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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66560 条记录，以下是481-490 订阅

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis 3

PhishGuard: A Convolutional Neural Network-Based Model for D...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Islam, Md Robiul Islam, Md Mahamodul Afrin, Mst. Suraiya Antara, Anika Tabassum, Nujhat Amin, Al Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Oslo Dept. of Informatics Oslo Norway Dept. of Building Engineering and Construction Management Khulna Bangladesh BRAC University Dept. of Electrical and Electronics Engineering Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Dept. of Computer Science American International Dhaka Bangladesh

ISBN: (纸本)9798350372120

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models' decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1 D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL. © 2024 IEEE.

关键词： Convolutional neural networks

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Knee Osteoarthritis detection and classification using a customized CenterNet with DenseNet201 3

Knee Osteoarthritis detection and classification using a cus...

引用

3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Shirisha, N. Shilpa, K. Meghana, C.H. Rahman, Mohammed Abdul MLR Institute of Technology Dept. of Computer Science and Engineering Hyderabad India MLR Institute of Technology Dept. of Computer Science and Engineering Nalgonda India

ISBN: (纸本)9798331523893

Knee osteoarthritis (KOA) is a prevalent joint disorder diagnosed using imaging modalities like MRI, CT scans, and X-rays, with X-rays being the most cost-effective. Early detection is crucial for effective management. This study presents an automated deep learning approach to detect and classify KOA severity based on the Kellgren-Lawrence (KL) grading system using single posteroanterior standing knee X-ray images. Utilizing the Osteoarthritis Initiative dataset, we employed transfer learning to fine-tune DenseNet-201, enhancing model performance. Additionally, knowledge distillation was applied to reduce computational complexity while maintaining accuracy. Our model achieved over 95% accuracy on both testing and cross-validation datasets, outperforming existing methods. This approach offers a reliable tool for early KOA diagnosis and grading, potentially aiding clinical decision-making © 2025 IEEE.

关键词： Diagnostic radiography

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Movement Attentive Graph Embedding for Improving Next Point of Access Prediction 18

Movement Attentive Graph Embedding for Improving Next Point ...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Kim, Taesoo Yang, Huigyu Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Superintelligence Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Next Point-of-Attachment (PoA) prediction is a deep learning-based approach to provide seamless connectivity in the mobile networks from heterogenous and dense user access. Existing recurrent neural models utilize time series characteristics to extract and learn the sequential association changes occurs between all Base Stations (BSs). Since the user moves from certain BS to its adjacent BS, the deployments of these BSs are a crucial feature for improving prediction accuracy. To this end, we propose a Graph Embedding - Long Short-Term Memory (GE-LSTM) model using Recurrent Neural Network (RNN) and Graph Neural Network (GNN) jointly. The time-series pattern of the user movement is extracted by LSTM, and the movement between adjacent BSs is emphasized as GNN through GE. The joint property of the proposed model is implemented through mixture-of-experts approach from GNN and LSTM. The performance is evaluated along with the RNN families and Transformer models, furthermore, it explores the significance of the graph-based mobility embeddings. In our experiment, the proposed model outperforms other SOTA models by achieving the maximum accuracy 94%. © 2024 IEEE.

关键词： Forecasting

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Efficacy of Customer Churn Prediction System 6

Efficacy of Customer Churn Prediction System

引用

6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Raj, Ritik Gupta, Siddhant Mishra, Ritik Malik, Medha Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science & Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350374629

Customer churn, defined as customer defection is a big problem for businesses in today's competition. This paper takes an in-dept. look at the application of machine learning (ML) in customer churn prediction to identify the best models for various industries. quantitative performance metrics of seven commonly used machine learning algorithms: Logistic Regression, Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, and XGBoost. The theoretical background, application context, and performance evaluation of each model are examined, focusing on precision, accuracy, recall, and F1 score. Evaluation of the performance of the model on different business data is also done to ensure that the application is real in the world. This comparison shows the advantages and disadvantages of each model and guides providers in their selection based on the specific needs of the business and profile features. Additionally, this paper explores future changes in predictions and highlights the changing role of machine learning technologies, especially deep learning, in improving the accuracy of predictions and personalization. This review is very useful for professionals and experts;It provides a better understanding of the current machine learning model for customer churn prediction and provides insight into future research and practical applications. ©2024 IEEE.

关键词： Forecasting

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Thermal Model Validation of a Power Electronics Module for Digital Twin Application

Thermal Model Validation of a Power Electronics Module for D...

