A Novel MEGNet for Classification of High-Frequency Oscillations in Magnetoencephalography of Epileptic Patients by Jun Liu, Siqi Sun, Yang Liu, Jiayang Guo, Hailong Li, Yuan Gao, Jintao Sun, Jing Xiang

“…After optimized configuration, the accuracy, precision, recall, and F1-score of the proposed detector reached 94%, 95%, 94%, and 94%, which were better than other classical machine learning models. In addition, we used the k-fold cross-validation scheme to test the performance consistency of the model. …”
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Advanced Fraud Detection: Leveraging K-SMOTEENN and Stacking Ensemble to Tackle Data Imbalance and Extract Insights by Nurafni Damanik, Chuan-Ming Liu

“…This study proposes an innovative solution for credit card fraud detection, utilizing a stacking ensemble of machine learning classifiers enhanced with sophisticated data resampling techniques. …”
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Revolutionizing Supply Chains: Unleashing the Power of AI-Driven Intelligent Automation and Real-Time Information Flow by Mohammad Shamsuddoha, Eijaz Ahmed Khan, Md Maruf Hossan Chowdhury, Tasnuba Nasir

“…This paper examines how AI, machine learning (ML), and robotic process automation (RPA) influence supply chain operations to adjust to the risks and vulnerabilities. …”
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Automatic Segmentation of Ischemic Stroke Lesions in CT Perfusion Maps Using Deep Learning Networks by Lida Zare Lahijan, Saeed Meshgini, Reza Afrouzian

“…Accordingly, several machine learning-based techniques have recently been proposed to improve the segmentation accuracy of ischemic stroke lesions. …”
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The extension of the largest generalized-eigenvalue based distance metric ) in arbitrary feature spaces to classify composite data points by Mosaab Daoud

“…Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. …”
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A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction by Tuong Le, Minh Thanh Vo, Bay Vo, Mi Young Lee, Sung Wook Baik

“…Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various datasets around the world. …”
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Metasurface-enabled multifunctional single-frequency sensors without external power by Masaya Tashiro, Kosuke Ide, Kosei Asano, Satoshi Ishii, Yuta Sugiura, Akira Uchiyama, Hiroki Wakatsuchi

“…Moreover, we provide a method for predicting physical parameters via the machine learning-based approach of random forest regression. …”
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Automated Classification of Circulating Tumor Cells and the Impact of Interobsever Variability on Classifier Training and Performance by Carl-Magnus Svensson, Ron Hübler, Marc Thilo Figge

“…The automated analysis of medical data is a growing field where different machine learning techniques are used to minimize the time-consuming task of manual analysis. …”
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BharatSim: An agent-based modelling framework for India. by Philip Cherian, Jayanta Kshirsagar, Bhavesh Neekhra, Gaurav Deshkar, Harshal Hayatnagarkar, Kshitij Kapoor, Chandrakant Kaski, Ganesh Kathar, Swapnil Khandekar, Saurabh Mookherjee, Praveen Ninawe, Riz Fernando Noronha, Pranjal Ranka, Vaibhhav Sinha, Tina Vinod, Chhaya Yadav, Debayan Gupta, Gautam I Menon

“…BharatSim uses a synthetic population created by applying statistical methods and machine learning algorithms to survey data from multiple sources, including the Census of India, the India Human Development Survey, the National Sample Survey, and the Gridded Population of the World. …”
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Efficient Preference Clustering via Random Fourier Features by Jingshu Liu, Li Wang, Jinglei Liu

“…Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks. Unlike approaches using the Nyström method, which randomly samples the training examples, we make use of random Fourier features, whose basis functions (i.e., cosine and sine ) are sampled from a distribution independent from the training sample set, to cluster preference data which appears extensively in recommender systems. …”
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Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network by Hao Peng, Han Wu, Junwu Wang

“…Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.…”
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Cellular senescence-associated genes in rheumatoid arthritis: Identification and functional analysis. by You Ao, Qing Lan, Tianhua Yu, Zhichao Wang, Jing Zhang

“…We then pinpointed three key genes (DHX9, CYR61, and ITGB) using random forest and LASSO Cox regression machine learning techniques. An integrated diagnostic model was created using these genes. …”
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Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images by Navya K. T, Seemitr Verma, Keerthana Prasad, Brij Mohan Kumar Singh

“…This study uses machine learning and computer vision techniques for the automatic identification of hypochromic microcytes from Peripheral Blood Smear (PBS) images to improve IDA diagnosis. …”
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A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil by Caio B. Wetterich, Ratnesh Kumar, Sindhuja Sankaran, José Belasque Junior, Reza Ehsani, Luis G. Marcassa

“…The overall objective of this work was to develop and evaluate computer vision and machine learning technique for classification of Huanglongbing-(HLB)-infected and healthy leaves using fluorescence imaging spectroscopy. …”
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On the use of clustering workflows for automated microstructure segmentation of analytical STEM datasets by Zhiquan Kho, Andy Bridger, Keith Butler, Ercin C. Duran, Mohsen Danaie, Alexander S. Eggeman

“…This study considers the issue of automated segmentation of scanning transmission electron microscopy (STEM) datasets using unsupervised machine learning approaches. To this end, a systematic comparison of two clustering workflows that had been established in previous literature was performed on two distinct material systems—an experimentally acquired Co2FeSi Heusler alloy and a simulated Au-matrix and Al2Cu precipitate. …”
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From Baseline to Best Practice: An Advanced Feature Selection, Feature Resampling and Grid Search Techniques to Improve Injury Severity Prediction by Soukaina EL Ferouali, Zouhair Elamrani Abou Elassad, Sara Qassimi, Abdelmounaîm Abdali

“…As far as we know and as of right now, there hasn’t been much interest in supporting a fusion-based system that critically reviews machine learning techniques using grid search optimization, feature selection, and smote technique and examines how injury severity prediction is affected by road crashes.…”
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A Bee Farm Records Management System: A Case Study of Kanyabugunga Kashambya Sub-County. by Maanige, Barnet, Tayebwa, Onesmas

“…Future avenues for enhancement include IoT integration for real-time hive monitoring, machine learning for pest detection, a dedicated mobile application, and expansion to other regions. …”
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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…Then three mainstream machine learning models are compared for SHAP analysis to obtain the significance results of relevant features. …”
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Usage of Optimized Least Square SVR to Volume Expansion Estimation of Cement Paste Including Fly Ash and Mgo Expansive Additive by Mazharul Islam, Sadia Afrin

“…To construct and assess machine learning-based algorithms to assess the volume expansion (𝑉𝑒) of cement paste, which consists of 𝐹𝐴 and 𝑀𝐸𝐴, 170 experimental findings from published studies are employed. …”
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Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement. by José L Safanelli, Tomislav Hengl, Leandro L Parente, Robert Minarik, Dellena E Bloom, Katherine Todd-Brown, Asa Gholizadeh, Wanderson de Sousa Mendes, Jonathan Sanderman

“…However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). …”
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