A NOVEL DEEP LEARNING APPROACHES FOR MULTI-CLASS HISTOPATHOLOGICAL SUB-IMAGE CLASSIFICATION USING PRIOR KNOWLEDGE by Riyam Ali Yassin, Morteza Valizadeh, Alaa Hussein Abdulaal

“…A new dataset comprising 3,600 sub-image histopathological images is presented, generated from the original Bach dataset. …”
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Counselling Career with Artificial Intelligence: A Systematic Review by Rifqi Muhammad, Patriana Patriana, Yusrain Yusrain, Astaman Astaman, Manja Manja

“…This study was conducted in Namibia, the USA, the UK, China, Romania, India, Sri Lanka, the Philippines, South Korea, and Finland. System: computer algorithm, artificial neural network, AIED, C3-IoC, Facebook Messenger, iAdvice, and online career counselor system (Chatbot and machine learning). …”
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Experience of implementing a value-based approach in oncodermatology by Yu. A. Zuenkova, D. I. Kicha, L. N. Izyurov

“…Validated questionnaires were used: Patient-Reported Outcome Measures (PROMs) (as a decision support tool), Patient-Reported Experience Measures (PREMs) (3 months after treatment to assess the patient's experience).Results. …”
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Automatic Characterization of Prostate Suspect Lesions on T2-Weighted Image Acquisitions Using Texture Features and Machine-Learning Methods: A Pilot Study by Teodora Telecan, Cosmin Caraiani, Bianca Boca, Roxana Sipos-Lascu, Laura Diosan, Zoltan Balint, Raluca Maria Hendea, Iulia Andras, Nicolae Crisan, Monica Lupsor-Platon

“…Further, when trained to differentiate each ISUP group, the accuracy was 80.3%. <b>Conclusions</b>: We developed an AI-based decision-support system that accurately differentiates between the two PCa prognostic groups using only T2 MRI acquisitions by employing radiomics with a robust machine-learning architecture.…”
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Serum Lipid Biomarkers for the Diagnosis and Monitoring of Neuromyelitis Optica Spectrum Disorder: Towards Improved Clinical Management by Li R, Wang J, Wang J, Xie W, Song P, Zhang J, Xu Y, Tian D, Wu L, Wang C

“…Ruibing Li,1,&ast; Jinyang Wang,1,2,&ast; Jianan Wang,1,&ast; Wei Xie,3 Pengfei Song,4 Jie Zhang,4 Yun Xu,5 Decai Tian,5 Lei Wu,3,&ast; Chengbin Wang1,&ast; 1Department of Laboratory Medicine, the First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China; 2School of Laboratory Medicine, Weifang Medical College, Weifang, Shandong, 261053, People’s Republic of China; 3Department of Neurology, the First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China; 4School of Advanced Technology, Xi’an Jiaotong - Liverpool University, Suzhou, 215000, People’s Republic of China; 5Center for Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Lei Wu; Chengbin Wang, The First Medical Centre of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China, Email wlyingsh@163.com; wangcb301@126.comBackground: Neuromyelitis optica spectrum disorder (NMOSD) is a group of immune-mediated disorders that often lead to severe disability. …”
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Review of open libraries for pharmacoeconomic analysis in R environment by I. A. Lackman, R. I. Sladkov, V. M. Timiryanova

“…The selected libraries can be divided into three classes: packages for calculating various quality of life indices, libraries for calculating indicators and indices of economic effectiveness of medical interventions (DALY, QALY, ICER), libraries for performing sensitivity analysis of the effect of medical interventions based on decision tree algorithms and Markov models. …”
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Preoperative Prediction of Macrotrabecular-Massive Hepatocellular Carcinoma Using Machine Learning-Based Ultrasomics by Li Y, Duan S, Ren S, Li D, Ma Y, Bu D, Liu Y, Li X, Cai X, Zhang L

“…Ultrasomics models were constructed based on the ultrasound image features of the training set using five different ML algorithms, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), decision tree (DT), and logistic regression (LR). …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…A classification problem is addressed by classifying failure into three prediction classes: (i) class 1, unlikely to fail within the first three years, (ii) class 2, likely to fail within three years; and (iii) class 3, likely to fail after three years from the date of commissioning. …”
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Development and validation of a multidimensional predictive model for 28-day mortality in ICU patients with bloodstream infections: a cohort study by Jun Jin, Jun Jin, Lei Yu, Qingshan Zhou, Qian Du, Xiangrong Nie, Hai-Yan Yin, Wan-Jie Gu

