Bioinformatics insights into mitochondrial and immune gene regulation in Alzheimer's disease by Tian Meng, Yazhou Zhang, Yuan Ye, Hui Li, Yongsheng He

“…Conclusions Five mitochondrial and immune biomarkers, i.e., TSPO, HIGD1A, NDUFAB1, NT5DC3, and MRPS30, with diagnostic value in Alzheimer's disease, were screened by machine-learning algorithmic models, which will be a guide for future clinical research of Alzheimer's disease in the mitochondria–immunity-related direction.…”
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Intricacies of human–AI interaction in dynamic decision-making for precision oncology by Dipesh Niraula, Kyle C. Cuneo, Ivo D. Dinov, Brian D. Gonzalez, Jamalina B. Jamaluddin, Jionghua Judy Jin, Yi Luo, Martha M. Matuszak, Randall K. Ten Haken, Alex K. Bryant, Thomas J. Dilling, Michael P. Dykstra, Jessica M. Frakes, Casey L. Liveringhouse, Sean R. Miller, Matthew N. Mills, Russell F. Palm, Samuel N. Regan, Anupam Rishi, Javier F. Torres-Roca, Hsiang-Hsuan Michael Yu, Issam El Naqa

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Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers by Alon Kukliansky, Marko Orescanin, Chad Bollmann, Theodore Huffmire

“…The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in quantum machine learning offering new avenues. …”
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Shipyard Manpower Digital Recruitment: A Data-Driven Approach for Norwegian Stakeholders by Bogdan Florian Socoliuc, Andrei Alexandru Suciu, Mădălina Ecaterina Popescu, Doru Alexandru Plesea, Florin Nicolae

“…The application of machine learning algorithms provides predictive insights that support real-time adjustments to job postings, optimizing recruitment strategies. …”
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A Secure Object Detection Technique for Intelligent Transportation Systems by Jueal Mia, M. Hadi Amini

“…Federated Learning is a decentralized machine learning technique that creates a global model by aggregating local models from multiple edge devices without a need to access the local data. …”
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Prioritize Effective Factors of Scheduling in Tehran Metro Station with Fuzzy TOPSIS Model by Vinh Phuc Dung, Minh Tien Nhung

“…This research tries to provide an optimal data mining approach and machine learning principles to predict the route and select the optimal path in metro lines with minimum time, best speed, and minor errors in routing. …”
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Enhancing real estate price prediction using optimized least squares moment balanced machine by Radian Khasani Riqi

“…The OLSMBM was benchmarked against five other machine learning models, including LSSVM, BPNN, ELSIM, Decision Tree, and Linear Regression. …”
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Hypertension Detection Using Passive-Aggressive Algorithm With The PA-I And PA-II Methods by M. Hafidz Ariansyah, Sri Winarno

“…Researchers use machine learning that can explore large amounts of data sets to produce knowledge that is beneficial to science. …”
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Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model by Basim Mahbooba, Mohan Timilsina, Radhya Sahal, Martin Serrano

“…Despite the growing popularity of machine learning models in the cyber-security applications (e.g., an intrusion detection system (IDS)), most of these models are perceived as a black-box. …”
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Analisis Kinerja Algoritma Klasifikasi Teks Bert dalam Mendeteksi Berita Hoaks by Assyfa Rasida Hanum, Ivykaeyla Adriana Zetha, Salwa Cahyani Putri, Rafifah Ayud Wulandari, Sherla Puspa Andina, Julia Nur Fajrina, Novanto Yudistira

“…Hasil evaluasi menunjukkan bahwa model klasifikasi BERT memiliki akurasi sebesar 76% pada data validasi dalam mengklasifikasikan berita hoaks, yang menunjukkan performa atau kinerja model Machine Learning dalam melakukan klasifikasi berita hoaks. …”
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Application of ensemble learning techniques to model the atmospheric concentration of SO2 by A. Masih

“…In general, it demonstrates that the performance of ensemble classifiers random forest, bagging and voting can outperform single base traditional statistical and machine learning algorithms such as linear regression, support vector machine for regression and multilayer perceptron to model the atmospheric concentration of sulphur dioxide.…”
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Data-Driven Approach for Modeling the Nonlane-Based Mixed Traffic Conditions by Narayana Raju, Shriniwas S. Arkatkar, Said Easa, Gaurang Joshi

“…The data were used to extract the parameters required for modeling the vehicles’ positions using machine learning algorithms. Three established supervised machine learning algorithms (k-NN, random forest, and regression tree) and deep learning are selected to model mixed traffic conditions. …”
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Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection by Ella Goldschmidt, Ella Rannon, Daniel Bernstein, Asaf Wasserman, Michael Roimi, Anat Shrot, Dan Coster, Ron Shamir

“…Our study demonstrates the potential of machine learning models to predict inappropriate empiric antibiotic treatment.…”
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Refining the prediction of user satisfaction on chat-based AI applications with unsupervised filtering of rating text inconsistencies by Hae Sun Jung, Jang Hyun Kim, Haein Lee

“…Subsequently, the authors conduct supervised sentiment analysis using various machine learning and deep learning algorithms. The experimental results confirm the effectiveness of the proposed approach showing improvement in prediction accuracy with cost efficiency. …”
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Online energy consumption forecast for battery electric buses using a learning-free algebraic method by Zejiang Wang, Guanhao Xu, Ruixiao Sun, Anye Zhou, Adian Cook, Yuche Chen

“…In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. …”
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Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree by Nasir Tayarani, Mehrdad Jalali

“…In this paper, a new hybrid approach of machine learning and vocabulary-based method to text-mining sentiment analysis on Twitter. …”
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A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems by Fahd A Alhaidari, Alia Mohammed Alrehan

“…It has garnered much interest from researchers studying the mitigation of attacks including distributed denial of service attacks. Machine learning techniques, which mainly rely on the quality of the datasets used, play a role in detecting many attacks with a high level of accuracy. …”
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Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning researchMendeley Data by Eram Mahamud, Md Assaduzzaman, Shayla Sharmin

“…The dataset aims to support the development of machine-learning models for precise disease detection and quality assessment in lentil cultivation. …”
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Improved ML-Based Technique for Credit Card Scoring in Internet Financial Risk Control by Shuangshuang Fan, Yanbo Shen, Shengnan Peng

“…In order to overcome the limitations of the credit card scoring model and evaluate credit risk better, this paper proposes a credit evaluation model based on extreme gradient boosting tree (XGBoost) machine learning (ML) algorithm to construct a credit risk assessment model for Internet financial institutions. …”
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Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems by Xianming Dou, Yongguo Yang

“…This is attributed partly to the considerable development of machine learning techniques that allow the scientific community to use these advanced tools for approximating the nonlinear processes affecting the variation of water vapor in terrestrial ecosystems. …”
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