Visual Impairment in Stroke Patients: a Two-Part Review. Part II — Rehabilitation Methods by Tatyana V. Marfina, Tatiana V. Konchugova, Tatiana V. Apkhanova, Detelina B. Kulchitskaya, Anastasiya A. Mukhina

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Monitoring of the Physicochemical Properties and Aflatoxin of <i>Aspergillus flavus</i>-Contaminated Peanut Kernels Based on Near-Infrared Spectroscopy Combined with Machine Learni... by Yingge Wang, Mengke Li, Li Xu, Chun Gao, Cheng Wang, Lu Xu, Shaotong Jiang, Lili Cao, Min Pang

“…The key innovation lies in the development of an optimized spectral processing pipeline that effectively overcomes moisture interference while maintaining high sensitivity to low aflatoxin concentrations. …”
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Hyperparameters optimization of evolving spiking neural network using artificial bee colony for unsupervised anomaly detection by Rehan Rabie, Sahran Shahnorbanun, Alyasseri Zaid Abdi Alkareem, Sani Nor Samsiah, Al-Betar Mohammed Azmi

“…Nowadays, anomaly detection in streaming data has gained considerable attention due to the exponential growth in the data gathered by Internet of Things applications. Analyzing and processing vast data volumes requires a system capable of working in real-time. …”
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Untargeted metabolomics for acute intra-abdominal infection diagnosis in serum and urine using UHPLC-TripleTOF MS by Zhenhua Dong, Shaopeng Zhang, Hongwei Zhang, Dingliang Zhao, Ziwen Pan, Daguang Wang

“…Following preliminary experimental processing, all serum and urinary samples were subjected to ultrahigh performance liquid chromatography-triple time-of-flight mass spectrometry analysis. …”
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Machine Learning for Identifying Damage and Predicting Properties in 3D-Printed PLA/Lygeum Spartum Biocomposites by Khalil Benabderazag, Moussa Guebailia, Zouheyr Belouadah, Lotfi Toubal, Salah Eddine Tachi

“…Specimens were fabricated using a bio-filament composed of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers and were subjected to tensile and flexural tests. The processed dataset, comprising six normalized features (cumulative rise, duration, count, frequency, energy, and amplitude) was used to train four ML models: Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT) implemented in Python using libraries such as scikit-learn, pandas, and numpy. …”
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Flood risk mapping and performance efficiency evaluation of machine learning algorithms: Best practice in northern Iran by M. Shirmohammadi, M. Shirmohammadi, S. Pirasteh, W. Li, D. Mafi-Gholami

“…In this study, we applied several ML algorithms, including Random Forest (RF), XGBoost (Extreme Gradient Boosting), LightGBM, CatBoost, and Support Vector Machine (SVM), to develop flood risk maps for a region in northern Iran. …”
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TSB-Forecast: A Short-Term Load Forecasting Model in Smart Cities for Integrating Time Series Embeddings and Large Language Models by Mohamed Mahmoud Hasan, Neamat El-Tazi, Ramadan Moawad, Amany H. B. Eissa

“…TSB-Forecast shows significant improvements reducing MAE by 38.7%, RMSE by 18.2%, and SMAPE by 50.6% compared to several baselines, including the official ENTSO-E forecast, Extra Trees Regressor (ETR), a hybrid model with basic text processing (M6), and a deep learning model combining GRU and CNN (CNN-GRU). …”
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Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops by Tariq Ali, Saif Ur Rehman, Shamshair Ali, Khalid Mahmood, Silvia Aparicio Obregon, Rubén Calderón Iglesias, Tahir Khurshaid, Imran Ashraf

“…These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. …”
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Optimizing drying and storage for edible mushrooms: Study on gamma irradiation levels, drying temperatures, and packaging materials with SVM-based predictions by Ehsan Fartash Naeimi, Mohammad Hadi Khoshtaghaza, Kemal Çağatay Selvi, Mariana Ionescu, Soleiman Abbasi

“…Nanocomposite packaging preserved the appearance characteristics of the dried mushrooms, and the SVM algorithm demonstrated strong potential for predicting quality changes prior to processing.…”
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Machine learning enables legal risk assessment in internet healthcare using HIPAA data by Shixian Liu, Hailing Liu, Siyu Fan, Leming Song, Zeyu Wang

