Deep learning for COVID-19 by X-ray images Analysis and Designing Diagnostic Application by Thamer Khaleel, Ali Kalakech

“…This has increased interest in the creation of AI-based automated detection systems, and deep learning is a group of machine learning algorithms used in AI that aim to automatically extract key properties from a dataset. …”
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Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining by Zhiwei Cheng, Luyu Ding, Cheng Peng, Helong Yu, Baozhu Yang, Ligen Yu, Qifeng Li

“…The algorithm’s optimization bolstered its scalability, making it more adaptable to future data expansions and complex knowledge integrations. …”
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Cervical cancer prediction using machine learning models based on routine blood analysis by Jie Su, Hui Lu, Ruihuan Zhang, Na Cui, Chao Chen, Qin Si, Biao Song

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Improving T2D machine learning-based prediction accuracy with SNPs and younger age by Cynthia AL Hageh, Andreas Henschel, Hao Zhou, Jorge Zubelli, Moni Nader, Stephanie Chacar, Nantia Iakovidou, Haralampos Hatzikirou, Antoine Abchee, Siobhán O’Sullivan, Pierre A. Zalloua

“…Background: This study aimed to evaluate whether integrating clinical and genomic data improves the performance of machine learning (ML) models for predicting Type 2 Diabetes (T2D) risk. …”
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Discrimination of the Skin Cells from Cellular-Resolution Optical Coherence Tomography by Deep Learning by Jui-Yun Yi, Sheng-Lung Huang, Shiun Li, Yu-You Yen, Chun-Yeh Chen

“…The deep learning algorithm is successfully and efficiently applied to discriminate the OCT skin cell images.…”
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Immune Microenvironment Characterization and Machine Learning-Guided Identification of Diagnostic Biomarkers for Ulcerative Colitis by Zheng Q, Wang L, Zhang Y, Peng J, Hou J, Wang H, Ma Y, Tang P, Li Y, Li H, Chen Y, Li J, Chen Y

“…Conventional treatments primarily focus on anti-inflammatory strategies but are often limited by relapses and a lack of durability.What is new here: This study identifies a distinct pattern of immune cell dysregulation in UC patients, involving abnormalities in macrophages, neutrophils, and T-cell subsets. It employs machine learning algorithms to construct diagnostic models, including an optimal 8-gene model (GATA2, IL8, LAT, NOLC1, SMARCA5, SMC3, STX10, ZMIZ1), which demonstrates high predictive performance (AUC of 0.964 in training datasets and 0.884 in testing datasets). …”
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Enhancing semi‐supervised contrastive learning through saliency map for diabetic retinopathy grading by Jiacheng Zhang, Rong Jin, Wenqiang Liu

“…Moreover, the performance of these algorithms is hampered by the scarcity of large‐scale, high‐quality annotated data. …”
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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…However, these methods are time-consuming and computationally costly, which makes ASR prediction challenging. Machine learning (ML) techniques can serve as effective alternatives for the early detection of expansion in concrete structures. …”
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An enhanced deep learning model for accurate classification of ovarian cancer from histopathological images by Anik Kumar Saha, Muntezar Rabbani, Anika Saba Ibte Sum, M. F. Mridha, Md Mohsin Kabir

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Predicting fertilizer treating of maize using digital image processing and deep learning approaches by Eshete Derb Emiru, Kassie Bishaw

“…The goal of this study is to develop a model for the recognition and classification of fertilizer treatment for maize based on maize leaf images, using deep learning algorithms to facilitate and improve the recognition and early control of fertilizer treatment for maize. …”
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Artificial intelligence and machine learning in modern cardiology: Advancements in diagnosis, treatment and patient monitoring by Szymon Kopciał, Dawid Piecuch, Edyta Hańczyk, Karolina Kornatowska, Natalia Pawelec, Weronika Mazur

