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12th International Conference on Orange Technology, ICOT 2024

作者： Sultan, Muhammad Ali Kaburuan, Emil R. Rasool, Fezan Suwito, Misni Harjo Informatics Engineering Department Mercu Buana University Jakarta Barat Indonesia Computer Science Department Lahore University of Management Sciences Lahore Pakistan Information System Department Mercu Buana University Jakarta Barat Indonesia

ISBN: (纸本)9798331523602

Eye-related disorders are among the driving causes of vision impedances worldwide. Accurate classification of retinal conditions is crucial for adequate treatment and management. This research explores the potential of three advanced deep-learning models, namely—-VGG16, DenseNet, and Swin Transformers, to classify four eye-related disorders utilizing the Optical Coherence Tomography Image Retinal Database (OCTID) taken from ICPSR. The OCTID dataset contains retinal OCT images representing four distinct pathological conditions: age-related macular degeneration, diabetic retinopathy, central serous retinopathy, macular holes, and normal retinas. Evaluation is done based on metrics such as accuracy, precision, recall, and F1 score. The comparison of these models, paired with interpretability techniques such as GRADCAM and Integrated Gradients, reveals that VGG16 achieves the highest F1-score, demonstrating robust classification performance. At the same time, Swin Transformers develops competitive accuracy with practical interpretability through Integrated Gradients. DenseNet showed slightly lower metrics but performed adequately on the GRADCAM outputs. This study underscores the critical role of advanced deep learning frames, which work to enhance diagnostic accuracy for retinal disorders and provide a foundation for future developments in ophthalmic disease classification. © 2024 IEEE.

关键词： Optical coherence tomography

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A review of federated learning in renewable energy applications:Potential,challenges,and future directions

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Energy and AI 2024年第3期17卷 444-457页

作者： Albin Grataloup Stefan Jonas Angela Meyer Bern University of Applied Sciences School of Engineering and Computer ScienceQuellgasse 21Biel2501Switzerland Universitàdella Svizzera italiana Faculty of InformaticsVia la Santa 1Lugano-Viganello6962Switzerland Delft University of Technology Department of Geoscience and Remote SensingStevinweg 1Delft2628The Netherlands

Federated learning has recently emerged as a privacy-preserving distributed machine learning *** learning enables collaborative training of multiple clients and entire fleets without sharing the involved training *** preserving data privacy,federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation,research and *** paper provides an overview of federated learning in renewable energy *** discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and *** also evaluate the potential and the challenges associated with federated learning applied in power and energy ***,we outline promising future research directions in federated learning for applications in renewable energy.

关键词： Federated learning Renewable energy Dis tributed learning Data heterogeneity Privacy Data sharing Wind energy Solar energy Buildings Review

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Hybrid Chaotic Salp Swarm with Crossover Algorithm for Underground Wireless Sensor Networks

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computers, Materials & Continua 2022年第8期72卷 2963-2980页

作者： Mariem Ayedi Walaa H.ElAshmawi Esraa Eldesouky First Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia MEDIATRON Lab. SUP’COMCarthage UniversityTunis2083Tunisia Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Faculty of Computer Science Misr International UniversityCairoEgypt

Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network *** intriguing design goal for such networks is to achieve balanced energy and spectral resource *** paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data,received from the buried source nodes through a lossy soil medium,to the aboveground base station.A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover(HCSSC)algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource *** proposed algorithm improves the standard Salp Swarm Algorithm(SSA)by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of *** experimental results,the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency ***,the network’s lifetime is ***,the proposed algorithm achieves an improvement performance of 23.6%and 20.4%for the resource efficiency and average remaining relay battery per transmission,***,simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.

关键词： Underground wireless sensor networks resource efficiency chaotic theory crossover algorithm salp swarm algorithm

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep Learning Machine Learning Protein Feature Encoding Protein Protein interaction Protein sequence

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A Survey on the Communication of UAVs with Charging and Control Stations

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2025年

作者： Chalkiadaki, Aikaterini Milidonis, Athanasios Mourgelas, Christos Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Ag. Spyridonos Str.Athens Egaleo12243 Greece

Unmanned Aerial Vehicles (UAVs) have a history of over a century of deployment, but in recent decades, they have progressed at a staggering rate. Nowadays, UAVs are used by a large number of civil and military applications. The communication functionality of a UAV with external systems for control and charging is strongly connected with evolving technologies and services. This leads to an increased number of alternatives when designing UAV communications. This review presents the information needed for choosing an efficient communication system between UAVs and two important elements, the Ground Control Station (GCS) and the Charging Station (CS). GCS is responsible for monitoring and controlling the UAV’s units, while CS is used for the formal charging of the UAV. This study aimed at collecting, classifying, and evaluating all of the necessary information in order to obtain the final decision about the kind of communication that is most efficient for a target UAV application. The features of the telemetry open-source protocols are presented for the UAV-GCS communication and evaluated according to the needs of the most significant application domains. Communication between UAVs and CSs is classified depending on the existence of an intermediate server and analyzed considering telemetry protocols and application domains. Communication algorithms are evaluated in terms of time and energy efficiency. Lastly, for the most significant application domains, the most suitable algorithms are matched. © 2024 Bentham Science Publishers.

