Deep Learning-Based Network Security Data Sampling and Anomaly Prediction in Future Network by Lan Liu, Jun Lin, Pengcheng Wang, Langzhou Liu, Rongfu Zhou

“…Then, through offline and real-time analyses, network security abnormal events are predicted in the future network. With the comparison of various algorithms and the adjustment of hyperparameters, the data characteristics and classification algorithms corresponding to different network security attacks are found. …”
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Prediction of adverse drug reactions based on pharmacogenomics combination features: a preliminary study by Mingxiu He, Mingxiu He, Yiyang Shi, Fangfang Han, Fangfang Han, Fangfang Han, Yongming Cai, Yongming Cai, Yongming Cai

“…We proposed a novel deep learning architecture, DGANet, based on the constructed features for ADR prediction. The algorithm uses Convolutional Neural Networks (CNN) and cross-features to learn the latent drug-gene-ADR associations for ADRs prediction.Results and DiscussionThe performance of DGANet was compared to three state-of-the-art algorithms with different genomic features. …”
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Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete by Waleed Bin Inqiad, Muhammad Faisal Javed, Deema Mohammed Alsekait, Naseer Muhammad Khan, Majid Khan, Fahid Aslam, Diaa Salama Abd Elminaam

“…The comparison of results revealed that XGB is the most accurate algorithm to predict plastic viscosity (training $$\:{R}^{2}=0.959$$ , testing $$\:{R}^{2}=0.947$$ ) and interface yield stress (training $$\:{R}^{2}=0.925$$ , testing $$\:{R}^{2}=0.965$$ ). …”
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Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

“…This study developed an advanced bankruptcy prediction model using Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Network (ANN) algorithms based on datasets from the UCI machine learning repository. …”
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Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). …”
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A Comparison of the Performance of Ensemble Tree and Neural Networks for The Prediction of Traffic Accident Duration by Hüseyin Korkmaz, Mehmet Ali Ertürk, Mehmet Adak

“…Statistical tests and machine learning algorithms were applied to the extracted data set and prediction of traffic accident duration was performed. …”
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INTERVAL PREDICTION OF NON-STATIONARY PROCESSES, DESCRIBED BY STOCHASTIC DIFFERENTIAL EQUATIONS WITH VARIABLE PARAMETERS by A. V. Ausiannikau

“…Algorithms of interval prediction in the discrete and continuous time are received. …”
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A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction by Saba Taheri Kadkhoda, Babak Amiri

“…Financial distress prediction is crucial to financial planning, particularly amid emerging uncertainties. …”
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Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures by Bader Huwaimel, Jowaher Alanazi, Muteb Alanazi, Tareq Nafea Alharby, Farhan Alshammari

“…Abstract This research article presents a thorough and all-encompassing examination of predictive models utilized in the estimation of viscosity for ionic liquid solutions. …”
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Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial by Kalapala Prasad, V. Ravi Kumar, R. Suresh Kumar, A. S. Rajesh, Anjani Kumar Rai, Essam A. Al-Ammar, Saikh Mohammad Wabaidur, Amjad Iqbal, Dawit Kefyalew

“…Thus, by using machine learning framework, the adsorption efficiency of paracetamol on a carbon-activated nanomaterial was predicted.…”
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Improving synergistic drug combination prediction with signature-based gene expression features in oncology by Mozhgan Mozaffarilegha, Sajjad Gharaghani

“…Machine learning (ML) and deep learning (DL) models have advanced drug synergy prediction by integrating diverse datasets and modeling the interactions between drugs and cell lines. …”
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Prediction-Based Filter Updating Policies for Top- Monitoring Queries in Wireless Sensor Networks by Jiping Zheng, Hui Zhang, Baoli Song, Haixiang Wang, Yongge Wang

“…In this paper, we propose a new top- k algorithm named PreFU which is based on prediction models to update window parameters of filters. …”
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A Detailed Review for Predicting the Quantity of Sugar From Sugarcane Using Various Models by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

“…The present analysis highlights that conventional regression algorithms can predict sugar content;however, multicollinearity restricts their effectiveness. …”
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Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast. by Jingru Dong, Ruijiao Lei, Feiyang Ma, Lu Yu, Lanlan Wang, Shangzhi Xu, Yunhua Hu, Jialin Sun, Wenwen Zhang, Haixia Wang, Li Zhang

“…We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. …”
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Meta-Learning-Based Prediction of Different Corn Cultivars from Color Feature Extraction by Abdullah Beyaz, Dilara Gerdan

“…The values were analyzed with the help of the Multilayer Perceptron (MLP), Decision Tree (DT), Gradient Boost Decision Tree (GBDT) and Random Forest (RF) algorithms by using the Knime Analytics Platform. The majority voting method was applied to MLP and DT for prediction fusion. …”
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Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients by Md. Shahid Ansari, Dinesh Jain, Sandeep Budhiraja

“…This study therefore investigated the risk factors, performance and effectiveness of eight different machine-learning algorithms to predict blood component transfusion requirements in confirmed dengue cases admitted to hospital. …”
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Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review by Suhila Sawesi, Arya Jadhav, Bushra Rashrash

“… Abstract BackgroundLeptospirosis, a zoonotic disease caused by Leptospira ObjectiveThis systematic review aimed to evaluate the application of machine learning (ML) and deep learning (DL) techniques in predicting and diagnosing leptospirosis, focusing on the most used algorithms, validation methods, data types, and performance metrics. …”
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AI-Driven Belt Failure Prediction and Prescriptive Maintenance with Motor Current Signature Analysis by João Paulo Costa, José Torres Farinha, Mateus Mendes, Jorge O. Estima

“…Through the integration of motor current signature analysis (MCSA) and machine learning algorithms, particularly long short-term memory (LSTM) networks, this study aims to predict and detect belt degradation in real time. …”
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TCDE: Differential Evolution for Topographical Contour-Based Prediction to Solve Multimodal Optimization Problems by Zhen Cheng, Yun Zhang, Caifu Fan, Xingwei Gao, Haohan Jia, Lei Jiang

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Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…The potential of various machine learning (ML) algorithms to predict Marshall Stability (MS) and Marshall Flow (MF) was investigated in this work. …”
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