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18th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Arifuddin, Lukman Nurba, Hardy Purnama Sarief, Ivany Setiawan, Asep Trisna Munastha, Ketut Abimanyu Lestari, Nina Universitas Sangga Buana Faculty of Engineering Department of Electrical Engineering Bandung Indonesia Universitas Bandar Lampung Faculty of Computer Science Lampung Indonesia

ISBN: (纸本)9798331527686

The demand for more efficient and reliable elevator monitoring systems has increased due to security and safety issues in high-mobility buildings. This paper proposes a microservice architecture for an elevator monitoring system to enhance scalability, flexibility, and fault tolerance by decomposing the system into more minor, loosely coupled services. This paper will discuss the proposed system's design, implementation, and evaluation. The system, equipped with ESP32Cam and ultrasonic sensors, excels in real-time monitoring. Data is processed and analyzed using Python and OpenCV, allowing the system to not only count the number of people in the elevator but also detect any anomalies, ensuring the safety of the occupants. LED notification alerts, managed by NodeMCU8266, are triggered based on ultrasonic sensor data. MongoDB handles user management and analytical data, while the dashboard offers a user-friendly interface for real-time monitoring and system management provide essential information for elevator operators. The architecture's use of MQTT ensures efficient communication between sensors and services, creating a robust, scalable, and efficient solution. © 2024 IEEE.

关键词： Ultrasonic sensors

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AI-Enabled Smart Hybrid Energy Optimization Management System for Green Hydrogen-Based Islanded Microgrids 6

AI-Enabled Smart Hybrid Energy Optimization Management Syste...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Mohamed, Noor El-Deen M. El Zoghby, Helmy M. Abdelhakam, Mostafa M. Elmesalawy, Mahmoud M. Helwan University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Helwan University Department of Electrical Power and Machines Engineering Cairo Egypt Helwan University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt

ISBN: (纸本)9798350378511

Renewable hybrid energy systems are crucial for ensuring energy sustainability. This work presents an advanced control and management system for green hydrogen production, leveraging artificial intelligence (AI) and Internet of Things (IoT) technologies. The system optimizes renewable energy utilization, improves hydrogen production efficiency, and ensures a reliable energy supply to meet varying demands. AI algorithms are used, including Random Forest, Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks to forecast photovoltaic (PV) energy production and load consumption. These models are integrated into a centralized control system that dynamically adjusts energy distribution between immediate consumption and hydrogen production. Using data from the Rye microgrid, the system's effectiveness is demonstrated in enhancing decision-making for energy management in small-scale islanded operations. © 2024 IEEE.

关键词： Clean energy

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Data-Driven Human Factors Enabled Digital Twin 49

Data-Driven Human Factors Enabled Digital Twin

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Kolesnikov, Mikhail V. Atmojo, Udayanto Dwi Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350331820

This paper presents a methodology for increasing human-centric production systems flexibility using human factors-enabled digital twins. The paper includes an analysis of the relevant projects that incorporate human-related data collection and processing. The proposed system is capable of collecting human factors-related data from various sources and then use a decision-making algorithm to schedule the tasks according to assessed human operator conditions in real-time. The formed Digital Twin is able to depict the condition of the labourer and production system status in real-time using Visual Components simulation environment. Shown results prove that existing production systems are capable of adapting to the changing condition of the worker flexibly, optimising workflow, distributing tasks with AGVs and cobots, and applying changes in workplace ergonomics to achieve better safety and performance of the worker. © 2023 IEEE.

关键词： Decision making

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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Video quality enhancement using different enhancement and dehazing techniques

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16607-16635页

作者： Ayoub, Abeer Naeem, Ensherah A. El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. El-Rabaie, El-Sayed M. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Electrical Department Faculty of Technology and Education Suez University Suez43527 Egypt Security Engineering Lab Computer Science Department 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

Video in an outdoor environment suffers from the haze problem due to smoke, dust, and other particles in the atmosphere. The quality of video under these atmospheric conditions is clearly/dramatically degraded, leading to poor contrast and information leakage. The proposed algorithm in this paper depends on pre-processing of video frames prior to the dehazing process. Different enhancement techniques are used to remove noise and control the dynamic range, because each frame has some noise due to sensor measurement errors. This noise might be magnified during the haze removal process, if totally neglected. Previous dehazing algorithms enhance the video contrast and reduce frame degradation. We use optimized dehazing algorithms, namely a dual-transmission-map dehazing algorithm, and a recursive Deep Residual Learning (DRL) network, as dehazing tools. We modify the previous dehazing algorithms to generate new algorithms to be suitable not only for visible frames but also for Near Infrared (NIR) frames. In the optimized dehazing algorithms, the haze effect is reduced, according to the hazing model. In the dual-transmission-map dehazing algorithm, the effect of attenuation parameter R and attenuation weight U on the dehazed frames is investigated. A dual-transmission-map algorithm, depending on the Dark Channel Prior (DCP) technique, is studied. We study the effect of a regularization parameter Ω on the visible and NIR dehazed frames without and with enhancement techniques. For the recursive deep residual learning algorithm, we increase the number of iterations in the DRL network from three iterations in the traditional DRL algorithm without denoising techniques to nine iterations, in order to explore the impact of increasing the number of iterations on the output dehazed frames. Since elapsed time grows with the number of iterations, we terminate the operation after the ninth iteration. A comparison between traditional algorithms and the proposed dehazing algorithms is