关键词： Signaling

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Performance Degrading Faults in Branch Target Buffers and Branch Predictors: Exploration and Remedy

SSRN

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SSRN 2023年

作者： Filippou, Filippos Keramidas, Georgios Mavropoulos, Michail Nikolos, Dimitris Department of Computer Engineering & Informatics University of Patras Greece School of Informatics Aristotle University of Thessaloniki Greece

Voltage scaling increases the impact of process variations resulting in an exponential ramp-up in the number of malfunctioning SRAM cells. Our experimental findings reveal that for several percentages of malfunctioning cells (pfails) and processor configurations, the disabling of the faulty Branch Target Buffers (BTB) entries can incur significant average performance (AvP) degradation and even larger worst-case performance (WCP) degradation. To mitigate the impact on performance, we propose alternative BTB designs and we explore their applicability for various pfails. It is already known that malfunctioning cells at Branch Predictors (BP) impact only the performance. Under the single stuck-at fault model (SaFM) as well as under the multiple SaFM, the AvP degradation is small, while the WCP degradation is significant in many cases. However, all studies are based on SaFM which do not model all the faults caused by random process variations. Hence, we performed extensive simulations adopting, besides the multiple SaFM, a more suitable model, the random value fault model. Under both fault models we confirm that the AvP degradation is relatively small while the WCP degradation is significant. Our investigation is based on cycle-accurate simulations, the SPEC2006 benchmarks, a plethora of fault maps, and representative pfails. © 2023, The Authors. All rights reserved.

关键词： Voltage scaling

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Introduction to Advances in Information, Intelligence, Systems and Applications 14th

Introduction to Advances in Information, Intelligence, Syste...

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14th International Conference on Information, Intelligence, Systems, and Applications, IISA 2023

作者： Tsihrintzis, George A. Virvou, Maria Bourbakis, Nikolaos G. Jain, Lakhmi C. Department of Informatics University of Piraeus Piraeus185 34 Greece Department of Electrical and Computer Engineering Wright State University DaytonOH45435-0001 United States KES International Shoreham-by-Sea United Kingdom

ISBN: (纸本)9783031674259

Information is widely available and accessible, but frequently leads to information overload and overexposure and the effort for coding, storing, hiding, securing, transmitting, and retrieving it may be excessive. Intelligence, inspired by biological and other paradigms, is required to manage information and extract knowledge from it. Information and Multimedia Systems, with an increasing level of Intelligence, are being developed that incorporate these advances. As a result, new Technologies, Protocols, and novel Applications are emerging. In turn, the novel applications lead to the collection of new information and the "Information → Intelligence → Systems → Applications" circle is repeated at ever higher levels. This book includes extended versions of selected research works that were presented at the 14th International Conference on Information, Intelligence, Systems, and Applications (IISA2023), Volos, Greece, July 10–12, 2024 (https://***/iisa2023) along with additional relevant papers by invited authors. These works cover a broad spectrum of topics from both novelties in theory and advanced methodologies as well as applications in the Energy, Agriculture, Medicine, Forensics, Healthcare, Education, Tourism, Inspection, Traffic and Software engineering. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Dynamic Self-Triggered Intelligent Path Tracking Control for Unmanned Agricultural Vehicles via Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 16487-16498页

作者： Zhang, Yongwei Liu, Derong South China Agricultural University College of Mathematics and Informatics Guangzhou 510642 China Southern University of Science and Technology School of Automation and Intelligent Manufacturing Shenzhen 518055 China University of Illinois Chicago Department of Electrical and Computer Engineering Chicago 60607 IL United States

This paper investigates the path tracking control of unmanned agricultural vehicles with disturbances under a dynamic self-triggered mechanism via reinforcement learning (RL). To begin with, a path tracking offset system is constructed based on the kinematic model of the unmanned agricultural vehicle, which transforms the path tracking control problem into an optimal control problem. Subsequently, a novel dynamic self-triggered second-order integral sliding mode control policy is developed to mitigate the impact of disturbances and to derive a nominal path tracking offset model. Afterward, to further alleviate the computing and communication burdens, a novel dynamic self-triggered mechanism is proposed for the optimal control policy. It can predict the next update time based on current information, thus avoiding the need for continuous monitoring of the triggering condition. Furthermore, a single critic network architecture is constructed to obtain an approximate path tracking control policy, and it is proven by Lyapunov stability theory that this policy ensures unmanned agricultural vehicles can maintain the predefined working path in the presence of disturbances. Finally, the effectiveness of the proposed path tracking control method is demonstrated by simulation experiments. © 2004-2012 IEEE.

关键词： adaptive dynamic programming dynamic self-triggered control integral sliding mode control reinforcement learning Unmanned agricultural vehicles

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Improving Communication Systems Through Federated Learning: An Optimization of Decision-Making 6

Improving Communication Systems Through Federated Learning: ...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Stergiou, Konstantinos D. Ishibashi, Yutaka Goudos, Sotirios K. Lagkas, Thomas Sarigiannidis, Panagiotis Psannis, Konstantinos E. University of Macedonia Department of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Aristotle University of Thessaloniki Department of Physics Thessaloniki Greece International Hellenic University Department of Computer Science Kavala Greece University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani Greece

ISBN: (纸本)9798350339505

This paper discusses the use of federated learning as a method for optimizing decision-making in communication systems. Federated learning is a machine learning technique that enables the training of models on decentralized data, allowing for the collection and analysis of data from multiple sources while maintaining the privacy and security of the data. This approach is particularly useful in communication systems, as it allows for the optimization of decision-making across a wide range of devices and networks. The paper examines the advantages of federated learning, including the ability to collect a large amount of data from a diverse range of devices, the protection of sensitive data, and the ability to adapt to changing conditions in real-time. The paper also provides specific examples of how federated learning can be used in the optimization of mobile networks and content delivery. The conclusion highlights the growing importance of federated learning in improving communication systems. © 2023 IEEE.

关键词： Learning systems

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