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Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

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Frontiers in Energy research 2024年 12卷

作者： Alhussan, Amel Ali Khafaga, Doaa Sami El-kenawy, El-Sayed M. Eid, Marwa M. Ibrahim, Abdelhameed Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia MEU Research Unit Middle East University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences. Copyright © 2024 Alhussan, Khafaga, El-kenawy, Eid and Ibrahim.

关键词： Charging stations

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Comparative Analysis of Machine Learning Models in Online Payment Fraud Prediction 2

Comparative Analysis of Machine Learning Models in Online Pa...

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2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Shdefat, Ahmed Younes Mohamed, Mennatallah Khaled, Shahd Hany, Farrah Fathi, Hanaa Abdelminaam, Diaa Salama College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Misr International University Faculty of Computer Science Cairo Egypt Applied Science Private University Applied Science Research Center Amman Jordan Jadara University Jadara Research Center Irbid21110 Jordan Benha University Faculty of Computers and Artificial Intelligence Information Systems department Benha Egypt MEU Research Unit Middle East University Amman11831 Jordan

ISBN: (纸本)9798350362633

With the substantial increase in Cybercrime, the pressing need for effective solutions in the light of the rising amount of corporate financial losses, is a concern f or many researchers. The widespread adoption of ecommerce and diverse online payment methods has contributed to the surge in a major form of cybercrime - online payment fraud. This further highlights the critical need to control and reduce the widespread danger of fraudulent behaviour on the internet. However, predicting online payment fraud is difficult as fraudsters are constantly changing their tactics, transactions happen quickly and in large volumes, transactions involve different regions and countries, stolen data is often used and other factors. Our goal is to improve the accuracy and usability of online payment fraud prediction through the use of machine learning. A well-established and widely recognized dataset specifically designed for studying online payment fraud was used. The six proposed algorithms trained and tested were (SVM), Decision Tree, Naïve Bayes, Random Forest, (KNN) and Logistic Regression. After rigorous testing, Decision Tree presented the highest accuracy in performance of 98.58%. However, as ten-fold cross validation was implemented on the dataset, Decision Tree suffered a loss while Random Forest had an increase in accuracy making it the best model with accuracy of 98.47%. As shown in this paper, machine learning has proven to be a reliable and ever-evolving approach for the prediction of Online Payment Fraud and results can be further improved in collaboration with financial and cybercrime specialists. © 2024 IEEE.

关键词： Contrastive Learning

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

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Clinical resistance predictors to first-line VEGFR-TKI monotherapy for metastatic renal cell carcinoma: a retrospective multicenter real-life case series

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Journal of Cancer Metastasis and Treatment 2023年第1期9卷 445-458页

作者： Gaetano Pezzicoli Silvana Quaglini Valentina Tibollo Melissa Bersanelli Camillo Porta Mimma Rizzo Department of Interdisciplinary Medicine University of Bari"Aldo Moro"Bari 70124Italy Department of Electrical Computer and Biomedical EngineeringUniversity of PaviaPavia 27100Italy Laboratory of Informatics and Systems Engineering for Clinical Research Scientific Clinical Institute Maugeri(ICS Maugeri)Pavia 27100Italy Medical Oncology Unit University Hospital of ParmaParma 43121Italy Division of Translational Oncology Scientific Clinical Institute Maugeri(ICS Maugeri)Pavia 27100Italy Medical Oncology Unit University Hospital-Polyclinic of BariBari 70124Italy

Aim:For many years,systemic treatment of metastatic Renal Cell Carcinoma(mRCC)was based on sequential targeted agent *** this real-life case series,we evaluated easily accessible clinical factors useful for disease course ***:We exploited patients'clinical pathological characteristics and systemic treatment outcomes in a real-world population of 365 mRCC patients who received sequential monotherapies in the targeted therapy era,and we identified an early progressors subpopulation,resistant to first-line VEGFR-TKI monotherapy in less than 6 ***:Early progressors(n=124)show a far worse OS compared with patients progressing beyond the sixth month of therapy(13.5 vs.44.8 months,P-value<0.0001,HR=0.41,95%CI:0.29-0.53).However,these patients did not show far worse performance in second and third-line settings compared to first-line *** the univariate analysis,IMDC risk class,sarcomatoid features,and Systemic Inflammation Index(SII)were correlated with first-line therapy Progression-Free Survival(PFS1).In multivariate analysis,variables correlated with first-line outcome were IMDC risk class,histotype,and number of metastatic sites at the ***:Real-world data can contribute to developing easy-to-use prognostic factors associated with refractory disease that could support clinicians in identifying the most appropriate treatment strategy for each patient.