“… Introduction and purpose: Artificial intelligence (AI) and machine learning (ML) are impacting cardiology by enhancing diagnostic accuracy, personalizing treatment and optimizing patient care. …”
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Target repositioning using multi-layer networks and machine learning: The case of prostate cancer by Milan Picard, Marie-Pier Scott-Boyer, Antoine Bodein, Mickaël Leclercq, Julien Prunier, Olivier Périn, Arnaud Droit

“…Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.…”
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Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…However, the scarcity of large-scale public PPG datasets acquired from wearable devices hinders the development of intelligent automatic AF detection algorithms unaffected by motion artifacts, saturated ambient noise, inter- and intra-subject differences, or limited training data. …”
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Enhancing Corn Image Resolution Captured by Unmanned Aerial Vehicles With the Aid of Deep Learning by Emilia Alves Nogueira, Bruno Moraes Rocha, Gabriel da Silva Vieira, Afonso Ueslei da Fonseca, Juliana Paula Felix, Antonio Oliveira-Jr, Fabrizzio Soares

“…Among them are the classical interpolation techniques such as Nearest Neighbor, Bilinear and Bicubic, as well as Super Resolution (SR) algorithms based on deep learning, such as MuLUT, LeRF and Real-ESRGAN. …”
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Aggregation-Based Ensemble Classifier Versus Neural Networks Models for Recognizing Phishing Attacks by Wojciech Galka, Jan G. Bazan, Urszula Bentkowska, Kamil Szwed, Marcin Mrukowicz, Pawel Drygas, Lech Zareba, Marcin Szpyrka, Piotr Suszalski, Sebastian Obara

“…Unlike other phishing datasets, this data provides dates which is important for the incremental learning approach. …”
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Automatic Detection of Occluded Main Coronary Arteries of NSTEMI Patients with MI-MS ConvMixer + WSSE Without CAG by Mehmet Cagri Goktekin, Evrim Gul, Tolga Çakmak, Fatih Demir, Mehmet Ali Kobat, Yaman Akbulut, Ömer Işık, Zehra Kadiroğlu, Kürşat Demir, Abdulkadir Şengür

“…In this study, a novel deep learning-based approach is used to automatically detect the occluded main coronary artery or arteries in NSTEMI patients. …”
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Detecting Fake Reviews in E-Commerce: A Case Study on Shopee Using Support Vector Machine and Random Forest by Khoirotulmuadiba Purifyregalia, Khothibul Umam, Nur Cahyo Hendro Wibowo, Maya Rini Handayani

“…These findings highlight the effectiveness of SVM in handling high-dimensional text data for fake review detection. The study contributes to the application of automated topic modeling (LDA) for labeling e-commerce reviews in the Indonesian context and opens opportunities for further enhancement using larger datasets and deep learning-based models to improve classification accuracy and scalability.…”
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An Immunology Inspired Flow Control Attack Detection Using Negative Selection with -Contiguous Bit Matching for Wireless Sensor Networks by Muhammad Zeeshan, Huma Javed, Amna Haider, Aumbareen Khan

“…This paper implemented an improved, decentralized, and customized version of the Negative Selection Algorithm (NSA) for data flow anomaly detection with learning capability. …”
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A multi-model approach for distance and angle estimation using a custom-designed tag by Emre Erkan

“…Recent advancements in deep learning-based object detection models have led to remarkable developments across various application domains. …”
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Method of Diagnostics of Operation Modes of Individual Heat Supply Units, Allowing to Detect Pre-Emergency Situations at an Early Stage by Dvortsevoy A.I., Borush O.V., Khoreva V.A., Yakovina I.N.

“…To achieve this goal, the following tasks were solved: creating and debugging methods for diagnosing IH operating modes; using the cluster analysis method, in particular the K-means algorithm, to identify pre-emergency situations at an early stage; analyzing the relationship between outdoor air temperature data and the pressures of direct and return network water in IHU operating modes using Novosibirsk as an example. …”
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