Machine vision-based automatic fruit quality detection and grading by Amna, Muhammad Waqar AKRAM, Guiqiang LI, Muhammad Zuhaib AKRAM, Muhammad FAHEEM, Muhammad Mubashar OMAR, Muhammad Ghulman HASSAN

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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis by S. Jeba Priya, C. Anand Deva Durai, M. S. P. Subathra, S. Thomas George, Andrew Jeyabose

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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. …”
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Soft computing approaches of direct torque control for DFIM Motor's by Zakariae Sakhri, El-Houssine Bekkour, Badre Bossoufi, Nicu Bizon, Mishari Metab Almalki, Thamer A.H. Alghamdi, Mohammed Alenezi

“…Our evaluation focuses on various aspects: torque and flux ripple reduction, speed tracking improvement, switching losses minimization, algorithmic complexity simplification, and sensitivity reduction to parameter variations. …”
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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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Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam. by Raja Das, Pham Van Tien, Karl W Wegmann, Madhumita Chakraborty

“…The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. …”
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Exploration of shared pathogenic factors and causative genes in early-stage endometrial cancer and osteoarthritis by Yiyun Bai, Sang Luo, Ruzhen Shuai, Xiaomei Zhang, Liwei Yuan, Dan Liu

“…Genes with diagnostic value were identified using multiple machine learning algorithms to construct EC prediction models and evaluate their performance. …”
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Statistical modeling and application of machine learning for antibiotic degradation using UV/persulfate-peroxide based advanced oxidation process by Musfekur Rahman Dihan, Md. Ashraful Alam, Surya Akter, Md. Abdul Gafur, Md. Shahinoor Islam

“…Pearson correlation and statistical multivariate linear regression (MLR) were applied to model the removal% and pHfinal of both antibiotics, along with the three machine learning algorithms, Artificial neural network (ANN), support vector machine (SVM), and Random Forest (RF), to make the same predictions. …”
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Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach. by Hamidreza Moradi, H Timothy Bunnell, Bradley S Price, Maryam Khodaverdi, Michael T Vest, James Z Porterfield, Alfred J Anzalone, Susan L Santangelo, Wesley Kimble, Jeremy Harper, William B Hillegass, Sally L Hodder, National COVID Cohort Collaborative (N3C) Consortium

“…Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction.…”
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Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives by Nicolás Cabrera, Jose R. Mora, Edgar A. Marquez

“…In order to improve the prediction of the pIC50 values, the aggregation of the individual models was performed through the construction of an ensemble, and the most robust one was constructed by two individual models (LR3 and RF1) by applying the IBK algorithm, and a substantial improvement in predictive performance is reflected in the values of R2ADJ = 0.982, Q2CV = 0.962, and Q2EXT = 0.918. …”
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The Growing Impact of Natural Language Processing in Healthcare and Public Health by Aadit Jerfy, Owen Selden, Rajesh Balkrishnan PhD

“…Automating labor intensive and tedious tasks with language processing algorithms, using text analytics systems and machine learning to analyze social media data and extracting insights from unstructured data allows for better public sentiment analysis, enhancement of risk prediction models, improved patient communication, and informed treatment decisions. …”
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Machine learning on multi‐spectral imagery to estimate nutrient yield of mixed‐species cover crops by Tulsi P. Kharel, Heather L. Tyler, Partson Mubvumba, Yanbo Huang, Ammar B. Bhandari, Reginald S. Fletcher, Saseendran Anapalli, Deepak R. Joshi, Alemu Mengistu, Girma Birru, Kabindra Adhikari, Madhav Dhakal, Mahesh L. Maskey, Krishna N. Reddy, David E. Clay

“…The chlorophyll absorption ratio index and the normalized difference vegetation index closely followed the biomass nutrients N, P, and K combined yield (Bio_NPK) trend. Machine learning algorithms random forest (RF) and partial least square (PLS) regression were better for biomass (R2 = 0.74 with RF) and N% (R2 = 0.72 with PLS) prediction compared to the Bio_NPK prediction. …”
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Assessing perioperative risks in a mixed elderly surgical population using machine learning: A multi-objective symbolic regression approach to cardiorespiratory fitness derived fro... by Pietro Arina, Davide Ferrari, Maciej R Kaczorek, Nicholas Tetlow, Amy Dewar, Robert Stephens, Daniel Martin, Ramani Moonesinghe, Mervyn Singer, John Whittle, Evangelos B Mazomenos

“…Preoperative cardiorespiratory fitness data from cardiopulmonary exercise testing (CPET), demographic and clinical data were extracted and integrated into advanced machine learning (ML) algorithms. Multi-Objective-Symbolic-Regression (MOSR), a novel algorithm utilizing Genetic Programming to generate mathematical formulae for learning tasks, was employed to predict patient morbidity at Postoperative Day 3, as defined by the PostOperative Morbidity Survey (POMS). …”
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Visual analysis of research trends in diabetes-associated dry eye via bibliometrics by Zhe Yang, Jia-Yi Jiang, Xiao-Hui Zhang

“…With high prevalence of dry eye in Asia, valuable resources like the Korea National Health and Nutrition Examination Survey (KNHANES) database offer crucial data for developing risk prediction models for DADE. Building risk prediction models using machine learning algorithms is a promising future research direction, enabling physicians to identify high-risk individuals and implement early interventions.…”
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Assessment of copeptin and obestatin levels in coronary artery disease patients by Saraa Ali, Fatma F. Abdel Hamid, Samia Hussein, Shaimaa Wageeh, Doaa M. Ibrahim

“…Also, it may be used for prediction of the course of stable angina as it was positively correlated with copeptin and Gensini score.…”
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A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis by Seref Sagiroglu, Ramazan Terzi, Emrah Celtikci, Alp Özgün Börcek, Yilmaz Atay, Bilgehan Arslan, Mustafa Caglar Sahin, Kerem Nernekli, Umut Demirezen, Okan Bilge Ozdemir, Kevser Özdem Karaca, Nuh Azgınoğlu

“…ROI and whole tumor segmentations were successfully performed and compared with seven algorithms with accuracies of 87.61% and 97.18%. The Grad-CAM model also demonstrated satisfactory accuracy across the tests that were conducted. …”
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Poisson random measure noise-induced coherence in epidemiological priors informed deep neural networks to identify the intensity of virus dynamics by Saima Rashid, Ayesha Siddiqa, Fekadu Tesgera Agama, Nazeran Idrees, Mohammed Shaaf Alharthi

“…Compartmental models have estimates of parameter complications, whereas machine learning algorithms struggle to understand MPV’s progression and lack elucidation. …”
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