Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking by Nikolaos Pavlidis, Andreas Sendros, Theodoros Tsiolakis, Periklis Kostamis, Christos Karasoulas, Eleni Briola, Christos Chrysanthos Nikolaidis, Vasilis Perifanis, George Drosatos, Eleftheria Katsiri, Despoina Elisavet Filippidou, Anastasios Manos, Pavlos S. Efraimidis

“…Unlike conventional centralized systems, FLORA ensures that sensitive information remains on users’ devices, with predictive algorithms powered by local computations. …”
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The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations—a narrative review by Prasanthi Samathoti, Rajasekhar Komarla Kumarachari, Sarad Pawar Naik Bukke, Eashwar Sai Komarla Rajasekhar, Ashish Ashokkumar Jaiswal, Zohre Eftekhari

“…Nanomedicine leverages nanoparticles for targeted drug delivery, enhancing therapeutic efficacy while minimizing adverse effects. Concurrently, AI algorithms facilitate early cancer detection, personalized treatment planning, and predictive analytics, thereby optimizing clinical outcomes. …”
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QSAR, docking and pharmacokinetic studies of 2,4-diphenyl indenol [1,2-B] pyridinol derivatives targeting breast cancer receptors by Auwal Isa, Adamu Uzairu, umar Umar, Muhammad Tukur Ibrahim, Abdullahi Umar

“…A robust quantitative structure-activity relationship (QSAR) model, developed using genetic algorithms and multilinear regression analysis, predicts chemical activity (pGI50) against breast cancer receptors. …”
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Quantum Computation of the Cobb–Douglas Utility Function via the 2D Clairaut Differential Equation by Isabel Cristina Betancur-Hinestroza, Éver Alberto Velásquez-Sierra, Francisco J. Caro-Lopera, Álvaro Hernán Bedoya-Calle

“…Our findings, validated through IBM-Q simulations, align with the predictions, demonstrating the robustness of our approach. …”
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Detection of Cyanuric Acid and Melamine in Infant Formula Powders by Mid-FTIR Spectroscopy and Multivariate Analysis by Edwin García-Miguel, Ofelia Gabriela Meza-Márquez, Guillermo Osorio-Revilla, Darío Iker Téllez-Medina, Cristian Jiménez-Martínez, Maribel Cornejo-Mazón, Diana Maylet Hernández-Martínez, Tzayhrí Gallardo-Velazquez

“…Chemometric models were constructed using the algorithms Partial Least Squares (PLS1, PLS2) and Principal Component Regression (PCR) in order to correlate the IR signal with the levels of melamine or cyanuric acid in the infant formula samples. …”
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Autonomous Aircraft Tactical Pop-Up Attack Using Imitation and Generative Learning by Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama

“…By applying imitation learning techniques and comparing three algorithms – Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) – the research trains models to predict aircraft control inputs through sequences of state-action pairs. …”
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Fine-scale carbon stocks mapping in the mangrove forests of Tumaco, Colombia using machine learning and remote sensing approaches by Laura Lozano-Arias, Bryan Ernesto Gallego-Pérez, John Josephraj Selvaraj

“…Additionally, spectral indices significantly enhanced the model’s ability to predict aboveground biomass.…”
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Crowd anomaly estimation and detection: A review by A. Hussein, M.W. Raed, A. Al-Shaikhi, M. Mohandes, B. Liu

“…We present a comprehensive literature review on crowd anomaly detection using disruptive technologies such as radio frequency identification, wireless sensor networks, Wi-Fi, and Bluetooth low energy, employing device-free noninvasive algorithms based on received signal strength indicator variations to detect the speed and direction of a moving crowd to predict the onset of a stampede. …”
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Advances in Breast Cancer Care: The Role of Artificial Intelligence and Digital Pathology in Precision Medicine by Ayşe Hümeyra Dur Karasayar, İbrahim Kulaç, Nilgün Kapucuoğlu

“…The application of machine learning algorithms has enhanced the ability of AI systems to classify breast cancer subtypes, grade tumors, and quantify key biomarkers, thereby improving diagnostic accuracy and prognostic precision. …”
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Decoding circRNA translation: challenges and advances in computational method development by Jingjing Zhang, Jingjing Zhang, Rui Zhou, Rui Zhou, Huiling Zhang, Yin Peng, Jintao Meng, Wenhui Xi, Yanjie Wei

