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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zheng, Shaolong Li, Zhenni Wang, Qianming Xie, Kan Liu, Ming Xie, Shengli Polycarpou, Marios School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Rsearch and Innovation Center of Excellence Nicosia Cyprus

ISBN: (纸本)9780936406350

Signals from global navigation satellite systems (GNSS) suffer from serious multipath errors in urban areas caused by building blockages and reflections. The use of deep neural networks offer great potential for predicting and eliminating complex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first is the inability to exploit effectively the irregular GNSS dataset because of inconsistent numbers of visible satellites in different epochs. The second is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network for predicting satellite visibility that effectively learns environment representations from the irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from the irregular GNSS measurements, a sky satellite graph is constructed as the input to a graph neural network by using the satellites captured in the same epoch, which can represent the spatial relationships between the satellites and enhance the model to enable learning of satellite-related features sufficiently well. To improve generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites for extracting the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, we develop a novel graph transformer neural network (GTNN) for predicting satellite visibility, which can not only handle irregular GNSS measurements but also learn an environment representation via graph attention. Comparative experiments were carried out on real-world GNSS meas

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Review of Phishing Attacks’ Effects on AI-Powered IoT Systems

Review of Phishing Attacks’ Effects on AI-Powered IoT Sys...

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Mohana, S.D. Rafiya Nusrath, D. Shiva Prakash, S.P. Krinkin, Kirill Department of School of Information Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Information Science JSS Science and Technology University Mysuru Mysore570017 India Co-evolutionary Artificial Intelligence Paphos Cyprus

ISBN: (纸本)9789819986606

The research review explores the intersection of AI, IoT, and phishing attacks, highlighting their applications, risks, and vulnerabilities. It discusses various types of phishing attacks and existing solutions for detection and mitigation. The paper also addresses future directions, challenges, and the importance of interdisciplinary collaboration and user awareness. Real-world examples are examined to illustrate the impact of phishing attacks. The conclusion emphasizes the need for ongoing research and a resilient, secure AI and IoT ecosystem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Classifying Tomorrow’s Currents: A Probabilistic Neural Network Approach to Forecasting Electricity Prices

Classifying Tomorrow’s Currents: A Probabilistic Neural Net...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Stylianos Loizidis Andreas Livera Andreas Kyprianou George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PH AET HON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Mechanical and Manufacturing Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

This study introduces a probabilistic neural network model for classifying next-day electricity prices into negative or positive categories. It addresses the challenge of price volatility exacerbated by renewable energy integration, presenting significant risks for market participants. Through a comparative analysis with the traditional support vector machine with radial basis function kernel using the German Day-Ahead market data of 2020, the probabilistic neural network demonstrates superior accuracy and yielded promising electricity price forecasting results. This approach equips market participants with valuable insights for strategic planning and risk mitigation.

关键词： Support vector machines Adaptation models Renewable energy sources Costs Electricity Neural networks Predictive models Probabilistic logic Forecasting Kernel

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Edge Artificial Intelligence in Large-Scale IoT Systems, Applications, and Big Data Infrastructures 8

Edge Artificial Intelligence in Large-Scale IoT Systems, App...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Karras, Aristeidis Giannaros, Anastasios Karras, Christos Giotopoulos, Konstantinos C. Tsolis, Dimitrios Sioutas, Spyros University of Patras Department of Computer Engineering and Informatics Patras Greece University of Patras Department of Management Science and Technology Patras Greece University of Patras Department of History and Archaeology Patras Greece

ISBN: (纸本)9798350383676

As the Internet of Things (IoT) landscape grows, with estimates exceeding 75 billion devices by 2025, effective data management and processing become primary challenges. Traditional cloud-centric models may struggle under this large data volume. This research presents Edge AI as an innovative solution, integrating artificial intelligence directly at data sources like sensors and cameras. This ensures real-time analytics and decision-making, promoting responsive and tailored actions. Our literature review details Edge AI's distinct characteristics and applications. The interaction of Edge AI with large-scale IoT domains is critically examined, emphasizing their combined potential. Within big data infrastructures, a comparative study contrasts Edge AI and cloud-based AI, investigating processing speeds, optimization techniques, and essential metrics. The in-herent limitations of Edge AI and current challenges are also discussed. In summary, Edge AI offers notable improvements in operational efficiency, data privacy, and bandwidth use. As IoT continues its rapid expansion, the strategic deployment of Edge AI becomes crucial, leading to a future where data is not just collected but smartly utilized. © 2023 IEEE.

