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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Švarcmajer, Miljenko Köhler, Mirko Marinčić, Juraj Perić, Juraj Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Osijek31000 Croatia

ISBN: (纸本)9783031805967

In the world of cryptocurrencies, there is the possibility of participating in numerous processes in which some kind of reward is obtained, most often in the form of cryptocurrency. This paper investigates the possibilities of using laboratory computers at the faculty of Electrical engineering, Computing and information Technologies in these processes when they are not used for teaching purposes. The hardware specifications of the computer were collected and an insight into the ways of cryptocurrency earnings mining and validation was provided. Mining is the most well-known earning process, and it was introduced with the Bitcoin cryptocurrency, and based on this cryptocurrency, this process is described in detail. Due to the hardware configuration of the computer, in this paper we focus on the profitability of CPU mining of cryptocurrencies that can be mined this way, namely Monero, Zephyr, Raptoreum, Skytere and Subi Network. This type of mining has been unprofitable for Bitcoin for years. Computing power does not have to be used only for the mining process. For example, Solana uses CPUs to validate transactions. This paper combines technical and economic analyses, taking into account computer specifications to assess the economic viability of cryptocurrency mining and validation on college computers. We highlight the importance of organization, maintenance, and economic factors in managing these processes, providing practical guidelines for optimal utilization of faculty resources for the purpose of cryptocurrency earnings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Earnings

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A Binary and Multi Classification Model on Tax Evasion:A Comparative Study 1

A Binary and Multi Classification Model on Tax Evasion:A Com...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Shujaaddeen, Abeer Abdullah Ba-Alwi, Fadl Mutaher Zahary, Ammar T. Alhegami, Ahmed Sultan Alsabry, Ayman Al-Badani, Abdulkader M. Faculty of Computer and Information Technology Sana’a University Sana’a Yemen International University of Technology Twintech Malaysia Department of Computer Science Faculty of Computing and Information Technology University of Science and Technology Sana’a University Sana’a Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen Faculty of Science and Engineering Department of Computers Aljazeera University Ibb Yemen

ISBN: (纸本)9798331533557

In this paper, a model was built to compare the performance of the following machine learning (ML) models: DT, RF, SVM, and MLP, using two types of classification: binary classification and multi classification. The researchers concluded that the MLP classifier was the most efficient using multi classifications, as the classifier gave an accuracy of 99.77%, a recall of 93.25%, a precision of 92.02%, and an F-score of 92.63%. Using the dataset provided by the Tax Authority of Yemen, which is related to the commercial and industrial profits tax explained in detail in other papers for the same authors, which consists of 1083 record, after the preprocessing of data. © 2024 IEEE.

关键词： Contrastive Learning

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Adaptive Data Forwarding Scheme for Diverse Infotainment Services in Vehicular Named Data Networking

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IEEE Transactions on Vehicular technology 2024年第5期74卷 8012-8022页

作者： Khan, Sajjad Ahmad Lim, Huhnkuk Chaudhry, Shehzad Ashraf Kocaeli University Software Engineering Department Izmit41000 Turkey Hoseo University Computer Engineering Department Korea Republic of Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering & Architecture Nisantasi University Istanbul Turkey

Providing diverse infotainment services is crucial for enhancing road safety and improving the overall driving experience in vehicular networks. However, delivering these services within Vehicular Named Data Networking (VNDN) environments presents significant challenges, primarily due to the data broadcast phenomenon and the varying Quality of Service (QoS) requirements of different applications. This paper introduces an adaptive data forwarding scheme designed to effectively manage these challenges and optimize the delivery of infotainment services in VNDN. Our proposed scheme enhances the data packet format by incorporating additional attributes that prioritize data based on its criticality, specifically categorizing it into critical, non-critical, and entertainment data. Furthermore, we introduce an Enhanced Weighted Fair Queuing (EWFQ) policy to optimize the scheduling of data packets according to their importance. Two innovative data forwarding schemes, namely Advanced Push-based Data Forwarding (APDF) and Enhanced Pull-based Data Forwarding (EPDF), are developed to address the needs of critical and non-critical information and entertainment data, respectively. Simulation results demonstrate that our adaptive data forwarding scheme significantly outperforms legacy fragment schemes in delivering infotainment data within dynamic VNDN environments, ensuring reliability and efficiency. This paper contributes to advancing data dissemination strategies in vehicular networks and lays the groundwork for future research in adaptive QoS management. © 1967-2012 IEEE.

