Hybrid Approach for Protein Secondary Structure Prediction with KNN, SVM, and Neural Network Algorithms by Benjamin Mukanya Ntumba, Jean Paul Ngbolua Koto-Te-Nyiwa, Blaise Bikandu Kapesa, Nathanael Kasoro Mulenda

“…We also discuss the implications of these results and propose future work to further improve the accuracy and robustness of the model. …”
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Analisis Sentimen: Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z by Muhammad Daffa Al Fahreza, Ardytha Luthfiarta, Muhammad Rafid, Michael Indrawan

“…Arifin Setiono's stemming algorithm on the Gaussian Naïve Bayes model achieved 81% precision, 81% recall, 81% f1-score, with 82% accuracy, while on Support Vector Machine, 88% precision, 86% recall, 86% f1-score, with 86% accuracy. …”
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Predicting the interfacial tension of CO2 and NaCl aqueous solution with machine learning by Kashif Liaqat, Daniel J. Preston, Laura Schaefer

“…In this work, multiple machine learning models, including linear regression (LR), support vector machine (SVM), decision tree regressor (DTR), random forest regressor (RFR), and multilayer perceptron (MLP), are used to predict the IFT of the CO2 and aqueous solution of NaCl. …”
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Transfer learning based feature selection for feedforward neural network for speech emotion classifier by D. V. Krasnoproshin, M. I. Vashkevich

“…This technique allowed us to significantly reduce initial feature vector size while increasing models’ prediction quality. …”
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Comparison of artificial intelligence approaches for estimating wind energy production: A real-world case study by Mohamed Bousla, Mohamed Belfkir, Ali Haddi, Youness El Mourabit, Badre Bossoufi

“…The precise prediction of wind power is essential not only for the smooth integration into the power grid but also for the optimization of unit commitment, maintenance scheduling, and the improvement of power traders' profitability. The present work investigates several forecasting methodologies for wind energy by employing sophisticated machine learning algorithms, including Support Vector Machines and Recurrent Neural Networks. …”
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Computational and Experimental Study of Local Characteristics of the Fluidization Process of Fuel Pellets by A. E. Karmanov, A. V. Mitrofanov, E. V. Prikhodko, S. V. Vasilevich, N. S. Shpeynova

“…Fluidized bed phase distributions were described by state vectors whose evolution was controlled by transition probability matrices. …”
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Utility of Certain AI Models in Climate-Induced Disasters by Ritusnata Mishra, Sanjeev Kumar, Himangshu Sarkar, Chandra Shekhar Prasad Ojha

“…As a result, multiple AI tools such as Random Forest (RF), Random Tree (RT), M5P (M5 model trees), M5Rules, Feed-Forward Neural Networks (FFNNs), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Support Vector Machines kernel-based model (SVM-Pearson VII Universal Kernel, Radial Basis Function) are tested in the present study using various combinations of datasets. …”
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Model of Interaction of Structural Elements of the Financial Market of Ukraine by Kochorba Valeriia Yu.

“…On the basis of vector auto-regressive models, the dynamic adaptation of market components to shocks is analyzed. …”
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DETERMINING THE EFFECTIVENESS OF CONCURRENT ENGINEERING THROUGH THE ANALYTICAL HIERARCHY PROCESSING OF PROJECT SUCCESS CRITERIA by N.A Aniekwu, A.C. Igboanugo, M.K. Onifade

“…Using the Analytical Hierarchy Process (AHP) model, these project success criteria were used as the primary criteria, along with its sub-criteria, to calculate the Eigenvectors, in order to synthesize a pair-wise comparison matrix of the criteria. …”
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Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features by Kennette James Basco, Alana Singh, Daniel Nasef, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

“…Five machine learning models—Gaussian Naive Bayes, support vector machines, random forest trees, extremely randomized trees, gradient boosted trees, and an ensemble of top-performing classifiers—were trained and optimized using stratified k-fold cross-validation. …”
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On the interpretability of the SVM model for predicting infant mortality in Bangladesh by Md Abu Sayeed, Azizur Rahman, Atikur Rahman, Rumana Rois

“…The interpretable SVM model based on the global surrogate model also reveals that working mothers who used polluted cooking fuel at home and working women who used less polluted cooking fuel but had a longer period between pregnancies than two years would have higher infant death rates. …”
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A model of mobile robots in networks with resolvability properties. by Carlos Camacho Campos, José Carlos Camacho Moreno, Dorota Kuziak, Zahid Raza, Ismael G Yero

“…A model for mobility of robots keeping the property of uniquely recognizing the vertices of a given network is considered in this work. …”
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Machine learning models for enhanced stroke detection and prediction by Shilpa Bajaj, Manju Bala, Mohit Angurala

“…The OzNet-mRMR-NB model integrates VGG19 for feature extraction, mRMR for feature selection, and Naive Bayes for classification, while Logistic Regression processes flattened feature vectors. …”
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An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification by Khaoula Taji, Ali Sohail, Tariq Shahzad, Bilal Shoaib Khan, Muhammad Adnan Khan, Khmaies Ouahada

“…This research proposes an ensemble deep learning based technique for classifying plant leaf diseases. In this work, two separate pre-trained deep learning models have been utilized and a hybrid approach is proposed. …”
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Autonomous Vehicle Security: Hybrid Threat Modeling Approach by Amal Yousseef, Yu-Zheng Lin, Shalaka Satam, Banafsheh Saber Latibari, Jesus Pacheco, Soheil Salehi, Salim Hariri, Pratik Satam

“…By integrating analytical modeling with broad system insights, this work provides actionable recommendations for advancing the cybersecurity posture of autonomous vehicles.…”
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Hybrid 2D/3D-quantitative structure–activity relationship studies on the bioactivities and molecular mechanism of antibacterial peptides by Qingguo Yan, Fangfang Wang, Bo Zhou, Xiangna Lin

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Speech Databases, Speech Features, and Classifiers in Speech Emotion Recognition: A Review by G. H. Mohmad Dar, Radhakrishnan Delhibabu

“…This review seeks to provide an in-depth examination of current methodologies in speech emotion recognition (SER), with a focus on databases, feature extraction techniques, and classification models. It has been done in the past using low-level descriptors (LLDs) like Mel-Frequency Cepstral Coefficients (MFCCs), linear predictive coding (LPC), and pitch-based features in methods like Support Vector Machines (SVM), Random Forests (RF), and Gaussian Mixture Models (GMM). …”
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Optimizing power generation in a hybrid solar wind energy system using a DFIG-based control approach by Muhammad Salman, Syed Abdul Rahman Kashif, Muhammad Salman Fakhar, Akhtar Rasool, Abdulkerim Sherefa Hussen

“…This research project aims to develop effective modeling and control techniques for a grid-connected HSWES. …”
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Collision Avoidance in Circular Motion of a Fixed-Wing Drone Formation Based on Rotational Modification of Artificial Potential Field by Tagir Muslimov

“…In this case, the path following algorithm developed in our previous works provides the circular motion. As a result, a collision avoidance algorithm has been developed that works efficiently by maintaining a coordinated circular motion of the autonomous drone formation without unnecessary turns. …”
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Modeling the Dynamics of Dengue Transmission with Awareness and Optimal Control Analysis. by Faishal F Herdicho, F Fatmawati, Cicik Alfiniyah, Farah P Fajrin, Ebenezer Bonyah, Muhammad A Rois, Olumuyiwa J Peter

“…This disease is spread around the world and still as a global health problem. In this work, we formulate the dengue model by considering the population of aware humans. …”
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