Detecting human activity types from 3D posture data using deep learning models

Analysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training

“…Sports posture detection effect analysis methods mainly include random forest, support vector machine, neural network, deep learning, heuristic optimisation algorithm and other methods [9]. Literature [10] extracted motion gesture detection features through questionnaires and other methods, and used the random forest method to construct a motion gesture detection method, which confirms the feasibility of intelligent motion detection technology; Literature [11] proposed an effect analysis method based on support vector machines by combing the analysis process of motion gesture detection and combining with machine learning algorithms; and Literature [12] designed a cloud-based multi-classification algorithm that has the ability to capture and analyse the leg and hip pressure features of human sitting state; literature [13] designed a Gaussian face algorithm and achieved significant accuracy on a face database; literature [14] used the KLT algorithm for face tracking, and trained a cascade classifier for face recognition, but the detection and recognition rate is low for people who are too high or too low; literature [15], after reflecting on the traditional sports gesture recognition method, discussed the joint position and face features as sports gesture recognition features, and proposed a neural network-based sports gesture recognition method; Literature [16] proposed three aspects of sports gesture recognition features, such as body joints, hand joints, and facial features, and meanwhile constructed a system for analysing the effects of sports gesture recognition, and proposed an effect analysis method based on deep learning algorithm; Literature [17] proposed a cascade classifier for detecting and recognizing faces, but the detection and recognition rate is low for tall and low people. analysis method; Literature [17] extracts posture features based on Mediapipe single person motion detection system, and proposes a human posture detection and recognition method based on support vector machine.…”

EfficientNetB0 and SVM Based Approach for Colon Cancer Recognition from Histopathological Images

“…Birbiri ile bağlantılı katmanlar aracılığı ile öznitelikler vektörel hale getirilerek aktivasyon fonksiyonları (sigmoid, softmax ve tanh) yardımı ile tahmin işlemi gerçekleştirilir [30,31]. Literatürde ESA mimarileri kullanılarak, asfalt çatlaklarının tespiti [32], fiziksel hareketlerden aktivite belirleme [33], yazılım güvenlik açıklarının sınıflandırılması [34]; ses, nefes ve öksürükten Covid-19 tespiti [35], alzaymır hastalığının sınıflandırılması [36], beyin tümörü tespiti [37], insan aktivite türlerinin tespiti [38], timpanik membran görüntülerinin sınıflandırılması [39] ve beyin kanaması tespiti [40] gibi birçok alanda başarılı bir şekilde uygulanmıştır.…”

A Novel Deep Dense Block-Based Model for Detecting Alzheimer’s Disease

“…Various ML techniques, including feature extraction, deep networks, and transfer learning (TL), have been proposed for the classification of AD and MCI [18][19][20][21]. Deep learning (DL), consisting of artificial neurons, has demonstrated superior performance in handling complex classification tasks compared to traditional ML methods [22,23]. CNNs, a specialized DL technique, have been widely utilized for AD diagnosis [24,25].…”