Shale volume estimation using machine learning methods from the southwestern fields of Iran by Parirokh Ebrahimi, Ali Ranjbar, Yousef Kazemzadeh, Ali Akbari

“…However, estimating shale volume presents significant challenges, particularly in complex formations. …”
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Driven early detection of chronic kidney cancer disease based on machine learning technique. by Wafa Almukadi, Sayed Abdel-Khalek, Adel A Bahaddad, Ahmed Mohammed Alghamdi

“…The combination of advanced computational methods with the assessment of kidney cancer HIs not only overcomes the demanding requirements for timely identification but also paves the way for more effective and reliable diagnostic processes in renal oncology. This study presents the Kidney Cancer Detection and Classification employing a Snake Optimizer with Deep Learning on Pathological Images (KCDC-SODLPI) technique. …”
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Detecting malicious code variants using convolutional neural network (CNN) with transfer learning by Nazish Younas, Shazia Riaz, Saqib Ali, Rafiullah Khan, Farman Ali, Daehan Kwak

“…Malware presents a significant threat to computer networks and devices that lack robust defense mechanisms, despite the widespread use of anti-malware solutions. …”
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Machine learning with environmental predictors to forecast hospital visits and admissions: a systematic review by Levi Monteiro Martins, Elsa Coz, Delphine Maucort-Boulch, Mohand-Saïd Hacid

“…To address this problem, forecasting models are essential for resource allocation and general management to improve patient outcomes. Machine Learning (ML), especially Deep Learning (DL), offers promise for forecasting patient volume. …”
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Deep Learning for Real-Time PPE Usage Monitoring Using Wearable IMU Sensors by Pedro Carvalho da Fonseca Guimaraes, Leonardo Braga de Cristo, Marcos Eduardo Pivaro Monteiro, Joao Luiz Rebelatto, Guilherme de Santi Peron, Ohara Kerusauskas Rayel, Guilherme Luiz Moritz

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Reinforcement Learning Agent for Multi-Objective Online Process Parameter Optimization of Manufacturing Processes by Akshay Paranjape, Nahid Quader, Lars Uhlmann, Benjamin Berkels, Dominik Wolfschläger, Robert H. Schmitt, Thomas Bergs

“…Optimizing manufacturing processes to reduce scrap and enhance process stability presents significant challenges, particularly when multiple conflicting objectives must be addressed concurrently. …”
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Optimized Motion Capture for Cricket Shot Classification Using Minimal Hardware and Machine Learning by J. Ishan Randika, Kanishka Rajamanthri, Avishka Kothalawala, Niroshan Gunawardana, Ashan Induranga, Pathum Weerakkody, Kaveendra Maduwantha, B. T. G. S. Kumara, Kaveenga Koswattage

“…However, the high cost of commercial systems and the disturbing nature of wearable devices limit their accessibility and practicality. This study presents a two-fold solution: first, the development of a low-cost, non-intrusive optical motion capture system; and second, the implementation of a machine learning model to classify cricket batting shots based on body angle variations over time. …”
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Intelligent transportation systems: A survey on modern hardware devices for the era of machine learning by Issam Damaj, Salwa K. Al Khatib, Tarek Naous, Wafic Lawand, Zainab Z. Abdelrazzak, Hussein T. Mouftah

“…The performance challenge has become more noticeable with the integration of Machine Learning (ML) techniques deployed in large-scale settings. …”
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Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy

“…Abstract This paper provides a novel approach to estimating CO₂ emissions with high precision using machine learning based on DPRNNs with NiOA. The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) as well as Blind Source Separation (BSS) to reduce noise as well as to improve feature selection. …”
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Neural correlates of sensorimotor adaptation: Thalamic contributions to learning from sensory prediction error by Shirin Mahdavi, Axel Lindner, Carsten Schmidt-Samoa, Anna-Lena Müsch, Peter Dechent, Melanie Wilke

“…Notably, the gradual shift in the predicted hand movement consequences was associated with activity in the cerebellum, motor cortex and thalamus (ventral lateral, medial dorsal, and medial pulvinar). Our study presents clear evidence for an involvement of the thalamus, both classical ‘motor’ and higher-order nuclei, in error-based motor learning.…”
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Hierarchical Cascade Deep Learning for EMG-Based Behavioral Biometrics: Gesture and Subject Classification by Berke Cansiz, Hatice Vildan Dudukcu, Murat Taskiran, Nihan Kahraman

