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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Rehman, Fazalur Mushtaq, Farhan Zaman, Hafsah National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan Department of Electrical Engineering Taxila Pakistan

ISBN: (纸本)9798350391862

This research introduces a Hybrid Intrusion Detection System (HIDS) that merges signature-based detection, with AI-powered anomaly detection to enhance the accuracy and effectiveness of identifying cyber threats. The proposed HIDS demonstrates an ability to detect uncommon and sophisticated cyber threats with an accuracy rate of 90.37%. By combining Gradient Boosting and K-Nearest Neighbors (KNN) algorithms the system improves detection precision, speeds up response times, and expands coverage across network traffic. This comprehensive approach overcomes the limitations of traditional methods by enabling threat responses while reducing false positive rates. The study highlights the potential of integrating signature-based and AI-driven techniques to strengthen cybersecurity defences which emphasizes the benefits of this approach. When the system detects a potential threat, alerts are sent to the Security Operations Center (SOC) or Network Operations Center (NOC), with details such as nature of the threat, and the affected system. This study establishes a foundation for real-time cyber threat detection and intelligence sharing in cloud environments, with future Hybrid IDS versions operating across multiple hosts and supported by a web service for cross-platform compatibility and centralized alert system. © 2024 IEEE.

关键词： Network intrusion

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A Genetic Algorithm-Based Improved Availability Framework for Controller Placement in SDN

A Genetic Algorithm-Based Improved Availability Framework fo...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Asamoah, Emmanuel Ampratwum, Isaac Nayak, Amiya School of Electrical Engineering and Computer Science University of Ottawa Canada

ISBN: (纸本)9798350335590

Thanks to the Software-Defined Networking (SDN) paradigm, which segregates the control and data layers of traditional networks, large and scalable networks can now be dynamically configured and managed. It is a game-changing networking technology that provides increased flexibility and scalability through centralized management. The Controller Placement Problem (CPP), however, poses a crucial problem in SDN because it directly impacts the efficiency and performance of the network. The CPP attempts to determine the most ideal number of controllers for any network and their corresponding relative positioning. This is to generally minimize communication delays between switches and controllers and maintain network reliability and resilience. In this paper, we present a modified Genetic Algorithm (GA) technique to solve the CPP efficiently. Our approach makes use the GA's capabilities to obtain the best controller placement correlation based on important factors such as network delay, reliability and availability. We further optimize the process by means of certain deduced constraints to allow faster convergence. © 2023 IEEE.

关键词： Genetic algorithms

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Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking 17

Solving 100-Digit Challenge with Score 100 by Extended Runni...

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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Zamuda, Aleš University of Maribor Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9789616165617

This paper assesses Distance-based Success History Differential Evolution for 100-Digit Challenge and Numerical Optimization Scenarios (DISHchain3e+12). DISHchain3e+12 algorithm is based on DISH algorithm, using a Differential Evolution (DE) parameters control mechanism based on Euclidean distance between trial and parent in a search space to weigh Lehmer means in scaling and crossover historical parameter memories. In this paper, the maximum number of fitness evaluations and initial population size parameters are tailored in the DISHchain3e+12 algorithm per problem, since the benchmark used allows tuning up to two of the same parameters independently for each problem. © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Population statistics

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A Linear Algebraic Toolbox for Analyzing Strongly Connected Components 27

A Linear Algebraic Toolbox for Analyzing Strongly Connected ...

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27th International Conference on Circuits, Systems, Communications and computers, CSCC 2023

作者： Davidrajuh, Reggie University of Stavanger Electrical Engineering and Computer Science Stavanger Norway

ISBN: (纸本)9798350337594

A strongly connected component of a directed graph is a group of vertices in which each vertex (source) has a path to the other vertices, and the path leads back to the source vertex. When a directed graph possesses two or more strongly connected components, injecting one or more edges makes the components merge into one component. On the other hand, by removing an inner edge, a strongly connected component can be split into multiple components. Traditionally, graph algorithms provide traversal algorithms for analyzing connected components in a graph. Though these traversal algorithms are usually efficient, taking polynomial running times (linear or quadratic), these were developed before parallel computing and linear algebraic techniques. This paper presents a linear algebraic toolbox (a set of functions developed on MATLAB) for analyzing strongly connected components. These functions are efficient, easy to understand, and simple enough to implement and extend. Also, these functions are added to the GPenSIM tool so that distributed cyber layers can be developed for the hardware of real-life industrial discrete systems. © 2023 IEEE.

关键词： Directed graphs

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Adaptive iterated local search algorithm for dynamic patient admission scheduling problems

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Soft Computing 2025年第7期29卷 1-11页

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Resource allocation

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Mean-Based Approach To Omnidirectional Pathfinding Algorithm Using Single Ultrasonic Sensor For Robotic Car

Mean-Based Approach To Omnidirectional Pathfinding Algorithm...

