Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment by An Zichen, Li Fan

“…Breast ultrasound (US) imaging technology plays a crucial role in the early diagnosis and intervention treatment of breast cancer patients. Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. …”
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Using transformer-based models and social media posts for heat stroke detection by Sumiko Anno, Yoshitsugu Kimura, Satoru Sugita

“…This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks.…”
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A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny by HU Wei, ZHAO Jumin, LI Dengao

“…In this study, a lightweight laser chip defect detection algorithm based on an improved YOLOv7-Tiny is proposed, aiming at addressing the high computational and parameter demands of deep learning applications in defect detection. [Methods] By employing a lightweight convolutional neural network as the feature extraction backbone and integrating multi-branch reparameterized convolution blocks, this algorithm not only significantly reduces resource consumption but also enhances feature representation capabilities. …”
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Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications by Aditya Venkatraman, Mark A. Wilson, David Montes de Oca Zapiain

“…The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. …”
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PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology by David P. G. Thomas, Carlos M. Garcia Fernandez, Reza Haydarlou, K. Anton Feenstra

“…Abstract Protein interactions are crucial for understanding biological functions and disease mechanisms, but predicting these remains a complex task in computational biology. Increasingly, Deep Learning models are having success in interface prediction. …”
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Segmentasi Citra X-Ray Dada Menggunakan Metode Modifikasi Deeplabv3+ by Rima Tri Wahyuningrum, Maughfirotul Jannah, Budi Dwi Satoto, Amillia Kartika Sari, Anggraini Dwi Sensusiati

“…To make it easier to make a diagnosis, we need a deep learning model that can help with this. DeepLabV3+ is a method that can carry out the segmentation process. …”
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Implementasi Algoritma Convolutional Neural Network untuk Klasifikasi Jenis Keris by Maria Mediatrix Sebatubun, Cosmas Haryawan

“…Penelitian ini akan mengimplementasikan metode deep learning dengan algoritma Convolutional Neural Network (CNN) yang dapat melakukan tugas klasifikasi secara langsung pada citra, untuk membangun sebuah model untuk klasifikasi jenis keris berdasarkan dhapur. …”
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Gradient pooling distillation network for lightweight single image super-resolution reconstruction by Zhiyong Hong, GuanJie Liang, Liping Xiong

“…In recent years, significant progress about SISR has been achieved through the utilization of deep learning technology. However, these deep methods often exhibit large-scale networks architectures, which are computationally intensive and hardware-demanding, and this limits their practical application in some scenarios (e.g., autonomous driving, streaming media) requiring stable and efficient image transmission with high-definition picture quality. …”
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Surface defect detection on industrial drum rollers: Using enhanced YOLOv8n and structured light for accurate inspection. by Guofeng Qin, Qinkai Zou, Mengyan Li, Yi Deng, Peiwen Mi, Yongjian Zhu, Hao Liu

“…Aiming at solving the problems that the traditional light source visual imaging system, which does not clearly reflect defect features, the defect detection efficiency is low, and the accuracy is not enough, this paper designs an image acquisition system based on line fringe structured light and proposes an improved deep learning network model based on YOLOv8n to achieve efficient detection of defects on the rolling surface of a drum roller. …”
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Temperature and Humidity Monitoring in Hydroponic Cultivation Based on Internet of Things: Dataset Development for Smart Agriculture by Simon Prananta Barus, Jeriko Ichtus Seo

“…Further research will integrate more monitoring parameters, conduct direct hydroponic cultivation trials, and apply artificial intelligence such as machine learning and deep learning to improve efficiency and effectiveness in hydroponic cultivation.…”
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An intelligent spam detection framework using fusion of spammer behavior and linguistic. by Amna Iqbal, Muhammad Younas, Muhammad Kashif Hanif, Muhammad Murad, Rabia Saleem, Muhammad Aater Javed

“…The unified representation of features is another challenging task in spam detection. Various deep learning approaches have been proposed for spam detection and classification but these methods are specialized in extracting the features but lack to capture feature dependencies effectively with other features but there is a lack of comprehensive models that integrate linguistic and behavioral features to improve the accuracy of spam detection. …”
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DFTD-YOLO: Lightweight Multi-Target Detection From Unmanned Aerial Vehicle Viewpoints by Yuteng Chen, Zhaoguang Liu

“…Due to the low detection accuracy of small and dense target objects in multi-target detection tasks from the unmanned aerial vehicle (UAV) perspective and the deployment of deep learning models for UAVs as embedded devices, these models must be lightweight. …”
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Research on YOLOv5 Oracle Recognition Algorithm Based on Multi-Module Fusion by Xinhang Zhang, Zhenhua Ma, Yaru Zhang, Huiying Ru

“…However, traditional methods and some deep learning models have limited ability to capture the complex forms and fine details of oracle bone script, which makes it difficult to fully detect subtle differences between characters. …”
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CLOUD COMPUTING AND FINANCIAL PERFORMANCE OF LISTED DEPOSIT MONEY BANKS IN NIGERIA by Hakeem Olayinka Hakeem Olayinka, Adetumilara Adepeju Dedire-Ampitan

“…The analysis includes variables such as Automated Chatbot Banking Services (ACBS), Deep Learning Machine in Credit Risk Assessment (DLM), Machine Learning Solutions (MLS), and Return on Asset (ROA). …”
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Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries by Azeez Okikiola Lawal, Tolutope Joseph Ogunniyi, Oriire Idunnuoluwa Oludele, Oluwaloseyi Ayomipo Olorunfemi, Olalekan John Okesanya, Jerico Bautista Ogaya, Emery Manirambona, Mohamed Mustaf Ahmed, Don Eliseo Lucero-Prisno

“…The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. …”
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Enhancing depression recognition through a mixed expert model by integrating speaker-related and emotion-related features by Weitong Guo, Qian He, Ziyu Lin, Xiaolong Bu, Ziyang Wang, Dong Li, Hongwu Yang

“…Thus, it outperforms state-of-the-art deep learning methods that use speech features. Additionally, our approach shows strong performance across Chinese and English speech datasets, highlighting its effectiveness in addressing cultural variations.…”
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An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data by Arjun Kumar Bose Arnob, M. F. Mridha, Mejdl Safran, Md Amiruzzaman, Md. Rajibul Islam

“…The contribution of this paper will be an addition to the deep learning techniques applied for the solution of intrusion detection systems (IDS), which will also allow the building and implementation of more efficient security mechanisms in IoT environments.…”
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Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs by Hanlin Feng, Zitong Zhang, Chunlei Zhang, Chengcheng Zhong, Qiaoyu Ma

“…Compared with machine learning and deep learning methods, the SVA-TCN demonstrates a remarkable accuracy of 99.00%, surpassing the accuracy of the comparison methods by 0.37–17.69%. …”
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Leveraging Quantum LSTM for High-Accuracy Prediction of Viral Mutations by Prashanth Choppara, Bommareddy Lokesh

“…The one-hot encoding technique is a standard technique in machine learning for encoding protein sequences into data that can be used in neural networks.The proposed QLSTM outperformed existing deep learning architectures such as the Attention-Augmented Convolutional Neural Network (AACNN), Stacked Recurrent Neural Network (Stacked RNN), Retention Network (RetNet), and Bidirectional Long Short Term Memory (BiLSTM). …”
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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification by Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed

“…This article introduces Least Absolute Shrinkage and Selection Operator–Multi-omics Gated Attention (LASSO–MOGAT), a novel graph-based deep learning framework that integrates messenger RNA, microRNA, and DNA methylation data to classify 31 cancer types. …”
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