“…The research methods include data collection and processing, construction and optimization of ML models, and the application of a risk assessment framework. …”
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JASBO: Jaya Average Subtraction Based Optimization with Deep Learning Model for Multi-Classification of Infectious Disease from Unstructured Data by Vian Sabeeh, Ahmed Bahaaulddin A. Alwahhab, Ali Abdulmunim Ibrahim Al-kharaz

“…Accordingly, enriching medical text processing is beneficial in health informatics. In this research, proposed Jaya Average Subtraction Based Optimization (JASBO), which is enabled by Deep Learning (DL) is used to classify infectious diseases into many categories from unstructured data. …”
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Modeling the thermal inactivation of non-pathogenic and avian-pathogenic Escherichia coli in broiler mash feed with high initial moisture content using a lab-based circulating wate... by Michael Carroll, Pratima Adhikari, Kelley Wamsley, Cangliang Shen, Timothy Boltz

“…This study aimed to apply existing models to quantify the thermal inactivation kinetics of two strains of E. coli during thermal processing between 75 and 95°C. Two-grams of feed sample were added with 200 µL of one of the two Nalidixic acid (NaL) adapted E. coli strains and submerged in a circulated water bath set at 75, 80, 85, 90, and 95°C, for heating between 0 and 180 s. …”
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New joint estimation method for emissivity and temperature distribution based on a Kriged Marginalized Particle Filter: Application to simulated infrared thermal image sequences by Thibaud Toullier, Jean Dumoulin, Laurent Mevel

“…However, the computational efficiency of the proposed method is significantly improved, reducing the processing time by seven orders of magnitude compared to MCMC and three orders of magnitude compared to CMA-ES. …”
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RESEARCH INTO THE AERODYNAMICS OF AIR FLOWS IN CYCLONES by V. Petrov, А. Аlеksashyn, S. Poznar

“…Models of vertical cyclones, worked out over the years, are widely used in the grain processing industry. Horizontal cyclones are less studied and have lower efficiency, but they are used in aggregate equipment, where they have certain advantages. …”
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Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy by Chenlong Fan, Ying Liu, Tao Cui, Mengmeng Qiao, Yang Yu, Weijun Xie, Yuping Huang

“…The associated methods and theoretical foundation provide a scientific basis for the quality control and processing of maize.…”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…In one experiment, Alkhatib et al. used Convolutional neural network (CNN) based deep trackers to track the median and sciatic nerve with a surprising accuracy of 0.87.2 Another study employed the same CNN model to locate and discriminate accurate images of sacrum, vertebral levels and intervertebral gaps during percutaneous spinal needle insertion.3 Another study used a different AI model called SVM (support vector machine) classification, image processing, and template matching to locate lumbar level L3-L4 and the ideal puncture site for epidural anaesthesia in real-time. …”
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Extracting Information from Unstructured Medical Reports Written in Minority Languages: A Case Study of Finnish by Elisa Myllylä, Pekka Siirtola, Antti Isosalo, Jarmo Reponen, Satu Tamminen, Outi Laatikainen

“…This highlights the challenge of medical language processing when working with minority languages. Moreover, it was noted that parameter tuning based on translated English reports did not significantly improve the detection rates, likely due to linguistic differences between the datasets. …”
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Predicting Diabetes Mellitus with Machine Learning Techniques by Heba Ahmed Jassim, Omar R. Kadhim, Zahraa Khduair Taha, Johnny Koh Siaw Paw, Yaw Chong Tak, Tiong Sieh Kiong

“…This research analyzes the effectiveness of various machine learning algorithms in processing datasets with minority classes. The evaluation was based on the classification report (including accuracy, precision, recall, and F1-score), the confusion matrix, and the ROC AUC. …”
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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

“…In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears.…”
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Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms by Zilong Pu, Miaomiao Yang, Mingzhi Jiao, Duan Zhao, Yu Huo, Zhi Wang

“…To assess the impact of time duration sensor data on the results, we selected four time windows of different length and extracted the corresponding sensor response data for subsequent processing. Initially, principal component analysis (PCA) was used to visualise the clustering of the three target gas samples at room temperature, providing a preliminary data analysis. …”
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