Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm by Gargi Pant Shukla, Santosh Kumar, Saroj Kumar Pandey, Rohit Agarwal, Neeraj Varshney, Ankit Kumar

“…After pre-processing, we proposed three learning algorithms for AD classification, that is random forest, XGBoost, and Convolution Neural Networks (CNN). Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57% and sensitivity is 97.60%.…”
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Novel transfer learning approach for hand drawn mathematical geometric shapes classification by Aneeza Alam, Ali Raza, Nisrean Thalji, Laith Abualigah, Helena Garay, Josep Alemany Iturriaga, Imran Ashraf

“…We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. …”
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A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease by Ahmed B. Salem Salamh, Abdulrauf A. Salamah, Halil Ibrahim Akyüz

“…The researchers used a new filter called Golden Key Tool (GK-Tool) which has confirmed the improvement in the quality and diagnostic efficacy of images acquired using our modified images. Further, Convolution Neural Networks (CNNs) architecture called VGG face was used to classify chest CT images. …”
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THE USE OF ARTIFICIAL INTELLIGENCE ON COLPOSCOPY IMAGES AND SEGMENTAL VOLUMES, CONSTRUCTED FROM MRI AND CT IMAGES, IN THE DIAGNOSIS AND STAGING OF PRECANCERS, CERVICAL CANCERS AND... by Hlescu Cristian Stefan, Tudor Florin Ursuleanu, Roxana Grigorovici, Andreea Roxana Luca, Ramona Elena Teiu, Maria Paula Comanescu, Alina Ionela Calin, Alexandru Grigorovici

“…Materials and methods The optimization of the method will involve the development and training of artificial intelligence models using convolutive neural networks (CNN) to identify precancers and cancers in colposcopic images. …”
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Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning by Yuanran Zhu, Jia Yin, Cian C Reeves, Chao Yang, Vojtěch Vlček

“…In this paper, we develop an operator-learning framework based on recurrent neural networks (RNNs) to address this challenge. …”
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A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification by Nagarjun Malagol, Tanuj Rao, Anna Werner, Reinhard Töpfer, Ludger Hausmann

“…Therefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN). We trained modified ResNet CNNs with a minimalistic number of images to efficiently classify the area covered by leaf hairs. …”
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