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6th International Conference on Dynamics of Information systems, DIS 2023

作者： Moosaei, Hossein Ketabchi, Saeed Syed, Mujahid N. Bazikar, Fatemeh Department of Informatics Faculty of Science Jan Evangelista Purkyně University Ústí nad Labem Czech Republic Department of Applied Mathematics Faculty of Mathematical Sciences University of Guilan Rasht Iran Department of Industrial and Systems Engineering Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals KSA Dhahran31261 Saudi Arabia Department of Computer Science Faculty of Mathematical Sciences Alzahra University Tehran Iran

ISBN: (纸本)9783031503191

The augmented Lagrangian method can be used for finding the least 2 - norm solution of a linear programming problem. This approach’s primary advantage is that it leads to the minimization of an unconstrained problem with a piecewise quadratic, convex, and differentiable objective function. However, this function lacks an ordinary Hessian, which precludes the use of a fast Newton method. In this paper, we apply the smoothing techniques and solve an unconstrained smooth reformulation of this problem using a fast Newton method. Computational results and comparisons are illustrated through multiple numerical examples to show the effectiveness of the proposed algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Newton-Raphson method

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Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Convolutional Recurrent Imputation Network

Enhanced Prediction of Fuel Consumption and Carbon Emissions...

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2025 International Conference on computer, electrical and Communication engineering, ICCECE 2025

作者： Lalitha, T. Szymański, Jerzy Ryszard Zurek-Mortka, Marta Sathiyanarayanan, Mithileysh Srilekha, K.R. Presidency University School of Information Science Karnataka Bengaluru India Casimir Pulaski University of Radom Faculty of Transport Electrical Engineering and Computer Sciences Department of Electrical Drives and Industrial Electronics Poland Lukasiewicz Research Network Institute for Sustainable Technologies Kazimierza Pulaskiego Radom Poland MIT Square London United Kingdom Vit University Tamilnadu Vellore India

ISBN: (纸本)9798331513894

Fuel utilization and emission estimation are essential for assessing the consequence of materials and strict emission control measures due to the startling rate of weather changes. The world's socioeconomic issues are significantly impacted by the growing worldwide average temperature and its effects on climate change. The melting of the polar ice caps, that is a direct result of this rise, leads to a number of other problems, such as the polar animal's death, coastal flooding, and exposure to bacteria and earliest microorganisms that are frozen in the snow and could cause many more global pandemics and invisible illnesses. In this paper, Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Dynamic Graph Convolutional Recurrent Imputation Network (EP-FCCE-DGCRIN) are proposed.. The effectiveness of the EP-FCCE-DGCRIN technique is compared to other existing methods. This research offers evidence-based suggestions to lessen the environmental effects of vehicles for both manufacturers and consumers depending upon the outcomes of the Descriptive Statistics analysis. © 2025 IEEE.

关键词： Low emission

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Towards Sustainable Buildings: Chilled Water System Analysis & Efficiency Modeling

Towards Sustainable Buildings: Chilled Water System Analysis...

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Global Power, Energy and Communication Conference (GPECOM)

作者： Wiam Ayrir Ahmed. M. Helmi Simon Abongmbo Driss Benhaddou Electrical Engineering Networks and Telecommunication Systems Department National School of Applied Sciences Ibn Tofail University Kenitra Morocco Computer Engineering Department College of Engineering and Information Technology Buraydah Private Colleges Buraydah KSA Computer and Systems Engineering Faculty of Engineering Zagazig University Zagazig Egypt Electrical and Computer Engineering Department University of Houston Houston Texas USA Engineering Technology Department University of Houston Houston Texas USA

ISBN: (数字)9798350351088

ISBN: (纸本)9798350351095

Optimizing the performance of heating, ventilation, and air conditioning (HVAC) systems constitutes a significant research topic in energy efficiency that brings together various disciplines. In particular, campus-like buildings use HVAC system implemented in a central plant as a form of the chiller system. The main objective is to minimize the energy consumption of these systems in different types of buildings and increase their efficiency while maintaining optimal interior comfort. This paper offers a thorough analysis and evaluation of a university’s chilled water system performance. The insights gained shed light on key optimization perspectives. Additionally, this paper employs regression modeling using Random Forest to analyze the HVAC system, aiming to understand the importance of features such as consumption, outside temperature, and other relevant factors in the chilled water system’s energy efficiency.

关键词： Energy consumption Analytical models HVAC Temperature System performance systems operation Buildings

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A Modified Dempster Shafer Approach to Classification in Surgical Skill Assessment

A Modified Dempster Shafer Approach to Classification in Sur...