关键词： Internet of things

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Design Amharic Text Sentiment Analysis Model Using Machine Learning Techniques. in Case of Restaurant Reviews 5

Design Amharic Text Sentiment Analysis Model Using Machine L...

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5th International Conference on Information and Communication technology for Development for Africa, ICT4DA 2023

作者： Gedif, Birku Alemu, Abebe Assefa, Yibeltal Nibret, Shegaw College of Informatics University of Gondar Information Science Ethiopia College of Informatics University of Gondar Computer Science Ethiopia Kombolcha Institute of Technology Wollo University Software Engineering Ethiopia Bahir Dar Institute of Technology Bahir Dar University ICT4D Research Center Ethiopia

ISBN: (纸本)9798350328486

Sentiment analysis is a type of natural language processing for tracking the attitude of the public about a particular product, service or topic. It is also highly challenging as natural language processing research topic, and covers many novel sub-problems. Now business organizations and academics are putting forward their efforts to find the best system for sentiment analysis. The focus of this study was an Amharic unstructured restaurant review on the web. The objective of the paper was to design Amharic text sentiment analysis model using supervised machine learning techniques and evaluate the performance of classifiers. This paper explored the supervised machine learning classification approaches (naive Bayes, support vector machine and k-nearest neighbor) with different feature selection schemes to obtain a sentiment analysis model for domain specific restaurant review dataset at sentence level. The proposed model has the following components: Data preparation, preprocessing such as tokenization, normalization, filter stop words, feature extraction and selection to prepare feature vector, polarity classification. Performance analysis carried out on classifiers, based on n-grams proposed. From the results of the experimental studies, all algorithms are known to be highly effective classifiers, and are able to achieve good accuracy in this experiment. The experiments show that Term frequency (TF) and the TF-IDF scheme gives maximum accuracy 80.43 % and 79.49 % respectively for SVM in bigram features. Term frequency and term occurrence also give maximum accuracy 78.37 % and 78.00 % respectively for Naive Bayes classifier at bigram features. TF-IDF also give maximum accuracy 78.00% for KNN at 4-gram. The challenge was opinion holders sometimes give objective text to express their opinion, but the classifier did not identify those facts from opinions. These kinds of complexities of natural languages make sentiment mining systems more challenging and to resolving th

关键词： Sentiment analysis

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A New Membrane Algorithm Based on Quasi-Affine TRansformation Evolution for Optimization Problems 1

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Wang, Xiaopeng Snášel, Václav Mirjalili, Seyedali Chu, Shu-Chuan Shehadeh, Hisham A. Bai, Zhonghai Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava70800 Czech Republic Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane4006 Australia College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Faculty of Computer Science and Informatics Amman Arab University Amman11953 Jordan

ISBN: (数字)9789819700684

ISBN: (纸本)9789819700677

This paper develops a new meta-heuristic algorithm, called Membrane Algorithm based on Quasi-Affine TRansformation Evolution (MA-QUATRE). The proposed algorithm combines the principles of QUATRE, the one-level membrane structure, and the rules of membrane systems. The different operations of QUATRE are for each inner elementary membrane and outer skin membrane, and the transformation/communication is like rules in membrane systems. For the effectiveness of MA-QUATRE, it is first evaluated on the CEC2022 benchmark and compared with five QUATRE-related algorithms. Further, the proposed algorithm is used to solve two engineering design problems (tension/compression spring and pressure vessel design) and compared with the original QUATRE and six state-of-the-art algorithms. Finally, experimental results show that the proposed MA-QUATRE is a competitive algorithm for optimization problems. The source codes of this algorithm can be found in https://***/matlabcentral/fileexchange/134372-ma-quatre and https://***/wangxper/***. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Membranes

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Stroke Risk Assessment Through Sparse AM-FM Decompositions of Carotid Plaque Ultrasound Images

Stroke Risk Assessment Through Sparse AM-FM Decompositions o...

