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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 shutterstockMental health researchRecurrent neural networkConvolutional neural networkSupport vector machineVector machineNeural networkLong short term memoryArtificial intelligencePost traumatic stress disorderMachine learningMental 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 initiativeDisease neuroimaging initiativeSupport vector machineMachine learningDeep learningNeurodegenerative 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 treeAmyaran sarvabad roadMachine learningRandom forestSupport 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 networkSupport vector machineGaussian mixture modelMean shift approachDeep neural networkDark netInfrared imagesMillimeter 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 teamGuangdong songshan polytechnicFacial compensationPrincipal component analysisSupport vector machineFindbiometrics editorial

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