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Iranian Conference on electrical engineering (ICEE)

作者： Arash Iranfar Mohammad Soleymannejad Behzad Moshiri Hamid D. Taghirad School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Advanced Robotics and Automated Systems (ARAS) Industrial Control Center of Excellence Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran

Artificial intelligence systems are usually implemented either using machine learning or expert systems. Machine learning methods are usually more accurate and applicable to a broader range of applications. Expert systems, on the other hand, require much less data for training and generate more comprehensible results. These characteristics are typically desired in the fields of surgery and medicine because there isn't much data available. In order to give a machine's decisions a deeper level of semantics, it is also advantageous to incorporate a doctor's expertise into it. Furthermore, it is safer to understand the reasoning behind a machine's choices. In this paper, a Dempster-Shafer Theory (DST) based expert system is suggested for the task of surgical training skill assessment. An interval-based probabilistic feature analysis was applied to the data to assign values to the mass functions. Zhang's rule of combination was applied to handle the conflicting evidence in the prediction phase. The performance of the proposed method was compared to another DST classifier, SVM, and XGBoost. Our method outperforms SVM and other DST classifiers, but it is not as precise as XGBoost. By reducing the size of the dataset, the added benefit of using an expert system as opposed to a machine learning method was explored further. The performance of the suggested method is not adversely affected by the size of the dataset, whereas the XGBoost classifier is.

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Performance Improvements of Vehicular PKI Protocol for the Security of V2X Communications 46

Performance Improvements of Vehicular PKI Protocol for the S...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Farran, Hassan Khoury, David Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services BudapestH-1111 Hungary American University of Science and Technology Department of Computer Science and Information and Communication Technology Beirut Lebanon

ISBN: (纸本)9798350303964

Vehicle-to-Everything (V2X) technology allows vehicles to communicate with numerous transportation system components. To provide safe communication between vehicles and other entities, V2X depends on Public Key Infrastructure (PKI). However, the present PKI employed in V2X has certain drawbacks, including scalability and high computational cost. This article offers an enhancement to the current PKI in V2X that tackles these constraints by managing and distributing keys using blockchain technology. The suggested Public Key Infrastructure enhancement is intended to be secure, efficient, and scalable, providing a more solid and trustworthy foundation for V2Xcommunication. The article explains the suggested PKI enhancement and examines its advantages and disadvantages. Overall, the suggested Public Key Infrastructure enhancement has the potential to increase the security and dependability of Vehicle-to-Everything communication, allowing for a secure and more effective transportation system. © 2023 IEEE.

关键词： Vehicle to Everything

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A Novel Binary Emperor Penguin Optimizer for Feature Selection Tasks

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computers, Materials & Continua 2022年第3期70卷 6239-6255页

作者： Minakshi Kalra Vijay Kumar Manjit Kaur Sahar Ahmed Idris Saban Oturk Hammam Alshazly Computer Science Department Government College BahadurgarhBahadurgarh124507India CSE Department National Institute of Technology Hamirpur177005India School of Engineering and Applied Sciences Bennett UniversityGreater Noida201310India College of Industrial Engineering King Khalid UniversityAbhaSaudi Arabia Department of Electrical and Electronics Engineering Amasya UniversityAmasyaTurkey Faculty of Computers and Information South Valley UniversityQena83523Egypt

Nowadays,due to the increase in information resources,the number of parameters and complexity of feature vectors *** offermore practical solutions instead of exact solutions for the solution of this *** Emperor PenguinOptimizer(EPO)is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor *** shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence *** traditional EPO overcomes the optimization problems in continuous search space,many problems today shift to the binary search ***,in this study,using the power of traditional EPO,binary EPO(BEPO)is presented for the effective solution of binary-nature *** algorithm uses binary search space instead of searching solutions like conventional EPO algorithm in continuous search *** this purpose,the sigmoidal functions are preferred in determining the emperor *** addition,the boundaries of the search space remain constant by choosing binary ***’s performance is evaluated over twenty-nine benchmarking *** evaluations are made to reveal the superiority of the BEPO *** addition,the performance of the BEPO algorithm was evaluated for the binary feature selection *** experimental results reveal that the BEPO algorithm outperforms the existing binary meta-heuristic algorithms in both tasks.