关键词： Renal cell carcinoma targeted therapy tyrosine kinase inhibitor drug resistance early progressor rapidly progressive prognostic factor real-world data

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A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

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computers, Materials & Continua 2022年第11期73卷 4291-4309页

作者： Kais Tissaoui Sahbi Boubaker Waleed Saud Alghassab Taha Zaghdoudi Jamel Azibi University of Ha’il Applied CollegeHail CitySaudi Arabia University of Tunis El Manar Faculty of Economic Sciences and Management of Tunisthe International Finance GroupTunisTunisia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Unit on Study of Systems and Renewable Energy National College of Engineering of MonastirUniversity of MonastirMonastirTunisia LAREQUAD&FSEGT University of Tunis El ManarTunisTunisia University of Jendouba Law FacultyManagement and Economic Sciences of JendoubaTunisia

The application of optimization methods to prediction issues is a continually exploring *** line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting *** complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown *** use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein *** indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,*** performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

关键词： Forecasting Cboe’s volatility index COVID-19 pandemic nonlinear polynomial hammerstein model hybrid particle swarm optimization

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Benchmarking GAN-Based vs Classical Data Augmentation on Biomedical Images 27th

Benchmarking GAN-Based vs Classical Data Augmentation on Bi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mantegna, Massimiliano Tronchin, Lorenzo Tortora, Matteo Soda, Paolo Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Rome Italy Department of Naval Electrical Electronics and Telecommunications Engineering University of Genova Via all’Opera Pia 11a Genoa16145 Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Messina Italy

ISBN: (纸本)9783031876592

The medical field faces significant data shortages due to the high image acquisition and maintenance costs. Data Augmentation aims to mitigate this by increasing data availability and enhancing image generalization. However, traditional DA methods often produce data with limited quality and diversity. Generative Adversarial Networks present a promising alternative, offering potential solutions to data scarcity issues. This paper evaluates the impact of GAN-based data augmentation in medical imaging and provides a benchmark for the efficacy of synthetic data in downstream classification tasks. To this aim, we performed a wide set of tests using three different GAN architectures on six 2D datasets from the standardized MedMNIST biomedical image collection, conducting a total of 696 experiments. Our results reveal that while GAN-based DA methods show promise with low-dimensional datasets, traditional DA methods still outperform them. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mammography

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Named Entity Recognition in Italian Lung Cancer Clinical Reports using Transformers

Named Entity Recognition in Italian Lung Cancer Clinical Rep...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Domenico Paolo Alessandro Bria Carlo Greco Marco Russano Sara Ramella Paolo Soda Rosa Sicilia Department of Engineering Unit of Computer Systems & Bioinformatics Università Campus Bio-Medico di Roma Italy Department of Electrical and Information Engineering University of Cassino and Southern Latium Cassino Italy Department of Medicine and Surgery Research Unit of Radiation Oncology Università Campus Bio-Medico di Roma Italy Operative Research Unit of Radiation Oncology Fondazione Policlinico Universitario Campus Bio-Medico Italy Operative Research Unit of Medical Oncology Fondazione Policlinico Universitario Campus Bio-Medico Italy Department of Radiation Sciences Radiation Physics Biomedical Engineering Umeå University Sweden

The widespread adoption of electronic health records (EHRs) offers a valuable opportunity to support clinical research by containing crucial patient information, including diagnoses, symptoms, medications, lab tests, and more. Despite the success of deep learning for biomedical Named Entity Recognition (NER), the literature in this field still presents a gap regarding applications focused on lung cancer for the Italian language. Hence, this paper presents a transformer-based approach to extract named entities from Italian clinical notes related to Non-Small Cell Lung Cancer (NSCLC). We introduce a novel set of 25 clinical entities related to NSCLC building a corpus annotated for NER. We apply a state-of the-art model pre-trained on Italian biomedical texts to the manually annotated clinical reports of a cohort of 257 patients suffering from NSCLC, successfully dealing with class-imbalance problems and obtaining promising performance (average F1-score of 84.3%). We also compared our method with two other pre-trained state-of-the-art models showing that the domain specific knowledge offered by the proposed approach is necessary to achieve higher performance. These findings also showcase the feasibility of using transformers to extract biomedical information in the Italian language.

