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检索条件"机构=Dept. of Computer Architecture and Information Technology"

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Detection Systems for Distributed Denial-of-Service (DDoS) Attack Based on Time Series: A Review 21

Detection Systems for Distributed Denial-of-Service (DDoS) A...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Adil Nafea, Ahmed Maad Hamdi, Mustafa Abdulhakeem, Baraa Saad Shakir, Ahmed Thair Alsumaidaie, Mustafa S. Ibrahim Shaban, Ali Muwafaq College of Computer Science and Information Technology University of Anbar Ramadi Dept. Artificail Inteligence Iraq College of Computer Science and IT University of Anbar Ramadi Dept. of Computer Science Iraq College of Computer Science and IT University of Anbar Ramadi Dept. of Information System Iraq College of Applied Sciences University of Anbar Ramadi Dept. of Biophysics Iraq College of Computer Science and Information Technology University of Anbar Ramadi Dept. Information System Iraq

ISBN: (纸本)9798350374131

The Distributed Denial-of-Service (DDoS) attacks are one of the most critical threats to the stability and security of the Internet. With the increasing number of devices connected to the Internet, the frequency and severity of DDoS attacks are also increasing. To mitigate the impact of DDoS attacks, intelligent detection systems are becoming increasingly important. This paper reviews the recent literature on intelligent techniques, including machine learning (ML), Deep Learning (DL), and artificial intelligence (AI), for detecting DDoS attacks. We will provide an overview of the existing research in the field and analyse the trends in using time series data analysis for DDoS attack detection. A taxonomy and conceptual framework for DDoS mitigation are presented. This study highlights the use of several intelligent techniques for detecting DDoS attacks and evaluates the performance utilizing real datasets and also discusses future research directions in this field. © 2024 IEEE.

关键词： Time series analysis

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Machine Learning for Detection and Classification of Human Brain Tumor: A Survey 5

Machine Learning for Detection and Classification of Human B...

引用

5th International Conference on information technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Al Hussen, Sara Ali Alsaadi, Elham Mohammed Thabit A. University Of Karbala College Of Computer Science And Information Technology Dept. Of Computer Science Karbala Iraq University Of Karbala College Of Computer Science And Information Technology Dept. Of Information Technology Karbala Iraq

ISBN: (纸本)9798350335118

Intracranial solid tumors arise due to uncontrolled and abnormal cell division. Classification of brain tumors depends on factors such as the tumor's location, the type of tissue it consists of, its malignant or benign nature, and other factors. Magnetic resonance imaging (MRI) is one of the techniques frequently used to detect tumors in the brain Magnetic resonance imaging (MRI). It provides important detail that is used in accurately scanning the human body's internal organization. problem with brain tumors is the complexity and variations in tumor location, shape, size, and intensity from patient to patient. Also, tumor boundaries are usually unclear and irregular. Some studies have appeared in the detection and classification of human brain tumors. Therefore, some of these studies will be discussed in this paper. Humans have developed many algorithms to get better results in brain tumor detection. They used the latest machine-learning techniques to solve this problem. Through these studies, we conclude that the best accuracy was achieved using deep learning techniques like Deep Auto-Encoder (DAE) based on Jaya Optimization Algorithm (JOA) for classification and Bayesian Fuzzy clustering (BFC) to segment up to 98.5%. © 2023 IEEE.

关键词： Magnetic resonance imaging

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A Review of Food Supply Chain Tracking Using Blockchain technology: Enabled Factors and Barriers 1

A Review of Food Supply Chain Tracking Using Blockchain Tech...

引用

1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Hashem, Buthaina Algaphary, Ghaleb Almutawkil, Adnan Dept. Information Technology Faculty of Computer and IT University of Sana’a Sana’a Yemen Dept. Computer Science Faculty of Computer and IT University of Sana’a Sana’a Yemen

ISBN: (纸本)9798331533557

The food supply chain (FSC) deals with issues like reliability, security, and traceability due to information contradiction. While blockchain technology (BCT) shows promise in improving FSC operations, its adoption in this sector remains early. This study systematically reviews BCT's applications in FSC tracking, focusing on its layers, types, platforms, and features. Through a methodical literature review spanning 2019 to 2023, 50 relevant articles were examined to identify barriers to BCT adoption, such as scalability and costs, and enabling factors like data integrity and decentralization. The review aims to identify gaps in the present research, setting the basis for offering a structured classification for blockchain-based FSC tracking and developing a conceptual framework for future BCT adoption in FSC. This framework aims to present strategic guidance for applying blockchain technology in FSC tracking applications, enhancing operational efficiency and transparency in the food supply chain sector. The finding of this study could help the government sector focus on building mechanisms to encourage the agriculture sector to invest in supply chain tracking. © 2024 IEEE.

