Discordance between a deep learning model and clinical-grade variant pathogenicity classification in a rare disease cohort by Sek Won Kong, In-Hee Lee, Lauren V. Collen, Michael Field, Arjun K. Manrai, Scott B. Snapper, Kenneth D. Mandl

“…This observation underscores ongoing challenges in clinical genetics, highlighting the need for continued refinement of computational methods in variant pathogenicity prediction.…”
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Mathematisation of specialised disciplines as the basis for fundamentalising IT training in universities by E. A. Perminov, V. A. Testov

“…Additionally, a significant gap has emerged in higher education regarding the development of basic education curriculum for IT training. …”
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Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health by Qiang Zhang, Diandong Lian, Yiqiao Zhang

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Logic, Dilemma and Realization of Digital Transformation of Classroom Teaching Assessment by LIN Ying, TIAN Yingqian, WANG Jinrong

“…Based on this, the digital transformation of classroom teaching assessment should be realized through: firstly, improving the digital literacy of assessment subjects and adhering to the origin of educating; secondly, strengthening multidimensional data collection and dynamic digital algorithms; thirdly, reshaping the content of embodied assessment to build a diverse and integrated assessment method; fourthly, regulate technology usages and improve data supervision and management systems.…”
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Critical Evaluation of SQL Injection Security Measures in Web Applications by Haneen mohammed adhab Al salmawi

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Implementation of Several Data Mining Strategies on Electronic Nose Data for Identifying Gluten in Cheese by Mohammad Nasiri-Galeh, Mahdi Ghasemi-Varnamkhasti

“…With analyzing 105 Permutations, Finally, the best suitable model to be used for data classification using the Random Forest algorithm and MinMaxScaler for scaling was selected with a prediction accuracy of 99.8% for both test and training datasets.…”
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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

“…The novel combination of ML and MPN analysis was shown to predict in 30 min the bacterial concentration of a water sample with a minimum prediction accuracy of 84%.…”
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Phonon-mediated high-temperature superconductivity in clathrate superhydride ThCeH18 under pressure by Prutthipong Tsuppayakorn-aek, Wei Luo, Thiti Bovornratanaraks

“…Abstract Predicting high-temperature superconductivity in hydrogen-rich dense metallic states under pressure remains a significant challenge. …”
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Intelligent resource allocation in internet of things using random forest and clustering techniques by Nahideh Derakhshanfard, Lida Hosseinzadeh, Fahimeh Rashid Jafari, Ali Ghaffari

“…A Random Forest model is then trained to accurately predict the resource needs of each cluster, enabling optimal allocation. …”
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Artificial intelligence in spinal deformity by Joash Suryavanshi, David Foley, Michael H. McCarthy

“…In spine surgery, AI can affect care in the following domains: pre-operative workup, surgical planning, and outcome prediction. The use of AI in adult spinal deformity (ASD) poses unique opportunities for growth, as outcomes after ASD are often difficult to predict due to disease complexity. …”
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Improving thyroid disorder diagnosis via innovative stacking ensemble learning model by Ayesha Hassan, Shabana Ramzan, Ali Raza, Muhammad Munwar Iqbal, Aseel Smerat, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…Five advanced machine learning (ML) algorithms, logistic regression, support vector machine, decision tree, random forest, and gradient boosting are employed to develop predictive models. …”
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Hydrodynamic coefficients identification of ship simplified modular model based on support vector regression by Lifei SONG, Yuqing WANG, Wei PENG, Peiyong LI, Yushan LIu, Yongfeng ZHANG

“…The calculated values of root mean square error (RMSE) and correlation coefficient (CC) fall within a favorable range. ConclusionsThe SVR algorithm successfully identifies the hydrodynamic derivatives of the modular model, the identified hydrodynamic coefficients exhibit high accuracy, and the established model demonstrates good predictive capability and robustness.…”
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Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy by Miguel Martins, Miguel Mascarenhas, Maria João Almeida, João Afonso, Tiago Ribeiro, Pedro Cardoso, Francisco Mendes, Joana Mota, Patrícia Andrade, Hélder Cardoso, Miguel Mascarenhas-Saraiva, João Ferreira, Guilherme Macedo

“…Deep-learning (DL) algorithms were developed for the detection of enteric and gastric lesions. …”
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Towards a greener future: The role of sustainable methodologies in metabolomics research by Chiara Spaggiari, Kgalaletso Othibeng, Fidele Tugizimana, Gabriele Rocchetti, Laura Righetti

“…Computational advancements, including AI-driven models, machine learning-based semi-quantification, and predictive algorithms for solvent selection, further enhance sustainability by reducing resource consumption. …”
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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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Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique by Daniyal Alghazzawi, Hayat Ullah, Naila Tabassum, Sahar K. Badri, Muhammad Zubair Asghar

“…Our methodology, along with an updated ensemble method, bridges the gap between Explainable AI and leverages a variety of machine learning algorithms to improve predictive accuracy. By leveraging Explainable AI’s interpretability to analyze the features, the model elucidates the reasoning behind its classifications leading to a comprehension of hidden patterns associated with suicidal ideations. …”
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Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol by Qunling Luo, Zhiqiang Guo, Danping Lin, Boxue Chang, Yinlan Ruan

“…The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. …”
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DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework by Arshak Rezvani, Nasrin Ravansalar, Mohammad Ali Akhaee, Andrew J. Greenshaw, Russell Greiner, Maryam S. Mirian, Muhammad Yousefnezhad, Martin J. McKeown

“…These synthetic samples contain novel movement patterns not present in the observed data; systems trained on this information have better prediction performance. In addition, we propose a novel classification algorithm that can learn a predictive model, from both observed training data and synthetic samples, to accurately assess PD severity. …”
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CBID: A Scalable Method for Distributed Data Aggregation in WSNs by Aristides Mpitziopoulos, Damianos Gavalas, Charalampos Konstantopoulos, Grammati Pantziou

“…This results in a significant reduction of the overall energy expenditure and response time. …”
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Comprehensive Fault Diagnosis of Three-Phase Induction Motors Using Synchronized Multi-Sensor Data Collection by Kevin Thomas, Ahasanur Rahman, Wesam Rohouma, Md. Faysal Ahamed, Fariya Bintay Shafi, Md. Nahiduzzaman, Amith Khandakar

“…The dataset, organized into ten distinct CSV files covering various operational states, provides a rich resource for developing and testing fault detection algorithms. A Random Forest classifier trained on this dataset achieved an accuracy of 99.82%, demonstrating its suitability for real-time fault diagnosis and predictive maintenance applications. …”
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