Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches by Nafiseh Hosseini, Hamid Tanzadehpanah, Amin Mansoori, Mostafa Sabzekar, Gordon A. Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The performance of the KNN model was compared with Artificial neural network (ANN) and support vector machine (SVM) models. …”
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A Pervasive Approach to EEG-Based Depression Detection by Hanshu Cai, Jiashuo Han, Yunfei Chen, Xiaocong Sha, Ziyang Wang, Bin Hu, Jing Yang, Lei Feng, Zhijie Ding, Yiqiang Chen, Jürg Gutknecht

“…Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. …”
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Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm by Mu Panliang, Sanjay Madaan, Siddiq Ahmed Babikir Ali, Gowrishankar J., Ali Khatibi, Anas Ratib Alsoud, Vikas Mittal, Lalit Kumar, A. Johnson Santhosh

“…The evaluated features are utilized for classifying face expressions by utilizing the deep neural network model, ResNet-50. The presented FER system has been tested using multi-pose facial expression benchmark datasets, including RaF (Radboud Faces) and KDEF (Karolinska Directed Emotional Faces). …”
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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. …”
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Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators by Shenqiong Jiang, Xiangju Cheng, Baoshan Shi, Dantong Zhu, Jun Xie, Zhihong Zhou

“…The evaluation results showed that the generalized regression neural network (GRNN) model optimized by particle swarm optimization (PSO) had the best prediction among all the models under the conditions of six input variables, namely COD, NH4+-N, DO, WT, TN, and pH. …”
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A real-world Pharmacovigilance study of brodalumab based on the FDA adverse event reporting system by Ke He, Kaidi Zhao, Tingyi Yin, Meng Liu, Jiashu Liu, Wenqian Du, Xinyi Liu, Baochen Cheng, Dewu Zhang, Yan Zheng

“…Techniques such as the Reporting Odds Ratio, Proportional Reporting Ratio, Multi-item Gamma Poisson Shrinker, and Bayesian Confidence Propagation Neural Network were utilized to analyze the adverse events associated with brodalumab. …”
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Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal by Supath Dhital, Kapil Lamsal, Sulav Shrestha, Umesh Bhurtyal

“…This project aims to forecast the next 2-hour Precipitation and Air Temperature for Pokhara Domestic Airport meteorological station and the next day's Precipitation, Maximum and Minimum Air Temperature forecast for Lumle, Begnas, and Lamachaur meteorological station, total of four meteorological stations of the Kaski District, Nepal using Long Short-Term Memory (LSTM): a Recurrent Neural Network (RNN) and deploy the outputs through the web portal. …”
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Physically Meaningful Surrogate Data for COPD by Harry J. Davies, Ghena Hammour, Hongjian Xiao, Patrik Bachtiger, Alexander Larionov, Philip L. Molyneaux, Nicholas S. Peters, Danilo P. Mandic

“…As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. …”
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Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases by Mosleh Hmoud Al-Adhaileh, Ebrahim Mohammed Senan, Waselallah Alsaade, Theyazn H. H Aldhyani, Nizar Alsharif, Ahmed Abdullah Alqarni, M. Irfan Uddin, Mohammed Y. Alzahrani, Elham D. Alzain, Mukti E. Jadhav

“…In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. …”
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Efficient Method for Robust Backdoor Detection and Removal in Feature Space Using Clean Data by Donik Vrsnak, Marko Subasic, Sven Loncaric

“…The steady increase of proposed backdoor attacks on deep neural networks highlights the need for robust defense methods for their detection and removal. …”
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Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot by Daiqing Tan, Hao Zang, Xinyue Zhang, Han Gao, Ji Wang, Zaijian Wang, Xing Zhai, Huixia Li, Yan Tang, Aiqing Han

“…Results: Experiments conducted on six distinct tongue image datasets demonstrated that the Tongue-LiteSAM model outperformed traditional convolutional neural network-based models and transformers, the original SAM model, and other related improved models in tongue image segmentation tasks. …”
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Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipel... by Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton, Hans W. Guesgen

