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Green Machine Learning Approach for QoS Improvement in Cellular Communications 2

Green Machine Learning Approach for QoS Improvement in Cellu...

2nd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Saeed, Mamoon M. Saeed, Rashid A. Azim, Mohammad Abdul Ali, Elmustafa Sayed Mokhtar, Rania A. Khalifa, Othman Faculty of Engineering Communications and Electronics Engineering Department Yemen Taif University College of Computers and Information Technology Department of Computer Engineering Taif21944 Saudi Arabia Asian University for Women Department of Computer Science Bangladesh Department of Electrical Electronic Engineering Port Sudan Sudan College of Electronics Engineering Khartoum Sudan International Islamic University Malaysia Faculty of Engineering Dept of Electrical and Electronics Engineering Malaysia

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Green cellular communications are becoming an important approach due to large-scale and complex radio networks. Due to the dynamic cellular network behaviors related to interference distribution, traffic bottlenecks, congestion points, and hotspots, there is a need to evaluate the dynamic processes in cellular systems in addition to ensuring spectrum availability. The delay, loss rate, and SNR are the most issues that may affect cellular communication performance. Artificial intelligent algorithms such as machine learning (ML) enable to detection of the dynamics in cellular networks by analyzing the complex cellular network processes and evaluating the spectrum and links qualities. It enables the extraction of spectrum knowledge from the network autonomously. The extracted information helps to know about every dynamic change in wireless parameters, related to frequency, modulation, route selection, etc. This paper provides details about the use of ML in green cellular networks to efficiently upgrade the communications and enhances different related approaches including quality of services (QoS), signal traffic load, and energy efficiency, which are critical issues of green cellular communication paradigms. The paper also presents the technical concept of green ML approaches to solve significant problems in cellular communications, in addition to future aspects and considerations for energy consumption minimization using the green ML approach in cellular radio communications. © 2022 IEEE.

关键词： Energy efficiency

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Development of a Digital Brain Phantom Population for Radionuclide Imaging Research in Parkinson's Disease

Development of a Digital Brain Phantom Population for Radion...

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2022 IEEE Nuclear science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Huang, Wenbo Mok, Greta S. P. University of Macau Faculty of Science and Technology The Center for Cognitive and Brain Sciences Institute of Collaborative Innovation Department of Electrical and Computer Engineering Taipa China

ISBN: (纸本)9781665488723

Parkinson's disease (PD) can be early diagnosed using radionuclide imaging. Digital phantoms and Monte Carlo (MC) simulations serve as important research tools for PD imaging physics research. Here we designed and developed a new digital brain phantom population for different stages of PD. We selected 206 T1 MRI brain images and 160 DaTscan SPECT reconstructed images for normal subjects and patients with different PD stages from Parkinson's Progression Markers Initiative (PPMI) datasets. We used the model-based segmentation tool FIRST to segment putamen and caudate region and used Brain Extraction Tool (BET) to extract brain region from the MRI images, generating striatum and brain maps, respectively. Striatum maps were then rigidly registered to a referenced striatum model using Elastix, and the corresponding brain maps were transformed using the same parameters. Different activities were set on the maps based on the striatal binding ratio (SBR) in PPMI and the SPECT Visual interpretation assessment scheme. New phantoms were generated by randomly pairing the striatum and brain maps. Corresponding CT maps were generated by non-rigidly registered the XCAT-attenuation map to MRI images corresponding to the brain maps and transformed using the same parameters. We then used the Simind MC code to generate 120 realistic noisy projections with full physics modelling for each new brain phantom, based on a clinical SPECT scanner mounted with low-energy high-resolution parallel-hole collimators. OS-EM algorithm (8 iterations, 4 subsets) was used to reconstruct projections with and without attenuation correction (AC) respectively. SBR was evaluated on reconstructed images. Our results show that we could generate realistic PD SPECT data with the newly developed digital brain phantom population combined with a MC code, and AC is important for PD SPECT based on both visual assessment and SBR analysis. Our phantoms could be applied for further PD radionuclide imaging study. © 2022

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Automated Taxi Booking Operations for Autonomous Vehicles

arXiv

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arXiv 2025年

作者： Van Ma, Linh Azam, Shoaib Munir, Farzeen Jeon, Moongu Choi, Jinho School of Electrical Engineering and Computer Science Gwanju Institute of Science and Technology Gwangju61005 Korea Republic of School of Information Technology Deakin University GeelongVIC Australia

In a conventional taxi booking system, all taxi operations are mostly done by a decision made by drivers which is hard to implement in unmanned vehicles. To address this challenge, we introduce a taxi booking system which assists autonomous vehicles to pick up customers. The system can allocate an autonomous vehicle (AV) as well as plan service trips for a customer request. We use our own AV to serve a customer who uses a mobile application to make his taxi request. Apart from customer and AV, we build a server to monitor customers and AVs. It also supports inter-communication between a customer and an AV once AV decided to pick up a customer. © 2025, CC BY.

