Deformation prediction in innovative implant design with machine learning approaches by Mehmet Onur Yağır, Muhammed Fatih Pekşen, Şaduman Şen, Uğur Şen

“…The data obtained were modeled using a finite element analysis system (ANSYS), and instantaneous deformation data were collected during the modeling process. These instantaneous deformation data were included as an additional feature in the ML dataset and used in the analysis processes.In the study, the Kernel Support Vector Machine (Kernel SVM), Kernel Logistic Regression (Kernel LR), and extreme gradient boosting (XGBoost) classification methods were employed to assess the impact of the implant on the jawbone. …”
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The Symbiotic Bacterial Profile of Laboratory-Reared and Field-Caught <i>Aedes albopictus</i> Mosquitoes from Greece by Elias Asimakis, Ioannis Galiatsatos, Georgia Apostolopoulou, Eleni C. Savvidou, Georgios Balatsos, Vasileios Karras, Vasiliki Evangelou, Eva Dionyssopoulou, Antonios Augustinos, Nikos T. Papadopoulos, Antonios Michaelakis, Panagiota Stathopoulou, George Tsiamis

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Optimizing Euclidean Distance Computation by Rustam Mussabayev

“…From spatial data structures and approximate nearest neighbor algorithms to dimensionality reduction, vectorization, and parallel computing, various approaches exist to accelerate Euclidean distance computation in different contexts. …”
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Related and independent variable fault detection method based on KPCA-SVM by GUO Jinyu, YU Huan, LI Yuan

“…In the real industrial process, some process variables are independent of other variables, a fault detection method of related and independent variable based on kernel principal component analysis and support vector machine (KPCA-SVM) is proposed to detect these independent variables separately from related variables. …”
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PERBAIKAN KONTRAS CITRA MAMMOGRAM PADA KLASIFIKASI KANKER PAYUDARA BERDASARKAN FITUR GRAY-LEVEL CO-OCCURRENCE MATRIX by Febri Liantoni, Agus Santoso

“…This method will expedite the process of recognition and classification of breast cancer. …”
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Improved Technique in Arabic Handwriting Recognition by Ammar A. Al-Hamadani, Maad Kamal Al-Anni, Gamil R. S. Qaid, Najran Nasser Hamood

“…  Arabic handwriting recognition has significant applications in fields like postal sorting, handwritten text identification, and cheque processing. The process involves several steps: preprocessing, feature extraction, and classification. …”
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Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…Initially, the pre-processing is accomplished using null value dropping and standard scaler normalization. …”
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Deep learning application to roughness classification of road surface conditions through an e-scooter’s ride quality by Asher Virin, Lalitphat Khongsomchit, Sakdirat Kaewunruen

“…Three machine learning models—Random Forest Classifier, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) with k-means clustering—were tested using various hyperparameter tuning, post-processing, and data splitting strategies. …”
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Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system by Kai Yang, Ming Zhao, Dimitrios Argyropoulos

“…For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). …”
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Assembly of ascovirus HvAV-3h long DNA fragment using the Transformation-Associated Recombination (TAR) approach in yeast cells by Heba A. H. Zaghloul, Zhengkun Xiao, Hengrui Hu, Guo-Hua Huang

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Prediction of anisotropic property of activated metal inert gas welding by employing different supervised machine learning models by Ruturaj U. Kakade, Nitin Khedkar, Amol Dalavi

“…Machine learning models Linear Regression, Random Forest Regression, and Support Vector Regression (SVR) were applied to predict TS based on welding parameters.• The SVR model achieved the best predictive performance, with an R² of 0.8750 and a model accuracy of 96.73 %.• The results confirm the potential of SVR for accurately forecasting TS in A-MIG welded EN10028, facilitating process optimization in pressure applications…”
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An Assessment of Land Use Land Cover Using Machine Learning Technique by V. Pushpalatha, H. N. Mahendra, A. M. Prasad, N. Sharmila, D. Mahesh Kumar, N. M. Basavaraju, G. S. Pavithra and S. Mallikarjunaswamy

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Enhanced Category-Feature Association Measure by Soran S. Badawi, Ari M. Saeed, Sara A. Ahmed, Diyari A. Hassan

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Unsupervised anomaly detection for gearboxes based on the deep convolutional support generative adversarial network by Chengguang Zhang, Zhen Guo, Chuan Li

“…Next, these reconstruction errors are utilized to train one-class support vector machine (OCSVM). During the testing phase, reconstruction errors are similarly calculated for the test data, and after being normalized using the same process as the training data, the errors are input into the trained OCSVM model for anomaly detection. …”
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Enhancing the interpretability of electricity consumption forecasting models for mining enterprises using SHapley Additive exPlanations by Pavel V. Matrenin, Alina I. Stepanova

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AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke by Ismail Ben Abdallah, Ismail Ben Abdallah, Yassine Bouteraa, Yassine Bouteraa, Ahmed Alotaibi, Ahmed Alotaibi

“…A ROS2-based architecture enables real-time signal processing, adaptive control, and remote supervision by clinicians. …”
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Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement by Sri Rossa Aisyah Puteri Baharie, Sugiyarto Surono, Aris Thobirin

“…This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. …”
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Building Fire Location Predictions Based on FDS and Hybrid Modelling by Yanxi Cao, Hongyan Ma, Shun Wang, Yingda Zhang

“…Different scenarios were built to simulate the spatial and temporal distributions of key parameters such as temperature, smoke, and CO concentration during the fire process. Combining convolutional neural networks (CNNs) and support vector machines (SVMs) for prediction, the fire-source location prediction model with temperature, smoke, and CO concentration as feature quantities was constructed, and the hyperparameters affecting the model accuracy and generalisation were optimised by the Crested Porcupine Optimizer (CPO) algorithm. …”
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The Effect of CDKN1A on the Expression of Genes Related to Milk Protein and Milk Fat Synthesis in Bovine Mammary Epithelial Cells by Yuanyuan Zhang, Junxi Liang, Kai Zhang, Hong Su, Daqing Wang, Min Zhang, Feifei Zhao, Zhiwei Sun, Zhimin Wu, Guifang Cao, Yong Zhang

“…Its content and composition directly affect the nutritional value, processing characteristics, and economic benefits of dairy products. …”
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Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…Seven classification algorithms – logistic regression, random forest (RF), support vector machine (SVM), Gaussian naive Bayes (GNB), gradient boosting (GB), K-nearest neighbors, and decision tree (DT) – were employed. …”
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