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2024 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2024

作者： Tandon, Anvit Sah, Shubham Gawre, Suresh Kumar Iiit Bhopal Department of Computer Science and Engineering India Manit Bhopal Department of Electrical Engineering India

ISBN: (纸本)9798350348460

Our project centers around creating and practically testing a pathfinding algorithm using a single ultrasonic sensor on a test RC car. The algorithm consists of two phases: normal and intensive detection. This algorithm provides a reliable method for determining an obstacle-free path. The intensive detection method uses a servo motor for a broader field of view, thus creating an omnidirectional composite sensor with just one monodirectional sensor. It reduces the number of potential false positives for the ideal path by a mean-based approach. © 2024 IEEE.

关键词： Arduino UNO DC Motors H-W 130 Motor Shield Servo Motor Ultrasonic Sensor

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Addressing IoT Security Challenges: A Framework for Determining Security Requirements of Smart Locks Leveraging MQTT-SN

Addressing IoT Security Challenges: A Framework for Determin...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Gupta, Hemant Nayak, Amiya School of Electrical Engineering and Computer Science University of Ottawa Canada

ISBN: (纸本)9798350335590

The Internet of Things (IoT) connects the physical world to the digital world, and wireless sensor networks (WSNs) play a significant role. There are billions of IoT products in the market. We found that security was not the primary focus of software developers. The first step of designing a secure product is to analyze and note down the security requirements. This research paper proposes a modified approach, incorporating elements from the SREP (Software Requirements engineering Process) and SQUARE (Security Quality Requirement engineering), to define security requirements for IoT products. The revised process is applied to determine the security requirements of a Smart Lock system that utilizes the publish/subscribe protocol MQTT-SN (Message Queuing Telemetry Transport for Sensor Networks) communication protocol architecture. © 2023 IEEE.

关键词： Locks (fasteners)

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CaT: Balanced Continual Graph Learning with Graph Condensation 23

CaT: Balanced Continual Graph Learning with Graph Condensati...

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23rd IEEE International Conference on Data Mining, ICDM 2023

作者： Liu, Yilun Qiu, Ruihong Huang, Zi The University of Queensland School of Electrical Engineering and Computer Science Australia

ISBN: (纸本)9798350307887

Continual graph learning (CGL) is purposed to continuously update a graph model with graph data being fed in a streaming manner. Since the model easily forgets previously learned knowledge when training with new-coming data, the catastrophic forgetting problem has been the major focus in CGL. Recent replay-based methods intend to solve this problem by updating the model using both (1) the entire new-coming data and (2) a sampling-based memory bank that stores replayed graphs to approximate the distribution of historical data. After updating the model, a new replayed graph sampled from the incoming graph will be added to the existing memory bank. Despite these methods are intuitive and effective for the CGL, two issues are identified in this paper. Firstly, most samplingbased methods struggle to fully capture the historical distribution when the storage budget is tight. Secondly, a significant data imbalance exists in terms of the scales of the complex newcoming graph data and the lightweight memory bank, resulting in unbalanced training. To solve these issues, a Condense and Train (CaT) framework is proposed in this paper. Prior to each model update, the new-coming graph is condensed to a small yet informative synthesised replayed graph, which is then stored in a Condensed Graph Memory with historical replay graphs. In the continual learning phase, a Training in Memory scheme is used to update the model directly with the Condensed Graph Memory rather than the whole new-coming graph, which alleviates the data imbalance problem. Extensive experiments conducted on four benchmark datasets successfully demonstrate superior performances of the proposed CaT framework in terms of effectiveness and efficiency. The code has been released on https://***/superallen13/CaT-CGL. © 2023 IEEE.

关键词： Condensation

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On the Relationship Between Linter Warning Density and Software Maintainability: An Empirical Study of JavaScript Projects 23

On the Relationship Between Linter Warning Density and Softw...

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6th International Conference on Software engineering and Information Management, ICSIM 2023

作者： Heričko, Tjaša Šumak, Boštjan Faculty of Electrical Engineering and Computer Science University of Maribor Slovenia

ISBN: (纸本)9781450398237

A common practice in software development is to include linters, static analysis tools that warn developers about potential issues in the code, in the software quality assurance process. Actionable warnings generated by linters upon violations of defined rules help detect, resolve, and reduce coding errors, quality flaws, code style inconsistencies, and deviations from best coding practices and conventions. However, little empirical evidence exists to fully understand the relationship between linter warnings and external software quality factors. To this end, an empirical investigation of the source code of 40 open-source JavaScript project releases was conducted to study whether there is a relation between software maintainability measured by the Maintainability Index and the density of linter warnings per Logical Lines of Code. The findings suggest a very weak to strong negative correlation between warning density and the value of the Index at a project- and a module-level. Changes in warning density between projects only slightly inversely correspond to changes in maintainability. Additionally, a statistically significant difference in maintainability was found between projects defining linters in their manifest file and those that do not, in favor of the former. © 2023 ACM.

关键词： Software design

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