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Artificial intelligence models for diagnosing post-traumatic stress disorder

Whether AI may be a promising strategy for post-traumatic stress disorder (PTSD) diagnosis. ....

Suriyawut Suriya Shutterstock , Mental Health Research , Recurrent Neural Network , Convolutional Neural Network , Support Vector Machine , Vector Machine , Neural Network , Long Short Term Memory , Artificial Intelligence , Post Traumatic Stress Disorder , Machine Learning , Mental Health ,

The most recent neuroimaging procedures and machine learning approaches for the prediction of Alzheimer's progression

Researchers examined recent studies exploring neuroimaging modalities aided by advanced machine learning (ML) algorithms. ....

Alzheimer Disease Neuroimaging Initiative , Disease Neuroimaging Initiative , Support Vector Machine , Machine Learning , Deep Learning , Neurodegenerative Disorder ,

"Landslide Susceptibility Mapping in a Mountainous Area Using Machine L" by Himan Shahabi, Reza Ahmadi et al.

Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locatio ....

Decision Tree , Amyaran Sarvabad Road , Machine Learning , Random Forest , Support Vector Machine ,

"Role of Thermal Images in Various Applications of Computer Vision" by Ravina Gupta, Sarika Jain et al.

Object detection is an advanced area of image processing and computer vision. Its major applications are in surveillance, autonomous driving, face recognition, anomaly detection, traffic management, agriculture etc. This paper focuses on various object detection techniques in thermal images. A thermal imaging sensor is a device that creates an image by analyzing temperature differences between different objects in a scene and detecting radiation from those objects. In recent years, many machine learning and deep learning algorithms have been used to recognize objects in thermal images. This study makes a comparison of YOLO, YOLO DarkNet, Retinex algorithm, CNN-based machine learning model Support Vector Machine (SVM), and Gaussian Mixture Model (GMM), Mixer of Gaussian (MoG), Mean Shift Approach, Faster R-CNN and Deep Neural Network along with different datasets. ....

Neural Network , Support Vector Machine , Gaussian Mixture Model , Mean Shift Approach , Deep Neural Network , Dark Net , Infrared Images , Millimeter Wave ,

Chinese Researchers Detail Improved 'Particle Swarm Optimization' Technique for Facial Recognition

Chinese Researchers Detail Improved 'Particle Swarm Optimization' Technique for Facial Recognition
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Findbiometrics Editorial Team , Guangdong Songshan Polytechnic , Facial Compensation , Principal Component Analysis , Support Vector Machine , Findbiometrics Editorial ,