关键词： Entertainment industry Quality of service Adaptive systems Delays Data dissemination Vehicle dynamics Scalability Road safety Real-time systems Vehicles

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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

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Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： COVID-19

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Using Supervised Machine Learning Model For Predicting Fake Deliveries 6

Using Supervised Machine Learning Model For Predicting Fake ...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Sadek, Mohamed A. Fouad, Khaled M. Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Egypt

ISBN: (纸本)9798350373875

The home delivery service is a crucial part of the delivery process. Still, it has the highest financial and trustworthiness costs because it's the only part that directly touches the customer. If it works, the customer will be happy, and the company will make money, but if it doesn't, the customer will be skeptical, and the product will not be trusted. One big issue with the delivery process that this study will try to address is the prevalence of fraudulent or inaccurate deliveries. Making the most money possible from each journey is the primary goal of most riders. The delivery management system allows riders to falsely record orders as completed, allowing them to receive many orders simultaneously. Because the consumer usually gets a message or notification saying their item has been delivered when it hasn't, this weakens their faith in the organization and messes up the delivery process. Return to Origin (RTO) rates rose, and consumer confidence in the brand fell due to the fraudulent shipments. The suggested approach is based on ML models that are supervised. © 2024 IEEE.

关键词： Sales

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Centromere Landscapes Resolved from Hundreds of Human Genomes

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Genomics, Proteomics & Bioinformatics 2024年第5期22卷 61-71页

作者： Shenghan Gao Yimeng Zhang Stephen J. Bush Bo Wang Xiaofei Yang Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China Center for Mathematical Medical The First Affiliated HospitalXi'an Jiaotong UniversityXi'an 710061China School of Life Science and Technology Xi'an Jiaotong UniversityXi'an 710049China Faculty of Science Leiden UniversityLeiden 2311 EZThe Netherlands

High-fidelity(HiFi)sequencing has facilitated the assembly and analysis of the most repetitive region of the genome,the ***,our current understanding of human centromeres is based on a relatively small number of telomere-to-telomere assemblies,which have not yet captured its full *** this study,we investigated the genomic diversity of human centromere higher order repeats(HORs)via both HiFi reads and haplotype-resolved assemblies from hundreds of samples drawn from ongoing pangenome-sequencing projects and reprocessed them via a novel HOR annotation pipeline,*** used this wealth of data to provide a global survey of the centromeric HOR landscape;in particular,we found that 23 HORs presented significant copy number variability between *** detected three centromere genotypes with unbalanced population frequencies on chromosomes 5,8,and *** inter-assembly comparison of HOR loci further revealed that while HOR array structures are diverse,they nevertheless tend to form a number of specific landscapes,each exhibiting different levels of HOR subunit expansion and possibly reflecting a cyclical evolutionary transition from homogeneous to nested structures and back.

关键词： Centromere sequence Genome evolution High-fidelity sequencing technology Higher order repeat annotation Human population

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Coverage Probability of EH-enabled LoRa networks - A Deep Learning Approach

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2025年第2期12卷 1-11页

作者： Nguyen, Thi-Tuyet-Hai Hung, Tran Cong Son, Nguyen Hong Hanh, Tan Duy, Tran Trung Tu, Lam-Thanh Faculty of Information Technology Posts and Telecommunications Institute of Technology Viet Nam School of Computer Science & Engineering Saigon International University Ho Chi Minh City Viet Nam Faculty of Telecommunications Posts and Telecommunications Institute of Technology Viet Nam Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam

The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the considered networks. Our study incorporates a general fading distribution, specifically the Nakagami-m distribution, and utilizes tools from stochastic geometry (SG) to model the spatial distributions of all nodes and end-devices (EDs) with EH capability. The DL approach is employed to overcome the limitations of model-based methods that can only evaluate the Pcov under simplified network conditions. Therefore, we propose a deep neural network (DNN) that estimates the Pcov with high accuracy compared to the ground truth values. Additionally, we demonstrate that DL significantly outperforms the Monte Carlo simulation approach in terms of resource consumption, including time and memory. © (2025), (European Alliance for Innovation). All rights reserved.