关键词： Reservation systems

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A Rule-Based Deep Learning Method for Predicting Price of Used Cars 4th

A Rule-Based Deep Learning Method for Predicting Price of Us...

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Ajagbe, Sunday Adeola Mishra, Nishchol Department of Computer Science McPherson University Seriki-Sotayo Abeokuta Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science and Communication Ostfold University College Halden Norway Department of Computer Engineering Ladoke Akintola University of Technology Ogbomoso Nigeria Rajiv Gandhi Technical University Bhopal India

ISBN: (纸本)9789819900466

The growth of internet used automobile shopping has been tremendous in recent years. It is important for sellers to correctly evaluate the price of the used cars. From the literature, classification is an approach that assists in data interpretation by analyzing data and providing a second opinion on data among other tasks most of the classifiers used for car price prediction suffered from lower accuracy ratios and higher error ratios. In this work, a hybrid feature selection model for predicting the price of used cars was developed. The developed model includes a CfsSubsetEval to determine the best feature subset, rule-based GA to search for the best solution, and a neural network for the final price prediction based on the selected features. The developed model was implemented in a Java programming language. The evaluation showed that the developed hybrid feature selection model achieved better performances with the correlation coefficient, mean absolute error and root mean square error of 0.9818, 1.1809, and 1.5643 respectively, compared to other related state-of-the-art algorithms. The results validated the efficiency of the developed model for used car price prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Java programming language

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Retraction Note: Optimizing radio access in 5G vehicle networks using novel machine learning-driven resource management

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Natarajan, Rajesh Mahadev, Natesh Alfurhood, Badria Sulaiman Ranjith, Christodoss Prasanna Zaki, John Manu, M. N. Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Shinas Oman Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Computer and Systems Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Information Science and Engineering SJBIT Bengaluru India

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Electric Vehicle Range Prediction using Random Forest Regression 8

Electric Vehicle Range Prediction using Random Forest Regres...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Sagar, B.S. Rajasekaran, Mothiram Sakthisaravanan, B. Jyothi, R. Leela Lalitha, K. Murugan, S. Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Pine Candle Way Saint AugustineFL United States Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

This research introduces a new method for predicting electric vehicles (EVs) range that combines cloud computing with random forest regression (RFR) approaches. Predicting the range properly is now critical for user comfort and efficient route planning due to the rising popularity of EVs. Factors like changing driving conditions and battery depletion might make traditional range calculation techniques inaccurate. It uses the processing power of the cloud to collect and analyze massive volumes of real-time data on variables like traffic, weather, and topography that have an impact on EVs range. Next, it uses a powerful machine learning (ML) algorithm for RFR to accurately forecast EVs range by modeling the intricate interactions between these factors. Results from experiments this technique works, with far better range estimate advanced approaches. Cloud computing scalability and flexibility also make it easy to predict models into preexisting EVs navigation systems, so drivers may get accurate range estimations in real time based on their own circumstances. It will improve EVs' usability and dependability, which will lead to more people buying and using these eco-friendly vehicles. © 2024 IEEE.

关键词： Prediction models

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Revolutionizing Dota 2 Viewing: Automated Battle Highlights in Esports Videos

Revolutionizing Dota 2 Viewing: Automated Battle Highlights ...

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International electrical engineering Congress (iEECON)

作者： Chananate Arthayukti Kritthanit Malathong Jessada Konpang Adisorn Sirikham Chanwit Musika Phatsakul Thitimahatthanakusol Department of Computer Science Faculty of Science and Technology Rajamangala University of Technology Bangkok Thailand Department of Electrical and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Bangkok Thailand Department of Telecommunication Engineering Faculty of Engineering and Technology Rajamangala University of Technology Isan Nakorn Ratchasima Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This research outlines a structured process for automatically generating video highlights from Dota 2, a multiplayer online battle arena (MOBA) game. Dota 2's dynamic battles make determining their duration challenging, often requiring manual intervention. However, the game's API provides developers access to critical in-game information, particularly player deaths, which serve as key indicators of battles. The process unfolds in six steps: capturing competition videos, extracting match details, refining the data to account for in-game time variations and game start times, automatic video trimming with attention to overlap resolution, consolidation of video segments, and, finally, the publication of the highlight video. This approach ensures engaging and concise highlight videos, enhancing the viewer experience.