“…Through a two-step variable selection process combining LASSO regression and Boruta algorithm, we identified 12 predictive variables from 58 initial clinical parameters. …”
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Development and validation of an interpretable multi-task model to predict outcomes in patients with rhabdomyolysis: a multicenter retrospective cohort studyResearch in context by Chunli Liu, Jie Shi, Fengjuan Wang, Duo Li, Yu Luo, Bofan Yang, Yunlong Zhao, Li Zhang, Dingwei Yang, Heng Jin, Jie Song, Xiaoqin Guo, Haojun Fan, Qi Lv

“…Ten machine learning (ML) algorithms were applied to construct multi-task prediction models. …”
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High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022 by Xining Zhao, Jichao Wang, Yelu Ding, Xiaodong Gao, Changjian Li, Hongwei Huang, Xuerui Gao

“…The research methodology involved four key steps: (1) Enhancing the sample dataset using phenological indices and the Dynamic Time Warping (DTW) algorithm; (2) Identifying crop planting intensity based on phenological growth curves; (3) Developing independent random forest classifiers tailored to agricultural climate zones; and (4) Constructing an optimal feature subset for crop classification. …”
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Lightweight CNC digital process twin framework: IIoT integration with open62541 OPC UA protocol by Arivazhagan Anbalagan, Waqir Yusuf Zanhar, Shone George, Marcos Kauffman, Tengfei Long

“…The layers include: (i) Physical – microcontroller with sensors; (ii) Virtual – modeling and GM code generation; (iii) Data – OPC UA-based transfer; (iv) Interaction – visualization via custom nodes; and (v) Decision – rule-based logic and analytics. …”
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A vehicular network–based intelligent transport system for smart cities by Tayyaba Zaheer, Asad Waqar Malik, Anis Ur Rahman, Ayesha Zahir, Muhammad Moazam Fraz

“…Our results show reduced travel times of up to 33.3% when compared to a traditional fixed route-selection algorithm.…”
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Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach by Longhao Yan, Ping Wang, Jingwen Yang, Yu Hu, Yu Han, Junfeng Yao

“…Firstly, a rescue path planning environment for emergency vehicles on congested urban arterial roads based on the Markov decision process is established, which focuses on the architecture of arterial roads, taking the traffic efficiency and vehicle queue length into consideration of path planning; then, the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. …”
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An extreme forecast index-driven runoff prediction approach using stacking ensemble learning by Zhiyuan Leng, Lu Chen, Binlin Yang, Siming Li, Bin Yi

“…EFI is introduced as an input into four machine learning models (Support Vector Regression, Multi-layer Perceptron, Gradient Boosting Decision Tree, and Ridge Regression) for runoff prediction with lead times of 24 h, 48 h, and 72 h. …”
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Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis by D. Dhinakaran, L. Srinivasan, S. Edwin Raja, K. Valarmathi, M. Gomathy Nayagam

“…In order to overcome these drawbacks, the new integrated algorithm is presented here: Synergistic Kruskal-RFE Selector and Distributed Multi-Kernel Classification Framework (SKR-DMKCF). …”
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Prediction of Metastasis in Paragangliomas and Pheochromocytomas Using Machine Learning Models: Explainability Challenges by Carmen García-Barceló, David Gil, David Tomás, David Bernabeu

“…The best-performing algorithm, Random Forest, achieved an accuracy of 96.3%, precision of 96.5%, and AUC of 0.963, among other metrics, combining strong predictive capability with explainability that fosters trust in clinical applications.…”
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Developing an AI-based application for caries index detection on intraoral photographs by Niha Adnan, Syed Muhammad Faizan Ahmed, Jai Kumar Das, Sehrish Aijaz, Rashna Hoshang Sukhia, Zahra Hoodbhoy, Fahad Umer

“…In contrast, junior dentists achieved 83.3% precision, 64.1% sensitivity, and an F1 score of 72.4%. …”
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A Capacitated Vehicle Routing Model for Distribution and Repair with a Service Center by Irma-Delia Rojas-Cuevas, Elias Olivares-Benitez, Alfredo S. Ramos, Samuel Nucamendi-Guillén

“…Additionally, a Variable Neighborhood Search (VNS) algorithm was implemented and compared with AMPL-Gurobi. …”
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Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty: a simplified and validated model based on 154,519 total hip... by M. A. Alagha, Justin Cobb, Alexander D. Liddle, Henrik Malchau, Ola Rolfson, Maziar Mohaddes

“…If the risk of fracture can be forecasted, it would aid the shared decision-making process related to cementless stems. …”
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