“…Unlike vision-based methods like cameras, EMG is immune to lighting conditions, clothing, or occlusions. This study presents a hierarchical cascade deep learning framework aimed at simultaneously performing hand gesture recognition and subject-specific biometric classification. …”
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Advanced clustering and transfer learning based approach for rice leaf disease segmentation and classification by Samia Nawaz Yousafzai, Fahd N. Al-Wesabi, Hadeel Alsolai, Shouki A. Ebad, Inzamam Mashood Nasir, Emad Fadhal, Adel Thaljaoui

“…In light of such drawbacks, this article introduces an improved deep learning and transfer learning method for diagnosing and categorizing rice leaf diseases proficiently. …”
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Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach by Kashif Iqbal Sahibzada, Shumaila Shahid, Mohsina Akhter, Muhammad Faisal, Reham A. Abd El Rahman, Muhammad Imran, Yangyong Lv, Dongqing Wei, Yuansen Hu

“…The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine learning model was introduced which classifies enzymes by their toxin degradation ability. …”
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Cancelable Multi-Branch Deep Learning Framework for Privacy-Preserving ECG Biometric Authentication by Mohamed Hammad, Ali Abdullah S. AlQahtani, Mohammed ELAffendi, Abdelhamied A. Ateya, Nebojsa Bacanin

“…However, storing and processing raw ECG data raises privacy and security concerns, requiring advanced methods to safeguard user data. This paper presents a novel cancelable ECG biometric authentication framework that integrates a Cancelable Dynamic Biometric Transformation (CDBT) with a multi-branch deep learning architecture to enhance both privacy and performance. …”
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Machine Learning in Estimating Daily Global Radiation in the Brazilian Amazon for Agricultural and Environmental Applications by Charles Campoe Martim, Rhavel Salviano Dias Paulista, Daniela Roberta Borella, Frederico Terra de Almeida, João Gabriel Ribeiro Damian, Érico Tadao Teramoto, Adilson Pacheco de Souza

“…The performance of SVM was better when compared to MLP in all statistical indicators. ML techniques presented better results than empirical models, and in general, the ordering of the best models in the three locations is achieved using SVM, MLP, and empirical models. …”
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A novel deep learning model for predicting marine pollution for sustainable ocean management by Michael Onyema Edeh, Surjeet Dalal, Musaed Alhussein, Khursheed Aurangzeb, Bijeta Seth, Kuldeep Kumar

“…The classification was done using two types of machine learning algorithms: two-step clustering and a fully convolutional network (FCN). …”
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Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images by Ling-Chun Sun, Chia-Chiang Lee, Hung-Yen Ke, Chih-Yuan Wei, Ke-Feng Lin, Shih-Sung Lin, Hsin Hsiu, Ping-Nan Chen

“…In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum. Results We present a MsCWT image-based deep learning machine for AF differentiation. …”
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A Multimodal Machine Learning Model in Pneumonia Patients Hospital Length of Stay Prediction by Anna Annunziata, Salvatore Cappabianca, Salvatore Capuozzo, Nicola Coppola, Camilla Di Somma, Ludovico Docimo, Giuseppe Fiorentino, Michela Gravina, Lidia Marassi, Stefano Marrone, Domenico Parmeggiani, Giorgio Emanuele Polistina, Alfonso Reginelli, Caterina Sagnelli, Carlo Sansone

“…To address these challenges, we present a novel multimodal ML framework that combines imaging and clinical data to enhance LOS prediction accuracy. …”
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Dual-Path Large Kernel Learning and Its Applications in Single-Image Super-Resolution by Zhen Su, Mang Sun, He Jiang, Xiang Ma, Rui Zhang, Chen Lv, Qiqi Kou, Deqiang Cheng

“…To surmount this limitation, this study introduces the application of Dual-path Large Kernel Learning (DLKL) to the task of image super-resolution. …”
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Three Years of VoiceMOS Challenges: Lessons Learned by the UWB-NTIS-TTS Team by Marie Kunesova, Jindrich Matousek, Jan Lehecka, Jan Svec, Daniel Tihelka, Zdenek Hanzlicek

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