关键词： electrical engineering Systematics Soft sensors Refining Games Manuals Aerodynamics

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Plant Disease Identification Using a Dual Self-Attention Modified Residual-Inception Network 15

Plant Disease Identification Using a Dual Self-Attention Mod...

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15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2023

作者： Chauhan, Rashi Karnati, Mohan Dutta, Malay Kishore Burget, Radim Amity University Computer Science and Engineering Uttar Pradesh Noida India Amity Centre for Artificial Intelligence Amity University Uttar Pradesh Noida India Brno University of Technology Faculty of Electrical Engineering Department of Telecommunications Czech Republic

ISBN: (纸本)9798350393286

The early detection of plant diseases reduces agricultural loss. In the field of computer vision and pattern recognition, deep learning (DL) techniques, particularly convolutional neural networks (CNNs), are widely employed. To identify plant diseases, researchers put forth various DL models. However, DL models require many parameters to learn the underlying patterns of the plant disease, increasing training time and making it challenging to deploy on small devices. This study introduces a novel DL model utilizing a dual self-attention modified residual-inception network (DARINet), which integrates the multi-scale, self-attention, and channel attention features with the residual connection. The proposed approach is evaluated on two plant disease datasets such as Cassava and Rice leaf, achieving an accuracy of 77.12% and 98.92%. In Comparision to state-of-the-art DL models, our proposed approach attains higher accuracy with fewer parameters. © 2023 IEEE.

关键词： deep convolutional neural networks deep learning modified inception Plant disease residual connection

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Path2Vec: A Deep Representation Learning Method for Trajectory Feature Extraction and HYSPLIT Uncertainty Quantification

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Applied Mathematics and Nonlinear sciences 2024年第1期9卷

作者： Ren, Ke Jin, Chengyao Song, Yuxuan Xu, Yang Zhang, Huijie Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian223003 China School of Information Science and Technology Northeast Normal University Changchun130117 China

Accurate quantification of the uncertainty in HYSPLIT model simulations is crucial for analyzing atmospheric pollution propagation paths and assessing environmental risks. This study introduces Path2Vec, a method based on deep representation learning for extracting trajectory features and measuring uncertainty. The method is capable of mining spatiotemporal-independent trajectory motion patterns in the HYSPLIT model. We first extract spatiotemporal-invariant features of the trajectories using a sliding window technique. Subsequently, we utilize a deep representation learning model that integrates a variational autoencoder (VAE) with long short-Term memory (LSTM) networks to encode high-quality deep representations of the trajectories. By measuring the similarity and performing clustering analysis on the generated trajectory deep representations, we can identify and classify different motion patterns, and quantify the uncertainty of HYSPLIT. Experimental results indicate that the Path2Vec method surpasses traditional similarity measurement techniques, such as Euclidean distance and Edit Distance on Real sequence, in extracting spatiotemporal-independent motion patterns and quantifying uncertainty. This study provides a novel and effective approach for trajectory feature extraction and uncertainty quantification, with wide-ranging applications in fields such as meteorological simulation and air pollution propagation path analysis. © 2024 Ke Ren, Chengyao Jin, Yuxuan Song, Yang Xu and Huijie Zhang, published by Sciendo.

关键词： Risk assessment

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A Dynamic Optimization Framework for Computation Rate Maximization in UAV-Assisted Mobile Edge Computing

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IEEE Transactions on Vehicular technology 2025年

作者： Chen, Yang Pi, Dechang Yang, Shengxiang Xu, Yue Wang, Bi Qin, Shuo Wang, Yintong Xi'an University of Science and Technology Artificial Intelligence and Computer Science Xi'an China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Jiangxi University of Science and Technology Faculty of Information Engineering Ganzhou China Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Nanjing Xiaozhuang University School of Information Engineering Nanjing China

Mobile edge computing (MEC) significantly boosts the computing power and reduces the energy consumption of Internet of Things (IoT) devices, serving as a valuable complement to cloud computing. The application of unmanned aerial vehicle (UAV) for MEC systems can effectively alleviate the issue of insufficient or damaged communication facilities in remote areas, further expanding the scope of MEC applications. In this article, we present a system model for UAV-assisted wireless-powered MEC systems in a dynamic environment with the objective of maximizing the computation rate of user devices. Due to the complexity of the optimization objective in dynamic environments, we propose a swarm intelligence-based optimization framework with a mechanism for responding to environmental changes, which is intended to enhance population diversity in both static and dynamic environments with the aim of overcoming premature convergence. We integrate particle swarm optimization and harmony search into the proposed framework, naming them DOPSO and DOHS, respectively. Simulation results for two offloading modes in UAV-assisted MEC systems indicate that the proposed framework significantly outperforms other dynamic optimization algorithms. © 1967-2012 IEEE.

关键词： Energy utilization

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