Smart Approach for Botnet Detection Based on Network Traffic Analysis by Alaa Obeidat, Rola Yaqbeh

“…In this article, we suggest a method for identifying the behavior of data traffic using machine learning classifiers including genetic algorithm to detect botnet activities. …”
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A convergent Deep Learning algorithm for approximation of polynomials by Després, Bruno

“…We propose a novel algorithm, where the functional equation is solved with a converging iterative algorithm which can be realized as a Machine Learning training method iteratively with respect to the number of layers. …”
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A Data Warehousing Framework for Predictive Analytics in Higher Education: A Focus on Student at-Risk Identification by Ismaili Burim, Besimi Adrian

“…This paper will examine the development of a data warehouse aimed at improving decision-making in higher education, which focuses on the identification of students at-risk of academic failure through machine learning techniques. This research utilizes South East European University (SEEU) as a case study to show how data warehousing can integrate various student data—including demographics, academic performance, grades, attendance, and engagement—into an integrated framework that enables predictive analytics.…”
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Modeling and Simulation of Real Estate Valuation Based on Genetic Neural Network Model by Shuli Shen

“…Based on this, the relationship model between characteristic variables and house prices is constructed by using the machine learning method. At the same time, genetic algorithm is used to screen the specific values. …”
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The early warning paradox by Hugh Logan Ellis, Edward Palmer, James T. Teo, Martin Whyte, Kenneth Rockwood, Zina Ibrahim

“…Machine learning models in healthcare aim to predict critical outcomes but often overlook existing Early Warning Systems’ impact. …”
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Improved KNN Algorithm Based on Preprocessing of Center in Smart Cities by Haiyan Wang, Peidi Xu, Jinghua Zhao

“…The KNN algorithm is one of the most famous algorithms in machine learning and data mining. It does not preprocess the data before classification, which leads to longer time and more errors. …”
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Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS by M. N. Afzal Khan, Nada Zahour, Usman Tariq, Ghinwa Masri, Ismat F. Almadani, Hasan Al-Nashah

“…Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. …”
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Advanced analysis of soil pollution in southwestern Ghana using Variational Autoencoders (VAE) and positive matrix factorization (PMF) by Raymond Webrah Kazapoe, Daniel Kwayisi, Seidu Alidu, Samuel Dzidefo Sagoe, Aliyu Ohiani Umaru, Ebenezer Ebo Yahans Amuah, Millicent Obeng Addai, Obed Fiifi Fynn

“…Integrating chemometric and machine learning techniques provides a dynamic system that can monitor pollution shifts early, to aid remediation efforts in highly affected areas.…”
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Clinical Big Data and Deep Learning: Applications, Challenges, and Future Outlooks by Ying Yu, Min Li, Liangliang Liu, Yaohang Li, Jianxin Wang

“…The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. …”
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Enhanced vehicle routing for medical waste management via hybrid deep reinforcement learning and optimization algorithms by Norhan Khallaf, Osama Abd-El Rouf, Abeer D. Algarni, Mohy Hadhoud, Ahmed Kafafy

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Feature Selection for Physical Activity Prediction Using Ecological Momentary Assessments to Personalize Intervention Timing: Longitudinal Observational Study by Devender Kumar, David Haag, Jens Blechert, Josef Niebauer, Jan David Smeddinck

“…ObjectiveThe aim of the study was to use machine learning to balance the feature set size of EMA questions with the prediction accuracy regarding of enacting PA. …”
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Human–computer interaction in healthcare: Comprehensive review by Meher Langote, Saniya Saratkar, Praveen Kumar, Prateek Verma, Chetan Puri, Swapnil Gundewar, Palash Gourshettiwar

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Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis by Termeh Sarrafan Sadeghi, Seyed AmirHossein Ourang, Fatemeh Sohrabniya, Soroush Sadr, Parnian Shobeiri, Saeed Reza Motamedian

“…Abstract Background Artificial intelligence (AI) methods, including machine learning and deep learning, are increasingly applied in orthodontics for tasks like assessing skeletal maturity. …”
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Kombinasi Seleksi Fitur Berbasis Filter dan Wrapper Menggunakan Naive Bayes pada Klasifikasi Penyakit Jantung by Siti Roziana Azizah, Rudy Herteno, Andi Farmadi, Dwi Kartini, Irwan Budiman

“…Data yang digunakan dalam penelitian ini adalah data penyakit jantung yang didapatkan dari UCI Machine Learning Repository. Dari implementasi pemodelan yang akan dilakukan menghasilkan nilai akurasi tertinggi sebesar 91.80% pada algoritma Naive Bayes dengan kombinasi union hasil seleksi fitur Information Gain dan Gain Ratio menggunakan perbandingan data latih dan data uji 80:20. …”
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Elucidating the emotional persona in the Romanian university students’ academic discourse: a corpus-based exploration by Diana Paula Dudău, Diana Paula Dudău, Madalina Chitez, Florin Alin Sava

“…Advanced data analysis techniques included supervised machine learning for language classification, network analysis to explore interactions among linguistic features, and cluster analysis to detect discipline- and genre-specific linguistic patterns.ResultsThe findings reveal distinct emotional patterns between Romanian and English academic writing. …”
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Clinical utility of receptor status prediction in breast cancer and misdiagnosis identification using deep learning on hematoxylin and eosin-stained slides by Gil Shamai, Ran Schley, Alexandra Cretu, Tal Neoran, Edmond Sabo, Yoav Binenbaum, Shachar Cohen, Tal Goldman, António Polónia, Keren Drumea, Karin Stoliar, Ron Kimmel

“…Here we explore the clinical utility of predicting receptor status from digitized hematoxylin and eosin-stained (H&E) slides using machine learning trained and evaluated on a multi-institutional dataset. …”
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Learning from Demonstrations and Human Evaluative Feedbacks: Handling Sparsity and Imperfection Using Inverse Reinforcement Learning Approach by Nafee Mourad, Ali Ezzeddine, Babak Nadjar Araabi, Majid Nili Ahmadabadi

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DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts by Chaonan Ji, Tonio Fincke, Vitus Benson, Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Fabian Gans, Guido Kraemer, Francesco Martinuzzi, David Montero, Karin Mora, Oscar J. Pellicer-Valero, Claire Robin, Maximilian Söchting, Mélanie Weynants, Miguel D. Mahecha

“…Abstract With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. …”
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New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret by T. Netzel, A. Miebach, T. Litt, A. Hense

“…This includes consideration of various machine learning (ML) algorithms for solving the classification problem of biome presence and absence, taking into account uncertainties in the proxy–climate relationship. …”
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Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study by Olivia L. Finnegan, R. Glenn Weaver, Hongpeng Yang, James W. White, Srihari Nelakuditi, Zifei Zhong, Rahul Ghosal, Yan Tong, Aliye B. Cepni, Elizabeth L. Adams, Sarah Burkart, Michael W. Beets, Bridget Armstrong

“…This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. …”
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