Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics by Saurabh Sharma, Vijay Kumar Gahlawat, Kumar Rahul, Rahul S Mor, Mohit Malik

“…This paper addresses the research concerning AI and big data analytics in the food industry, including machine learning, artificial neural networks (ANNs), and various algorithms. …”
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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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THE INVERSE GAUSSIAN PLUME METHOD FOR ESTIMATING THE LEVEL OF AIR POLLUTION by Volodymyr Hura, Liubomyr Monastyrskyi

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Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…In contrast to human-intensive methods, machine learning may offer a viable alternative for solids content estimation due to its pattern-recognition capability. …”
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Associations between forest vertical structure and habitat preferences of black-and-white snub-nosed monkeys (Rhinopithecus bieti) in high-elevation environments by Zhipang Huang, Yuling Chen, Haitao Yang, Yihao Fang, Kai Cheng, Hongcan Guan, Cyril C. Grueter, Wen Xiao, Qinghua Guo

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Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning. by Ridhanya Sree Balamurugan, Yusuf Asad, Tommy Gao, Dharmakeerthi Nawarathna, Umamaheswara Rao Tida, Dali Sun

“…Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. …”
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Machine learning applications in the analysis of sedentary behavior and associated health risks by Ayat S Hammad, Ayat S Hammad, Ali Tajammul, Ismail Dergaa, Ismail Dergaa, Ismail Dergaa, Maha Al-Asmakh, Maha Al-Asmakh

“…As prolonged inactivity becomes a growing public health concern, researchers are increasingly utilizing machine learning (ML) techniques to examine and understand these patterns. …”
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Machine Learning Techniques to Model and Predict Airflow Requirements in Underground Mining by Maria Karagianni, Andreas Benardos

“…With this twin model, several scenarios are developed and evaluated and more importantly data are gathered, allowing for the training of the ML algorithms used to assess and predict the required ventilation airflow, taking into account air quality data, the number of workers, and machine fleet.…”
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Predicting Students’ Performance Using a Hybrid Machine Learning Approach by Ropafadzo Duwati, Tawanda Mudawarima

“…The Linear Support Vector Classifier (SVC) captured linear patterns within the data, and Logistic Regression was employed as a meta-learner to make the final predictions. …”
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An Assessment of Land Use Land Cover Using Machine Learning Technique by V. Pushpalatha, H. N. Mahendra, A. M. Prasad, N. Sharmila, D. Mahesh Kumar, N. M. Basavaraju, G. S. Pavithra and S. Mallikarjunaswamy

“…Remote sensing imagery, Geographic Information System (GIS) tools, and machine learning algorithms are leveraged to process and interpret satellite data for accurate land-cover classification. …”
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Improving medical machine learning models with generative balancing for equity and excellence by Brandon Theodorou, Benjamin Danek, Venkat Tummala, Shivam Pankaj Kumar, Bradley Malin, Jimeng Sun

“…This paper introduces FairPlay, a synthetic data generation approach leveraging large language models, to address these issues. FairPlay enhances algorithmic performance and reduces bias by creating realistic, anonymous synthetic patient data that improves representation and augments dataset patterns while preserving privacy. …”
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Model Klasifikasi Machine Learning untuk Prediksi Ketepatan Penempatan Karir by Hendri Mahmud Nawawi, Agung Baitul Hikmah, Ali Mustopa, Ganda Wijaya

“…That is becoming increasingly popular is the use of Machine Learning  algorithms in the decision-making process. …”
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Specifics of predicting the profitability of individual bank products based on machine learning by Inna Strelchenko, Dmytro Stognii, Anatolii Strelchenko

“…It explores the use of machine learning to build adaptive predictive models that can identify hidden patterns in financial data and provide more accurate estimates of the future profitability of banking products. …”
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Unlocking biological complexity: the role of machine learning in integrative multi-omics by Ravindra Kumar, Rajrani Ruhel, Andre J. van Wijnen

“…It offers sophisticated algorithms that can identify and discover hidden patterns and provide insights into complex biological networks. …”
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Machine learning applied to the design and optimization of polymeric materials: A review by Sudarsan M. Pai, Karim A. Shah, Sruthi Sunder, Rodrigo Q. Albuquerque, Christian Brütting, Holger Ruckdäschel

“…ML approaches can analyze vast amounts of data, uncover hidden patterns, and generate predictive models that significantly reduce the time needed to develop materials with desired properties. …”
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Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…This paper presents a novel, data-driven approach for predicting Building Energy Ratings (BER) in urban environments, using advanced Machine Learning (ML) algorithms. Focusing on Dublin, we integrate diverse geospatial datasets with building-specific and neighbourhood-scale features to classify BER. …”
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Machine Learning Applications in Use-Wear Analysis: A Critical Review by Anastasia Eleftheriadou, Shannon P. McPherron, João Marreiros

“…Use-wear analysis examines the macroscopic and microscopic patterns of traces left on tool surfaces as a result of use. …”
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Advancing agriculture with machine learning: a new frontier in weed management by Mohammad MEHDIZADEH, Duraid K. A. AL-TAEY, Anahita OMIDI, Aljanabi Hadi Yasir ABBOOD, Shavan ASKAR, Soxibjon TOPILDIYEV, Harikumar PALLATHADKA, Renas Rajab ASAAD

“…This review examines the potential of machine learning in chemical weed management. Machine learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate predictions. …”
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Practical Recommendations for Artificial Intelligence and Machine Learning in Antimicrobial Stewardship for Africa by Tafadzwa Dzinamarira, Elliot Mbunge, Claire Steiner, Enos Moyo, Adewale Akinjeji, Kaunda Yamba, Loveday Mwila, Claude Mambo Muvunyi

“…The deployment of AI‐driven solutions presents unprecedented opportunities for optimizing treatment regimens, predicting resistance patterns, and improving clinical workflows. However, successfully integrating these technologies into Africa's health systems faces considerable obstacles, including limited human capacity and expertise, widespread public distrust, insufficient funding, inadequate infrastructure, fragmented data sources, and weak regulatory and policy enforcement. …”
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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. …”
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