Optimization of the Naive Bayes Algorithm with SMOTETomek Combination for Imbalance Class Fraud Detection by Arief Tri Arsanto, Arif Faizin, Moch lutfi, Zulfatun Nikmatus Saadah

“…Class imbalance in a dataset is a significant problem in machine learning that can affect overall model performance. …”
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Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia by Bojana Ivošević, Nina Pajević, Sanja Brdar, Rana Waqar, Maryam Khan, João Valente

“…This dataset is a valuable resource for researchers working on habitats mapping and assessment for biodiversity monitoring studies on one side and researchers working on novel machine learning methods for land cover classification.…”
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Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers by Rodolfo Brizola Toscan, Wojciech Lesiński, Piotr Stomma, Balakrishnan Subramanian, Paweł P. Łabaj, Witold R. Rudnicki, Witold R. Rudnicki

“…Using metagenomic data from the CAMDA 2023 challenge, we applied tools such as AMR++, Bowtie, AMRFinderPlus, and RGI for resistome profiling, along with clustering, normalization, and machine learning techniques to identify predictive markers. …”
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Correlation feature and instance weights transfer learning for cross project software defect prediction by Quanyi Zou, Lu Lu, Shaojian Qiu, Xiaowei Gu, Ziyi Cai

“…Abstract Due to the differentiation between training and testing data in the feature space, cross‐project defect prediction (CPDP) remains unaddressed within the field of traditional machine learning. Recently, transfer learning has become a research hot‐spot for building classifiers in the target domain using the data from the related source domains. …”
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The role of histone lactylation genes in hepatocellular carcinoma prognostic models and their immune cell infiltration features: a comprehensive analysis of single-cell, spatial tr... by Shanjie Luan

“…Methods A variety of machine learning methods were used to construct and validate a model of differentially expressed Kla genes with comprehensive evaluations included ROC, Kaplan‒Meier curve, Cox regression, decision curve. …”
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Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals by Arezoo Sanati Fahandari, Sara Moshiryan, Ateke Goshvarpour

“…Furthermore, the SVM classifier surpassed the other machine learning algorithms when all features were integrated, attaining an accuracy of 89.06%, a sensitivity of 88.97%, an F1 score of 94.16%, and a precision of 100% for the discrimination of depression in the gamma band. …”
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LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns by Mehran Motamedi, Reza Shidpour, Mehdi Ezoji

“…Abstract In this paper, we present a machine learning-based approach that leverages Long Short-Term Memory (LSTM) networks combined with a sliding window technique for feature extraction, aimed at accurately predicting point defect percentages in semiconductor materials based on simulated X-ray Diffraction (XRD) data. …”
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Enhancing Business Intelligence by Means of Suggestive Reviews by Atika Qazi, Ram Gopal Raj, Muhammad Tahir, Erik Cambria, Karim Bux Shah Syed

“…A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. …”
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Improving Text Recognition Accuracy for Serbian Legal Documents Using BERT by Miloš Bogdanović, Milena Frtunić Gligorijević, Jelena Kocić, Leonid Stoimenov

“…High-quality datasets, considered a prerequisite for many supervised machine learning algorithms, are often only available in very limited quantities. …”
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Innovative Driver Monitoring Systems and On-Board-Vehicle Devices in a Smart-Road Scenario Based on the Internet of Vehicle Paradigm: A Literature and Commercial Solutions Overview by Paolo Visconti, Giuseppe Rausa, Carolina Del-Valle-Soto, Ramiro Velázquez, Donato Cafagna, Roberto De Fazio

“…Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management.…”
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Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets. …”
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Managing the University’s Educational Process Based on Predicting Students’ Academic Performance by E. V. Alikina, D. V. Maltsev

“…Methods of mathematical statistics were used to analyze the data: determining the type of data distribution using the Shapiro-Wilk test, establishing the presence of multicollinearity in the construction of multiple regression by the Pearson criterion, establishing correlation dependencies by Spearman’s rank correlation method. Machine learning methods are implemented in the Python programming language (v. 3.8) using the freely distributed Keras library.   …”
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Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm by Nana Zhang, Qi An, Shuai Zhang, Huanhuan Ma

“…Considering that traditional time series models and machine learning models are facing challenges in making predictions with high accuracy and robustness, this paper proposes a Light gradient boosting machine model based on the boosting ensemble learning algorithm to predict prices for three representative types of fresh agricultural products (bananas, beef, crucian carp). …”
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The role of force and torque in friction stir welding: A detailed review by Mostafa Akbari, Milad Esfandiar, Amin Abdollahzadeh

“…A notable advancement in this field is the implementation of machine learning tools, which enhance the ability to predict potential weld defects and improve overall weld quality. …”
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Unified regularity measures for sample-wise learning and generalization by Chi Zhang, Meng Yuan, Xiaoning Ma, Yu Liu, Haoang Lu, Le Wang, Yuanqi Su, Yuehu Liu

“…Abstract Fundamental machine learning theory shows that different samples contribute unequally to both the learning and testing processes. …”
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Knowledge graph based entity selection framework for ad-hoc retrieval by Pankaj Singh, Plaban Kumar Bhowmick

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Identification and Development of Synovial B-Cell-Related Genes Diagnostic Signature for Rheumatoid Arthritis by Jifeng Tang, Jinfang Xia, Huiming Sheng, Jinpiao Lin

“…B-cell-related differential expression genes (BRDEGs) were screened, and BRDEGs-based model was developed by machine learning algorithms and evaluated by an external validation set and clinical RA cohort, then biological functions were further analyzed. …”
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ECG-based transfer learning for cardiovascular disease: A scoping review by Sharifah Noor Masidayu Sayed Ismail, Siti Fatimah Abdul Razak, Nor Azlina Ab Aziz

“…Advancements in medical diagnosis using artificial intelligence have popularised transfer learning among researchers in machine learning and deep learning. This scoping review focuses on the application of ECG-based transfer learning for CVD identification, examining the most common types of CVD studied, the input formats used, frequently referenced databases, and the extent of transfer learning's application in diagnosing CVD through ECG analysis. …”
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Achieving Equity via Transfer Learning With Fairness Optimization by Xiaoyang Wang, Chia-Hsuan Chang, Christopher C. Yang

“…Machine learning algorithms are increasingly used in real-world decision-making systems, raising concerns about potential biases and unfairness. …”
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Cholec80-Boxes: Bounding Box Labelling Data for Surgical Tools in Cholecystectomy Images by Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Herag Arabian, Alberto Battistel, Paul David Docherty, Hisham ElMoaqet, Thomas Neumuth, Knut Moeller

“…Acquiring surgical data is challenging due to ethical constraints and the complexity of establishing data recording infrastructures. For machine learning tasks, there is also the large burden of data labelling. …”
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