention mechanism is integrated into both models. Experimental results show that the inclusion of SE attention mechanism provides a noticeable improvement in classification accuracy. This study not only contributes to the development of effective skin lesion classification systems, but also It also provides valuable insights into the synergistic effects of combining neural network architectures with attention mechanisms. © 2024 IEEE.

关键词： Convolutional neural networks

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Survey on Ethical Challenges of Implementing AI in Healthcare 7

Survey on Ethical Challenges of Implementing AI in Healthcar...

引用

7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Lalwani, Nikita Ur Rahim, Muhammad Abdul Basit Anand, Aravind Shaik, Sumair Pasha Moghe, Gauri Shashank Datta, Debarati California State University Long Beach Dept. of Computer Engineering and Computer Science Long Beach United States

ISBN: (纸本)9798350350890

In today's world, Artificial Intelligence (AI) and its usage concerning human tasks have become an integral part of our daily lives. Humans depend upon AI to provide faster and more efficient solutions. In the world of medicine, where every decision made by a doctor, physician, or consultant directly impacts the lives of those being treated or diagnosed, AI is making its way to help efficiently provide such results as the intelligence of a human being. Healthcare systems around the world are now making use of AI to help provide humane solutions using algorithms and Machine Learning (ML). Fallacies or inaccuracies in the arena of healthcare can have horrendous outcomes for the patient on whom the procedure is being carried out. Sometimes, certain ethics could be traded off by the system, such as confidentiality, autonomy, justice, and biased decisions leading to false results and victim blaming, which directs the masses into not trusting AI-related healthcare solutions. This situation can lead to mishaps like 'AI winter,' where the public could start disbelieving the results of AI solutions. This survey review makes an effort to analyze and address these pertinent ethical issues underlying the requirement for algorithmic translucency, concealment, and safeguarding the interests of all those involved. © 2024 IEEE.

关键词： Adversarial machine learning

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KinderEye: Kindergarten Child Performance Monitoring System 9

KinderEye: Kindergarten Child Performance Monitoring System

引用

9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Rathnayaka, B. Rabel, W.T.S. Namasingha, C.N.W. Hettiarachchi, H.D. Hansika, Mahaadikara M.D.J.T. Wellalage, Sasini Nuwanthika Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350325218

Kindergarten is the most important beginning of a child's formal education. Since the brain is rapidly developing in early childhood, a lack of brain development at kindergarten can create a weak foundation for learning, health, and behavioral problems throughout life. Digital learning and monitoring applications for young children are widely available but often lack opportunities for physical engagement, which can negatively impact executive function development. In this paper, we introduce a system for continuously monitoring mental development in terms of working memory, decision-making ability, language skills, and likelihood of having ADHD. The system consists of three physically playable games that collect performance data from digital sensors and encourage children to be involved in physical activities while increasing enthusiasm for evaluations. Continuous performance measurement allows teachers and parents to know the development of children and guide them or direct them to have suitable medical treatments. Memory development evaluation is done from the button-pressing game which allows giving inputs according to a pattern previously indicated. For decision-making ability measurement, we are using a remote controllable robot and maze in which children navigate the robot toward the winning point of the maze. Language development is measured by giving several activities through an attractive device that measures listening, speaking, reading, and writing. Furthermore, checking ADHD is done by lively tracking child behavior from a camera while playing a game and invalid game inputs. © 2023 IEEE.

关键词： Decision making

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Design of a Hybrid Framework for Detecting Attacks in Fog-based IoT Architecture using Deep Learning 15

Design of a Hybrid Framework for Detecting Attacks in Fog-ba...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pokale, Navnath B. Sharma, Pooja Mane, Deepak T. Dept. of Computer Engineering School of Engineering and Technology D. Y. Patil University Ambi Pune410507 India Dept. of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798331300579

IoT-Fog computing offers a broad variety of services for IoT-based end-systems. End IoT devices communicate with cloud nodes and fog nodes to administer client tasks. During the data collection process between the fog and cloud layers, IoT endpoints are more likely to be compromised by critical attacks such as DDoS and other security threats. It is necessary to detect these network vulnerabilities early on. By extracting features and placing the danger in the network, DL is crucial in predicting end-user behavior. Due to the limited processing power and less storage, Deep Learning algorithms cannot be directly implemented on IoT devices. In this article, we present a three stage Deep Hybrid Detection Model for Attack Detection in IoT-Fog Architecture. Enhanced Z-score normalization based data preparation will be carried out in the beginning. On the basis of preprocessed data, features such as IG, raw data, entropy, and enhanced MI are extracted in the second step. The collected characteristics are used as an input to hybrid classifiers called DBN and optimized DeepMaxout in the third stage to classify the assaults based on the input dataset. A hybrid optimization model known as the BMJFO (Blue Monkey Jelly Fish Optimization) algorithm is presented for the best DeepMaxout training. The suggested model produced increased accuracy, precision, sensitivity, and specificity. The suggested model produced superior accuracy, precision, sensitivity, and specificity of 95.80%, 94.94%, 96.38%, and 97.94%, respectively. © Grenze Scientific Society, 2024.

关键词： Fog computing

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EXAMSENTRY: Secure, Verify, Excel 7

EXAMSENTRY: Secure, Verify, Excel

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7th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Bhalgat, Kushal Bawdekar, Jaee Siroya, Chirag Mandlecha, Sanjana Girme, Shital Dept. of Computer Engineering Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798331515683

In today's digital world, online assessments are essential for companies to evaluate candidates effectively. However, they face challenges like malpractice and test integrity. We aim to create a scalable, secure platform that ensures fairness and reliability in online evaluations. The platform monitors the test environment by blocking browser extensions and media interference while verifying participants' identities using advanced methods. Eye tracking will ensure that candidates stay focused on the screen, while the system checks the stability of their internet connection and browser settings. Artificial intelligence will detect suspicious behavior during the exam, and cloud infrastructure will manage high user traffic for smooth performance under heavy loads. Results will be automatically generated and securely delivered to companies, providing a comprehensive solution for large-scale assessments. © 2025 IEEE.

关键词： application development in the education domain cloud-based solutions distributed systems Online assessments remote examinations scalable testing environments secure testing platforms security test proctoring

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