关键词： Coverage probability deep learning energy harvesting long range power beacon

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Forecasting renewable energy for microgrids using machine learning

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Discover Applied Sciences 2025年第5期7卷 1-28页

作者： Sudasinghe, Piyumi Herath, Damayanthi Karunarathne, Isiwara Weeratunge, Hansani Jayasuriya, Lahiru Department of Mechanical Engineering University of Peradeniya Peradeniya Sri Lanka Department of Computer Engineering University of Peradeniya Peradeniya Sri Lanka Department of Mechanical Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Electrical and Electronic Technology Faculty of Technology Rajarata University of Sri Lanka Mihintale Sri Lanka

Evaluated ML-based renewable energy forecasting models by implementing 1-D CNN and LSTM models using real-world data. Proposed 1-D CNN performs better than LSTM and baseline models, achieving higher accuracy and computational efficiency. Accurate forecasting of PV and wind energy generation enhances grid stability, reduces backup power dependency, and supports sustainable energy integration. © The Author(s) 2025.

关键词： Wind power integration

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Dynamic Graph Convolution Recurrent Neural Network for Traffic Flow Prediction 3

Dynamic Graph Convolution Recurrent Neural Network for Traff...

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3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Lou, Haijie Ma, Ying Li, Jianmin Faculty of Computer and Information Engineering Xiamen University of Technology Xiamen China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9781510671485

To forecast the condition of traffic networks in the future, it is crucial to model the spatial and temporal correlation of traffic series. The majority of current research has been on creating complicated graph neural networks that can capture common patterns using preconfigured graphs. In this paper, we claim that predefined graphs may be avoided and that adaptive graphs can be used to capture spatial correlations between traffic series and improve the performance of graph neural networks. In order to capture the temporal relationships of sequences, we also aggregated gated recurrent neural networks. Then, we encode the relative time position of the sequence in order to fully extract the characteristics of the traffic sequence. Finally, we add the values of the previous day and the same day of the previous week as a reference in the final prediction to improve the accuracy of our prediction. Experimental results on two sets of real-word traffic data (PeMSD4 and PeMSD8) demonstrate that our method is better than the existing methods. © 2023 SPIE.

关键词： Graph neural networks

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Carbon Dioxide Emission Forecasting using Machine Learning and Time Series Statistical Models 25

Carbon Dioxide Emission Forecasting using Machine Learning a...

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25th International Arab Conference on information technology, ACIT 2024

作者： Hassan, Tahir Mansour, Khalid Waseemullah, Waseemullah Memon, Muhammad Qasim Ned University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Arab Open University Faculty of Computer Studies Bahrain Sindh Bhitshah Pakistan

ISBN: (纸本)9798331540012

Climate change is considered a global disaster that has wreaked havoc worldwide. Climate change conditions are primarily driven due to emission of carbon dioxide and other greenhouse gases. Around the globe, several countries are facing serious challenges related to global warming and climate change. The adverse effects of global warming and climate change are recently observed in the form heat waves, floods, extreme rainfall events and drought conditions. The escalating CO2 emission level causes threats of food security and water scarcity. To overcome this, timely and accurate CO2 emissions forecasting is essential for developing effective mitigation strategies in such a manner that CO2 emission impacts can be reduced. The aim of this study is to forecast the total CO2 emissions of upcoming years using various statistical, machine learning models such as ARIMA (Auto-Regressive Integrated Moving Average), SARIMA (Seasonal ARIMA), Linear Regression, and Decision tree regressor. We had forecasted CO2 emission on the basis of influencing factors, which highly contributes in CO2 emission. The performance of the models is validated using multiple evaluation metrics such as MAE, MSE RMSE,MAPE and R2 square. The results show that CO2 emission is highly affected by fossil fuel consumption. In particular, Linear regression performs better among all applied models, with R2 value 0.93 and MAE, RMSE and MAPE values 5.52, 6.47 and 0.03, respectively. Our research delineates the results and insights that will assist concerned authorities, policymakers and environmental agencies. The present study also aim to inform authorities, helping them to better understand the future of CO2 emission trajectories and make timely efforts and interventions to tackle the climate change and global warming challenges. © 2024 IEEE.

关键词： Low emission

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