关键词： Barriers Blockchain technology Enabling Factors Food Supply Chains Tracking

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Leveraging Deep Quantum Convolutional Neural Networks for Student Facial Expression Identifcation and Mode Assessments 4

Leveraging Deep Quantum Convolutional Neural Networks for St...

引用

4th International Conference on Ubiquitous Computing and Intelligent information Systems, ICUIS 2024

作者： Hossain, Javed Alam, Md. Nur Akib, Mohammad Yasin Nur Kogoya, Krisdahim Nanjing University of Information Science and Technology Dept. of Artificial Intelligence Nanjing China Nanjing University of Information Science and Technology Dept. of Computer Science and Technology Nanjing China

ISBN: (纸本)9798331529635

The incorporation of modern technologies into education has transformed how learning is seen and assessed, with a rising emphasis on understanding student behavior using facial expression recognition (FER). Recognizing students' emotional and cognitive states can provide vital insights into their engagement and learning styles, allowing teachers to adjust their teaching tactics in real time. Traditional machine learning algorithms have been used to solve this problem, but they frequently struggle with the complexity of facial expressions and big datasets, resulting in limitations in accuracy and processing efficiency. This study explores the potential of quantum computing in addressing these challenges through the development of a Deep Quantum Convolutional Neural Network (QCNN) architecture. By combining the advanced image processing capabilities of convolutional neural networks (CNNs) with the computational power of quantum features such as superposition and entanglement, we aim to enhance the detection of facial expressions that reflect students' engagement, concentration, frustration, and satisfaction. Finally, the result demonstrates a classification accuracy of 91.58 © 2024 IEEE.

关键词： Convolutional neural networks

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Application to Enhance the Programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This proposal outlines the development of a comprehensive educational platform aimed at bridging the information technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical information technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances information technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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Ultra-Wideband Pattern Reconfigurable Antenna for Base station Applications 5

Ultra-Wideband Pattern Reconfigurable Antenna for Base stati...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Bhickta, Shalini Jaglan, Naveen Kumar, Amit Jaypee University of Information Technology Dept. of Electronics and Communication Engineering Waknaghat Solan India Jaypee University of Information Technology Dept. of Computer Science & Engineering and Information Technology Waknaghat Solan India

ISBN: (纸本)9798350376753

The proposed work aims at 5G base station antenna communication applications. The Reconfigurable Antenna is built with an idea of parasitic loaded elements in antenna. The proposed design has a zig-zag S-shape structure with a driven Monopole antenna located along the central axis and a parasitic structure enfolding around the monopole. Reconfiguration is accomplished by adjusting the PIN diodes bias voltages. The 2.44-9.46 GHz frequency range i.e., the Ultra-Wide-Band frequency spectrum is used at which this reconfigurable antenna is designed to operate. This paper has three radiation pattern which are obtained by two PIN diodes. Beam-steering capability in the elevation plane is achieved i.e., θ (0°-180°). The outcome shows the design's potential and its wide range concurrent applications for base station antennas. © 2024 IEEE.

关键词： Monopole antennas

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Survey on Use of AI/ML Algorithms in Enhancing Filesystem Performance and Efficiency 11

Survey on Use of AI/ML Algorithms in Enhancing Filesystem Pe...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Olekar, Vijay Patil, Yash Pawar, Vishvam Pitale, Shubham Jaiswal, V.R. Naranje, Vishal Pune Institute of Computer Technology Pune Dept. of Information Technology Pune India School of Engineering Architecture and Interior Design Amity University Dubai United Arab Emirates

ISBN: (纸本)9798350350357

The processing power of CPUs is increasing day by day, but systems often struggle to fully utilize this power due to the bottleneck of file access time from the disk. Over the years, various techniques have been devised to reduce I/O latency while considering various file parameters. Our survey paper aims to shed light on these techniques, efficiently comparing them and highlighting the benefits and drawbacks of each approach. For our evaluation, we have considered various file parameters, such as access time, file size, directory structure, and kernel execution flow. We have combined these parameters with a range of supervised and unsupervised machine learning algorithms, including Decision Tree, ABLE, RNN, GNN, CART, XGBoost, CNN, and more. This survey aims to highlight the use of ML/AI algorithms in the intelligent detection of file access and I/O patterns to optimize the performance of modern applications. © 2024 IEEE.

