APAH: An autonomous IoT driven real-time monitoring system for Industrial wastewater by Nishant Chavhan, Resham Bhattad, Suyash Khot, Shubham Patil, Aditya Pawar, Tejasvi Pawar, Palomi Gawli

“…APAH utilizes advanced technologies including, the Internet of Things (IoT) and Machine learning (ML) to provide real-time monitoring and control of wastewater treatment processes. …”
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Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan by Moiz Qureshi, Khushboo Ishaq, Muhammad Daniyal, Hasnain Iftikhar, Mohd Ziaur Rehman, S. A. Atif Salar

“…This study analyzes and forecasts the CVD deaths in the Sindh province of Pakistan using classical time series models, including Naïve, Holt-Winters, and Simple Exponential Smoothing (SES), which have been adopted and compared with a machine learning approach called the Artificial Neural Network Auto-Regressive (ANNAR) model. …”
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Temporal Convolutional Network Approach to Secure Open Charge Point Protocol (OCPP) in Electric Vehicle Charging by Ikram Benfarhat, Vik Tor Goh, Chun Lim Siow, Muhammad Sheraz, Teong Chee Chuah

“…The primary challenge within EVCS architecture lies in defending against various cyberattacks. Several machine learning models, including convolutional neural networks, recurrent neural networks, and long short-term memory, have been employed to enhance EVCS security. …”
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Risk prediction model of cognitive performance in older people with cardiovascular diseases: a study of the National Health and Nutrition Examination Survey database by Hui Wang, Sensen Wu, Dikang Pan, Yachan Ning, Cong Wang, Jianming Guo, Yongquan Gu

“…After which, based on the selected relevant variables, we developed a machine learning model that was predictive of cognitive impairment such as Alzheimer’s diseases in the older people. …”
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An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction by Jerry Emmanuel, Itunuoluwa Isewon, Jelili Oyelade

“…The results were compared with standard optimization methods such as traditional Bayesian optimization, genetic algorithms, evolutionary strategies, and other machine learning models. The optimized model achieved an accuracy of 89.3 %, outperforming other models across all metrics, including a sensitivity of 85.4 % and a precision of 91.6 %. …”
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Assessment of grazing livestock balance on the Eastern Mongolian Plateau based on remote sensing monitoring and grassland carrying capacity evaluation by Menghan Li, Juanle Wang, Kai Li, Yaping Liu, Altansukh Ochir, Davaadorj Davaasuren

“…This study focuses on the grassland regions of 8 provinces in eastern Mongolia (MNG) and 7 leagues in Inner Mongolia (IMNG), China, during the period from 2018 to 2022. Machine learning methods were employed for land cover classification and above-ground biomass (AGB) estimation. …”
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Enhanced ensemble learning-based uncertainty and sensitivity analysis of ventilation rate in a novel radiative cooling building by Majid Mohsenpour, Mohsen Salimi, Atieh Kermani, Majid Amidpour

“…This study proposes a novel machine learning-based ensemble stacking model to predict ventilation rates in passive cooling buildings, addressing the challenges of black-box modeling. …”
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GnRH pulse generator activity in mouse models of polycystic ovary syndrome by Ziyue Zhou, Su Young Han, Maria Pardo-Navarro, Ellen G Wall, Reena Desai, Szilvia Vas, David J Handelsman, Allan E Herbison

“…Examining the prenatal androgen (PNA) model of PCOS, we observed highly variable patterns of pulse generator activity with no significant differences detected in ARNKISS neuron SEs, pulsatile LH secretion, or serum testosterone, estradiol, and progesterone concentrations. However, a machine learning approach identified that the ARNKISS neurons of acyclic PNA mice continued to exhibit cyclical patterns of activity similar to that of normal mice. …”
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Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models by M. Sinčić, S. Bernat Gazibara, M. Rossi, S. Mihalić Arbanas

“…Some of the novelties in LSA are the following: scenarios using stretched landslide conditioning factor values or classification with more than 10 classes prove more reliable; certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others; all the tested machine learning methods give the best landslide susceptibility model performance using continuous stretched landslide conditioning factors derived from high-resolution input data. …”
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Automated skin melanoma diagnostics based on mathematical model of artificial convolutional neural network by D. A. Gavrilov, E. I. Zakirov, E. V. Gameeva, V. Yu. Semenov, O. Yu. Aleksandrova

