Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Recent research investigations have shown that Machine Learning (ML) algorithms can identify geochemical anomalies associated with mineralization that represent targets for mineral exploration. …”
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On the construction of a large-scale database of AI-assisted annotating lung ventilation-perfusion scintigraphy for pulmonary embolism (VQ4PEDB) by Amir Jabbarpour, Eric Moulton, Eric Moulton, Sanaz Kaviani, Sanaz Kaviani, Siraj Ghassel, Wanzhen Zeng, Wanzhen Zeng, Ramin Akbarian, Ramin Akbarian, Anne Couture, Aubert Roy, Richard Liu, Yousif A. Lucinian, Nuha Hejji, Nuha Hejji, Sukainah AlSulaiman, Sukainah AlSulaiman, Farnaz Shirazi, Farnaz Shirazi, Eugene Leung, Eugene Leung, Sierra Bonsall, Samir Arfin, Bruce G. Gray, Ran Klein, Ran Klein, Ran Klein, Ran Klein

“…The annotated data was then ingested into Deep Lake, a SQL-based database, for AI model training. Quality assurance involved manual inspections and algorithmic validation.ResultsA query of The Ottawa Hospital's data warehouse followed by initial data screening yielded 2,137 V/Q studies with 2,238 successfully retrieved as DICOM studies. …”
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Machine learning in Alzheimer’s disease genetics by Matthew Bracher-Smith, Federico Melograna, Brittany Ulm, Céline Bellenguez, Benjamin Grenier-Boley, Diane Duroux, Alejo J. Nevado, Peter Holmans, Betty M. Tijms, Marc Hulsman, Itziar de Rojas, Rafael Campos-Martin, Sven van der Lee, Atahualpa Castillo, Fahri Küçükali, Oliver Peters, Anja Schneider, Martin Dichgans, Dan Rujescu, Norbert Scherbaum, Jürgen Deckert, Steffi Riedel-Heller, Lucrezia Hausner, Laura Molina-Porcel, Emrah Düzel, Timo Grimmer, Jens Wiltfang, Stefanie Heilmann-Heimbach, Susanne Moebus, Thomas Tegos, Nikolaos Scarmeas, Oriol Dols-Icardo, Fermin Moreno, Jordi Pérez-Tur, María J. Bullido, Pau Pastor, Raquel Sánchez-Valle, Victoria Álvarez, Mercè Boada, Pablo García-González, Raquel Puerta, Pablo Mir, Luis M. Real, Gerard Piñol-Ripoll, Jose María García-Alberca, Eloy Rodriguez-Rodriguez, Hilkka Soininen, Sami Heikkinen, Alexandre de Mendonça, Shima Mehrabian, Latchezar Traykov, Jakub Hort, Martin Vyhnalek, Nicolai Sandau, Jesper Qvist Thomassen, Yolande A. L. Pijnenburg, Henne Holstege, John van Swieten, Inez Ramakers, Frans Verhey, Philip Scheltens, Caroline Graff, Goran Papenberg, Vilmantas Giedraitis, Julie Williams, Philippe Amouyel, Anne Boland, Jean-François Deleuze, Gael Nicolas, Carole Dufouil, Florence Pasquier, Olivier Hanon, Stéphanie Debette, Edna Grünblatt, Julius Popp, Roberta Ghidoni, Daniela Galimberti, Beatrice Arosio, Patrizia Mecocci, Vincenzo Solfrizzi, Lucilla Parnetti, Alessio Squassina, Lucio Tremolizzo, Barbara Borroni, Michael Wagner, Benedetta Nacmias, Marco Spallazzi, Davide Seripa, Innocenzo Rainero, Antonio Daniele, Fabrizio Piras, Carlo Masullo, Giacomina Rossi, Frank Jessen, Patrick Kehoe, Tsolaki Magda, Pascual Sánchez-Juan, Kristel Sleegers, Martin Ingelsson, Mikko Hiltunen, Rebecca Sims, Wiesje van der Flier, Ole A. Andreassen, Agustín Ruiz, Alfredo Ramirez, EADB, Ruth Frikke-Schmidt, Najaf Amin, Gennady Roshchupkin, Jean-Charles Lambert, Kristel Van Steen, Cornelia van Duijn, Valentina Escott-Price

“…ML approaches successfully captured all genome-wide significant genetic variants identified in the training set and 22% of associations from larger meta-analyses. …”
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Modeling and validation of wearable sensor-based gait parameters in Parkinson’s disease patients with cognitive impairment by Guo Hong, Guo Hong, Fengju Mao, Fengju Mao, Mingming Zhang, Fei Zhang, Fei Zhang, Xiangcheng Wang, Kang Ren, Kang Ren, Zhonglue Chen, Zhonglue Chen, Xiaoguang Luo, Xiaoguang Luo

“…The logistic regression model demonstrated superior predictive performance (test set AUC: 0.957), outperforming other machine learning algorithms. SHAP analysis revealed that Step Length, UPDRS-III score, Duration of PD, and Peak angular velocity during steering were the most influential predictors in the logistic regression model. …”
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Digital augmentation of aftercare for patients with anorexia nervosa: the TRIANGLE RCT and economic evaluation by Janet Treasure, Katie Rowlands, Valentina Cardi, Suman Ambwani, David McDaid, Jodie Lord, Danielle Clark Bryan, Pamela Macdonald, Eva Bonin, Ulrike Schmidt, Jon Arcelus, Amy Harrison, Sabine Landau

