Attention-enhanced corn disease diagnosis using few-shot learning and VGG16 by Ruchi Rani, Jayakrushna Sahoo, Sivaiah Bellamkonda, Sumit Kumar

“…The proposed work uses a pre-trained convolution neural network, VGG16, as the backbone, fine-tuned on the corn disease dataset. …”
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Vision-based manipulation of transparent plastic bags in industrial setups by F. Adetunji, F. Adetunji, A. Karukayil, A. Karukayil, P. Samant, P. Samant, S. Shabana, S. Shabana, F. Varghese, F. Varghese, U. Upadhyay, U. Upadhyay, R. A. Yadav, R. A. Yadav, A. Partridge, E. Pendleton, R. Plant, Y. R. Petillot, Y. R. Petillot, M. Koskinopoulou, M. Koskinopoulou

“…Integrating autonomous systems, including collaborative robots (cobots), into industrial workflows is crucial for improving efficiency and safety.MethodsThe proposed system employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), for identifying transparent plastic bags under diverse lighting and background conditions. …”
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Automation of quantum dot measurement analysis via explainable machine learning by Daniel Schug, Tyler J Kovach, M A Wolfe, Jared Benson, Sanghyeok Park, J P Dodson, J Corrigan, M A Eriksson, Justyna P Zwolak

“…While image-based classification tools, such as convolutional neural networks (CNNs), can be used to verify whether a given measurement is good and thus warrants the initiation of the next phase of tuning, they do not provide any insights into how the device should be adjusted in the case of bad images. …”
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Advancing the application of the analytical renal pathology system in allograft IgA nephropathy patients by Xumeng Liu, Huiwen Fang, Dongmei Liang, Qunjuan Lei, Jiaping Wang, Feng Xu, Shaoshan Liang, Dandan Liang, Fan Yang, Heng Li, Jianghua Chen, Yuan Ni, Guotong Xie, Caihong Zeng

“…Background The analytical renal pathology system (ARPS) based on convolutional neural networks has been used successfully in native IgA nephropathy (IgAN) patients. …”
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Prospects for the Use of Quasi-Mersen Numbers in the Design of Parallel-Serial Processors by Aruzhan Kadyrzhan, Kaisarali Kadyrzhan, Akhat Bakirov, Ibragim Suleimenov

“…Fulfillment of this criterion ensures the possibility of convenient use of the considered RNS for calculating partial convolutions developed for the convenience of using convolutional neural networks. …”
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Editorial by Teddy Surya Gunawan

“…Healthcare and safety remain pivotal in this issue, with studies delving into early autism screening using federated learning and diabetic retinopathy detection leveraging deep convolutional neural networks. These works underscore the transformative potential of artificial intelligence in improving diagnostic accuracy and protecting sensitive medical data. …”
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Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society. by Muhammad Tayyab Zamir, Fida Ullah, Rasikh Tariq, Waqas Haider Bangyal, Muhammad Arif, Alexander Gelbukh

“…This research employs Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning classifiers, and afterwards compares the obtained results. …”
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Simplified Physical Stability Assessment of Chilean Mine Waste Storage Facilities Using GIS and AI: Application in the Antofagasta Region by Gabriel Hermosilla, Gabriel Villavicencio, Giovanni Cocca-Guardia, Vicente Aprigliano, Manuel Silva, Juan Carlos Quezada, Pierre Breul, Vinicius Minatogawa, Jaime Morales

“…By integrating Geographic Information Systems (GIS) and Artificial Intelligence (AI)—utilizing models like YOLOv11 and convolutional neural networks—we automate the detection and characterization of WRD and LWD from satellite imagery, extracting critical parameters for PS assessment. …”
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Rapid literature mapping on the recent use of machine learning for wildlife imagery by Nakagawa, Shinichi, Lagisz, Malgorzata, Francis, Roxane, Tam, Jessica, Li, Xun, Elphinstone, Andrew, Jordan, Neil R., O'Brien, Justine K., Pitcher, Benjamin J., Van Sluys, Monique, Sowmya, Arcot, Kingsford, Richard T.

