Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models by Abir Das, Saurabh Singh, Jaejeung Kim, Tariq Ahamed Ahanger, Anil Audumbar Pise

“…The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. …”
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Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation by Bingnan Lu, Yifan Liu, Guo Ji, Yuntao Yao, Zhao Yang, Bolin Zhu, Lei Wang, Keqin Dong, Yuanan Li, Jiaying Shi, Junzhe He, Runzhi Huang, Wang Zhou, Xinming Cui, Xiuwu Pan, Xingang Cui

“…Then, we defined 33 MDPGs and successfully constructed MDPC with three different subtypes (DPP4+MSMB+ MDPC, NHP2+NVL+ MDPC, COL1A1+MYLK+ MDPC). …”
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Preoperative ternary classification using DCE-MRI radiomics and machine learning for HCC, ICC, and HIPT by Peng Xie, Zhong-Jian Liao, Lu Xie, Junyuan Zhong, Xiaodong Zhang, Wei Yuan, Yujin Yin, Tianxian Chen, Huizhen Lv, Xinglin Wen, Xiaochun Wang, Ling Zhang

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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…These models are trained on two open-source datasets, using the PyCaret library in Python. (3) <b>Results</b>: The findings suggest that an ensemble of Random Forest and Logistic Regression models performs best for the 2C classification, while the Extra Trees classifier performs best for the 3C classification. …”
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CLASSIFICATION OF IRRIGATION MANAGEMENT PRACTICES IN MAIZE HYBRIDS USING MULTISPECTRAL SENSORS AND MACHINE LEARNING TECHNIQUES by João L. G de Oliveira, Dthenifer C. Santana, Izabela C de Oliveira, Ricardo Gava, Fábio H. R. Baio, Carlos A da Silva Junior, Larissa P. R. Teodoro, Paulo E. Teodoro, Job T de Oliveira

“…Three accuracy metrics were utilized to evaluate the algorithms in the classification of irrigation management: correct classifications (CC), Kappa coefficient and F-Score. …”
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Smartphone‐Embedded Artificial Intelligence‐Based Regression for Colorimetric Quantification of Multiple Analytes with a Microfluidic Paper‐Based Analytical Device in Synthetic Tea... by Meliha Baştürk, Elif Yüzer, Mustafa Şen, Volkan Kılıç

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Machine Learning Techniques in Chronic Kidney Diseases: A Comparative Study of Classification Model Performance by Nguyen Dong Phuong, Nguyen Trung Tuyen, Vu Thi Thai Linh, Nghi N Nguyen, Thanh Q Nguyen

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A Fusion of Deep Learning and Time Series Regression for Flood Forecasting: An Application to the Ratnapura Area Based on the Kalu River Basin in Sri Lanka by Shanthi Saubhagya, Chandima Tilakaratne, Pemantha Lakraj, Musa Mammadov

“…Thus, this study introduces a novel hybrid model that combines a deep leaning technique with a traditional Linear Regression model to first forecast water levels and then detect rare but destructive flood events (i.e., major and critical floods) with high accuracy, from 1 to 3 days ahead. …”
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Gated-LNN: Gated Liquid Neural Networks for Accurate Water Quality Index Prediction and Classification by Sreeni Chadalavada, Suleyman Yaman, Abdulkadir Sengur, Abdul Hafeez-Baig, Ru-San Tan, Prabal Datta Barua, Ravinesh C. Deo, Makiko Kobayashi, U. Rajendra Acharya

“…The proposed gated-LNN model achieved a high R2 of 0.9995 for WQI prediction and 99.74% accuracy for three-class water quality classification into &#x201C;Good,&#x201D; &#x201C;Poor,&#x201D; and &#x201C;Unsuitable&#x201D; classes, outperforming state-of-the-art models in both regression and classification tasks. …”
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Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson’s disease classification by Sameer Bhat, Piotr Szczuko

“…Four datasets are created from an original dataset: $$DS_0$$ (normal dataset), $$DS_1$$ ( $$DS_0$$ subjected to Canny edge detection and Hessian filtering), $$DS_2$$ (augmented $$DS_0$$ ), and $$DS_3$$ (augmented $$DS_1$$ ). We evaluate a range of ML models-Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), XGBoost (XBG), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost (AdB)-on these datasets, analyzing prediction accuracy, model size, and prediction latency. …”
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Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming by Giuseppe Altieri, Sabina Laveglia, Mahdi Rashvand, Francesco Genovese, Attilio Matera, Alba Nicoletta Mininni, Maria Calabritto, Giovanni Carlo Di Renzo

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Exploration of the ubiquitination-related molecular classification and signature to predict the survival and immune microenvironment in colon cancer by Ji-Zhong Xu, Tian-Qi Wan, Jin-Song Su, Jun-Min Song

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Distribution and classification of macrozoobenthos in Peter the Great Bay of Japan Sea in relation to contamination of bottom sediments by A. V. Moshchenko, T. A. Belan, B. M. Borisov

“…By these parameters, using the fuzzy sets algorithm, the taxa are classified to five groups: i) extremely sensitive; ii) highly sensitive; iii) moderately tolerant; iv) tolerant; and v) extremely tolerant to pollution (ES, S, MT, T and ET, respectively). …”
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Automated Classification Model for Elementary Mathematics Diagnostic Assessment Data Based on TF-IDF and XGBoost by Seonghyun Park, Seungmin Oh, Woncheol Park

“…After preprocessing, TF-IDF was employed to extract relevant features, and XGBoost was used to train a classification model. To validate the model’s performance, comparative experiments were conducted with Logistic Regression, Support Vector Machine (SVM), LightGBM, BERT, and DistilBERT. …”
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Classification of Signature-Based Phenotypes of Aging-Related Genes to Identify Prognostic and Immune Characteristics in HCC by Junjie Zhao, Chong Li, Qinggang Li, Shen Shen, Xiaobo Hu, Zihui Dong, Yize Zhang, Jiyuan Xing

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Evaluating the relationship between RMR and Q-system for improved classification of faulted rocks and weak rocks by Jun-Sik Park, Young-Woo Go, Tae-Min Oh

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APPLICATION OF BAGGING CART IN THE CLASSIFICATION OF ON-TIME GRADUATION OF STUDENTS IN THE STATISTICS STUDY PROGRAM OF TANJUNGPURA UNIVERSITY by Widad Imtiyaz, Neva Satyahadewi, Hendra Perdana

“…The characteristics of students who tend to graduate not or on time can be analyzed using classification techniques. Classification and Regression Tree (CART) is one of the classification tree methods. …”
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Sex classification accuracy through machine learning algorithms - morphometric variables of human ear and nose by Tej Kaur, Kewal Krishan, Akanksha Sharma, Ankita Guleria, Vishal Sharma

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Factors associated with macrosomia among singleton live-birth: A comparison between logistic regression, random forest and artificial neural network methods by Payam Amini, Saman Maroufizadeh, Omid Hamidi, Reza Omani Samani, Mahdi Sepidarkish

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Sample Denoising and Optimization Technique Based on Noise Filtering and Evolutionary Algorithms for Imbalanced Data Classification by Fhira Nhita, Asniar, Isman Kurniawan, Adiwijaya

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