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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Sarcevic, Ana Vranic, Mihaela Pintar, Damir Krajna, Agneza University of Zagreb Faculty of Electrical Engineering and Computing Croatia

ISBN: (纸本)9789532331035

Predicting the outcome of sporting events has always been a popular area of interest for sports professionals as well as the general public. Although domain experts have traditionally been the main contributors for generating sports predictions, relying solely on human expertise and intuition is not a scalable, time-effective, nor a cost-effective solution. Hence, computers are taking the leading role in predicting the outcomes of sporting events, in terms of estimating the final score or guessing the winner, but also for predicting the occurrence of various events that may happen during the game. Predictive analytics has been effectively applied to a wide range of sports, including popular team sports as well as individual sports. The nature of sport plays a significant role in the adaptation of different predictive techniques based on diverse statistical and mathematical models. Tennis has a strongly defined structure and a rigid scoring system which, along with the sport's popularity and large and easily accessible datasets, makes tennis match modeling a hot topic in scientific literature. This paper provides an overview of scientific papers on the prediction of tennis match outcomes, from the first regression-based models all the way to complex models based on machine learning. © 2022 Croatian Society MIPRO.

关键词： Tennis

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Crop Yield Forecasting Based on Echo State Network Tuned by Crayfish Optimization Algorithm 2

Crop Yield Forecasting Based on Echo State Network Tuned by ...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Bacanin, Nebojsa Jovanovic, Luka Djordjevic, Marko Petrovic, Aleksandar Zivkovic, Tamara Zivkovic, Miodrag Antonijevic, Milos Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia School of Electrical Engineering University of Belgrade Belgrade Serbia

ISBN: (纸本)9798350383652

The agriculture sector, once reliant only on external inputs for increased yields, faces repercussions of diminished natural systems and significant environmental impact. High-precision agriculture aims to curb excessive usage of fertilizers and pesticides while prioritizing sustainability. Modern technologies can shape decisions toward the betterment of soil health, plant protection, and yield enhancement. Predictive models employing machine learning aid farmers in crop performance estimation, disease detection, soil health assessment, and yield prediction among others. These insights inform field operations, logistics, and contract negotiations. Leveraging machinery data, weather data, and drone imagery using machine learning can help us refine farming practices. Recurrent neural networks (RNN) can aid in various farming challenges like plant stress detection and irrigation. Yet, challenges persist in optimizing hyperparameters, a task we tackled using the nature-inspired crayfish optimization algorithm (COA) adapted to tune echo state networks (ESN) for multivariate time series forecasting of crop yield. This study demonstrates the utility of machine learning in agriculture, enabling yield assessment for improved sustainability. © 2024 IEEE.

关键词： Recurrent neural networks

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Special Session: Implementation of models and innovative solutions in the operation of smart distribution networks 5

Special Session: Implementation of models and innovative sol...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Capuder, Tomislav University of Zagreb Faculty of electrical engineering and computing Croatia

ISBN: (纸本)9781665482158

Traditional power systems were characterized by passive end-users, oriented mostly on power delivery. Also, power system operators planned the generation of large power plants so that it matches electricity demand. In recent years, the paradigm of planning and operation of power systems, and especially distribution systems have significantly changed. © 2022 IEEE.

关键词： Electric power distribution

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Analysis of LiDAR-Camera Fusion for Marine Situational Awareness with Emphasis on Cluster Selection in Camera Frustum 15

Analysis of LiDAR-Camera Fusion for Marine Situational Aware...

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15th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles, CAMS 2024

作者： Obradović, Juraj Fabijanić, Matej Batoš, Matko Mišković, Nikola Faculty of Electrical Engineering and Computing University of Zagreb Department LABUST Laboratory for Underwater Systems and Technologies Unska 3 Zagreb Croatia

Providing accurate information about all objects in the environment surrounding an autonomous vehicle is essential for the development of a maritime navigation system used on board autonomous and unmanned vessels. In this paper, we introduce a method that integrates LiDAR and camera data for precise 3D object detection and tracking. The approach utilizes LiDAR for acquiring object position information and ensures accurate object classification through data from the camera. The proposed architecture is presented with a detailed explanation of all algorithms used. Several scenarios have been created in a simulated environment to compare three approaches for the selection of target object in the camera frustum (the Nearest Center, Gaussian Likelihood, and Intersection-Over-Union). Finally, the full algorithm setup is tested in a simulator environment with multiple vessels and a background coast. © 2024 Elsevier B.V.. All rights reserved.

