Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images by Arezoo Borji, Taha-Hossein Hejazi, Abbas Seifi

“…Several deep-learning and traditional machine-learning models have been used to detect AD. In this paper, three deep-learning models, namely VGG16 and AlexNet, and a custom Convolutional Neural Network (CNN) with 8-fold cross-validation, have been used for classification. …”
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Leveraging Quantum LSTM for High-Accuracy Prediction of Viral Mutations by Prashanth Choppara, Bommareddy Lokesh

“…The one-hot encoding technique is a standard technique in machine learning for encoding protein sequences into data that can be used in neural networks.The proposed QLSTM outperformed existing deep learning architectures such as the Attention-Augmented Convolutional Neural Network (AACNN), Stacked Recurrent Neural Network (Stacked RNN), Retention Network (RetNet), and Bidirectional Long Short Term Memory (BiLSTM). …”
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Approximate CNN Hardware Accelerators for Resource Constrained Devices by P Thejaswini, Gautham Suresh, V. Chiraag, Sukumar Nandi

“…The performance of our proposed architectures demonstrates significant acceleration and reduced power consumption compared to popular edge machine learning framework - TinyML TensorFlow Lite. FHA achieves a significant accuracy improvement of 6.2% along with a speedup of 1.07x and AFHA achieves accuracy enhancement of 4.3% and an impressive speedup of 1.42x.…”
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Registered report protocol: A scoping review to identify potential predictors as features for developing automated estimation of the probability of being frail in secondary care. by Dirk H van Dalen, Angèle P M Kerckhoffs, Esther de Vries

“…<h4>Conclusion</h4>The identified potential predictors of being frail can be used as evidence-based input for machine learning based automated estimation of the probability of being frail using routine EHR data in the near future.…”
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The Discriminative Lexicon: A Unified Computational Model for the Lexicon and Lexical Processing in Comprehension and Production Grounded Not in (De)Composition but in Linear Discr... by R. Harald Baayen, Yu-Ying Chuang, Elnaz Shafaei-Bajestan, James P. Blevins

“…The discriminative lexicon also incorporates the insight from machine learning that end-to-end modeling is much more effective than working with a cascade of models targeting individual subtasks. …”
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Ecological momentary interventions for mental health: A scoping review. by Andreas Balaskas, Stephen M Schueller, Anna L Cox, Gavin Doherty

“…Recent years have seen increased exploration of the use of sensors and machine learning, but the role of humans in the delivery of EMI is also varied. …”
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RASGRF2 as a potential pathogenic gene mediating the progression of alcoholic hepatitis to alcohol-related cirrhosis and hepatocellular carcinoma by Zhengyuan Chen, Danfeng Fan, Tianyi Hang, Xiaoqing Yue

“…After screening with two machine learning algorithms, five shared genes remained. …”
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Erythrocyte modified controlling nutritional status as a biomarker for predicting poor prognosis in post-surgery breast cancer patients by Jingjing Hu, Jiaming Dong, Xiang Yang, Zhiyi Ye, Guoming Hu

“…The predictive effects of nutritional and inflammatory indicators on DFS were evaluated. Machine learning was used to evaluate and rank laboratory indicators, select relatively important variables to modify nutritional or inflammatory indicators with the best predictive power, and evaluate their predictive role in patients’ postoperative recurrence and metastasis. …”
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Ensemble Deep Learning Technique for Detecting MRI Brain Tumor by Rasool Fakhir Jader, Shahab Wahhab Kareem, Hoshang Qasim Awla

“…As a result, this paper concentrated on the tasks of segmentation, feature extraction, classifier building, and classification into four categories using various machine learning algorithms. The authors used VGG-16, ResNet-50, and AlexNet models based on the transfer learning algorithm for three models to classify images as an ensemble model. …”
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A Novel Hybrid Model for Credit Risk Assessment of Supply Chain Finance Based on Topological Data Analysis and Graph Neural Network by Kosar Farajpour Mojdehi, Babak Amiri, Amirali Haddadi

