Deep learning-based technique for investigating the behavior of MEMS systems with multiwalled carbon nanotubes and electrically actuated microbeams by Muhammad Amir, Jamshaid Ul Rahman, Ali Hasan Ali, Ali Raza, Zaid Ameen Abduljabbar, Husam A. Neamah

“…The primary aim of this study is to investigate the nonlinear vibration properties of MEMS oscillators, specifically those related to nanotubes and electrically actuated microbeams, using DNN algorithms. Different optimizers are used to analyze the performance and capability of these non-linear dynamical models. …”
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Real-Time Dolphin Whistle Detection on Raspberry Pi Zero 2 W with a TFLite Convolutional Neural Network by Rocco De Marco, Francesco Di Nardo, Alessandro Rongoni, Laura Screpanti, David Scaradozzi

“…Specifically, a CNN model previously developed for classifying dolphins’ vocalizations and originally implemented with TensorFlow was converted to TensorFlow Lite (TFLite) with architectural optimizations, reducing the model size by 76%. …”
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Prediction of Metal Additively Manufactured Bead Geometry Using Deep Neural Network by Min Seop So, Mohammad Mahruf Mahdi, Duck Bong Kim, Jong-Ho Shin

“…The DNN model architecture consists of multiple hidden layers with varying neuron counts, trained using backpropagation, and optimized using the Adam optimizer. …”
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Identification of High-Photosynthetic-Efficiency Wheat Varieties Based on Multi-Source Remote Sensing from UAVs by Weiyi Feng, Yubin Lan, Hongjian Zhao, Zhicheng Tang, Wenyu Peng, Hailong Che, Junke Zhu

“…Vegetation indices (VIs) and texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), and BP Neural Network (BPNN)) were employed to construct predictive models for nine photosynthetic indices across multiple growth stages. …”
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A Dual-Feature Framework for Enhanced Diagnosis of Myeloproliferative Neoplasm Subtypes Using Artificial Intelligence by Amna Bamaqa, N. S. Labeeb, Eman M. El-Gendy, Hani M. Ibrahim, Mohamed Farsi, Hossam Magdy Balaha, Mahmoud Badawy, Mostafa A. Elhosseini

“…The extracted features were used to train machine learning models, with hyperparameter optimization performed using Optuna. …”
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Enhancing Dense-Scene Millet Appearance Quality Inspection Based on YOLO11s with Overlap-Partitioning Strategy for Procurement by Leilei He, Ruiyang Wei, Yusong Ding, Juncai Huang, Xin Wei, Rui Li, Shaojin Wang, Longsheng Fu

“…The optimized model was deployed in a custom-developed mobile application, enabling low-cost, real-time millet inspection directly on smartphones. …”
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Comprehensive energy consumption evaluation of a dry roots vacuum Pump: An experimental, dynamic and FEM research by Chongzhou Sun, Zhilong He, Dantong Li, Wei Liu, Xiaoqian Chen, Manguo Yan

“…These models were well-validated by experimental results. …”
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A study of connectivity features analysis in brain function network for dementia recognition by Siying Li, Peng Wang, Zhenfeng Li, Lidong Du, Xianxiang Chen, Jie Sun, Libin Jiang, Gang Cheng, Zhen Fang

“…The K-nearest neighbors (KNN) machine learning model achieves the highest accuracy of 0.978 with the optimal subset when the threshold is 0. …”
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Attention Deficit Hyperactivity Disorder identification: FMRI data analyzed with CNN and seed-based approach by Anika Siamin Oyshi, Mohammad Hasan, Md. Khabir Uddin Ahamed, Md. Sydur Rahman, Md. Mahfuzul Haque, Mahmudul Alam

“…Three classifiers were assessed: a Neural Network (Keras Sequential Model), a Support Vector Machine (SVM), and a Random Forest Classifier. …”
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Robust Deep Neural Network for Classification of Diseases from Paddy Fields by Karthick Mookkandi, Malaya Kumar Nath

