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32nd Annual Conference of the European Association for Education in Electrical and Information engineering, EAEEIE 2023

作者： Laura, Grindei Lorente, Vicent Posadas-Yague, Juan-Luis Grout, Ian Fischer, Thomas Technical University of Cluj-Napoca Department of Electrotechnics and Measurements Cluj-Napoca Romania Universitat Politècnica de València School of Informatics Department of Computer Engineering Valencia Spain University of Limerick Department of Electronic and Computer Engineering Limerick Ireland University of Applied Sciences Campus Vienna Department Engineering Vienna Austria

ISBN: (纸本)9789038656908

This paper presents the Erasmus+ DIG-SENSING project which is a Blended Intensive Program (BIP) that has the main objective to increase the motivation of students in achieving knowledge and practical experience in the field of electronic circuits. This is to be achieved through projects that integrate the design of electronic circuits, and the programming of microcontrollers through an initial online series of Webinars and local activities within the partner institutions from March to May 2023 that are then followed by a physical Summer School in July 2023. The structure and implementation of DIG-SENSING, with the benefits for students, academic staff, and partner universities will be presented. In this project, the participating students must develop and tackle a challenge that brings together Information and Communication Technologies in the framework of solving socio-environmental *** paper also presents and evaluation of students perception after the first webinar. © 2023 Fontys University of Applied Science.

关键词： Students

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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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Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13571-13584页

作者： Liu, Wei Liu, Jierong Peng, Tao Wang, Guojun Balas, Valentina Emilia Geman, Oana Chiu, Hung-Wen School of Information and Communication Engineering Hainan University Hainan Haikou570228 China School of Computer Science and Cyber Engineering Guangzhou University Guangdong Guangzhou510006 China College of Biomedical Information and Engineering Hainan Medical University Street Hainan Haikou571199 China Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad310130 Romania Department of Health and Human Development Stefan cel Mare University of Suceava Suceava720229 Romania Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan

Parkinson’s disease (PD) is a progressive disorder of the nervous system that affects movement. Early prediction of PD can increase the chances of earlier intervention and delay the onset of the disease. Vocal impairment is one of the most important signs in the early stages of PD. Therefore, PD detection based on speech analysis and vocal patterns has attracted significant attention recently. In this paper, we propose a vowel-based artificial neural network (ANN) model for PD prediction based on single vowel phonation. Firstly, we propose a novel multi-layer neural network based on speech features to predict PD. The speech samples from 48 PD patients and 20 healthy individuals are processed into four types: vowel, number, word, and short sentence. Secondly, we establish ANN models with single-type speech samples versus combinations of multi-type speech samples, respectively. Comparative experiments demonstrate that the single-type vowel model is superior to other single-type models as well as multi-type models. Finally, we build a vowel-based ANN model for PD prediction and evaluate its performance. Extensive experiments demonstrate that the proposed model has a prediction accuracy of 91%, sensitivity of 99%, specificity of 82%, and area under the receiver operating characteristic curve (AUC) of 91%, which is superior to the performance of previous methods. Overall, this study demonstrates that the proposed model can provide good classification accuracy for predicting PD and can improve the rate of early diagnosis. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Network layers

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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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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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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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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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EvAlignUX: Advancing UX Evaluation through LLM-Supported Metrics Exploration 25

EvAlignUX: Advancing UX Evaluation through LLM-Supported Met...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Zheng, Qingxiao Chen, Minrui Sharma, Pranav Tang, Yiliu Oswal, Mehul Liu, Yiren Huang, Yun Computer Science and Engineering University at Buffalo BuffaloNY United States Informatics University of Illinois Urbana-Champaign ChampaignIL United States Computer Science University of Illinois Urbana-Champaign ChampaignIL United States

ISBN: (纸本)9798400713941

Evaluating UX in the context of AI's complexity, unpredictability, and generative nature presents unique challenges. How can we support HCI researchers to create comprehensive UX evaluation plans? In this paper, we introduce EvAlignUX , a system powered by large language models and grounded in scientific literature, designed to help HCI researchers explore evaluation metrics and their relationship to research outcomes. A user study with 19 HCI scholars showed that EvAlignUX improved the perceived quality and confidence in UX evaluation plans while prompting deeper consideration of research impact and risks. The system enhanced participants' thought processes, leading to the creation of a "UX Question Bank"to guide UX evaluation development. Findings also highlight how researchers' backgrounds influence their inspiration and concerns about AI over-reliance, pointing to future research on AI's role in fostering critical thinking. In a world where experience defines impact, we discuss the importance of shifting UX evaluation from a "method-centric"to a "mindset-centric"approach as the key to meaningful and lasting design evaluation. © 2025 Copyright held by the owner/author(s).

关键词： User experience

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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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