Review of models for estimating 3D human pose using deep learning by Sani Salisu, Kamaluddeen Usman Danyaro, Maged Nasser, Israa M. Hayder, Hussain A. Younis

“…Human pose estimation (HPE) is designed to detect and localize various parts of the human body and represent them as a kinematic structure based on input data like images and videos. …”
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Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence by Chadaga Krishnaraj, Khanna Varada Vivek, Prabhu Srikanth, Sampathila Niranjana, Chadaga Rajagopala, Palkar Anisha

“…Hence, supervised machine learning (ML) algorithms and several hyperparameter tuning techniques, including Bayesian optimization, have been utilized in this study to predict MS in patients. …”
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Classification of Cavendish Banana Quality using Convolutional Neural Network by Ajeng Ayu Suryani, Ummi Athiyah, Yohani Setiya Rafika Nur, Warto

“…We perform classification using one of the deep learning algorithms, namely Convolutional Neural Network. The experiment uses 1047 images, divided into 65% training data, 15% validation data, and 20% testing data by using epochs 20 times with 16 batch sizes, the accurate results obtained are 99%. …”
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Hybrid Automata-Based Control Framework for Real-Time Optimization in Space-Based Solar Power Transmission by Patil Ankita, Ranjan Mritunjay, Deore Kalyani, Sonje Pranjal, Patil Kiran, Patil Rutuja

“…While usual machine learning algorithms may predict the production of energy, they cannot respond sufficiently in real time to alter according to dynamic environmental conditions. …”
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The Impact of Anatomic Racial Variations on Artificial Intelligence Analysis of Filipino Retinal Fundus Photographs Using an Image-Based Deep Learning Model by Carlo A. Kasala, MD, Kaye Lani Rea B. Locaylocay, MD-MBA, Paolo S. Silva, MD

“…Conclusions: The availability and sources of AI training datasets can introduce biases into AI algorithms. In our dataset, racial differences in retinal morphology, such as differences in retinal pigmentation, affected the accuracy of AI image-based analysis. …”
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Digital Transition as a Driver for Sustainable Tailor-Made Farm Management: An Up-to-Date Overview on Precision Livestock Farming by Caterina Losacco, Gianluca Pugliese, Lucrezia Forte, Vincenzo Tufarelli, Aristide Maggiolino, Pasquale De Palo

“…The increasing integration of sensing devices with smart technologies, deep learning algorithms, and robotics is profoundly transforming the agricultural sector in the context of Farming 4.0. …”
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Battery Management System for Solar Power Plants in Uganda: An IoT-Driven Approach by Ssembalirwa, Denis, Cartland, Richard, Bature, U. I., Kitone, Isaac

“…Additionally, the system disconnects power when battery temperature exceeds 30°C (ambient temperature: 25°C) and detects abnormal current levels above 0.16A to mitigate faults such as short circuits. …”
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Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population by Daniel Gordon, Jason Hoffman, Keren Gamrasni, Yotam Barlev, Alex Levine, Tamar Landau, Ronen Shpiegel, Avishai Lahad, Ariel Koren, Carina Levin, Osnat Naor, Hannah Lee, Xin Liu, Shwetak Patel, Gilad Chayen, Michael Brandwein

“…It identifies important sample collection parameters and design, provides critical algorithms for the pre-processing of fingernail data, and reports an initial capability to detect anemia with 87% sensitivity and 84% specificity. …”
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SurVIndel2: improving copy number variant calling from next-generation sequencing using hidden split reads by Ramesh Rajaby, Wing-Kin Sung

“…SurVindel2 builds on statistical techniques we previously developed, but also employs a novel type of evidence, hidden split reads, that can uncover many CNVs missed by existing algorithms. We use public benchmarks to show that SurVIndel2 outperforms other popular callers, both on human and non-human datasets. …”
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Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only by Yonina Ron, Tchelet Ron, Naomi Fridman, Anat Goldstein

“…Our approach offers caregivers an advanced screening tool to detect myopia risk using readily available data.…”
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Integrating Machine Learning and IoT for Effective Plant Disease Management by Bhoi Manjulata, Dubey Ahilya

“…Alerts are sent to the farmers real time when a disease is detected so that intervention is done early to reduce the spread of disease and save on crop loss. …”
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Small RNAs as biomarkers to differentiate benign and malign prostate diseases: An alternative for transrectal punch biopsy of the prostate? by Lukas Markert, Jonas Holdmann, Claudia Klinger, Michael Kaufmann, Karin Schork, Michael Turewicz, Martin Eisenacher, Andreas Savelsbergh

“…In addition, machine-learning algorithms were used to identify a panel of 22 additional miRNAs, whose interaction makes it possible to differentiate the groups as well. …”
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The impact of the protein interactome on the syntenic structure of mammalian genomes. by Isa Kristina Kirk, Nils Weinhold, Søren Brunak, Kirstine Belling

“…Our analyses demonstrate that subsets of syntenic blocks are associated with highly connected proteins, and that PPI connectivity can be used to detect conserved synteny even if sequence conservation drifts beyond what orthology algorithms normally can identify.…”
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A Portable UV-LED/RGB Sensor for Real-Time Bacteriological Water Quality Monitoring Using ML-Based MPN Estimation by Andrés Saavedra-Ruiz, Pedro J. Resto-Irizarry

“…Fluorescence signals from individual wells are captured by the RGB sensors and analyzed using Multilayer Perceptron Neural Network (MLPNN) and Support Vector Machine (SVM) algorithms, which can quickly determine if individual wells will be positive or negative by the end of a 24 h period. …”
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Emerging and Pioneering AI Technologies in Aesthetic Dermatology: Sketching a Path Toward Personalized, Predictive, and Proactive Care by Diala Haykal

“…Some of the key benefits of AI in aesthetic dermatology include the ability to detect subtle skin changes, simulate treatment outcomes, and adjust interventions in real time. …”
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Zero-Shot Prediction of Conversational Derailment With Large Language Models by Kenya Nonaka, Mitsuo Yoshida

“…Previous studies have trained machine learning algorithms to detect conversational derailment using supervised methods. …”
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Computer‐aided biochemical programming of synthetic microreactors as diagnostic devices by Alexis Courbet, Patrick Amar, François Fages, Eric Renard, Franck Molina

“…We show that proof‐of‐concept protosensors integrating diagnostic algorithms detect specific patterns of biomarkers in human clinical samples. …”
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Towards a Digital Twin for Gas Turbines: Thermodynamic Modeling, Critical Parameter Estimation, and Performance Optimization Using PINN and PSO by Jian Tiong Lim, Achnaf Habibullah, Eddie Yin Kwee Ng

“…Ultimately, the proposed methods aim to enhance GT operations via a data-driven digital twin concept combination of deep learning and optimization algorithms.…”
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Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study by Andrew Johnson, Luke Oakden-Rayner, Catherine M Jones, Jarrel Seah, Cyril Tang, Quinlan D Buchlak, Nazanin Esmaili, Luke Danaher, Michael R Milne

“…Objectives Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. …”
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Post-Marketing Pharmacovigilance of Canakinumab from the FDA Adverse Event Reporting System (FAERS) by Weidong Zhang, Yunzhou Chen, Zeyu Yao, Mengling Ouyang, Minghui Sun, Shupeng Zou

“…<b>Methods:</b> We used a disproportionate analysis to quantify canakinumab-related adverse events (AEs) using four algorithms. Clinical prioritization of the detected signals was assessed with a semiquantitative score method. …”
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