“…The classification performance of the proposed technique is evaluated according to accuracy, sensitivity, specificity, F1 score, MCC, area under curve (AUC), and receiver operating characteristic (ROC). The model was fine-tuned by setting the hyperparameters (such as batch size, learning rate, optimizer, epoch, and train and test ratio). …”
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Redivision of reference intervals of the red blood cell distribution width in a healthy Chinese population based on natural geographic environment by Lei Zhang, Shuyan Yin, Miao Ge, Congxia Wang, Chuwan He

“…Medical indicator data were matched with geographical data and environmental pollution data to predict RDW reference values for the whole region of China using ridge regression analysis, support vector machine and BP neural network. Taylor diagrams and assignment methods were used to select the optimal model, and the spatial distribution was mapped according to the prediction results of the optimal model, and the intervals of RDW reference values for different age groups of healthy people in China were redefined. …”
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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism by Sakorn Mekruksavanich, Wikanda Phaphan, Anuchit Jitpattanakul

“…Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. …”
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Research on the acquisition and identification methods of critical microscopic quality characteristics in the manufacturing process by Zhongyi Wu, Zhenyang Xu, Cheng Liang, Kan Lv, Bin Qin

“…This paper, based on manufacturing process unit information, mines the systematic information of the process unit through the process constituent elements model and also constructs the feature thesaurus of different process domains by using the Function-Principle-Behaviour-Structure-Resource-Environment (FPBSRE) model. …”
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Orientational Capability Analysis of a 2UPR-SPR Parallel Manipulator by Che Linxian, Zhang Rong, Zhang Caifang, Wang Taofen

“…The parallel module of Exechon machine tool is developed based on the 3 degree-of-freedom (DOF) 2UPR-SPR mechanism, which is a parallel spindle head with two rotational (orientational) DOFs and one translational DOF. …”
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Evaluating Feasibility of Pose Detection with Image Rotation for Monitoring Elderly People at Home by Sinan Chen, Masahide Nakamura

“…In this study, abnormal behaviors in in-home elderly individuals were detected using high-precision pose detection technologies, such as the MoveNet model, based on machine learning. However, there was a discrepancy in pose detection accuracy between the standing and lying positions. …”
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Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting by Jeonghoe Lee, Bingjiang Xia

“…In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. …”
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Automatic Identification of Amharic Text Idiomatic Expressions Using a Deep Learning Approach by Habtamu Hunegnaw Limenih, Abebe Belay Adege, Abrham Yaregal Alene, Habtamu Tariku Demasu, Habtamu Molla Belachew

“…The meaning of an idiomatic phrase cannot be inferred from individual words. Developing a model to identify Amharic idiomatic terms is helpful for different NLP applications like machine translation, sentiment analysis, spam classification, intent recognition, and so on. …”
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Data-driven strategies for digital native market segmentation using clustering by Md Ashraf Uddin, Md. Alamin Talukder, Md. Redwan Ahmed, Ansam Khraisat, Ammar Alazab, Md. Manowarul Islam, Sunil Aryal, Ferdaus Anam Jibon

“…To tackle these challenges, we introduce a Machine Learning-based Digital Native Market Segmentation designed to cater specifically to the interests of digital natives. …”
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UAV-Based Remote Sensing Monitoring of Maize Growth Using Comprehensive Indices by Tingrui Yang, Jinghua Zhao, Ming Hong, Mingjie Ma, Shijiao Ma, Yingying Yuan

“…SHAP analysis revealed that GNDVI contributed most significantly to the predictive accuracy of the growth model. Spatial imaging distribution of maize inversion using the optimal CGMICT-RF model revealed discernible differences in overall crop growth. …”
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Dynamic SOFA component scores-based deep learning for short to long-term mortality prediction in sepsis survivors by Juan Wei, Feihong Lin, Tian Jin, Qian Yao, Sheng Wang, Di Feng, Xin Lv, Wen He

“…This model has the potential to assist clinicians in optimizing post-discharge management and improving follow-up care.…”
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