Deepfake Audio Detection for Urdu Language Using Deep Neural Networks by Omair Ahmad, Muhammad Sohail Khan, Salman Jan, Inayat Khan

“…Therefore, developing effective algorithms to distinguish fake audio from real audio is critical to preventing such frauds. …”
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Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks by Udeme C. Ukpong, Olabode Idowu-Bismark, Emmanuel Adetiba, Jules R. Kala, Emmanuel Owolabi, Oluwadamilola Oshin, Abdultaofeek Abayomi, Oluwatobi E. Dare

“…This work presents the development of Deep RL (DRL) models for enhanced DSA in TV Whitespace (TVWS) cognitive radio networks using Deep Q-Networks (DQN) and Quantile-Regression (QR-DQN) algorithms. The implementation was done in the Radio Frequency Reinforcement Learning (RFRL) Gym, a training environment of the RF spectrum designed to provide comprehensive functionality. …”
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Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches by M. Sadeghi malekabadi, A.R. Davari

“…However, the extensive numerical computations required for accurate evaluation often hinder the implementation of multi-objective optimization strategies. …”
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Deep learning-based analysis of daily activity patterns of farmed dromedary camels by Rama Al-Khateeb, Nabil Mansour, Nabil Mansour, Shaher Bano Mirza, Fouad Lamghari

“…In Phase 2, the study expanded to include six camels, enabling an evaluation of individual behavioral variations. The YOLOv7 object detection algorithm was used to train and validate the model on images extracted from the recordings, achieving high accuracy in detecting and classifying the defined activities.ResultsThe results showed notable variations in activity patterns between Phases 1 and 2. …”
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Consensus Guidelines of Russian Society of Radiology (RSR) and Russian Association of Specialists in Ultrasound Diagnostics in Medicine (RASUDM) «Role of Imaging (X-ray, CT and US)... by V. E. Sinitsyn, I. E. Tyurin, V. V. Mitkov

“…If the correct procedure is followed, correct indications are selected, and trained medical personnel is available, this study is highly sensitive in detecting interstitial changes and consolidations in lung tissue, but only in their subpleural location. …”
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Constructing a predictive model for acute mastitis in lactating women based on machine learning by Liujing Zhu, Zuyan Huang, Yan Chen, Guangqiu Li, Liwen Liu

“…By using machine learning (ML) algorithms (Logistic Regression (LR), Naive Bayes (NB), XGBoost, Multilayer Perceptron (MLP)) to train and validate the above data, it aimed to construct a predictive model of the risk factors for the occurrence of acute mastitis in lactating women, and simultaneously analyzed the other influences and effects of these factors on acute mastitis. …”
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An explorative study on movement detection using wearable sensors in acute care hospital patients by Joris Kirchberger, Dominik Kunz, Guido Perrot, Sven Hirsch, Maren Leifke, Bianca Hölz, Lukas Geissmann, Miro Käch, Samuel Wehrli, Jens Eckstein

“…However, elderly patients often present distinct gait patterns due to walking aids or co-morbidities, and most existing monitoring solutions are trained on data from healthy individuals. Therefore, the main study goal was to develop a wearable based algorithm prototype for three wearing locations (ankle, thigh, wrist) and assess its comparative classification accuracy to determine the optimal location for classifying patient activities during hospitalization. …”
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Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach by Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi

“…Three (3) classification algorithms including Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were trained using the QoS dataset and then evaluated in order to determine the most effective model based on certain evaluation metrics – accuracy, precision, F1-Score and recall. …”
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AirQuaNet: A Convolutional Neural Network Model With Multi-Scale Feature Learning and Attention Mechanisms for Air Quality-Based Health Impact Prediction by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Ravinesh C. Deo, Abdul Hafeez-Baig, Tracy Kolbe-Alexander, Niranjana Sampathila, U. Rajendra Acharya

“…AirQuaNet was evaluated on two public datasets, the Air Quality and Health Impact Dataset and the Comprehensive Health Data for Asthma. …”
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Integrating data from unmanned aerial vehicles and Sentinel-2 with PROSAIL-5D-driven machine learning for fuel moisture content estimation in agroecosystems by Jinlong Liu, Jia Jin, Jing Huang, Mengjuan Wu, Shaozheng Hao, Haoyi Jia, Tengda Qin, Yuqing Huang, Dan Chen, Nathsuda Pumijumnong

