Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…This stage features a dropout layer for regularization, a dense layer with 512 units for further pattern learning, and a final dense layer with 8 units and a soft max activation function, producing a probability distribution across different disease classes. …”
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A Deep Paraphrase Identification Model Interacting Semantics with Syntax by Leilei Kong, Zhongyuan Han, Yong Han, Haoliang Qi

“…Then, DPIM-ISS learns the paraphrase pattern from this representation interacting the semantics with syntax by exploiting a convolutional neural network with convolution-pooling structure. …”
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Sentiment Analysis of Tweets on Prakerja Card using Convolutional Neural Network and Naive Bayes by Pahlevi Wahyu Hardjita, Nurochman, Rahmat Hidayat

“…Convolutional neural network (CNN) is a deep learning algorithm that uses one or more layers commonly used for pattern recognition and image recognition. Having applied these methods, this research found that the CNN model with the GlobalMaxPooling layer is the best model of the other two CNN models. …”
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A new framework for mental illnesses diagnosis using wearable devices aided by improved convolutional neural network by Hend S. Saad, John F. W. Zaki, Mohamed M. Abdelsalam

“…A new framework based on analyzing the motor activity data measured by smart watches is presented to diagnose mental illnesses such as schizophrenia and depression as well as analyze complex behavioral patterns. The framework encodes the behavioral time series data into image patterns using modified Markov Transition Field. …”
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Microclimate Refugia Are Transient in Stable Old Forests, Pacific Northwest, USA by Julia A. Jones, Christopher Daly, Mark Schulze, Christopher J. Stlll

“…The 45‐year seasonal patterns and rates of warming were similar throughout the forested landscape and matched regional rates observed at 88 standard meteorological stations in Oregon and Washington, indicating buffering, but not decoupling of long‐term climate change rates. …”
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DSGAU: Dual-Scale Graph Attention U-Nets for Hyperspectral Image Classification With Limited Samples by Hongzhuang Ji, Leying Song, Zhaohui Xue, Hongjun Su

“…This enables the simultaneous learning of local spectral features and global contextual patterns within HSI data. However, the convolutional operations in traditional GCNs require the inclusion of all data points during graph construction, leading to significant computational overhead, particularly for large-scale datasets. …”
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DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method. …”
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Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques by Sandy Putra Siregar, Imam Akbari, Poningsih Poningsih, Anjar Wanto, Solikhun Solikhun

“…Convolutional Neural Networks (CNNs) perform well in image classification and pattern identification, although they are prone to overfitting. …”
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An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics Perceptions by Ravi Raj, Andrzej Kos

“…CNNs are developed to autonomously and effectively acquire spatial patterns of characteristics using backpropagation, leveraging an array of elements, including convolutional layers, pooling layers, and fully connected layers. …”
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Active travel in the university setting: Assessing the effects of social behavior, socioeconomics, and spatial location by Isabel Cunha, Catarina Cadima

“…To further explore the likelihood of active mobility uptake at university campuses, this study explored university students’ mobility patterns over time, examining the role of social behavior, socioeconomics, and spatial location factors. …”
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The impact of the Brazilian family health strategy on selected primary care sensitive conditions: A systematic review. by Mayara Lisboa Bastos, Dick Menzies, Thomas Hone, Kianoush Dehghani, Anete Trajman

“…In 13 studies about hospitalizations due to PCSC, no clear pattern of association was found. In four studies, there was no effect on child and elderly vaccination or low-birth weight. …”
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Fault Diagnosis of Wind Turbine Blades Based on One-Dimensional Convolutional Neural Network-Bidirectional Long Short-Term Memory-Adaptive Boosting and Multi-Source Data Fusion by Kangqiao Ma, Yongqian Wang, Yu Yang

“…Fault data are first preprocessed by segmenting and labeling the fault patterns. Features are extracted through the convolutional layers, followed by dimensionality reduction and denoising using the pooling layers, and feature fusion. …”
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Climate Warming in the Eastern Mediterranean: A Comparative Analysis of Beirut and Zahlé (Lebanon, 1992–2024) by Rabih Zeinaldine, Salem Dahech

“…The Eastern Mediterranean region is experiencing accelerated climate warming, yet localized patterns remain poorly understood, particularly in areas with complex topography. …”
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Image-Based Malware Detection Using Deep CNN Models by hawraa omran musa, Muhanad Tahrir Younis

“…Deep learning (DL) presents a promising strategy for malware detection by utilizing advanced algorithms that are capable of discerning intricate patterns from extensive datasets. This study presents a model based on deep learning with Convolutional Neural Network (CNN) for malware classification. …”
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Enhancement of Chest X-Ray Images to Improve Screening Accuracy Rate Using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier by Chia-Hung Lin, Jian-Xing Wu, Chien-Ming Li, Pi-Yun Chen, Neng-Sheng Pai, Ying-Che Kuo

“…The accuracy rate is thus improved. Pooling is performed to reduce the dimensions of the feature patterns and speed up complex computations. …”
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Leveraging U-Net and ASPP for effective fault detection in photovoltaic modules by Khalfalla Awedat, Masoud Alajmi, Mustafa Elfituri

“…We propose and evaluate three modified configurations U-Net-ASPP_Cent, U-Net-ASPP_Diag, and U-Net-ASPP_Hybrid each designed to address specific fault localization challenges, including central and diagonal fault patterns. These configurations aim to overcome the limitations of conventional U-Net-ASPP by enhancing multiscale feature extraction and improving segmentation accuracy in complex PV thermal images. …”
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A multi-dilated convolution network for speech emotion recognition by Samaneh Madanian, Olayinka Adeleye, John Michael Templeton, Talen Chen, Christian Poellabauer, Enshi Zhang, Sandra L. Schneider

“…As the two-dimensional representation of the spectrogram includes more speech characteristics, research interest in convolution neural networks (CNNs) or advanced image recognition models is leveraged to learn deep patterns in a spectrogram to effectively perform SER. …”
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Deep convolutional neural network architecture for facial emotion recognition by Dayananda Pruthviraja, Ujjwal Mohan Kumar, Sunil Parameswaran, Vemulapalli Guna Chowdary, Varun Bharadwaj

“…By combining convolutional and pooling layers, the model effectively captures both subtle facial details and higher-level emotional patterns. …”
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Public-Private Partnership as a Model of Public Infrastructure Development by A. S. Bednyakov

“…The study of these prerequisites reveals the development patterns of PPP development, including its effectiveness, that allows politicians to harness them to develop an optimal state policy in the area. …”
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Combining acoustic survey and citizen science data yields enhanced species distribution models for tropical rainforest birds. by Reid Rumelt, Carla Mere Roncal, Arianna Basto, Zuzana Buřivalová, Christopher Searcy

“…Our approach combined automated acoustic monitoring detections and eBird citizen science data available for the region as well as a high resolution land cover dataset of the region's key ecological gradients. The pooled models outperformed models constructed solely with eBird data for predicting fine grain species responses to habitat gradients in intact forest, but also retained information from the citizen science dataset about species occurrence patterns in non-vegetated areas away from intact forest, including those subject to human disturbance. …”
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