Hybrid quantum enhanced federated learning for cyber attack detection by G. Subramanian, M. Chinnadurai

“…The presented model utilizes a hierarchical model aggregation procedure which dynamically groups nodes into regions based on the network condition and data similarity. …”
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Machine Learning Techniques In Wdm-Fso Systems: Comparative Study by Ranim Younes, Mohammad Nassr

“…Furthermore, machine learning algorithms were used to predict the quality factor of the proposed system and then the performance metrics R2 and RMSE (Root Mean Square Error) were used to compare between the algorithms. …”
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Objective classification for solid hydrometeor particles using deep learning by Asuka Yoshimura, Kazuhisa Tsuboki, Taro Shinoda, Tadayasu Ohigashi, Kensaku Shimizu

“…Abstract Various small particles are present in the clouds. Hydrometeor videosonde (HYVIS) is an in situ instrument to observe such particles. …”
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Adoption of e-learning in Zimbabwe’s higher education: Opportunities and challenges by Pinias Chikuvadze, Joseph Zuva, Samuel Mugijima, Patrick C. Rusike

“…In conclusion the adoption of e-learning in Zimbabwe's higher education presents both significant opportunities and notable challenges. …”
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Applying deep learning techniques to identify tonsilloliths in panoramic radiography by Ezgi Katı, Sümeyye Coşgun Baybars, Çağla Danacı, Seda Arslan Tuncer

“…An evaluation was conducted to analyze the learning capacity, intricacy, and compatibility of each model with the problem at hand. …”
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A model of digital transformation of the learning process in higher education by Olga V. Ivanova

“…Examples of both lecture and seminar classes following the compiled learning model are presented. Theoretical analysis of mathematical, scientific, methodological literature on digital transformation of the learning process in Russia and some foreign countries was conducted. …”
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Identification of Vehicle Logos in Deep Learning: A Comprehensive Survey by Mustafa Noaman Kadhim, Ammar Hussein Mutlag, Dalal Abdulmohsin Hammood, Nurulisma Binti Hj. Ismail

“…The survey concludes by delineating anticipated challenges and proposing future directions, thereby presenting a roadmap for researchers and practitioners seeking to advance logo detection using deep learning techniques. …”
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Evaluating Game-Based Learning in informal contexts with Data Science by Xavier Rubio-Campillo, Kevin Marín-Rubio, Celia Corral-Vázquez

“…The emergence of digital Game-Based Learning (GBL) has sparked interest in assessing its efficacy. …”
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Enhancing temporal learning in recurrent spiking networks for neuromorphic applications by Ismael Balafrej, Soufiyan Bahadi, Jean Rouat, Fabien Alibart

“…Training Recurrent Spiking Neural Networks (RSNNs) with binary spikes for tasks of extended time scales presents a challenge due to the amplified vanishing gradient problem during back propagation through time. …”
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Detection of Wild Mushrooms Using Machine Learning and Computer Vision by Christos Chaschatzis, Chrysoula Karaiskou, Chryssanthi Iakovidou, Panagiotis Radoglou-Grammatikis, Stamatia Bibi, Sotirios K. Goudos, Panagiotis G. Sarigiannidis

“…Our method integrates low-cost hardware with advanced deep learning and vegetation index analysis (NDRE) to enable real-time identification of mushrooms in forested environments. …”
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Deep-Learning-Based High-Precision Localization With Massive MIMO by Guoda Tian, Ilayda Yaman, Michiel Sandra, Xuesong Cai, Liang Liu, Fredrik Tufvesson

“…This paper presents an ML pipeline to solve localization tasks. …”
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Advances in Application of Federated Machine Learning for Oncology and Cancer Diagnosis by Mohammad Nasajpour, Seyedamin Pouriyeh, Reza M. Parizi, Meng Han, Fatemeh Mosaiyebzadeh, Yixin Xie, Liyuan Liu, Daniel Macêdo Batista

“…Our main goal was to present a comprehensive overview of the development of federated learning in the field of oncology. …”
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Cloudlet Federation Based Context-Aware Federated Learning Approach by Sana Latif, Muhammad Ziad Nayyer, Imran Raza, Syed Asad Hussain, M. Hasan Jamal, Soojung Hur, Imran Ashraf

“…This paper presents a novel context-aware cloudlet federated learning solution for COVID-19 detection that monitors the resources of a cloudlet using a broker thereby minimizing latency without any impact on the accuracy of the deep learning model. …”
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Bio-primed machine learning to enhance discovery of relevant biomarkers by David M. Henke, Alexander Renwick, Joseph R. Zoeller, Jitendra K. Meena, Nicholas J. Neill, Elizabeth A. Bowling, Kristen L. Meerbrey, Thomas F. Westbrook, Lukas M. Simon

“…The advent of high-throughput technologies presents unprecedented opportunities to explore molecular disease mechanisms but also challenges due to high dimensionality and collinearity among features. …”
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A deep reinforcement learning approach for wind speed forecasting by Shahab S. Band, Ting Jia Lin, Sultan Noman Qasem, Rasoul Ameri, Danyal Shahmirzadi, Muhammad Shamrooz Aslam, Hao-Ting Pai, Ely Salwana, Amir Mosavi

“…The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of  Empirical Wavelet Transform (EWT) and Deep Reinforcement Learning (DRL) for wind speed modeling to overcome the forecasting challenges. …”
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Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

“…Previous autoencoders for feature selection do not simultaneously incorporate feature and target information simultaneously, potentially limiting their effectiveness in improving predictive performance. This study presents a novel approach that combines a target-concatenated autoencoder (TCA) with ensemble learning to enhance the accuracy of tourism demand predictions. …”
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Pointwise prediction of protein diffusive properties using machine learning by Rasched Haidari, Achillefs N Kapanidis

“…In response to the challenge, we present M3, a machine learning method for pointwise inference of diffusive coefficients, anomalous exponents, and states along noisy heterogenous protein trajectories. …”
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Predicting the Severity of COVID-19 Respiratory Illness with Deep Learning by Connor Shorten, Taghi M. Khoshgoftaar, Javad Hashemi, Safiya George Dalmida, David Newman, Debarshi Datta, Laurie Martinez, Candice Sareli, Paula Eckard

“…With COVID-19 care, one of the most important indicators of care urgency is the severity of respiratory illness. We present an early analysis of 5,584 patient records, of whom 5,371 (96.2%) have returned a positive COVID-19 test, to understand how well we can predict the severity of a respiratory illness given other features describing a patient using Deep Learning methods. …”
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An Innovative Approach for Calibrating Hydrological Surrogate Deep Learning Models by Amir Aieb, Antonio Liotta, Alexander Jacob, Iacopo Federico Ferrario, Muhammad Azfar Yaqub

“…Developing data-driven models for spatiotemporal hydrological prediction presents challenges in managing complexity, capturing fine spatial and temporal resolution, and ensuring model resilience across diverse regions. …”
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Two-choice discrimination learning in African elephants (Loxodonta africana) by Charles Hyatt

“…This study extends learning research to the African species and indicates potential for further cognitive skill development. …”
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