“…To address the challenge of sparse ground observations, a calibrated PROSAIL-5D radiative transfer model was used to simulate diverse spectral responses, augmenting the training dataset. A genetic algorithm-optimized backpropagation neural network was then applied to assess the effectiveness of the fused remote sensing data and PROSAIL-5D simulation in improving FMC retrieval accuracy. …”
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Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines by Qin Li, Nan Lin, Zuheng Wang, Yuexi Chen, Yuli Xie, Xuemei Wang, Jirui Tang, Yuling Xu, Min Xu, Na Lu, Yiqian Huang, Jiamin Luo, Zhenfang Liu, Li Jing

“…Seven machine learning(ML) algorithm (XGBoost, Logistic Regression, LightGBM, RandomForest, AdaBoost, GBDT and GNB) were trained using 10-fold cross-validation and model performance was evaluated with the ROC, calibration plots, decision and learning curves and the Shapley Additive Explanations (SHAP) analysis. …”
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Deep learning-based prediction of nodal metastasis in lung cancer using endobronchial ultrasoundCentral MessagePerspective by Tsukasa Ishiwata, MD, PhD, Terunaga Inage, MD, PhD, Masato Aragaki, MD, PhD, Alexander Gregor, MD, PhD, Zhenchian Chen, MD, Nicholas Bernards, PhD, Kamran Kafi, MD, Kazuhiro Yasufuku, MD, PhD

“…Image frames were randomly selected and split into training and validation datasets on a per-patient basis. …”
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Deep Reinforcement Learning for Efficient Scheduling of Ground-based Astronomical Observations by Hai Cao, Shaoming Hu, Junju Du, Xu Chen, Shuqi Liu, Shuai Feng, Bo Zhang, Yuchen Jiang

“…To achieve this, we implement a pointer network with temporal attention that is capable of planning observations while accounting for time-varying factors such as moonlight interference, target altitude, and air mass, which impact the exposure time and image quality. To support the training of the deep neural network, we propose a scoring mechanism to evaluate the effectiveness of the observations, which is optimized through a refined REINFORCE algorithm with a baseline. …”
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Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…The models’ performances were evaluated on the test set using the Area Under the Receiver Operating Characteristic Curve (AUC). …”
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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

“…Six machine learning algorithms were selected and trained by our dataset. …”
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The value of a radiomics model in predicting ovarian malignancy: a retrospective multi-center comparison with O-RADS and radiologists by Junjie Jin, Xijia Deng, Ling Long, Meiling Liu, Meimei Cao, Hao Gong, Huan Liu, Xiaosong Lan, Lili Liu, Jiuquan Zhang

“…Features were selected using minimum redundancy, maximum relevance, and the least absolute shrinkage and selection operator algorithm. Diagnostic performance of the radiomics model, O-RADS, and independent assessments by junior and senior radiologists was evaluated via the area under the receiver operating characteristic curve (AUC) and compared using DeLong’s test. …”
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Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study by Bing Wen, Chengwei Li, Qiuyi Cai, Dan Shen, Xinyi Bu, Fuqiang Zhou

“…The dataset was sourced from Center A (training set: N = 240; internal test set: N = 60) and Center B (external test set: N = 60). …”
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Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study by Xinxin Qin, Binxu Qiu, Litao Ge, Song Wu, Yuye Ma, Wei Li

“…We applied six machine learning algorithms to construct a model that can predict distant metastasis of gastric cancer. …”
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Energy-Efficient Secure Cell-Free Massive MIMO for Internet of Things: A Hybrid CNN–LSTM-Based Deep-Learning Approach by Ali Vaziri, Pardis Sadatian Moghaddam, Mehrdad Shoeibi, Masoud Kaveh

“…This study employs secrecy energy efficiency (SEE) as a key performance metric to evaluate the trade-off between power consumption and secure communication efficiency. …”
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SK-TreePCN: Skeleton-Embedded Transformer Model for Point Cloud Completion of Individual Trees from Simulated to Real Data by Haifeng Xu, Yongjian Huai, Xun Zhao, Qingkuo Meng, Xiaoying Nie, Bowen Li, Hao Lu

“…The 3D radiative transfer model LESS, which can simulate various TLS data over highly heterogeneous scenes, is employed to generate massive point clouds with training labels. Among the various point cloud completion methods evaluated, SK-TreePCN exhibits outstanding performance regarding the Chamfer distance (CD) and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mn>1</mn></mrow></semantics></math></inline-formula> Score. …”
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