“…This jerk is largely due to the emergence and development of the technology of deep convolu onal neural networks.Recent developments in the fi eld of image processing and machine learning open up the prospect of crea ng systems based on ar fi cial neural convolu onal networks, superior to humans in problems of image classifi ca on, in par cular, in solving problems of analysis of various medical images. …”
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Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification by Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing

“…The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. …”
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Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images by Sultan Refa Alotaibi, Manal Abdullah Alohali, Mashael Maashi, Hamed Alqahtani, Moneerah Alotaibi, Ahmed Mahmud

“…Lately, computer-aided diagnosis (CAD) based on HI has progressed rapidly with the increase of machine learning (ML) and deep learning (DL) based models. …”
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Harnessing Internet Search Data as a Potential Tool for Medical Diagnosis: Literature Review by Gregory J Downing, Lucas M Tramontozzi, Jackson Garcia, Emma Villanueva

“…Leveraging advancements in machine learning, researchers have explored linking search data with health records to enhance screening and outcomes. …”
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ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI by Yuqi Fang, Junhao Zhang, Linmin Wang, Qianqian Wang, Mingxia Liu

“…Moreover, current studies usually focus on analyzing fMRI using conventional machine learning models that rely on human-engineered fMRI features, without investigating deep learning models that can automatically learn data-driven fMRI representations. …”
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Rapid and accurate multi-phenotype imputation for millions of individuals by Lin-Lin Gu, Hong-Shan Wu, Tian-Yi Liu, Yong-Jie Zhang, Jing-Cheng He, Xiao-Lei Liu, Zhi-Yong Wang, Guo-Bo Chen, Dan Jiang, Ming Fang

“…In the present study, we have developed a multi-phenotype imputation method based on mixed fast random forest (PIXANT) by leveraging efficient machine learning (ML)-based algorithms. We demonstrate by extensive simulations that PIXANT is reliable, robust and highly resource-efficient. …”
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Legal aspects of functional security standardisation of the Internet of Things by P. S. Klimushyn, V. Ye. Roh, T. P. Kolisnyk

“…To support the Internet of Things, technologies such as built-in devices, cloud and fog computing, big data processing, machine learning, and artificial intelligence are used to produce intelligent physical objects. …”
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Evaluation of the Impact of Morphological Differences on Scale Effects in Green Tide Area Estimation by Ke Wu, Tao Xie, Jian Li, Chao Wang, Xuehong Zhang, Hui Liu, Shuying Bai

“…Green tide patches were categorized into small, medium, and large sizes, and morphological features such as elongation, compactness, convexity, fractal dimension, and morphological complexity were designed and analyzed. Machine learning models, including Extra Trees, LightGBM, and Random Forest, among others, classified medium and large patches into striped and non-striped types, with Extra Trees achieving outstanding performance (accuracy: 0.9844, kappa: 0.9629, F1-score: 0.9844, MIoU: 0.9637). …”
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Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework by Arvind R. Singh, R. Seshu Kumar, K. Reddy Madhavi, Faisal Alsaif, Mohit Bajaj, Ievgen Zaitsev

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Automatic Classification of Difficulty of Texts From Eye Gaze and Physiological Measures of L2 English Speakers by Javier Melo, Leigh Fernandez, Shoya Ishimaru

“…In this study (<inline-formula> <tex-math notation="LaTeX">$N=30$ </tex-math></inline-formula>) we determined L2 speakers&#x2019; subjective difficulty while reading using language proficiency and objective text difficulty, combined with physiological data. We compared machine learning classifiers combining eye, skin and heart sensor data against models using each modality separately. …”
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Futility in TAVI: A scoping review of definitions, predictive criteria, and medical predictive models. by Charlie Ferry, Jade Fiery-Fraillon, Mario Togni, Stephane Cook

“…Medical predictive models showed moderate sensitivity and specificity, except for machine learning, which shows promise for the future. However, few articles delve deeply into non-quantifiable parameters such as patient goals and objectives or ethical questions. …”
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