Breast Cancer Detection Using Mammography: Image Processing to Deep Learning by Shahzad Ahmad Qureshi, Aziz-Ul-Rehman, Lal Hussain, Touseef Sadiq, Syed Taimoor Hussain Shah, Adil Aslam Mir, Muhammad Amin Nadim, Darnell K. Adrian Williams, Tim Q. Duong, Qurat-Ul-Ain Chaudhary, Natasha Habib, Asrar Ahmad, Syed Adil Hussain Shah

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Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences by Janet M. Baker, Peter Cariani

“…., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. …”
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Fault diagnosis method of mine hoist main bearing with small sample based on VAE-WGAN by Fan JIANG, Hongyan SONG, Xi SHEN, Zhencai ZHU, Shuman CHENG

“…In order to improve the feature extraction ability and fault diagnosis accuracy of fault diagnosis models, based on the lightweight convolutional neural network MobileNetV2, the convolutional block attention mechanism CBAM is integrated into the deep feature mapping of MobileNetV2, and an attention mechanism convolutional classification network CBAM-MobileNetV2 is constructed. …”
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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…Weighted gene co-expression network analysis (WGCNA) was employed to identify genetic modules associated with MI. …”
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CosPoint Transformer: Enhancing 3-D Semantic Segmentation With Cosine Similarity Attention and Cross-Attention by Thien Huynh-The, Minh-Thanh Le, Won Jae Ryu, Anh-Kiet Vo

“…In this article, we introduce the CosPoint Transformer, a novel Transformer-based neural network for 3-D semantic segmentation using superpoints. …”
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A tailored deep learning approach for early detection of oral cancer using a 19-layer CNN on clinical lip and tongue images by Pinjie Liu, Kambiz Bagi

“…This research introduces a custom-designed, 19-layer convolutional neural network (CNN) for the automated diagnosis of oral cancer using clinical images of the lips and tongue. …”
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Dynamic quantitative characterization and automatic identification of the buried hill reservoir types in Yakela block by REN Hong,LI Weiqi,GUO Zhongchun,YANG Xiaoteng,XU Jian,WANG Xiao

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New AODV based clustering routing strategy by ZHENG Kai1, WANG Neng1, LIU Ai-fang2

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Prediction of induction motor faults using machine learning by Ademola Abdulkareem, Tochukwu Anyim, Olawale Popoola, John Abubakar, Agbetuyi Ayoade

“…The Random Forest (RF) model achieved the highest accuracy at 0.91, closely followed by the Artificial Neural Network (ANN) and k-nearest Neighbors (k-NN) models, both achieving an accuracy of 0.9. …”
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Advancing e-waste classification with customizable YOLO based deep learning models by P. Akhil Rajeev, Vivek Dharewa, D. Lakshmi, G. Vishnuvarthanan, Jayant Giri, T. Sathish, Mubarak Alrashoud

“…The standout performer, YOLOv8, demonstrated exceptional capabilities with its enhanced feature pyramid networks and improved CSPDarknet53 backbone with 53 convolutional layers, achieving superior precision and accuracy. …”
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Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records by Abdulrahman Al-Dailami, Hulin Kuang, Jianxin Wang

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Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis by Li L, Yang W, Jia H

“…Feature importance analysis indicated that variables such as smoking, surgery and a history of oral contraceptive use were most important in predicting recurrence.Conclusion: This study demonstrates that, compared to logistic regression and random forest models, neural networks have superior performance in predicting the recurrence of granulomatous mastitis. …”
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Emotion Classification from Electroencephalographic Signals Using Machine Learning by Jesus Arturo Mendivil Sauceda, Bogart Yail Marquez, José Jaime Esqueda Elizondo

“…This study aimed to evaluate the performance of three neural network architectures—ShallowFBCSPNet, Deep4Net, and EEGNetv4—for emotion classification using the SEED-V dataset. …”
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Short-term train arrival delay prediction: a data-driven approach by Qingyun Fu, Shuxin Ding, Tao Zhang, Rongsheng Wang, Ping Hu, Cunlai Pu

“…It innovatively compares and analyzes the model's performance using both full data and delay data formats. Additionally, the evaluation of the network's predictive capabilities considers different scenarios, providing a comprehensive demonstration of the model's predictive performance.…”
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Continuous Speech-Based Fatigue Detection and Transition State Prediction for Air Traffic Controllers by Susmitha Vekkot, Surya Teja Chavali, Charan Tej Kandavalli, Rama Sai Abhishek Podila, Deepa Gupta, Mohammed Zakariah, Yousef Ajami Alotaibi

“…MFCC and statistical features were extracted from the labelled continuous speech and input into various Recurrent Neural Networks, such as bi-LSTM and Bi-GRU, for fatigue state prediction tasks. …”
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Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNN by Komang Wahyu Trisna, Jinjie Huang, Yuanjian Chen, I Gede Juliana Eka Putra

“…The MCNN architecture effectively captures local patterns in text using multiple filter sizes, enabling comprehensive feature extraction with low computational overhead. …”
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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenari... by Cecilia Parracciani, Daniela Gigante, Onisimo Mutanga, Stefania Bonafoni, Marco Vizzari

“…Understanding grassland habitat dynamics in space and time is crucial for evaluating the effectiveness of protection measures and developing sustainable management practices, specifically within the Natura 2000 network and in light of the European Biodiversity Strategy. …”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

“…Here, we developed and validated STIED, a simple yet powerful supervised DL algorithm combining two convolutional neural networks with temporal (1D time-course) and spatial (2D topography) features of MEG signals inspired from current clinical guidelines. …”
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An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation by Shiva Arjmandmazidi, Hamid Reza Heidari, Tohid Ghasemnejad, Zeinab Mori, Leila Molavi, Amir Meraji, Shadi Kaghazchi, Elnaz Mehdizadeh Aghdam, Soheila Montazersaheb

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Dual stream graph augmented transformer model integrating BERT and GNNs for context aware fake news detection by Hejamadi Rama Moorthy, N. J. Avinash, N. S. Krishnaraj Rao, K. R. Raghunandan, Radhakrishna Dodmane, Jeremy Joseph Blum, Lubna A Gabralla

“…The objective is to enhance fake news detection by leveraging both linguistic and network-based features. The proposed method employs Graph Attention Networks (GAT) and Graph Transformers to extract contextual relationships, while an attention-based fusion mechanism effectively integrates textual and graph embeddings for classification. …”
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