Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier by Ashutosh Kumar Singh, SVN Sreenivasu, U.S.B. K. Mahalaxmi, Himanshu Sharma, Dinesh D. Patil, Evans Asenso

“…The binary particle swarm optimization is applied for the selection of these hybrid features followed by the classification with random forest classifier to get the simulation results. …”
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Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

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Fasting Before Cardiac Catheterization: Still Necessary? A Systematic Review and Meta‐Analysis of Randomized Clinical Trials by Adil Salihu, Stephane Fournier, Sarah Hugelshofer, Quentin Liabot, Victor Weerts, Georgios Tzimas, Valentina Rancati, Clemence Ferlay, Aurelia Zimmerli, Olivier Muller, Henri Lu, Panagiotis Antiochos, Orestis Efthimiou, David Meier

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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

“…On the other hand, results showed that the proposed hybrid method outperformed the feature selection methods with an increase of about 3% in both classifications. The AdaBoost model achieved an accuracy of 99.28% with binary classification by using 18 features, and the RF model achieved an accuracy of 86.62% with multi-classification by using 22 features. …”
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Evaluation of the adequacy of phase equilibria modeling based on various sets of experimental data by A. V. Frolkova, V. G. Fertikova, E. V. Rytova, A. K. Frolkova

“…The purpose of the paper is to compare the adequacy of mathematical models of vapor–liquid equilibrium (VLE) and their ability to reproduce the phase behavior of the ternary system benzene–cyclohexane–chlorobenzene using different experimental data sets to evaluate binary interaction parameters.Methods. The research methodologies were mathematical modeling of VLE in the Aspen Plus V.10.0 software package using activity coefficient models (Non-Random Two-Liquid (NRTL), Wilson) and the Universal quasichemical Functional-group Activity Coefficients (UNIFAC) group model, which allows for independent information. …”
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Understanding the basal texture initiation in a randomly-oriented AZ31B alloy during cold-rolling by Wenhan Jin, Baolin Wu, Li Zhang, Jie Wang, Claude Esling, Marie-Jeanne Philippe

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Injury-Severity Analysis of Highway-Railroad Grade Crossing Crashes Based on a Random Parameters Logit Model With Heterogeneity in Means and Variances by Lan Wu, Jiayu Zhou, Haigen Min, Gen Li

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Effect of levetiracetam on cognition in patients with cognitive decline: A systematic review and meta‐analysis of randomized controlled trials by Claudia Faini, Arjune Sen, Michele Romoli

“…Abstract We conducted a systematic review and meta‐analysis of randomized controlled trials (RCTs) evaluating the efficacy of levetiracetam (LEV) compared to placebo in improving cognitive performance in people with cognitive decline. …”
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Efficacy of Alpha Lipoic Acid Supplementation in Sperm Parameters: A Systematic Review and Meta-Analysis of Randomized Trials by Iago Zang Pires, Marília Oberto da Silva Gobbo, Renan Yuji Ura Sudo, Tanize Louize Milbradt, Nilson Marquardt Filho, Gustavo Franco Carvalhal, Carlos Teodosio Da Ros

“…Material and Methods: Pubmed, Embase, Cochrane Library, and Scopus databases were searched from inception to June 2024. A random-effects model was employed to compute mean differences and risk ratios for continuous and binary endpoints. …”
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Application of the digital annealer unit in optimizing chemical reaction conditions for enhanced production yields by Shih-Cheng Li, Pei-Hua Wang, Jheng-Wei Su, Wei-Yin Chiang, Tzu-Lan Yeh, Alex Zhavoronkov, Shih-Hsien Huang, Yen-Chu Lin, Chia-Ho Ou, Chih-Yu Chen

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Logistic regression model for predicting failure of dual antihypertensive therapy: a prospective comparative non-randomized clinical trial by T. O. Okorokova, O. N. Kryuchkova

“…The paper presents a prospective comparative non-randomized clinical trial. The recruiting of participants and recording of results were carried out in March–December 2019 with 3 months of the follow-up period. …”
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Synchronized stepwise control of firing and learning thresholds in a spiking randomly connected neural network toward hardware implementation by Kumiko Nomura, Yoshifumi Nishi

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Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

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Developing a Prediction Model for Real-Time Incident Detection Leveraging User-Oriented Participatory Sensing Data by Md Tufajjal Hossain, Joyoung Lee, Dejan Besenski, Branislav Dimitrijevic, Lazar Spasovic

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Mitigating Algorithmic Bias Through Probability Calibration: A Case Study on Lead Generation Data by Miroslav Nikolić, Danilo Nikolić, Miroslav Stefanović, Sara Koprivica, Darko Stefanović

“…The evaluated models included Binary Logistic Regression with polynomial degrees of 1, 2, 3, and 4, Random Forest, and XGBoost classification algorithms. …”
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A Prospective Randomized Trial Comparing 2 Different Paclitaxel-Coated Balloons in De Novo Coronary Artery Disease by Eun-Seok Shin, MD, PhD, Yongwhi Park, MD, PhD, Jong-Young Lee, MD, PhD, Ae-Young Her, MD, PhD, Min-Ku Chon, MD, PhD, Sunwon Kim, MD, PhD, Seung-Woon Rha, MD, PhD, Gyu Chul Oh, MD, PhD, Deok-Kyu Cho, MD, PhD, Bitna Kim, MS, Jang-Whan Bae, MD, PhD

“…There was comparable late lumen enlargement (44.7% vs 42.7%; P = 0.903) and binary restenosis rates (3.2% vs 6.7%; P = 0.442) following treatment with shellac and vitamin E-based PCB and reference PCB, respectively. …”
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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

“…Then, we applied four machine learning methods—random forest, gradient boosting decision tree, extreme gradient boosting, and multi-layer perceptron—to develop binary classification models for N-dealkylation. …”
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Lightweight Deepfake Detection Based on Multi-Feature Fusion by Siddiqui Muhammad Yasir, Hyun Kim

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Accurate and Efficient Fluid Flow Regime Classification Using Localized Texture Descriptors and Machine Learning by Manimaran Renganathan, Palani Thanaraj Krishnan, C. Christopher Columbus, T. Sunil Kumar

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Exploring the determinants of thyroid disorders in high-altitude western Himalaya: a geospatial and epidemiological study by Arshad Ahmed, Kheraj, Alireza Mohammadi, Robert Bergquist

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