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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Clinically Significant Prostate Cancer Detection and Diagnosis in Bi-Parametric MRI with Deep Learning Models 9

Clinically Significant Prostate Cancer Detection and Diagnos...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Asoh-Itambi, Clinton Binda Yüzkat, Mecit Varli, Songül Yildiz Technical University Faculty of Electrical and Electronics Department of Computer Engineering Istanbul Turkey Muş Alparslan University Health Institutes of Türkiye Faculty of Engineering and Architecture Department of Software Engineering Istanbul Turkey Yildiz Technical University Health Institutes of Türkiye Faculty of Electrical and Electronics Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

This study presents a significant improvement in the detection and diagnosis of clinically significant prostate cancer (csPCa) in bi-parametric magnetic resonance imaging (bpMRI) by adapting the nnU-Net framework. We address the inherent limitations of traditional imaging analysis techniques by modifying the loss function used in nnU-Net, replacing the default combination of Cross-Entropy and soft Dice loss with a novel integration of Cross-Entropy loss and Focal loss. This modification targets class imbalance and enhances the detection sensitivity for less represented, clinically significant lesions, which are crucial for effective csPCa management while minimizing false diagnosis. Employing a semi-supervised learning approach, the modified nnU-Net was trained and validated on the PI-CAI (Prostate Imaging: Cancer AI) Public Training dataset (1500 cases), the current benchmark dataset for csPCa detection and diagnosis. It was also tested on the PI-CAI Hidden Testing cohort dataset consisting of 100 unseen cases. These datasets offer a comprehensive and diverse collection of prostate MRI exams, providing a robust foundation for model training and testing. We conducted a rigorous 5-fold cross-validation to ensure the robustness and reproducibility of our findings. The model's performance was evaluated with Average Precision (AP) at the lesion level and Area Under the Receiver Operating Characteristics curve (AUROC) at the patient level. Our model with AUROC and AP of 0.824 and 0.603 respectively on the Hidden Tuning cohort, outperformed the state-of-the-art U-Net, nnDetection models, and other nnU-Net variants. This work contributes to ongoing efforts to refine diagnostic tools in medical imaging, offering the potential for more accurate and timely prostate cancer screenings. © 2024 IEEE.

关键词： Magnetic resonance imaging

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AI and IoT in Energy Conservation: Challenges, Opportunities, and Trends 12

AI and IoT in Energy Conservation: Challenges, Opportunities...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Shirinabadi, Parisa Bonab, Amirreza Samadi Siddiqui, Khadim Moin University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran S.R. Institute of Management & Technology Electrical & Electronics Engineering Department Lucknow India

ISBN: (纸本)9798331525132

The rapid advancements in Artificial Intelligence (AI) and the Internet of Things (IoT) have paved the way for innovative solutions to address global energy challenges. This paper explores the integration of AI and IoT in energy conservation, focusing on their combined potential to optimize energy consumption, enhance system efficiency, and support sustainable development. AI-driven predictive analytics and machine learning algorithms enable accurate energy demand forecasting and real-time decision-making, while IoT devices facilitate seamless data collection, monitoring, and control of energy systems. The synergy of these technologies has been instrumental in transforming energy management across various sectors, including smart grids, industrial automation, and smart buildings. However, their adoption comes with significant challenges, such as data security, interoperability, and the high cost of deployment. This review discusses the current state of AI and IoT in energy conservation, identifies key challenges, highlights emerging opportunities, and examines future trends, such as the role of blockchain and edge computing in decentralized energy systems. The paper concludes by emphasizing the need for interdisciplinary research and collaboration to unlock the full potential of AI and IoT in creating sustainable energy solutions. © 2025 IEEE.

