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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Kardawi, Muhammad Yusuf Gunawan, Alexander Agung Santoso Bina Nusantara University Profesional Engineer Program Faculty of Engineering Jakarta11480 Indonesia Faculty of Computer Science University of Indonesia Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

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computers, Materials & Continua 2024年第8期80卷 1753-1808页

作者： Zheyi Chen Liuchang Xu Hongting Zheng Luyao Chen Amr Tolba Liang Zhao Keping Yu Hailin Feng College of Mathematics and Computer Science Zhejiang A&F UniversityHangzhou311300China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Mathematics and Computer Science Department Faculty of ScienceMenofia UniversityShebin El KomMenoufia Governorate32511Egypt School of Computer Science Shenyang Aerospace UniversityShenyang110136China Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language *** modeling,crucial for AI development,has evolved from statistical to neural models over the last two ***,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training *** the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models *** advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal *** survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language ***,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI ***,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

关键词： Artificial intelligence large language models large multimodal models foundation models

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PA-Star2: Fast Optimal Multiple Sequence Alignment for Asymmetric Multicore Processors 33

PA-Star2: Fast Optimal Multiple Sequence Alignment for Asymm...

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Sundfeld, Daniel Teodoro, George Melo, Alba C. M. A. Faculty of Science & Tech. in Engineering Brasilia Brazil Department of Computer Science Belo Horizonte Brazil Department of Computer Science Brasilia Brazil

ISBN: (纸本)9798331524937

Multiple Sequence Alignment (MSA) is an important operation in Bioinformatics, used to simultaneously compare 3 or more sequences. The MSA problem was proven NP-Hard, so strategies have been proposed to reduce the search space and solve it in parallel. Recently, asymmetric multicore processors (AMPs) have become popular, with performance and energy-efficient cores, like the P-Cores and E-cores from Intel. However, parallel MSA applications have complex access patterns and adapting them for AMPs can be challenging. In this paper, we propose PA-Star21, an asymmetric-aware strategy based on A-Star, which computes optimal MSAs taking asymmetry into account when distributing the search space among threads. Our experimental results show that the proposed optimizations can reduce considerably the average execution time of PA-Star2 achieving a speedup of up to 7.70×. We also show that the asymmetric-aware strategy can reduce the average execution time for one of the hardest sequences set from the BAliBASE benchmark, when compared to the symmetric counterpart. Finally, we show that our approach is energy-efficient.11PA-Star2 is open source and the code is publicly available at1PA-Star2 is open source and the code is publicly available at https://***/danielsundfeld/astar_msa © 2025 IEEE.

关键词： Parallel architectures

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ML Driven Priority-Based Constrained Routing for Pollution Management in ITS 11

ML Driven Priority-Based Constrained Routing for Pollution M...

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11th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Hegazy, Aly Soubra, Hassan Aboulyousr, Youssef German University in Cairo Faculty of Computer Science and Engineering Cairo Egypt ECE Faculty of Computer Science and Engineering France

ISBN: (纸本)9798350322101

Intelligent Transportation Systems (ITS) seek to enhance traffic safety, mobility, and efficiency through advanced technologies. Priority assignment, a fundamental component of ITS, allocates priority to various types of vehicles or roads based on their significance and urgency. In this study, we explore the implementation of priority-based constrained routing for road vehicles in ITS, leveraging machine learning techniques and dynamic routing algorithms. Our objective is to enhance traffic management and efficiency by addressing the limitations of traditional routing methods that do not account for vehicle priorities. By employing machine learning models within a priority-based routing system, dynamic priorities can be accurately assigned to vehicles in real-time, facilitating more effective traffic management decisions. This approach mitigates congestion and reduces travel times for high-priority vehicles, presenting an innovative means of refining traffic control strategies. The findings hold substantial implications for real-world ITS systems, as they can alleviate traffic congestion, improve traffic flow, enhance efficiency, and manage pollution levels in specific areas. © 2023 IEEE.