关键词： Metaheuristics optimization algorithms emperor penguin optimizer intensification diversification feature selection

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Comparison and optimization of machine learning methods for fault detection in district heating and cooling systems

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Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第3期73卷

作者： Çinar, Mehmet Aslan, Emrah Özüpak, Yıldırım Bitlis Eren University Organized Industrial Zone Vocational School Electrical Department Bitlis Turkey Mardin Artuklu University Faculty of Engineering and Architecture Department of Computer Engineering Mardin Turkey Dicle University Silvan Vocational School Electrical Department Diyarbakır Turkey

In this study, the methods used for the detection of sub-station pollution failures in district heating and cooling (DHC) systems are analyzed. In the study, high, medium, and low-level pollution situations are considered and machine learning methods are applied for the detection of these failures. Random forest, decision tree, logistic regression, and CatBoost regression algorithms are compared within the scope of the analysis. The models are trained to perform fault detection at different pollution levels. To improve the model performance, hyperparameter optimization was performed with random search optimization, and the most appropriate values were selected. The results show that the CatBoost regression algorithm provides the highest accuracy and overall performance compared to other methods. The CatBoost model stood out with an accuracy of 0.9832 and a superior performance. These findings reveal that CatBoost-based approaches provide an effective solution in situations requiring high accuracy, such as contamination detection in DHC systems. The study makes an important contribution as a reliable fault detection solution in industrial applications. © 2025 The Author(s).

关键词： Random forests

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Automatic Root Length Estimation from Images Acquired In Situ without Segmentation

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Plant Phenomics 2024年第1期6卷 79-93页

作者： Faina Khoroshevsky Kaining Zhou Sharon Chemweno Yael Edan Aharon Bar-Hillel Ofer Hadar Boris Rewald Pavel Baykalov Jhonathan EEphrath Naftali Lazarovitch Department of Industrial Engineering and Management Ben-Gurion University of the NegevBeer ShevaIsrael The Jacob Blaustein Center for Scientific Cooperation The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael The Albert Katz International School for Desert Studies The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael Department of Communication Systems Engineering School of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer ShevaIsrael Institute of Forest Ecology Department of Forest and Soil SciencesUniversity of Natural Resources and Life SciencesVienna(BOKU)ViennaAustria Faculty of Forestry and Wood Technology Mendel University in BrnoBrnoCzech Republic Vienna Scientific Instruments GmbH AllandAustria French Associates Institute for Agriculture and Biotechnology of Drylands The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevSede BoqerIsrael

Image-based root phenotyping technologies,including the minirhizotron(MR),have expanded our understanding of the in situ root responses to changing environmental *** conventional manual methods used to analyze MR images are time-consuming,limiting their *** study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total(cumulative)root length(TRL)per MR image without requiring *** data were derived from manual annotations in Rootfly,commonly used software for MR image *** compared TRL estimation with 2 models,a regression-based model and a detection-based model that detects the annotated points along the ***,the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR *** models were trained and tested with 4,015 images acquired using 2 MR system types(manual and automated)and from 4 crop species(corn,pepper,melon,and tomato)grown under various abiotic *** datasets are made publicly available as part of this *** coefficients of determination(R2),between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets,demonstrating that this tool is accurate and *** analyses were conducted to examine the effects of(a)the data acquisition system and thus the image quality on the models'performance,(b)automated differentiation between images with and without roots,and(c)the use of the transfer learning *** approaches can support precision agriculture by providing real-time root growth information.

关键词： Image Root image

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Transmission System Expansion Planning Using Optimal Power Flow and Genetic Algorithm

Transmission System Expansion Planning Using Optimal Power F...

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2022 International Conference on Power, Energy and Innovations, ICPEI 2022

作者： Wiwechpaisankul, Weeratep Rattananatthawon, Ongorn Sirisumrannukul, Somporn Sabpayakom, Noppatee. Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand College of Industrial Technology of Power Electronics Engineering Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9781665460194

ISBN: (纸本)9781665460194

Transmission system expansion planning (TSEP) is the process of identifying time, location, and the number of transmission facilities will be needed in the future. TSEP is an optimization problem with the objective to minimize the investment cost to ensure supply-demand adequacy, subject to constraints on the power balance and the apparent power capability limit of transmission lines. This paper presents transmission system expansion planning based on an optimal power flow and a genetic algorithm (GA). The OPF problem was solved by the PowerWorld simulator, and the GA was coded in Python and developed on the built-in SimAuto function in the PowerWorld simulator. The simulation results from a case study of a modified Garver's 6-bus power system show that the developed methodology can determine the optimal sites and capacities of transmission lines to be added to the system. © 2022 IEEE.

关键词： Genetic algorithms

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