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

“…Three machine learning models—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were used to predict aflatoxin levels. …”
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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

“…The model uses Sentence Bidirectional Encoder Representations from Transformers (SBERT) to extract semantic characteristics from textual news and Time to Vector (Time2Vec) to capture temporal patterns, acquiring cyclical behavior and context-sensitive impacts. …”
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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

“…Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. …”
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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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MANAGEMENT OF INNOVATIVE DEVELOPMENT OF ENTERPRISES IN THE CONTEXT OF A CHOICE OF ENERGY SECURITY STRATEGY by Oksana Mykoliuk, Nataliia Prylepa

“…Tasks: to determine the choice of alternative strategic perspectives of energy security; to analyze the main approaches to formation of energy security strategy in the conditions of innovative development; to develop scientific and methodological recommendations for neutralization of threats of the energy strategy of enterprise in internal and external environment, revealed in innovation development process. The following results were obtained: defined the main selection criteria of energy security strategy of enterprise; the author’s interpretation of the concept "energy security strategy of enterprise" is proposed, which is based on the vector of innovative development of enterprise in the field of energy security, which is aimed at rational and efficient use of energy and natural energy resources for achievement of strategic innovation aimed goals of energy policy; a structure for the energy security monitoring of enterprise has been formed and the main tasks of the enterprise’s energy security subdivision have been defined. …”
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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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Live Weight Prediction in Norduz Sheep Using Machine Learning Algorithms by Cihan Çakmakçı

“…The MANN algorithm, on the other hand, required a longer runtime to process the same dataset.…”
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A Deep Learning Approach for Extracting Cyanobacterial Blooms in Eutrophic Lakes From Satellite Imagery by Nan Wang, Zhenyu Tan, Chen Yang, Jinge Ma, Hongtao Duan

“…Moreover, a novel labeling technique using remote sensing indices simplified the labeling process. Experiments showed that MBAUNet achieved over 90% precision and recall, with an F1 score of 94.01%, outperforming vanilla UNet, DeepLabV3+, random forest, and support vector machine, while halving the number of parameters and training time compared to UNet. …”
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Optimizing Outdoor Micro-Space Design for Prolonged Activity Duration: A Study Integrating Rough Set Theory and the PSO-SVR Algorithm by Jingwen Tian, Zimo Chen, Lingling Yuan, Hongtao Zhou

“…This study proposes an optimization method based on Rough Set Theory (RST) and Particle Swarm Optimization–Support Vector Regression (PSO-SVR), aimed at enhancing the emotional dimension of outdoor micro-space (OMS) design, thereby improving users’ outdoor activity duration preferences and emotional experiences. …”
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Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…Tissue heterogeneity complicates the delivery process, requiring the extensive optimization of pulsing protocols currently empirically optimized. …”
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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

“…Additionally, character-based network features are extracted using Bi-LSTM model. Then, vector representation is concatenated with two separate character-level extractions from Bi-LSTM and CNN. …”
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Analyzing the influence of manufactured sand and fly ash on concrete strength through experimental and machine learning methods by S Sathvik, Solomon Oyebisi, Rakesh Kumar, Pshtiwan Shakor, Olutosin Adejonwo, Adithya Tantri, V Suma

“…Furthermore, machine learning (ML) techniques were engaged to predict the compressive strength of the concrete samples using Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). …”
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Research on the characteristics of the Maerkang MS6.0 earthquake based on the joint analysis of annual changes in gravity and magnetic fields by Dong Liu, Yufei Zhao, Yong Zhang, Jiangpei Huang, Yuqin Wu

“…The annual variation of the lithospheric magnetic field revealed that the horizontal vector of the magnetic field weakened overall before the earthquake, with the epicentral region continuing to weaken and the periphery converging, followed by a reverse weakening. …”
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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

“…Summary: The Food Safety Modernization Act (FSMA) has stimulated the need for research into feed sanitation practices for feed producers, particularly breeder and backyard flocks. Feed can be a vector for poultry pathogens, such as Escherichia coli (E. coli) transmission to poultry, and appropriate feed manufacturing can help mitigate the feed pathogens with thermal treatments. …”
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An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model by Hao Yan, Fangcao Zhang, Xingao Li, Chenchen Zhang, Yunjia Zhang, Yongfei Feng

“…In the rehabilitation training process for stroke patients, the level of excitement in the patient’s physiological state has a positive impact on the efficacy of the training. …”
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Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools by Shailendra Pawanr, Kapil Gupta

“…Two models, Least-Squares Support Vector Machine (LSSVM) and Multi-Gene Genetic Programming (MGGP), were trained and evaluated on various statistical metrics. …”
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