Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…Advanced machine-learning approaches are being used to understand student performance variables as educational data grows. A big dataset from several Chinese institutions and high schools is used to develop a credible student performance prediction technique. …”
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AI-Assisted identification of sex-specific patterns in diabetic retinopathy using retinal fundus images. by Parsa Delavari, Gulcenur Ozturan, Eduardo V Navajas, Ozgur Yilmaz, Ipek Oruc

“…To minimize confounding variables, we curated 2,967 fundus images from a larger dataset of DR patients acquired from EyePACS, matching male and female groups for age, ethnicity, severity of DR, and hemoglobin A1C levels. …”
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An investigation on energy-saving scheduling algorithm of wireless monitoring sensors in oil and gas pipeline networks by Zhifeng Ma, Zhanjun Hao, Zhenya Zhao

“…Firstly, this study designs a deep learning-based Transformer model that learns from historical data on energy consumption patterns and environmental variables to predict the energy and data transmission needs of each sensor node. …”
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. …”
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Force output in giant-slalom skiing: A practical model of force application effectiveness. by Matt R Cross, Clément Delhaye, Jean-Benoit Morin, Maximilien Bowen, Nicolas Coulmy, Frédérique Hintzy, Pierre Samozino

“…Ski athletes (N = 15) were equipped with ski-mounted force plates and a global navigation satellite system to compute the following variables over 14 turns: path length (L), velocity normalized energy dissipation [Δemech/vin], radial force [Fr], total force (both limbs [Ftot], the outside limb, and the difference between limbs), and a ratio of force application (RF = Fr/Ftot). …”
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From pixels to planning: scale-free active inference by Karl Friston, Karl Friston, Conor Heins, Tim Verbelen, Lancelot Da Costa, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, Christopher Buckley, Thomas Parr

“…This model generalizes partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active inference and learning in a dynamic setting. …”
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Modeling Temperature in the Ecuadorian Paramo Through Deep Learning by Marco Javier Castelo Cabay, Jose Antonio Piedra-Fernandez, Rosa Maria Ayala

“…The prediction integrates key variables such as humidity, precipitation, and wind speed through multivariate neural networks. …”
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Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…Progressions in disease forecasts aid farmers in making informed decisions, minimizing crop losses, and reducing pesticide use through targeted application of agrochemicals with the use of AI-driven variable rate sprayers. This leads to healthier crops, market stability, and a more sustainable farming environment.…”
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Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning by Umile Giuseppe Longo, Sergio De Salvatore, Alice Piccolomini, Nathan Samuel Ullman, Giuseppe Salvatore, Margaux D'Hooghe, Maristella Saccomanno, Kristian Samuelsson, Rocco Papalia, Ayoosh Pareek

“…The highest area under the curve (=1) was reported in the preoperative planning outcome variable and utilized CNN. All 20 studies demonstrated a high level of quality and low risk of bias, with a modified MINORS score of at least 7/8 (88%). …”
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Prediction of Sea Surface Chlorophyll-a Concentrations by Remote Sensing and Deep Learning by Qingfeng Ruan, Delu Pan, Difeng Wang, Xianqiang He, Fang Gong, Qingjiu Tian

“…Current methods struggle to capture short-term variability and periodic trends in Chl-a, especially in noise-prone coastal regions. …”
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Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement by Li Dan, Lin Wen, Liu Qun, Feng Hongfang, Hu Shuping, Wang Zhihai

“…By comparing various machine learning and linear regression models, it is found that CNN and quomial regression perform relatively well when the regional average surface rainfall is taken as the statistical variable, with the determination coefficient of CNN being 0.516 and RMSE being 1.097 mm. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…The dynamic learning of spatial correlations, combined with the integration of road characteristics and contextual variables, significantly enhances the accuracy of traffic accident predictions. …”
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Image-Based Breast Cancer Histopathology Classification and Diagnosis Using Deep Learning Approaches by Lama A. Aldakhil, Haifa F. Alhasson, Shuaa S. Alharbi, Rehan Ullah Khan, Ali Mustafa Qamar

“…We believe that by examining several factors and variables and conducting an in-depth analysis of the state of the art, this study will contribute to the state of the art and benefit researchers in both computing and medical domains.…”
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Keypoints-Based Multi-Cue Feature Fusion Network (MF-Net) for Action Recognition of ADHD Children in TOVA Assessment by Wanyu Tang, Chao Shi, Yuanyuan Li, Zhonglan Tang, Gang Yang, Jing Zhang, Ling He

“…This work proposes a novel keypoints-based system, the Multi-cue Feature Fusion Network (MF-Net), for recognizing actions and behaviors of children with ADHD during the Test of Variables of Attention (TOVA). The system aims to assess ADHD symptoms as described in the DSM-V by extracting features from human body and facial keypoints. …”
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Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent by Karim Malik, Colin Robertson

“…We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks.…”
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Modeling and Evaluating the Impact of Mobile Usage on Pedestrian Behavior at Signalized Intersections: A Machine Learning Perspective by Faizanul Haque, Farhan Ahmad Kidwai, Ishwor Thapa, Sufyan Ghani, Lincoln M. Mtapure

“…Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). …”
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Method of tail beam posture prediction of top coal caving hydraulic support based on LSTM by Yupeng YAO, Jinglin ZHANG, Wu XIONG

“…The absolute coordinates of the support bottom plate, the inclination of the tail beam, the relative height of the tail beam, the frame shifting rate and the column pressure related to the tail beam caving action were used as the input variables of the RNN convolutional network and the LSTM neural network. …”
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Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height, and Cover from High-Resolution, Multi-Sensor Satellite Imagery by Manuel Weber, Carly Beneke, Clyde Wheeler

“…We further show that our pre-trained model facilitates seamless transferability to other GEDI variables due to its multi-head architecture.…”
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Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN by Syed Waqad Ali, Muhammad Munaf Rashid, Muhammad Uzair Yousuf, Sarmad Shams, Muhammad Asif, Muhammad Rehan, Ikram Din Ujjan

“…Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing factors. …”
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Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks by Kun Mei, Zikang Feng, Hui Liu, Min Wang, Chao Ce, Shi Yin, Xiaoying Zhang, Bin Wang

“…Abstract Objective The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. …”
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