“…We found that an increasing number of studies used convolutional neural networks (i.e., deep learning). …”
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Estimating and forecasting daily reference crop evapotranspiration in China with temperature-driven deep learning modelsMendeley Data by Jia Zhang, Yimin Ding, Lei Zhu, Yukuai Wan, Mingtang Chai, Pengpeng Ding

“…Five deep learning (DL) models were employed in this study, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks Bi-LSTM (CNN-BiLSTM), and CNN-BiLSTM-Attention. …”
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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…For example, low-level feature fusion utilizes basic attributes such as color, shapes and texture to distinguish fruits from backgrounds, while high-level feature learning employs more complex models like convolutional neural networks to interpret the contextual relationships within the data. …”
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CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People by Fahad Ashiq, Muhammad Asif, Maaz Bin Ahmad, Sadia Zafar, Khalid Masood, Toqeer Mahmood, Muhammad Tariq Mahmood, Ik Hyun Lee

“…For object detection and recognition, a deep Convolution Neural Network (CNN) model is employed with an accuracy of 83.3%, whereas the dataset contains more than 1000 categories. …”
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Advanced TSGL-EEGNet for Motor Imagery EEG-Based Brain-Computer Interfaces by Xin Deng, Boxian Zhang, Nian Yu, Ke Liu, Kaiwei Sun

“…Additionally, this work also uses the Grad-CAM to visualize the frequency and spatial features that are learned by the neural network.…”
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Partial Attention in Global Context and Local Interaction for Addressing Noisy Labels and Weighted Redundancies on Medical Images by Minh Tai Pham Nguyen, Minh Khue Phan Tran, Tadashi Nakano, Thi Hong Tran, Quoc Duy Nam Nguyen

“…Recently, the application of deep neural networks to detect anomalies on medical images has been facing the appearance of noisy labels, including overlapping objects and similar classes. …”
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Editorial by Christian Gütl

“…Ruchika Malhotra and Madhukar Cherukuri from India look in their research into Software Defect Categorization (SDC) models and apply convolutional neural networks in their empirical study. …”
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Effectiveness of the Spatial Domain Techniques in Digital Image Steganography by Rosshini Selvamani, Yusliza Yusoff

“…In addition to using statistics as a foundation, convolution neural networks (CNN), generative adversarial networks (GAN), coverless approaches, and machine learning are all used to construct steganographic methods. …”
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Secured DICOM medical image transition with optimized chaos method for encryption and customized deep learning model for watermarking by R. Abirami, C. Malathy

“…The chaotic encryption technique makes use of the Lorenz map and a Customized Deep Learning Model (CDLM) based on Convolution Neural Networks (CNNs) are presented for watermarking. …”
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A dataset of blood slide images for AI-based diagnosis of malariaDataverse by Rose Nakasi, Joyce Nakatumba Nabende, Jeremy Francis Tusubira, Aloyzius Lubowa Bamundaga, Alfred Andama

“…The labelled image data can be used to build computational models implemented with convolution neural networks. The dataset has 3000 labelled thick blood smear images and 1000 labelled thin blood smear images. …”
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STA-HAR: A Spatiotemporal Attention-Based Framework for Human Activity Recognition by Md. Khaliluzzaman, Md. Furquan, Mohammod Sazid Zaman Khan, Md. Jiabul Hoque

“…Furthermore, the utilization of an attention mechanism serves the purpose of dynamically selecting the significant segments within the sequence, thereby improving the model’s comprehension of context and enhancing the efficacy of deep neural networks (DNNs) in the domain of human activity recognition (HAR). …”
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Edge and texture aware image denoising using median noise residue U-net with hand-crafted features by Soniya S., Sriharipriya K. C.

“…Unfortunately, the existing works have focussed only on the peak signal to noise ratio (PSNR) metric and have shown no attention to edge features in a reconstructed image. Although fully convolution neural networks (CNN) are capable of removing the noise using kernel filters and automatic extraction of features, it has failed to reconstruct the images for higher values of noise standard deviation. …”
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