引用

2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zoller, Svenja Cabre-Piqueras, Claudia Schilder, Boris Filba-Martinez, Alber Acker, Lukas Frankfurt University of Applied Science Faculty of Computer Science and Engineering Frankfurt Germany Power Systems Dept. Barcelona Spain

ISBN: (纸本)9798350364446

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Combining Deep Learning Models for Improved Drug Repurposing: Advancements and an Extended Solution Methodology 7

Combining Deep Learning Models for Improved Drug Repurposing...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kose, Utku Deperlioglu, Omer Kucuksille, Ecir Ugur Turan, Gokhan Suleyman Demirel University Dept. of Computer Engineering Isparta Turkey Afyon Kocatepe University Dept. of Computer Technologies Afyonkarahisar Turkey Burdur Mehmet Akif Ersoy University Dept. of Computer Technologies Burdur Turkey

ISBN: (纸本)9798350359299

Nowadays, major advancements through Artificial Intelligence (AI) were led by Deep Learning-based solutions. Considering their robust and extensive data processing mechanisms, Deep Learning (DL) models ensure great role in advancing solutions for real-world problems. Especially medical applications have been significantly improved by research studies as a result of intensive DL synergy. At this point, drug discovery has been one of the most remarkable fields where DL has been used in especially last few years. In the context of drug discovery studies, drug repurposing has a unique place to enable known drugs to be used for different diseases. As this is a remarkable way of optimizing discovery and treatment phases, use of DL for drug repurposing applications has still open areas to go. Objective of this paper is to examine the potential of combined DL models for improving drug repurposing and introduce a solution methodology, which includes use of multiple DL models to build a decision support system. It has been also aimed to support the system with computational models and Generative AI route to extend the capabilities towards a Digital Twin related approach. © 2024 IEEE.

关键词： Medical applications

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Real-Time Defogging using Dark Channel Prior for Enhanced Visibility 3

Real-Time Defogging using Dark Channel Prior for Enhanced Vi...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Mushtaq, Zohaib Rehman, Obaid Ur Ali, Usman Latif, Muhammad A. Dept. of Electrical Electronics and Computer systems University of Sargodha Sargodha Pakistan Dept. of Computer and Information System Cleveland State University OH44115 United States

ISBN: (纸本)9798331504861

The presence of fog is a notable obstacle for capturing images, particularly in the early morning hours. The diminished sight impedes the capacity to get distinct and comprehensive information from the surroundings. In order to tackle this issue, a rapid and live vision-based dehazing method is suggested. The objective of the technology is to dissipate the fog and enhance visual acuity. It does this by implementing four steps: using an exponential moving average filter to diminish the impact of noise and enhance clarity, preprocessing the filtered frames, detecting fog with the Gaussian blur technique, and applying the dark channel prior approach to achieve optimum contrast and remove fog. Comprehensive testing were carried out on live recordings to showcase the system's ability to significantly improve sight in different foggy situations. This technique demonstrates the superior performance in terms of following parameters like, an accuracy, precision, recall, and F1 score compared to previous investigations. Our methodology achieved the greatest accuracy when compared to state-of-the-art transfer learning convolutional neural networks. ©2024 IEEE.

关键词： Convolutional neural networks

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Binary and Multiclass Classification of Attacks in Edge IIoT Networks

Binary and Multiclass Classification of Attacks in Edge IIoT...

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2024 Advances in Science and engineering Technology International Conferences, ASET 2024

作者： Aslam, Sidra Alshoweky, Mohammad M.R. Saad, Mohamed University of Sharjah Dept. of Computer Engineering United Arab Emirates

ISBN: (纸本)9798350344134

The Internet of Things (IoT) refers to a smart network of digitally connected physical devices and systems, with applications ranging from predictive maintenance, optimization, enhanced efficiency in manufacturing and critical infrastructure. Over the years the applications of IoT have been extended from domestic to Industrial Internet of Things (IIoT). The increased integration of IoT in real-world applications exposes the networks to cyber threats, e.g., Man in the Middle and ransomware attacks. Such attacks on IoT networks impact their operational integrity, data confidentiality, and overall system reliability, especially in IIoT networks. Therefore, the timely and accurate detection of cyber attacks can save these networks from unauthorized access, malware propagation, and denial of service attacks. In this paper, we present a cyber attack detection technique using Edge-IIoTset dataset for IIoT applications. The proposed detection approach is based on binary and multi-class classification of cyber attacks in IIoT networks, leveraging machine learning (i.e., logistic regression, decision tree) and deep learning (i.e., recurrent and convolutional neural networks) algorithms. The proposed classification techniques showcase the effectiveness of the applied algorithms achieving an average accuracy of 90 %. © 2024 IEEE.

关键词： Cyber attacks

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