关键词： Marine navigation

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SecureML: Machine Learning-Powered Malware Detection 2

SecureML: Machine Learning-Powered Malware Detection

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Awasthi, Animay Ahsan, Mohd Vanusha, D. Delsi Robinsha, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India

ISBN: (纸本)9798350383867

As malware threats become increasingly complex, the development of advanced detection systems is necessary to protect digital assets. SecureML Using Machine Learning presents an innovative approach to combat the escalating threat of malicious software by leveraging machine learning algorithms. This research paper outlines the design, implementation, and evaluation of SecureML, emphasizing its effectiveness in detecting both known and novel malware strains. Through comprehensive feature selection techniques and algorithmic comparison, SecureML identifies crucial features and selects the Random Forest algorithm as the most effective for malware detection, achieving an impressive accuracy rate of 99.40% on the test set. The system's low false positive and false negative rates validate its robustness and reliability in accurately distinguishing between benign and malicious files. SecureML demonstrates significant potential in bolstering cybersecurity defenses and safeguarding digital assets against evolving malware threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Frontier of digitalization in Biomass-to-X supply chain:opportunity or threats?

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Journal of Bioresources and Bioproducts 2023年第2期8卷 101-107页

作者： Adrian Chun Minh Loy Karen Gah Hie Kong Juin Yau Lim Bing Shen How Department of Chemical and Biological Engineering Monash UniversityMelbourneAustralia Biomass Waste-to-Wealth Special Interest Group Research Centre for Sustainable TechnologiesFaculty of EngineeringComputing and ScienceSwinburne University of TechnologyKuching 93350Malaysia Integrated Engineering Department of Environmental Science and EngineeringCollege of EngineeringKyung Hee UniversityGyeonggi-do 17104Republic of Korea

The escalating climate crisis necessitates an urgent shift towards a sustainable business ***-der the context of bioeconomy,it has offered a promising alternative through its“Biomass-to-X”strategy for converting biological resources into value-added products or ***,the adoption of this approach remains scarce,which highlights the need to leverage digital technolo-gies to enhance its ***,this paper provides a comprehensive overview of the potential role of digital technologies in the Biomass-to-X supply chain,encompassing the entire value chain from upstream to downstream activities,specifically in the areas of 1)lab-to-fabrication transla-tion,2)biomanufacturing stage,and lastly,3)supply chain management ***,this study identifies and discusses research gaps in each niche area,along with potential future re-search prospects to facilitate the transition towards a sustainable bioeconomy,making it a crucial reference for stakeholders involved in decision-making processes.

关键词： Biomass valorization Internet of nano-things Bioeconomy Biomanufacturing Blockchain Biomass-to-X

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Evaluation of Two-Part rain attenuation model at Ku-band for tropical and equatorial regions 3

Evaluation of Two-Part rain attenuation model at Ku-band for...

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3rd International Conference on Emerging Electrical Energy, Electronics and computing Technologies 2021, ICE4CT 2021

作者： Yagasena Faculty of Computing & Engineering Quest International University Malaysia

The current ITU rain attenuation model that is used in the design of satellite-earth communication link was derived using data collected predominantly from temperate regions and has limitations when applied to tropical and equatorial regions that are characterized by heavy rainfall. Elevation and frequency scaling was applied to the rain attenuation data obtained from tropical and equatorial regions to validate the linear relationship for rain rates above 90 mm/h in the TPRA model. Analysis of the data from these regions also confirms the linear relationship between the rain attenuation and rain rates above 90 mm/h. The TPRA model can be considered as an alternative model for a reliable satellite-earth link for use in the tropical and equatorial regions. © Published under licence by IOP Publishing Ltd.