关键词： Energy efficiency

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Enhanced Modified Contrast Enhancement and Exposure Fusion: a visionary dehazing approach for clarity in hazy imagery

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Multimedia Tools and Applications 2025年 1-24页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In the field of image processing, haze is a type of atmospheric scattering that reduces contrast and clarity of images, frequently masking small details and far-off objects. It is mostly caused by microscopic airborne particles like smoke, dust, and water droplets that scatter incoming light. The scattered light, producing a hazy image, diminishes the direct light that reaches the camera sensor. Many image processing methods have been presened to solve this problem and lessen the impacts of haze. Usually, these methods work by determining how much haze is there in an image, and then using this information to get the image back to its assumed previous quality. In this work, we present the proposed Modified Contrast Enhancement and Exposure Fusion (MCEEF) technique. The MCEEF dehazing technique falls under the umbrella of enhancement-based dehazing techniques. In this technique, hazy frames or images undergo sharpening through a Smoothing-Sharpening Image Filter (SSIF) designed to accentuate the disparity between the haze and objects in images. Subsequently, Gamma Correction (GC) and Color Preserving Adaptive Histogram Equalization (CP-AHE) enhance the sharpened hazy images by augmenting their contrast. Dehazed images are then obtained by fusing the results of the CP-AHE- and GC-enhanced images. The MCEEF dehazing technique is aided with enhanced hazy images or frames as input for the dehazing process. Moreover, essential enhancement tools precede the MCEEF dehazing technique. These tools include homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), which are instrumental in controlling the dynamic range before the dehazing phase. In the proposed approach, a hazy image or frame is initially subjected to homomorphic processing, followed by the application of CLAHE, and finally, the MCEEF dehazing technique is employed. To demonstrate the effectiveness of the proposed approach, we apply it on both visible and Near Infrared (NIR) frames. We

关键词： Demulsification

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Design and Analysis of a UWB Slotted Vivaldi Antenna for Microwave Imaging Applications

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 350-357页

作者： Paul, Liton Chandra Majumder, Akash Rani, Tithi Hossain, Md. Najmul Rahim, Md. Abdur Shin, Jungpil Yun, Keun Soo Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Electronics & Telecommunication Engineering Faculty of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu / Fukushima Aizuwakamatsu965-8580 Japan School of Computer and Information Technology Ulsan College / Dong-gu Ulsan44610 Korea Republic of

An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3 in size. The proposed compact Vivaldi antenna showed good radiation characteristics and spanned an ultra-wide bandwidth of 10 GHz, ranging from 3.6 to 13.6 GHz, using a microstrip line feed with a radial stub, tapered slots, and resonant cavities. After analyzing the simulation findings, the proposed compact slotted UWB Vivaldi antenna was compared with the basic Vivaldi antenna. The maximum gain and efficiency were 7.35 dB and 82%, respectively. The antenna was simulated in the time domain using the computer simulation technology (CST) microwave studio. The frequency domain solver was also used for cross-validation, and the results were equally good. In conclusion, the proposed compact Vivaldi antenna is a good option for microwave imaging applications. © 2023 The Institute of electronics and Information Engineers.

关键词： Cavity resonators

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Optimizing the Operation of Energy Storage Systems Using Artificial Intelligence for Economic Performance and Energy Transition 14

Optimizing the Operation of Energy Storage Systems Using Art...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Zournatzis, Athanasios C. Kontargyri, Vassiliki T. Nicolopoulou, Eleni P. Christodoulou, Christos A. School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Electrical and Electronics Engineering Faculty of Engineering University of West Attica Athens Greece

ISBN: (纸本)9781837242689

The utilization of energy storage systems (ESS) and the expansion of their installed capacity are crucial steps towards achieving energy neutrality goals. These systems, often integrated with photovoltaic installations, represent an intermediate stage that introduces new challenges for distribution management and financial burdens for consumers and society. To maximize the benefits of ESS, it is necessary to design an optimization system that considers the battery lifecycle, installed capacity, and economic benefits. A key objective is the determination and reduction of the per-unit usage cost of these systems, which represents a significant innovation of this work. This paper proposes an optimization model that incorporates factors such as energy pricing, charging/discharging efficiency, battery lifespan, and renewable energy forecasts. The model leverages artificial intelligence (AI), including machine learning and deep learning, for data analysis and decision-making based on historical data and patterns. This solution is designed to address the needs of both residential and industrial clients, further expanding its applicability. This approach optimizes the economic performance of ESS, minimizes energy transition costs, and fosters favorable conditions for a successful transition. © The Institution of engineering & Technology 2024.