关键词： Cyber Physical System

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Revolutionizing Traditional Supermarkets: A Comprehensive Solution for Enhancing Efficiency, Security and Customer Satisfaction 2024

Revolutionizing Traditional Supermarkets: A Comprehensive So...

引用

16th International Conference on Contemporary Computing, IC3 2024

作者： Tanna, Om H. Vasani, Ankur M. Thakkar, Krishna H. Gotecha, Malav J. Dept. of Information Technology MPSTME NMIMS University India Dept. of Information Technology Shri Bhagubhai Mafatlal Polytechnic India Dept. of Computer Engineering DJ Sanghvi College of Engineering India

ISBN: (纸本)9798400709722

This research proposes an Artificially Intelligent enabled Smart Supermarket System for enhanced efficiency, security and reduces man power as compared to traditional Supermarkets. In order to ensure a perfect omnichannel shopping experience the system uniquely includes an Artificially Intelligent model. Customers can scan items on the go with the app in their smartphones, select product variants as required, add the product to their virtual cart and pay online for the total cart value. With the help of YOLOv5 computer vision algorithm, eliminating long checkout queues and manpower the system proves to be revolutionizing the traditional supermarket procedures. Moreover, the system also incorporates the indoor navigation assistance on customer's app to facilitate easy access and navigation to preferred items. This futuristic solution not only provides valuable insights for business development but also is proven to be more efficient and helpful for customers saving their time and companies resources. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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Factors on the UTAUT2 to Identify the Influence on the Usage Behavior of AI-Based Air Quality 3

Factors on the UTAUT2 to Identify the Influence on the Usage...

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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Aini, Qurotul Hartomo, Kristoko Dwi Dwi Purnomo, Hindriyanto Wahab, Abdul Rahardja, Untung Satya Wacana Christian University Dept. of Computer Science and Technology Salatiga Indonesia Satya Wacana Christian University Dept. of Information Technology Salatiga Indonesia International Islamic University Malaysia Dept. of Information and Computer Technology Kuala Lumpur Malaysia University of Raharja Master of Information Technology Tangerang Indonesia

ISBN: (纸本)9798350367492

This study investigates the effect of AI-based air quality detection apps on user behavior in Indonesia, ranked 26th globally for air pollution. Utilizing an online survey with 287 participants, focusing on 156 active users, and employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the research assesses user satisfaction in environmental monitoring using the Unified Theory of Acceptance and Use of technology 2 (UTAUT2). This model examines 9 key variables: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), Habit (HT), Behavioral Intention (BI), Usage Behavior (UB), Considered Risk (CR), and Considered Trust (CT). Key findings emphasize the crucial roles of PE and BI in determining user's usage intentions and behaviors. These insights are vital for enhancing air quality monitoring technologies for developers and policymakers. The study also identifies areas for future research, such as exploring external and demographic influences, to further understand user behavior and satisfaction with AI-based environmental tools. Overall, it highlights the importance of user-centric approaches in technological development for improving environmental health outcomes. © 2024 IEEE.

关键词： Environmental monitoring

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A Study on Apriori based Movie Recommendation 2

A Study on Apriori based Movie Recommendation

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Behera, Tapas Mahapatra, Sarita Mohanty, Kritik Ranjan Farhan, Mohammad Tripathy, Sachidananda Priyadarshini, Sushree Bibhuprada B. Siksha 'o' Anusandhan Dept. of Computer Science & Information Technology Bhubaneswar India

ISBN: (纸本)9798350370188

With so many movies available nowadays, it might be challenging for people to choose one that suit their preferences and tastes. We have created a system for suggesting movies to users based on their viewing interests and history. A dataset of user movie viewing history and preferences is first gathered and preprocessed by the movie recommendation system followed by employing an Apriori technique to find the common item sets from frequently watched movies. The recommendations are created using these item sets as a foundation. A real-world movie dataset is applied in extended trials for movie recommendation system while considering evaluation metrics like recall, precision, and accuracy. The experimental findings show that the suggested approach is capable of giving users precise and individualized movie suggestions by utilizing the Apriori algorithm. © 2024 IEEE.

关键词： Recommender systems

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