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The impact of Lacticaseibacillus paracasei GMNL-143 toothpaste on gingivitis and oral microbiota in adults: a randomized, double-blind, crossover, placebo-controlled trial

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BMC Oral Health 2024年第1期24卷 1页

作者： Lee, Min-Kang Chen, I-Hui Hsu, I-Ling Tsai, Wan-Hua Lee, Tzong-Yi Jhong, Jhih-Hua Liu, Bai-Chia Huang, Tsui-Yin Lin, Fang-Kuei Chang, Wen-Wei Wu, Ju-Hui Department of Dentistry Kaohsiung Medical University Hospital No.100 Shih-Chuan 1st Road Sanmin Dist Kaohsiung 807378 Taiwan Research and Development Department GenMont Biotech Incorporation Tainan 741014 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300193 Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan 320315 Taiwan Departement of Biomedical Sciences Chung Shan Medical University No. 110 Section 1 Chien-Kuo N. Rd Taichung 402306 Taiwan Department of Medical Research Chung Shan Medical University Hospital Taichung 402306 Taiwan Department of Oral Hygiene College of Dental Medicine Kaohsiung Medical University Kaohsiung 807378 Taiwan

Background: This study examines the oral health benefits of heat-killed Lacticaseibacillus paracasei GMNL-143, particularly its potential in oral microbiota alterations and gingivitis improvement. Methods: We assessed GMNL-143’s in vitro interactions with oral pathogens and its ability to prevent pathogen adherence to gingival cells. A randomized, double-blind, crossover clinical trial was performed on gingivitis patients using GMNL-143 toothpaste or placebo for four weeks, followed by a crossover after a washout. Results: GMNL-143 showed coaggregation with oral pathogens in vitro, linked to its surface layer protein. In patients, GMNL-143 toothpaste lowered the gingival index and reduced Streptococcus mutans in crevicular fluid. A positive relationship was found between Aggregatibacter actinomycetemcomitans and gingival index changes, and a negative one between Campylobacter and gingival index changes in plaque. Conclusion: GMNL-143 toothpaste may shift oral bacterial composition towards a healthier state, suggesting its potential in managing mild to moderate gingivitis. Trial registration: ID NCT04190485 (https://***/);09/12/2019, retrospective registration. © The Author(s) 2024.

关键词： Gingivitis Lacticaseibacillus paracasei Oral microbiota Probiotic toothpaste

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Meta-heuristic computing knacks for target angle estimation in monostatic radar system with coprime arrays

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Ain Shams engineering Journal 2024年第5期15卷

作者： Akbar, Sadiq Sohail, Muhammad Asif Zahoor Raja, Muhammad Zaman, Fawad Ullah, Rizwan Abdul Rehman Khan, Muhammad Ajavakom, Nopdanai Phanomchoeng, Gridsada Department of Mechanical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Hefei230026 China Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical & Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical & Computer Engineering COMSATS University Islamabad Attock Campus 43600 Pakistan Human-Robot Collaboration and Systems Integration Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves. © 2024 THE AUTHORS

关键词： Genetic algorithms

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Deep Trajectory Classification Model for Congestion Detection in Human Crowds

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computers, Materials & Continua 2021年第7期68卷 705-725页

作者： Emad Felemban Sultan Daud Khan Atif Naseer Faizan Ur Rehman Saleh Basalamah Department of Computer Engineering College of Computing and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science National University of TechnologyIslamabadPakistan Science and Technology Unit Umm Al-Qura UniversityMakkahSaudi Arabia Institute of Consulting Research and Studies Umm Al-Qura UniversityMakkahSaudi Arabia

In high-density gatherings,crowd disasters frequently occur despite all the safety *** detection of congestion in human crowds using automated analysis of video footage can prevent crowd *** work on the prevention of crowd disasters has been based on manual analysis of video *** methods also measure crowd congestion by estimating crowd ***,crowd density alone cannot provide reliable information about *** paper proposes a deep learning framework for automated crowd congestion detection that leverages pedestrian *** proposed framework divided the input video into several temporal *** then extracted dense trajectories from each temporal segment and converted these into a spatio-temporal image without losing information.A classification model based on convolutional neural networks was then trained using spatio-temporal ***,we generated a score map by encoding each point trajectory with its respective class *** this,we obtained the congested regions by employing the non-maximum suppression method on the score ***,we demonstrated the proposed framework’s effectiveness by performing a series of experiments on challenging video sequences.

关键词： Crowd congestion trajectory classification crowd analysis

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