Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation Using Deep Learning-Based Image Processing Techniques by Samitha Vidhanaarachchi, Janaka L. Wijekoon, W. A. Shanaka P. Abeysiriwardhana, Malitha Wijesundara

“…This paper presents a study conducted in Sri Lanka, demonstrating the effectiveness of employing transfer learning-based Convolutional Neural Network (CNN) and Mask Region-based-CNN (Mask R-CNN) to identify WCWLD and CCI at their early stages and to assess disease progression. …”
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Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) by Ahmad Fauzi, Annisya E Chandra, Sofyah Imammah, Malvin Zapata, Marza I Marzuki, Soni Prayogi

“…This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. …”
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Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions by Asmaa A. Abdelwahab, Mustafa A. Elattar, Sahar Ali Fawzi

“…This review provides a comprehensive exploration of how graph-based computational techniques, including Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have emerged as powerful tools for modeling complex CYP enzyme interactions and predicting ADMET properties with improved precision. …”
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Enhancing elderly care services through integrated sentiment analysis and knowledge reasoning: A deep learning approach by Yongguan Ai, Shiwei Chu, Juan Wang, Nianfang Xu

“…The model utilizes advanced deep learning techniques, such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to analyze multimodal data comprising speech, facial expressions and body language. …”
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Hybrid deep learning model for identifying the cancer type by Singamaneni Krishnapriya, Hyma Birudaraju, M. Madhulatha, S. Nagajyothi, K.S. Ranadheer Kumar

“…In this work, we introduce a hybrid deep learning-based framework for accurate cancer type and subtype identification by using pre-trained convolutional neural networks, custom deep learning networks, and traditional machine learning classifiers. …”
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Utilizing machine learning and digital twin technology for rock parameter estimation from drilling data by Abdullah Khan, Yiming Li, Muhammad Shoaib, Umair Sajjad, Fuxin Rui

“…It emphasizes the growing application of ML algorithms such as artificial neural networks (ANNs), support vector regression (SVR), random forest (RF), and convolutional neural networks (CNNs) for rock property estimation, underscoring the diversity of techniques utilized. …”
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Speech Databases, Speech Features, and Classifiers in Speech Emotion Recognition: A Review by G. H. Mohmad Dar, Radhakrishnan Delhibabu

“…But the development of deep learning techniques has completely changed the field. Models like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have shown that they are better at capturing the complex temporal and spectral features of speech. …”
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Artificial Intelligence-based Rice Variety Classification: A State-of-the-art Review and Future Directions by Md. Masudul Islam, Galib Muhammad Shahriar Himel, Md. Golam Moazzam, Mohammad Shorif Uddin

“…The study examines key steps in the automation process, including image acquisition, pre-processing, feature extraction, and classification algorithms, with particular emphasis on machine learning and deep learning methods such as Convolutional Neural Networks (CNNs), which have demonstrated exceptional performance in recent research. …”
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Image Augmentation Approaches for Building Dimension Estimation in Street View Images Using Object Detection and Instance Segmentation Based on Deep Learning by Dongjin Hwang, Jae-Jun Kim, Sungkon Moon, Seunghyeon Wang

“…This research presents a methodology that applies eight distinct augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, translation augmentation, and a combined “sum of all” approach—to train models in two tasks: object detection with Faster Region-Based Convolutional Neural Networks (Faster R-CNNs) and instance segmentation with You Only Look Once (YOLO)v10. …”
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Precision Agriculture: Utilizing Machine Learning for Accurate Plant Disease Diagnosis by Divya Banti, Tandan Gajendra

“…The efforts on the task of disease symptom identification have demonstrated high success with techniques like Convolutional Neural Networks (CNNs) and forms of ensembles. …”
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Developing an Innovative Seq2Seq Model to Predict the Remaining Useful Life of Low-Charged Battery Performance Using High-Speed Degradation Data by Yong Seok Bae, Sungwon Lee, Janghyuk Moon

“…Comparative analysis of fully connected neural networks, convolutional neural networks, and long short-term memory networks revealed their limitations in extrapolating to untrained conditions. …”
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YOLOv8-DEE: a high-precision model for printed circuit board defect detection by Feifan Yi, Ahmad Sufril Azlan Mohamed, Mohd Halim Mohd Noor, Fakhrozi Che Ani, Zol Effendi Zolkefli

“…Firstly, an improved backbone network incorporating depthwise separable convolution (DSC) modules is designed to enhance the network’s ability to extract PCB defect features. …”
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Deep learning for algorithmic trading: A systematic review of predictive models and optimization strategies by MD Shahriar Mahmud Bhuiyan, MD AL Rafi, Gourab Nicholas Rodrigues, MD Nazmul Hossain Mir, Adit Ishraq, M.F. Mridha, Jungpil Shin

“…We analyze and synthesize the key DL architectures, such as recurrent neural networks (RNN), long short-term memory (LSTM), convolutional neural networks (CNN), and hybrid models, to evaluate their performance in predicting stock prices, volatility, and market trends. …”
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The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case Study by Mohamed Abdul-Al, George Kumi Kyeremeh, Rami Qahwaji, Nazar T. Ali, Raed A. Abd-Alhameed

“…The survey highlights novel contributions such as using Generative Adversarial Networks (GANs) to generate synthetic disguised faces, Convolutional Neural Networks (CNNs) for feature extractions, and Fuzzy Extractors to integrate biometric verification with cryptographic security. …”
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Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope by Ling Guo, PhD, Gregg S. Pressman, MD, Spencer N. Kieu, BS, Scott B. Marrus, MD, PhD, George Mathew, PhD, John Prince, PhD, Emileigh Lastowski, MS, Rosalie V. McDonough, MD, MSc, Caroline Currie, BA, John N. Maidens, PhD, Hussein Al-Sudani, MD, Evan Friend, BA, Deepak Padmanabhan, MD, Preetham Kumar, MD, Edward Kersh, MD, Subramaniam Venkatraman, PhD, Salima Qamruddin, MD

“…Objectives: The authors developed and validated a convolutional neural network (CNN) model using single-lead electrocardiogram and phonocardiogram inputs captured by a digital stethoscope to assess its utility in detecting individuals with actionably low ejection fractions (EF) in a large cohort of patients. …”
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images by Zilong Lian, Yulin Zhan, Wenhao Zhang, Zhangjie Wang, Wenbo Liu, Xuhan Huang

“…With the development of computer science, deep learning models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), Transformers, and diffusion models, have recently been introduced into the field of spatiotemporal fusion, resulting in efficient and accurate algorithms. …”
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State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…However, in cases where the input data exhibit limited variation over time and consist of low-dimensional features, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) may tend toward overfitting. …”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…In this paper, we present a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) alongside a 3D-Echo Fusion approach and a Dual Attention Model for heart valve disease classification using echocardiogram videos. …”
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Optimising window size of semantic of classification model for identification of in-text citations based on context and intent. by Arshad Iqbal, Abdul Shahid, Muhammad Roman, Muhammad Tanvir Afzal, Umair Ul Hassan

“…Additionally, Word2Vec embedding is employed in conjunction with deep learning models and machine learning models such as Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), Decision Trees, and Naive Bayes.The evaluation employs precision, recall, F1-score, and accuracy metrics for each combination of window sizes. …”
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Emotion Recognition from Speech in a Subject-Independent Approach by Andrzej Majkowski, Marcin Kołodziej

“…Machine learning techniques such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and support vector machines with a cubic kernel (cubic SVMs) were employed in the emotion classification task. …”
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