关键词： Deep learning

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Developing an Anti -Phishing Large Language Model: A Focus Group Study on Human, Technological, and Legal Factors 15

Developing an Anti -Phishing Large Language Model: A Focus G...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Thomopoulos, George A. Kiortsi, Panagiota Sigalas, Damianos Dumi Kosmopoulos, Dimitris Igglezakis, Ioannis Fidas, Christos University of Patras Department of Electrical and Computer Engineering Greece University of Thessaloniki Faculty of Law Aristotle Greece University of Patras Department of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350368833

The phishing problem poses a significant threat in modern information systems, putting both individuals and businesses at risk of financial and professional harm. Owing to social media's rapid development and widespread appeal, deception of this sort is hurting millions of people and growing more dangerous. The current work, as part of the AILA (Artificial Intelligence-driven Framework and Legal Advice Tools for Phishing Prevention and Mitigation in Information Systems) project, aims to specify and validate an AI-driven multifactor (human, technology and legal) anti-phishing data model, with the implementation of focus group studies. The findings assist to provide human, technology, and legislative user model endpoints that will be identified and discussed for explicit and implicit user modeling, which will guide the development of the corresponding AI-driven user modeling and profiling mechanisms. To this end a Large Language System is planned to be employed. © 2024 IEEE.

关键词： Phishing

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Enhanced Indoor Mapping via Integration of Lidar and HoloLens 2 Data Using Iterative Closest Point Algorithm 22

Enhanced Indoor Mapping via Integration of Lidar and HoloLen...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Szalka, Panka Hideg, Attila Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Automation and Applied Informatics Budapest Hungary

ISBN: (纸本)9798350385601

This study presents an innovative method to improve the accuracy of indoor mapping by deploying and integrating a sensor that transmits two-dimensional data with a three-dimensional mesh created by another device. The Iterative Closest Point algorithm was used to coordinate the different data sets and transform their different coordinate systems into a common indoor map. Prior to this, rigorous image cleaning and matching processes are conducted to ensure the quality of the data used. The effectiveness of this methodology has been validated through multiple tests using a Lidar scanner and a HoloLens 2 Augmented Reality device. This method not only offers potential for enhancing indoor navigation and safety protocols like emergency evacuation but also presents opportunities for advancing asset tracking and other applications. © 2024 IEEE.

关键词： Indoor positioning systems

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Bitcoin Price Forecasting with Generative Adversarial Networks 29

Bitcoin Price Forecasting with Generative Adversarial Networ...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Manian, Mehdi Rastin, Niloofar Department of Computer Engineering Faculty of Engneering and Technology Shahid Ashrafi Esfahani University Isfahan Iran School of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (纸本)9798331523114

Forecasting bitcoin prices using deep learning techniques has gained significant attention recently. Despite the success of Generative Adversarial Networks (GANs) across various domains, there is a crucial issue in bitcoin price prediction: the ability to accurately capture correlations between temporal data points. Moreover, GANs face notable inherent challenges, particularly training instability and mode collapse. In this paper, we employ windowing and conditioning techniques to capture correlations between prices and other relevant features such as technical indicators. Additionally, we use a hybrid Long Short-Term Memory (LSTM)-based generator that combines a convolution layer and LSTM to improve learning from temporal data. Appropriate loss functions are also utilized for the discriminator and generator to enhance training stability and mitigate mode collapse. The proposed method was evaluated on the historical Bitcoin data sourced from the Yahoo Finance website. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods. The source code for the proposed method is available on GitHub: https://***/mahdimanian/dragan_btc/ © 2025 IEEE.

关键词： Generative adversarial networks

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Privacy, Security, Trust and Applications in Internet of Things 6

Privacy, Security, Trust and Applications in Internet of Thi...

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6th IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Yaklaf, Sliman K.A.A. Elmezughi, Abdurrezagh S. Naas, Salah Mohamed H. Ekreem, Nasser Bashir Azzaytuna University Faculty of Engineering Electrical and Electronics Engineering Department Tarhuna Libya Azzaytuna University Faculty of Engineering Computer Engineering Department Tarhuna Libya

ISBN: (纸本)9798350321029

Internet of Things (IoT) is a network to connect anything with the Internet. While its purpose is to provide efficient and effective solutions, security of the network devices is a challenging issue. Therefore, security and privacy has emerged as a significant challenge for the IoT. Within the available Internet infrastructure, IoT devices collecting data through wireless technology. Now Days, IoT is research area with many devices are connected through internet. IoT concept brings a new challenge concerning privacy, trust, security, and reliability. To build smart systems, IoT technology has become important technology for people use. In this paper, IoT technologies, applications and characteristics will be briefly discussed. This paper gives a complete image of IoT security issues and its applications. The platform is required to secure exchange between consumers of their information and IoT devices. © 2023 IEEE.

关键词： Internet of things

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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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