关键词： Smart city

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In-service performance of emergency shutdown valves and dependent operational relationships in the offshore oil and gas industry

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Petroleum 2023年第4期9卷 613-620页

作者： Keith Cameron Andrew Lewis Diogo Montalvao Mohammad Reza Herfatmanesh School of Physics Engineering and Computer ScienceUniversity of HertfordshireUnited Kingdom Department of Design and Engineering Faculty of Science and TechnologyBournemouth UniversityUnited Kingdom

Industrial process plants use emergency shutdown valves(ESDVs)as safety barriers to protect against hazardous events,bringing the plant to a safe state when potential danger is *** ESDVs are used extensively in offshore oil and gas processing plants and have been mandated in the design of such systems from national and international standards and *** paper has used actual ESDV operating data from four mid/late life oil and gas production platforms in the North Sea to research operational relationships that are of interest to those responsible for the technical management and operation of *** first of the two relationships is between the closure time(CT)of the ESDV and the time it remains in the open position,prior to the close *** has been hypothesised that the CT of the ESDV is affected by the length of time that it has been open prior to being closed(Time since the last stroke).In addition to the general analysis of the data series,two sub-categories were created to further investigate this possible relationship for CT and these are“above mean”and“below mean”.The correlations(Pearson's based)resulting from this analysis are in the“weak”and“very weak”*** second relationship investigated was the effect of very frequent closures to assess if this improves the *** operational records for six subjects were analysed to find closures that occurred within a 24 h period of each ***,no discriminating trend was apparent where CT was impacted positively or negatively by the frequent closure *** was concluded that the variance of ESDV closure time cannot be influenced by the technical management of the ESDV in terms of scheduling the operation of the ESDV.

关键词： Emergency Shutdown Valve Safety instrumented systems Closure time IEC-61511

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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly 3

A Review on Meta-Learning: How Artificial Intelligence and M...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Khadse, Shrikant Gourshettiwar, Palash Pawar, Adesh Faculty of Engineering and Technology Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings. © 2025 IEEE.

关键词： Contrastive Learning

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Malicious Node Detection Model with Enhanced QoS Mechanisms in Wireless Sensor Network for Secure Data Transmission 5

Malicious Node Detection Model with Enhanced QoS Mechanisms ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Aruna, O. Midhunchakkaravarthy Lincoln University College Computer Science and Engineering Malaysia Lincoln University College Faculty of Computer Science and Multimedia Malaysia

ISBN: (纸本)9798331504403

Random deployment and wide surveillance of a geographical area can be carried out in the Wireless Sensor Networks (WSN). A wireless sensor network is called a network with several sensor nodes that operate in wireless mode. The nodes are inherently self-configuring because of the security issues these networks face. The sorts of malicious nodes in the network lead to passive or active attacks on these networks. The total life of the network is decreased and energy consumption is more when the route selected for communication is unreliable or if the network experiences attacks. By examining the routes between nodes in the WSN environment, secure data transfer among network nodes is increased effectively. WSN network sensors monitor the surrounding information and communicate it to the central unit or sensor. In the WSN environment, all sensors are otherwise termed a node and each node has its data transmission range. Attacks on WSNs are common because of the large number of nodes and extensive network coverage. A decrease in system performance is inevitable as a result of network attacks. The WSN attackers attack a particular low-energy node that turns this node into an aggressive node. Malicious node creation is the prime reason for node-link failures. The proposed research work designs an efficient model for route selection and to identify malicious attacks in the network to improve the Quality of Service (QoS) in the network. © 2024 IEEE.

关键词： Sensor nodes

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Geometric Interpretation of Sensitivity to Structured Uncertainties in Spintronic Networks

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IEEE Control Systems Letters 2025年 9卷 192-197页

作者： O'Neil, S.P. Jonckheere, E.A. Schirmer, S. United States Military Academy Department of Electrical Engineering and Computer Science NY United States University of Southern California Dept of Electrical and Computer Engineering CA United States Swansea University Faculty of Science and Engineering Physics United Kingdom

We present a geometric model of the differential sensitivity of the fidelity error for state transfer in a spintronic network based on the relationship between a set of matrix operators. We show an explicit dependence of the differential sensitivity on the fidelity (error), and we further demonstrate that this dependence does not require a trade-off between the fidelity and sensitivity. We prove that for closed systems, ideal performance in the sense of perfect state transfer is both necessary and sufficient for optimal robustness in terms of vanishing sensitivity. We demonstrate the utility of this geometric interpretation of the sensitivity by applying the model to explain the sensitivity versus fidelity error data in two examples. © 2017 IEEE.

关键词： Sensitivity analysis

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Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

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computers, Materials & Continua 2023年第1期74卷 2133-2148页

作者： Ahmed Y.Hamed M.Kh.Elnahary Faisal S.Alsubaei Hamdy H.El-Sayed Department of Computer Science Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the *** a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud *** primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence *** and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor *** diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing *** a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the *** research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing *** basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal *** assess our algorithm’s performance by running it through three scenarios with varying numbers of *** findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

关键词： Heterogeneous processors cooperation search algorithm task scheduling cloud computing

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