“…Results demonstrate that the proposed BallMapper- Graph Neural Network (BM-GNN) model achieves higher accuracy and F1-scores, outperforming traditional machine learning approaches. Notably, incorporating network-based features alongside financial ratios yields the most favorable results in credit risk assessment. …”
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Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation by Mikhail Arbatsky, Ekaterina Vasilyeva, Veronika Sysoeva, Ekaterina Semina, Ekaterina Semina, Valeri Saveliev, Kseniya Rubina

“…Here we narrow our focus down to a small set of mathematical methods applied upon standard processing of scRNA-seq data: preprocessing, dimensionality reduction, integration, and clustering (using machine learning methods for clustering). Normalization and scaling are standard manipulations for the pre-processing with LogNormalize (natural-log transformation), CLR (centered log ratio transformation), and RC (relative counts) being employed as methods for data transformation. …”
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On the implications of artificial intelligence methods for feature engineering in reliability sector with computer knowledge graph by Heling Jiang, Yongping Xia, Changjie Yu, Zhao Qu, Huaiyong Li

“…To improve operational efficiency and lower long-term maintenance costs, policy ideas include standardizing data collection techniques, investing in real-time monitoring systems, and implementing machine learning-based predictive maintenance across industries.…”
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Instruction and demonstration-based secure service attribute generation mechanism for textual data by LI Chenhao, WANG Na, LIU Aodi

“…Traditionally, the calibration of secure service attribute for textual data has been primarily reliant on human experts and machine learning methods, yet the efficiency and few-shot ability are insufficient. …”
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A pediatric emergency prediction model using natural language process in the pediatric emergency department by Arum Choi, Chohee Kim, Jisu Ryoo, Jangyeong Jeon, Sangyeon Cho, Dongjoon Lee, Junyeong Kim, Changhee Lee, Woori Bae

“…Various NLP models, including four machine learning (ML) models with Term Frequency-Inverse Document Frequency (TF-IDF) and two DL models based on the KM-BERT framework, were trained to differentiate emergency cases using clinician transcripts. …”
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IMPLEMENTATION OF LEARNING MANAGEMENT SYSTEMS WITH GENERATIVE ARTIFICIAL INTELLIGENCE FUNCTIONS IN THE POST-PANDEMIC ENVIRONMENT by Denis-Cătălin Arghir

“…To demonstrate the system's effectiveness, a curriculum was crafted for a specialized field of study - Artificial Intelligence (AI), with a specific focus on the practical application of Machine Learning algorithms. This curriculum incorporates theoretical and practical application components, complemented by a suite of assessment tools and assignments tailored to the proposed subject. …”
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Very Short-Term Blackout Prediction for Grid-Tied PV Systems Operating in Low Reliability Weak Electric Grids of Developing Countries by Benson H. Mbuya, Aleksandar Dimovski, Marco Merlo, Thomas Kivevele

“…A very short-term power outage prediction model framework based on a hybrid random forest (RF) algorithm was developed using open-source Python machine learning libraries and using a dataset generated from the pilot project’s experimental microgrid. …”
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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification by Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed

“… The application of machine learning (ML) methods to analyze changes in gene expression patterns has recently emerged as a powerful approach in cancer research, enhancing our understanding of the molecular mechanisms underpinning cancer development and progression. …”
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Allosteric Fluorescent Detection of Saccharides and Biomolecules in Water from a Boronic Acid Functionalized Arene Ruthenium Assembly Hosting Fluorescent Dyes by Alaa Maatouk, Thibaud Rossel, Bruno Therrien

“…All data were analyzed by unsupervised machine learning technologies (PCA and cluster analysis), suggesting that such systems with multiple analyte–response behaviors are offering new perspectives for the development of highly sensitive, easily tunable, water-soluble, fluorescent-based sensing arrays for biomolecules and other analytes.…”
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A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things by Mudita Uppal, Deepali Gupta, Nitin Goyal, Agbotiname Lucky Imoize, Arun Kumar, Stephen Ojo, Subhendu Kumar Pani, Yongsung Kim, Jaeun Choi

“…A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. …”
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An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain by Zeynep Hilal Kilimci, A. Okay Akyuz, Mitat Uysal, Selim Akyokus, M. Ozan Uysal, Berna Atak Bulbul, Mehmet Ali Ekmis

“…For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. …”
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