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IEEE Southwest Symposium on Image Analysis and Interpretation

作者： Kyriacos P. Constantinou Ioannis P. Constantinou Efthyvoulos Kyriacou Christos P. Loizou Constantinos S. Pattichis Andreas Panayides Marios S. Pattichis University of Cyprus Nicosia Cyprus Istognosis Ltd Nicosia Cyprus Cyprus University of Technology Limassol Cyprus CYENS Center of Excellence University of Cyprus Nicosia Cyprus Center of Excellence CYENS Nicosia Cyprus Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

The objective of this work was to investigate a new sparse multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on multiple Gabor filterbanks representations where component selection was carried out using the elastic net regularization equation. The AM-FM histogram features sets of instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency were computed from carotid plaque ultrasound images to assess the risk of stroke. A total of 100 carotid plaque ultrasound images (50 asymptomatic and 50 symptomatic) were analyzed following manual segmentation by an expert. Classification modelling was carried out using the Support Vectors Machine to classify asymptomatic versus symptomatic plaques. An overall classification accuracy of 74% was achieved, demonstrating that the new sparse multiscale AM-FM analysis provided robust features. These findings are comparable with classification models trained with traditional AM-FM feature sets as well as classical texture feature sets. Moreover, the proposed analysis provides new sparse image representations that allow us to reduce the number of AM-FM components needed to explain the local spatial-frequency content and can further facilitate the desired explanatory interpretation in stroke risk assessment.

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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Performance Improvements of Vehicular PKI Protocol for the Security of V2X Communications 46

Performance Improvements of Vehicular PKI Protocol for the S...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Farran, Hassan Khoury, David Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services BudapestH-1111 Hungary American University of Science and Technology Department of Computer Science and Information and Communication Technology Beirut Lebanon

ISBN: (纸本)9798350303964

Vehicle-to-Everything (V2X) technology allows vehicles to communicate with numerous transportation system components. To provide safe communication between vehicles and other entities, V2X depends on Public Key Infrastructure (PKI). However, the present PKI employed in V2X has certain drawbacks, including scalability and high computational cost. This article offers an enhancement to the current PKI in V2X that tackles these constraints by managing and distributing keys using blockchain technology. The suggested Public Key Infrastructure enhancement is intended to be secure, efficient, and scalable, providing a more solid and trustworthy foundation for V2Xcommunication. The article explains the suggested PKI enhancement and examines its advantages and disadvantages. Overall, the suggested Public Key Infrastructure enhancement has the potential to increase the security and dependability of Vehicle-to-Everything communication, allowing for a secure and more effective transportation system. © 2023 IEEE.

关键词： Vehicle to Everything

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Improving DL-Learner on a Malware Detection Use Case 36

Improving DL-Learner on a Malware Detection Use Case

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36th International Workshop on Description Logics, DL 2023

作者： Bisták, Tomáš Švec, Peter Kluka, Ján Šimko, Alexander Balogh, Štefan Homola, Martin Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava842 48 Slovakia Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovicova 3 Bratislava812 19 Slovakia

Automation of malware characterization has become increasingly important for early malware detection over the past decades. Since it is crucial to be able to perform malware detection transparently, explainable machine-learning methods may be considered particularly suitable for this application. In our previous work, we thus employed algorithms for learning description-logics concept expressions to obtain structured characterizations of malicious software. The results of our initial experiments, however, lead us to several inconsistencies in the behaviour of the studied algorithms included in DL-Learner, a widely adopted framework for concept learning. Hence, in this work, we make a couple of corrections to DL-Learner, along with a few further enhancements, and repeat the experiments to empirically assess the effects of our changes. The outcomes show that the modified algorithms can mark an improvement in terms of the predictability of their behaviour as well as evaluation metrics. © 2023 CEUR-WS. All rights reserved.

关键词： Malware

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