Effective Facial Expression Recognition System Using Artificial Intelligence Technique by Imad S. Yousif, Tarik A. Rashid, Ahmed S. Shamsaldin, Sabat A. Abdulhameed, Abdulhady Abas Abdullah

“…ANNS are inspired by the neural architecture of human brain capable of learning and recognizing patterns in unchartered data after trained examples, on the other hand GAs come from fundamental principles underlying natural selection perform optimization process based-on evolutionary methods which includes fitness evaluation, comparison, selection, crossover, and mutation. …”
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Artificial Intelligence in ECG interpretation - review article by Łukasz Fussek, Jagoda Niewiadomska, Borys Bondos, Aleksandra Stępień, Alicja Paluch, Jakub Skrzypek, Aleksandra Język Polski, Robert Kochan, Ewelina Wieczorek, Kacper Lee

“…The biggest limitations of artificial intelligence are the need to verify the diagnosis made by the algorithm in order to detect possible errors. In addition, the use of personal data to train an artificial intelligence algorithm to diagnose specific medical conditions can be controversial, which can interfere with data protection rules. …”
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Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management by Pardis Sadeghi, Shahriar Noroozizadeh, Rania Alshawabkeh, Nian Xiang Sun

“…The current study integrates machine learning with a molecularly imprinted polymer biosensor for detecting D-glucose in the exhaled breath condensate or aerosol. …”
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Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning by Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Somaye Mohammadi, Len Gelman

“…The results show that transfer learning with fine-tuning, combined with the resonance region identification algorithm, significantly enhances fault detection accuracy. …”
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Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only by Yonina Ron, Tchelet Ron, Naomi Fridman, Anat Goldstein

“…Our approach offers caregivers an advanced screening tool to detect myopia risk using readily available data.…”
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Using machine learning methods to investigate the role of volatile organic compounds in non-alcoholic fatty liver disease by Chih-Hao Shen, Ruei-Hao Huang, Yaw-Kuen Li, Ta-Wei Chu, Ta-Wei Chu, Dee Pei, Dee Pei

“…This study aimed to explore whether NAFLD could be effectively detected using 341 volatile organic compounds (VOCs) via 10 machine learning (Mach-L) algorithms in a cohort of 1,501 individuals.MethodsParticipants were selected from the Taiwan MJ cohort, which includes comprehensive demographic, biochemical, lifestyle, and VOCs data. …”
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Multiclass Fault Diagnosis in Power Transformers Using Dissolved Gas Analysis and Grid Search-Optimized Machine Learning by Andrew Adewunmi Adekunle, Issouf Fofana, Patrick Picher, Esperanza Mariela Rodriguez-Celis, Oscar Henry Arroyo-Fernandez, Hugo Simard, Marc-André Lavoie

“…The performance of six machine learning classifiers was systematically evaluated using training and testing data generated through four widely recognized gas ratio schemes. …”
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Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers by B. Shamreen Ahamed, Meenakshi S. Arya, S. K. B. Sangeetha, Nancy V. Auxilia Osvin

“…These problems are caused by a series of factors interrelated to one another such as age, gender, family history, BMI, and Blood Glucose. Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. …”
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Different Approaches to Artificial Intelligence–Based Predictive Maintenance on an Axle Test Bench with Highly Varying Tests by Markus Siebert, Michael Fister, Christian Spieker, Daniel Stengler

“…The implementation of a machine learning and a deep learning algorithm for predictive maintenance through early damage detection on an electric rear axle test bench is presented in this paper. …”
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Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT by Meysam Dadgar, Amaryllis Verstraete, Jens Maebe, Yves D’Asseler, Stefaan Vandenberghe

“…Conclusion This study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.…”
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Improved early detection of wheat stripe rust through integration pigments and pigment-related spectral indices quantified from UAV hyperspectral imagery by Anting Guo, Wenjiang Huang, Binxiang Qian, Kun Wang, Huanjun Liu, Kehui Ren

“…Pigments, including chlorophylls (Cab), carotenoids (Car), anthocyanins, Cab/Car, and 11 pigment-related SIs, were extracted from UAV hyperspectral images using radiative transfer modeling. The early detection model for wheat stripe rust was developed using these parameters and machine learning algorithms. …”
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A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning by Yuzhu Yang, Hongda Li, Miao Sun, Xingyu Liu, Liying Cao

“…The average R² and RPD increased by 0.056 and 1.418 in comparison to the base learner algorithm. The study results lay a foundation for the fast detection of soil moisture content in black soil areas and provide a data source for intelligent irrigation in agriculture.…”
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Crowdsourcing geographic information for terrorism-related disaster awareness and mitigation: perspectives and challenges by Michaelmary Chukwu, Xiao Huang, Siqin Wang, Di Yang, Xinyue Ye

“…Our advocacy is for increased studies from the domains of geography, earth observation, and big data. Simultaneously, we encourage advancements in existing ML algorithms to enhance the accurate real-time detection of planned and onset terrorism disasters.…”
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DA-YOLOv7: A Deep Learning-Driven High-Performance Underwater Sonar Image Target Recognition Model by Zhe Chen, Guohao Xie, Xiaofang Deng, Jie Peng, Hongbing Qiu

“…Affected by the complex underwater environment and the limitations of low-resolution sonar image data and small sample sizes, traditional image recognition algorithms have difficulties achieving accurate sonar image recognition. …”
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LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network by Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge

“…Our system operates in conjunction with a preliminary algorithm that detects increasing trends in flux. These flagged instances are then passed to LensNet for further classification, allowing for timely alerts and follow-up observations. …”
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Deep Learning-Based Multi-Floor Indoor Localization Using Smartphone IMU Sensors With 3D Location Initialization by Jin-Woo Kim, Yoan Shin

“…In this paper, we explain conventional IMU sensor-based PDR methods and describe the problems of PDR as well as the limitations of barometer-based floor transition detection techniques. The proposed initial 3D position estimation method detects the initial floor using a deep learning model trained on magnetic field sequences from the user’s first 10 steps, and estimates the 2D position by comparing the current magnetometer data with a pre-built magnetic field map using the k-nearest neighbors algorithm. …”
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Proposal of a Methodology Based on Using a Wavelet Transform as a Convolution Operation in a Convolutional Neural Network for Feature Extraction Purposes by Nora Isabel Pérez-Quezadas, Héctor Benítez-Pérez, Adrián Durán-Chavesti

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Assisting human annotation of marine images with foundation models by Eric C. Orenstein, Eric C. Orenstein, Benjamin Woodward, Lonny Lundsten, Kevin Barnard, Brian Schlining, Kakani Katjia

“…Marine scientists have been leveraging supervised machine learning algorithms to analyze image and video data for nearly two decades. …”
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Predicting the Attraction and Retention of Customers in Sports Pools in Isfahan City Using a Decision Tree: Presenting a Data Mining-Based Model by Seyed Masoud Mirsaeidi, Davood Nasr Esfahani

“…The purpose of this research is collective learning techniques and a combination of classification algorithms in data mining, evaluated the performance of these algorithms to predict whether customers of Isfahan sports pools would drop over a 6-year period. …”
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AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis by Esther Ugo Alum

“…Existing gaps include data quality, algorithmic transparency, and ethical concerns around privacy, among others. …”
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