“…Subsequently, we provide a systematic introduction to existing circRNA translation analysis tools and their underlying algorithms, with emphasis on benchmarking the performance of sequence-based methods using a unified dataset. …”
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Deep-Learning and Dynamic Time Warping-Based Approaches for the Diagnosis of Reactor Systems by Hoejun Jeong, Jihyun Kim, Doyun Jung, Jangwoo Kwon

“…This study proposes a novel methodology for diagnosing the size and position of clamping force degradation in core support barrels, combining deep-learning techniques and dynamic time warping (DTW) algorithms. DTW is applied to the magnitude data of the ex-core neutron noise signal obtained in the frequency domain, thereby enabling the effective learning of changes in sensor data values. …”
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Early Detection of Parkinson’s Disease Using AI Techniques and Image Analysis by Marilena Ianculescu, Corina Petean, Virginia Sandulescu, Adriana Alexandru, Ana-Mihaela Vasilevschi

“…Methods: The best approach is selected to be integrated in a neurodegenerative disease management platform called NeuroPredict. The most innovative aspects of the presented approaches are related to the employed feature extraction techniques that convert hand-drawn spirals into a frequency spectra, so that frequency features may be extracted and utilized as inputs for various classification algorithms. …”
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Artificial intelligence in soil science: Where do we go now? by Jose Pablo Castro, Caley K. Gasch, Paulo Flores

“…Abstract Recognizing the fast advancement of artificial intelligence (AI) in soil science, the main objective of this commentary paper is to discuss how this technology is being incorporated into the discipline, focusing on the most common algorithms and their applications. Employing a discursive and reflective methodology, the article draws insights from the authors' expertise and opinions. …”
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Infra-Red Imaging to Detect Respirator Leak in Healthcare Workers During Fit-Testing Clinic by Darius Chapman, Campbell Strong, Kathryn D Tiver, Dhani Dharmaprani, Even Jenkins, Anand N Ganesan

“…To achieve this, we explored the use of infra-red (IR) imaging combined with machine learning algorithms on the thermal gradient across the respirator during inhalation. …”
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Interpretability in deep learning for finance: A case study for the Heston model by Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini, Haitz Sáez de Ocáriz Borde

“…In this study, we focused on the calibration of a stochastic volatility model, a subject recently tackled by deep-learning algorithms. We analyzed the Heston model in particular, as this model’s properties are well known, resulting in an ideal benchmark case. …”
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Tracking Sumatran Tiger (Panthera tigris sumatrae Pocock, 1929) distribution in Gunung Leuser National Park: The influence of prey presence and environmental variables on habitat s... by M Hadi Saputra, Salmah Widyastuti, Wanda Kuswanda, R. Taufiq Purna Nugraha, Hendra Helmanto, Nunu Anugrah, U Mamat Rahmat, Rudianto Saragih Napitu, Andrinaldi Adnan, Iskandarrudin, Agus Purwoko, Pindi Patana

“…The algorithms used are maximum entropy and random forest. …”
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Optimizing Energy Supply for Full Electric Vehicles in Smart Cities: A Comprehensive Mobility Network Model by Victor Fernandez, Virgilio Pérez, Rosa Roig

“…This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. …”
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Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…The proposed GBDT-BO model shows promise for predicting surrounding rock quality and guiding dynamic tunnel excavation and support.…”
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Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices by Maurizio Morisio, Emanuela Noris, Chiara Pagliarani, Stefano Pavone, Amedeo Moine, José Doumet, Luca Ardito

“…Three supervised binary classification algorithms were used to build models capable of predicting the status of the tree quadrant, using the VIs as predictors. …”
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Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks by Seyed Alireza Khanghahi, Hadi Kamkar, Seyedehsamaneh Shojaeilangari, Abdollah Allahverdi, Parviz Abdolmaleki

“…Unlike prior studies that focused on specific algorithms or limited descriptors, we benchmark a wide range of models, from traditional models like Random Forest, logistic regression, and SVM to modern algorithms like CNNs, GRUs, and LSTMs, on both descriptor-based features obtained by Dragon and descriptor-free inputs, including raw SMILES string, and Morgan fingerprints. …”
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