关键词： Energy economics

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Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained engineering Optimization

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Journal of Bionic engineering 2024年第1期21卷 374-408页

作者： Mojtaba Ghasemi Mohsen Zare Amir Zahedi Mohammad-Amin Akbari Seyedali Mirjalili Laith Abualigah Department of Electronics and Electrical Engineering Shiraz University of TechnologyShirazIran Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahromFrasIran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehranIran Department of Electrical and Computer Engineering University ofCyprusNicosiaCyprus Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD 4006Australia University Research and Innovation Center Obuda University1034 BudapestHungary Department of Electrical and Computer Engineering Lebanese American UniversityByblos 13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization *** efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark ***,GEA has been applied to several real-parameter engineering optimization problems to evaluate its *** addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization *** results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and *** that the source code of the GEA is publicly available at https://***/projects/gea.

关键词： Nature-inspired algorithms Real-world and engineering optimization Mathematical modeling Geyser algorithm(GEA)

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Optimizing Automotive Cybersecurity with Cost-Sensitive Decision Forest Algorithm in IoV 6

Optimizing Automotive Cybersecurity with Cost-Sensitive Deci...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Qureshi, Salwa Umar Souri, Alireza Inanç, Nihat Department of Software Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey

ISBN: (纸本)9798331539603

In the quickly changing world of connected cars, having strong cybersecurity is essential. To protect vehicular networks from cyber threats, this study provides an Embedded Intrusion Detection System (IDS) that has been optimized using the Cost-Sensitive Decision Forest (CSForest) optimization algorithm. To improve data usefulness and model performance, a thorough preprocessing and feature engineering process was carried out using a real vehicular GPS tracking process from ToN-IoT dataset. After evaluating several machine learning algorithms, CSForest received the best results, scoring 100% accuracy along with flawless precision, recall, and F-measure scores. Our results highlight how well the CSForest-based IDS can detect and stop possible cyberattacks in connected cars, which will improve automotive cybersecurity and create a safer driving environment for vehicular communication systems. The study also emphasizes how crucial it is to keep updating IDS models to handle the ever-changing nature of cyberattacks and maintain the dependability and resilience of vehicular communication systems in the future. The discovery has practical consequences as it can improve connected and autonomous car cybersecurity infrastructure substantially, lowering vulnerabilities and guaranteeing a safer transportation ecosystem. © 2024 IEEE.

关键词： Cybersecurity

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Enhancing Solar Energy Production Forecasting with Ensemble-based Learning Techniques 6

Enhancing Solar Energy Production Forecasting with Ensemble-...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Abdelsalam, Hany Souri, Alireza İnanç, Nihat Department of Software Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey

ISBN: (纸本)9798331539603

The global trend in renewable energy solutions has emphasized the urgent need for accurate forecasting of solar energy production. This study examines the potential of ensemble-based learning techniques in predicting solar energy production by leveraging historical data collected from 11 solar photovoltaic installations in Calgary, Canada, spanning from September 2015 to March 2023. The dataset was carefully preprocessed to handle missing values, duplicates, and to extract meaningful features that align with the nature of time series data. A range of ML models were implemented and assessed by several forecasting evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2). The experimental results demonstrate the superior performance of ensemble methods, particularly XGBoost and Light GBM, in solar energy forecasting. XGBoost achieved the best performance, with an MAE of 25.36, an MSE of 3599.66, and R2 of 0.957, outperforming models such as Random Forest and Neural Networks. The practical implications of these findings suggest that accurate solar energy forecasting can significantly enhance operational planning and optimize energy management in solar power plants, contributing to more reliable and efficient renewable energy systems. © 2024 IEEE.

关键词： ensemble methods machine learning renewable energy solar energy forecasting time series analysis XGBoost

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