关键词： Tropics

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Hybrid Deep Learning (CNN-BiLSTM-LSTM) Model for Prediction of Short Power Consumption 8

Hybrid Deep Learning (CNN-BiLSTM-LSTM) Model for Prediction ...

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8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Amirkhanloo, Danial Chitsaz, Hamid Mohajerzadeh, Amirhossein Ferdowsi University of Mashhad Department of Computer Engineering Mashhad Iran Sohar University Faculty of Computing and Information Technology Sohar Oman

ISBN: (纸本)9798350374674

The increase in electricity-based equipment caused by technological advances results in inordinate energy consumption and request for day-to-day power. Furthermore, energy forecasting for individual residential customers acts increasingly as an important factor in operating and designing future grids. A variety of methodologies has been used in order to project energy usage at distinct layers of distribution and transferring systems. In the current study, a new hybrid deep learning approach is presented to estimate power usage in smart homes. First, our model processes the data, excludes missing values, scales the data, and transforms them to assure the model performance. The pre-processed data is subsequently introduced to the proposed joint model as input, which uses CNN, LSTM, Bi-LSTM to choose hybrid discriminative features. Eventually, the fully connected layers are employed. The experiments were carried out to assess and compare the forecasting performance of the proposed method with recent models using the IHEPC dataset, including several variants. The outcomes show that the suggested network has the lowest mean MSE, RMSE and MAPE, compared to other cutting-edge models. © 2024 IEEE.

关键词： Forecasting

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Artificial Intelligence in Wireless Communications: An Overview of Present-day Paradigms 5

Artificial Intelligence in Wireless Communications: An Overv...

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5th IEEE International Conference on Emerging Trends in Electrical, Electronic and Communications engineering, ELECOM 2024

作者： Akinsolu, Mobayode O. Wrexham University Faculty of Arts Computing and Engineering Wales WrexhamLL11 2AW United Kingdom

ISBN: (纸本)9798331532475

The integration of artificial intelligence (AI) into wireless communications is growing rapidly. This growth is primarily driven by machine learning (ML) techniques, which can be broadly categorized into supervised learning, unsupervised learning, and reinforcement learning. Today, AI-based paradigms are transforming the field of wireless communications by enhancing various aspects, from the rapid design and optimization of components and devices to the robust analysis and characterization of entire systems and networks. This includes advanced systems such as present-day fifth-generation mobile (5G) and the upcoming sixth-generation mobile (6G) systems and networks. AI techniques also offer promising solutions to numerous design and development challenges in modern wireless communications. These challenges encompass enhancing power and energy effi-ciency, meeting stringent performance criteria, and improving the overall reliability of wireless communication devices, systems, and networks. This paper provides an overview of current paradigms demonstrating the application of AI, particularly ML techniques, in wireless communications. © 2024 IEEE.

关键词： Adversarial machine learning

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Alzheimer’s Disease and Frontotemporal Dementia: Differential Diagnosis Using Electroencephalogram Signal 1

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Rostamikia, Mehran Sarbaz, Yashar Farzamnia, Ali Biomedical System Modeling Lab Biomedical Engineering Department Electrical and Computer Engineering Faculty University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Dementia is a neurological disorder that affects a person’s cognitive and social skills, leading to a decline in their overall mental functioning. Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) are two common types of dementia. Frontotemporal dementia mostly affects the frontal and temporal lobes of the brain. These areas are responsible for executive functions, decision-making, language, behavior regulation, and personality traits. Alzheimer’s disease primarily damages the cerebral cortex, which is responsible for higher cognitive functions such as memory, language, and perception. The electroencephalogram (EEG) signal has many advantages in diagnosing these disorders, including low cost and high temporal resolution. This study compares AD and FTD patients with healthy subjects by extracting specific features from EEG signals. Two machine learning algorithms were used for the separation, Support Vector Machines (SVMs) and k-Nearest Neighbors (KNNs), and 10-Fold Cross-Validation was applied to validate the performance of this method and an accuracy of 91.2% (sd = 7.8) was achieved using the SVM classifier for diagnosing the disease and 71.1% (sd = 8.6) for classifying AD and FTD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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