Employing SAE-GRU deep learning for scalable botnet detection in smart city infrastructure by Usman Tariq, Tariq Ahamed Ahanger

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Atmospheric Modeling for Wildfire Prediction by Fathima Nuzla Ismail, Brendon J. Woodford, Sherlock A. Licorish

Subjects: “…one-class machine learning models…”
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EXAMINING THE IMPACT OF FEATURE SELECTION TECHNIQUES ON MACHINE AND DEEP LEARNING MODELS FOR THE PREDICTION OF COVID-19 by Hafiza Zoya Mojahid, Jasni Mohamad Zain, Marina Yusoff, Abdul Basit, Abdul Kadir Jumaat, Mushtaq Ali

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Developing a hybrid feature selection method to detect botnet attacks in IoT devices by Alshaeaa H.Y., Ghadhban Z.M., Ministry of Education, Iraq

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Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

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Bio inspired optimization techniques for disease detection in deep learning systems by A. Ashwini, Vanajaroselin Chirchi, S. Balasubramaniam, Mohd Asif Shah

Subjects: “…Bio-inspired deep learning models…”
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Optimizing Personalized Recommender Systems for Teachers’ Digital Learning Models Using Deep Learning Algorithms by Jun Zhong, Wenjuan Zhang

Subjects: “…Digital learning models for teachers…”
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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

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Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models by Benedictor Alexander Nguchu, Benedictor Alexander Nguchu, Yifei Han, Yanming Wang, Peter Shaw

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Comparative analysis of machine learning models for the detection of fraudulent banking transactions by Pedro María Preciado Martínez, Ricardo Francisco Reier Forradellas, Luis Miguel Garay Gallastegui, Sergio Luis Náñez Alonso

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Meteorological Factors and the Spread of COVID‐19: A Territorial Analysis in Italy by Vito Telesca, Gianfranco Castronuovo, Gianfranco Favia, Mariarosaria Marra, Marica Rondinone, Alessandro Ceppi

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Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques by Stephanie Ness, Vishwanath Eswarakrishnan, Harish Sridharan, Varun Shinde, Naga Venkata Prasad Janapareddy, Vineet Dhanawat

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Flood hazard zones prediction using machine-learning-based geospatial approach in lower Niger River basin, Nigeria by Adedoyin Benson Adeyemi, Akinola Adesuji Komolafe

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IN SILICO AND IN VITRO ACTIVITY OF FRIEDELIN N-DERIVATIVES AGAINST PROMASTIGOTE FORMS OF Leishmania amazonensis by Mauro Lúcio Gonçalves de Oliveira, Lucienir Pains Duarte, Grácia Divina de Fátima, Silvia Ribeiro de Souza, Misléia Rodrigues de Aguiar Gomes, Sidney Augusto Vieira-Filho, Vera Lucia de Almeida, Júlio César Dias Lopes

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Evaluating the efficacy and site-specific performance of machine learning approaches: A comprehensive review of autism detection models by Deblina Mazumder Setu, Tania Islam, Md Maklachur Rahman, Samrat Kumar Dey, Tazizur Rahman

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Prediction of compressive strength of fiber-reinforced concrete containing silica (SiO2) based on metaheuristic optimization algorithms and machine learning techniques by Hamed Shokrnia, Ashkan KhodabandehLou, Peyman Hamidi, Fedra Ashrafzadeh

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Study on the spatiotemporal evolution and prediction of internal porosity in concrete specimens under sulfate attack based on machine learning models by Chun Gong, Dunwen Liu, Kunpeng Cao, Yinghua Jian

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Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset by Ahmed E. Mansour Atwa, El-Sayed Atlam, Ali Ahmed, Mohamed Ahmed Atwa, Elsaid Md. Abdelrahim, Ali I. Siam

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A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

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Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models by Yin Kan Phua, Tsuyohiko Fujigaya, Koichiro Kato

Subjects: “…Machine learning models…”
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