“…Fathers in the spotlight: parental burden and the effectiveness of a parental skills training for anorexia nervosa in mother–father dyads. …”
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Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a “seek out” machine learning approach by Marianna Rania, Anna Procopio, Paolo Zaffino, Elvira Anna Carbone, Teresa Vanessa Fiorentino, Francesco Andreozzi, Cristina Segura-Garcia, Carlo Cosentino, Franco Arturi

“…This study is the first to use metabolic hallmarks to train ML algorithms for detecting BED in individuals at high risk for metabolic complications. …”
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Deep Ocean Learning of Small Scale Turbulence by Ali Mashayek, Nick Reynard, Fangming Zhai, Kaushik Srinivasan, Adam Jelley, Alberto Naveira Garabato, Colm‐cille P. Caulfield

“…Here, we show that supervised machine learning algorithms can be trained on the existing turbulence data to develop skillful predictions of the key properties of turbulence from T, S, Z, and topographic data. …”
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Integrating Sentiment Analysis With Machine Learning for Cyberbullying Detection on Social Media by Maram Fahaad Almufareh, Noor Zaman Jhanjhi, Mamoona Humayun, Ghadah Naif Alwakid, Danish Javed, Saleh Naif Almuayqil

“…State-of-the-art solutions predominantly rely on pre-trained language models and machine learning algorithms; however, these methods are often associated with substantial computational overheads and the development of advanced cyberbullying detection algorithms remains limited. …”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

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Spontaneous emergence of metacognition in neuronal computation by Hengyuan Ma, Wenlian Lu, Jianfeng Feng

“…We showcase this capability through diverse cognitive tasks and learning algorithms, including reservoir computing and backpropagation. …”
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Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis. by Ramasamy Srinivasagan, Mohammed Shawky El Sayed, Mohammed Ibrahim Al-Rasheed, Ali Saeed Alzahrani

“…In collaboration with avian researchers, a diverse dataset of poultry vocalizations representing a range of health and environmental conditions was created to train and validate the algorithms. Digital Signal Processing (DSP) blocks of the Edge Impulse platform were used to generate spectral features for studying fowl vocalization. …”
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Computed tomography enterography radiomics and machine learning for identification of Crohn’s disease by Qiao Shi, Yajing Hao, Huixian Liu, Xiaoling Liu, Weiqiang Yan, Jun Mao, Bihong T. Chen

“…This study aims to develop a non-invasive method for detecting bowel lesions associated with Crohn’s disease using CT enterography radiomics and machine learning algorithms. …”
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Target repositioning using multi-layer networks and machine learning: The case of prostate cancer by Milan Picard, Marie-Pier Scott-Boyer, Antoine Bodein, Mickaël Leclercq, Julien Prunier, Olivier Périn, Arnaud Droit

“…Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.…”
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Predicting 30-Day Venous Thromboembolism Following Total Joint Arthroplasty: Adjusting for Trends in Annual Length of Stay by Johnathan R. Lex, MBChB, MASc, Robert Koucheki, MD, MEng, Aazad Abbas, MD, Jesse I. Wolfstadt, MD, MSc, FRCSC, FAAOS, Alexander S. McLawhorn, MD, MBA, Bheeshma Ravi, MD, PhD, FRCSC

“…Predictive models (logistic regression, random forest, and XGBoost) were trained and tested based on year of surgery with different oversampling algorithms used to address data imbalance. …”
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A Machine Learning–Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study by Mengqing Ma, Caimei Chen, Dawei Chen, Hao Zhang, Xia Du, Qing Sun, Li Fan, Huiping Kong, Xueting Chen, Changchun Cao, Xin Wan

“…ObjectiveThis study aimed to establish and validate predictive models for AKI in hospitalized patients with CAP based on machine learning algorithms. MethodsWe trained and externally validated 5 machine learning algorithms, including logistic regression, support vector machine, random forest, extreme gradient boosting, and deep forest (DF). …”
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Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito, Massimo Celino

“…In the present work, we report cutting-edge research, where we explored a wide range of compositions of cathode materials for Na-ion batteries by first-principles calculations using workflow chains developed within the AiiDA framework. We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. …”
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Perceptual objective evaluation for multimodal medical image fusion by Chuangeng Tian, Juyuan Zhang, Lu Tang

“…Specifically, we employ a Multi-scale Transform structure that simultaneously processes these multi-scale images using an ImageNet pre-trained ResNet34. Subsequently, we incorporate an online class activation mapping mechanism to focus visual attention on the lesion region, enhancing representative discrepancy features closely associated with MFI quality. …”
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Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda by Sandra Ruth Babirye, Mike Nsubuga, Gerald Mboowa, Charles Batte, Ronald Galiwango, David Patrick Kateete

“…We also assessed the model’s generalizability on another dataset from South Africa. Results We trained ten machine learning algorithms on a dataset comprising of 182 MTB isolates with clinical data variables (age, sex, HIV status) and SNP mutations across the entire genome as predictor variables and phenotypic drug-susceptibility data for the four drugs as the outcome variable. …”
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Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding by Ibrahim Almubark

“…The proposed ensemble model outperformed conventional methods in addressing the challenges associated with CC fraud detection, as demonstrated by a comparative analysis. …”
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Chatbot Programmes’ ‘Arms Race’: Africa and Artificial Intelligence (AI) Ethics by Tapiwa Chagonda

“…The benefits of AI generative technologies such as chatbots in fields such as the academy; health; agriculture; music and art, have been touted in recent times, but the ethical concerns around issues of bias; possible proliferation of misinformation from algorithms that are trained on datasets that are not fully representative of the global South’s realities, especially Africa; breaches in privacy issues and threats of job losses, still linger. …”
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