“…While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. …”
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Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles by Adeel Khan, Adeel Khan, Haroon Khan, Nongyue He, Zhiyang Li, Heba Khalil Alyahya, Yousef A. Bin Jardan

“…To transform the qualitative colorimetric approach into a quantitative operation, we developed an intelligent convolutional neural network (CNN)-powered quantitative analyzer for chromaticity in the form of a smartphone app named ExoP, thereby achieving the intelligent analysis of chromaticity with minimal user intervention or additional hardware attachments for the sensitive and specific quantification of PD-L1@EVs. …”
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Multi-omic spatial effects on high-resolution AI-derived retinal thickness by V. E. Jackson, Y. Wu, R. Bonelli, J. P. Owen, L. W. Scott, S. Farashi, Y. Kihara, M. L. Gantner, C. Egan, K. M. Williams, B. R. E. Ansell, A. Tufail, A. Y. Lee, M. Bahlo

“…We processed the UK Biobank OCT images using a convolutional neural network to produce fine-scale retinal thickness measurements across > 29,000 points in the macula, the part of the retina responsible for human central vision. …”
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Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar, Abdullatif Albaseer, Muhammad Asif Khan, Mohamed Abdallah, Hamid Menouar, Saif Al-Kuwari, Ala Al-Fuqaha

“…Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. …”
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PCMMD: A Novel Dataset of Plasma Cells to Support the Diagnosis of Multiple Myeloma by Caio L. B. Andrade, Marcos V. Ferreira, Brenno M. Alencar, Jorge L. S. B. Filho, Matheus A. Guimaraes, Iarley Porto Cruz Moraes, Tiago J. S. Lopes, Allan S. dos Santos, Mariane M. dos Santos, Maria I. C. S. e Silva, Izabela M. D. R. P. Rosa, Gilson C. de Carvalho, Herbert H. M. Santos, Márcia M. L. Santos, Roberto Meyer, Luciana M. P. B. Knop, Songeli M. Freire, Ricardo A. Rios, Tatiane N. Rios

“…We also share a Deep Neural Network model, as a benchmark, trained to identify and count plasma and non-plasma cells automatically. …”
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Ultrasonic-assisted extraction of luteolin from peanut shells using ionic liquid and its molecular mechanism by Liwei Niu, Siwen Zhang, Xiaoyu Si, Yuhan Fang, Shuang Wang, Lulu Li, Zunlai Sheng

“…Further optimization of the extraction conditions was performed using response surface methodology and neural network analysis, resulting in a significantly enhanced luteolin yield of 3.71 ± 0.06 mg/g. …”
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Comparative analysis of deep learning models for crack detection in buildings by S. Siva Rama Krishnan, M. K. Nalla Karuppan, Adil O. Khadidos, Alaa O. Khadidos, Shitharth Selvarajan, Saarthak Tandon, Balamurugan Balusamy

“…The development of Artificial Intelligence (AI) techniques, provide favourable solutions in-order to handle, manage and solve building cracks, through analysis using deep image neural network models, that perform classification of the building with crack images. …”
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Design a Robust DDoS Attack Detection and Mitigation Scheme in SDN-Edge-IoT by Leveraging Machine Learning by Habtamu Molla Belachew, Mulatu Yirga Beyene, Abinet Bizuayehu Desta, Behaylu Tadele Alemu, Salahadin Seid Musa, Alemu Jorgi Muhammed

“…We evaluated four popular classifiers (K-Nearest Neighbor (K-NN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and FeedForward Neural Network (FFNN)) on benchmark datasets CICIDS2017 and Edge-IIoTset, conducting both binary and multi-class classifications. …”
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Advancing Horticultural Crop Loss Reduction Through Robotic and AI Technologies: Innovations, Applications, and Practical Implications by H. W. Gammanpila, M. A. Nethmini Sashika, S. V. G. N. Priyadarshani

“…For instance, Ji et al. in 2007 developed an artificial neural network (ANN)-based system for rice yield prediction in Fujian, China, improving accuracy over traditional models. …”
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