A Robust Hybrid CNN+ViT Framework for Breast Cancer Classification Using Mammogram Images by Vasudha Rani Patheda, Gunda Laxmisai, B. V. Gokulnath, S. P. Siddique Ibrahim, S. Selva Kumar

“…Breast cancer is the most frequent type of cancer largely experienced by women currently, although it could happen to men also. …”
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Short-Term Target Maneuvering Trajectory Prediction Using DTW–CNN–LSTM by Haifeng Guo, Jinyi Yang, Xianyong Jing, Peng Zhang

“…This approach allows us to identify and select the most analogous historical data, which we then utilize as our training dataset. …”
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A survey: Breast Cancer Classification by Using Machine Learning Techniques by Ruaa Hassan Mohammed Ameen, Nasseer Moyasser Basheer, Ahmed Khazal Younis

“…The Naïve Bayes, the K-nearest neighbors (KNN), the Support Vector Machine (SVM), the Random Forest, the Logistic Regression, Multilayer Perceptron (MLP), fuzzy classifier, and Convolutional Neural Network (CNN) classifiers, are the most widely used technologies in this field. …”
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Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks by Mirosław Łącki

“…The research results confirm that it is possible to create a practical lightweight detection system with convolutional neural networks that calculates safety level in real time.…”
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Review of image classification based on deep learning by Fu SU, Qin LV, Renze LUO

“…In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.…”
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Mathematical Analysis and Performance Evaluation of the GELU Activation Function in Deep Learning by Minhyeok Lee

“…Selecting the most suitable activation function is a critical factor in the effectiveness of deep learning models, as it influences their learning capacity, stability, and computational efficiency. …”
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Can Artificial Intelligence Technology Help Achieving Good Governance: A Public Policy Evaluation Method Based on Artificial Neural Network by Zhinan Xu, Zijun Liu, Hang Luo

“…By leveraging empirical data and a deep learning model based on convolutional neural networks (CNN), the model achieves a high accuracy of 93.40%, surpassing most comparable models. …”
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Multitemporal Difference and Dynamic Optimization Framework for Multiscale Motion Satellite Video Super-Resolution by Qian Zhao, Youming Guo, Lei Min, Changhui Rao

“…Motion alignment is a critical task in satellite video super-resolution. Most existing methods rely on optical flow or deformable convolution for motion alignment. …”
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Few-shot biomedical NER empowered by LLMs-assisted data augmentation and multi-scale feature extraction by Di Zhao, Wenxuan Mu, Xiangxing Jia, Shuang Liu, Yonghe Chu, Jiana Meng, Hongfei Lin

“…Simultaneously, we employ dynamic convolution to capture multi-scale semantic information in sentences and enhance feature representation based on PubMedBERT. …”
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A Reaction-Based Approach to Colorimetric Detection of Organic Analytes in Water Using a Chlorine-Containing Carbocyanine Dye and Hypochlorite by Anna V. Shik, Evgenii V. Skorobogatov, Ramil M. Akhmetov, Irina A. Doroshenko, Tatyana A. Podrugina, Gleb K. Sugakov, Mikhail K. Beklemishev

“…Water quality control employs techniques mostly targeting individual analytes; group detection is also practiced, but the choice of group methods is limited, which supports interest in developing such methods. …”
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Distinguishing Difficulty Imbalances in Strawberry Ripeness Instances in a Complex Farmland Environment by Yang Gan, Xuefeng Ren, Huan Liu, Yongming Chen, Ping Lin

“…Firstly, a partial convolution-based compact inverted block is developed, which significantly enhances the feature extraction capability of the model. …”
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Bird Species Detection Net: Bird Species Detection Based on the Extraction of Local Details and Global Information Using a Dual-Feature Mixer by Chaoyang Li, Zhipeng He, Kai Lu, Chaoyang Fang

“…The dual-branch feature mixer extracts features from dichotomous feature segments using global attention and deep convolution, expanding the network’s receptive field and achieving a strong inductive bias, allowing the network to distinguish between similar local details. …”
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BiAttentionNet: a dual-branch automatic driving image segmentation network integrating spatial and channel attention mechanisms by Ruijun Liu, Yijun Zhang, Jieying Chen, Zhigang Wu, Yaohui Zhu, Jun Liu, Min Chen

“…Abstract Real-time semantic segmentation is one of the most researched areas in the field of computer vision, and research on dual-branch networks has gradually become a popular direction in network architecture research. …”
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Solar Wind Speed Prediction via Graph Attention Network by Yanru Sun, Zongxia Xie, Haocheng Wang, Xin Huang, Qinghua Hu

“…Second, our approach employs the dilated causal convolution to extend the receptive field and prolong the prediction time. …”
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Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm by Gargi Pant Shukla, Santosh Kumar, Saroj Kumar Pandey, Rohit Agarwal, Neeraj Varshney, Ankit Kumar

“…After pre-processing, we proposed three learning algorithms for AD classification, that is random forest, XGBoost, and Convolution Neural Networks (CNN). Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57% and sensitivity is 97.60%.…”
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Injecting structure-aware insights for the learning of RNA sequence representations to identify m6A modification sites by Yue Yu, Shuang Xiang, Minghao Wu

“…N6-methyladenosine (m6A) represents one of the most prevalent methylation modifications in eukaryotes and it is crucial to accurately identify its modification sites on RNA sequences. …”
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AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics by Jian Feng, Lang Yu, Rui Ma

“…Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. …”
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BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection by Ruicheng Cao, Ruiteng Zhang, Xinyue Yan, Jian Zhang

“…A feature-guided module connects the shallow convolution layers of the two branches. When training the image enhancement branch, the object detection subnet in the enhancement branch guides the image enhancement subnet to be optimized towards the direction that is most conducive to the detection task. …”
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Evaluation of Dose Calculation Algorithms Accuracy for ISOgray Treatment Planning System in Motorized Wedged Treatment Fields by Sajjad Raghavi, Hamid-Reza Sadoughi, Mohammad Ehsan Ravari, Marziyeh Behmadi

“…ISOgray treatment planning system (TPS) uses Clarkson, collapsed cone convolution (CCC), and fast Fourier transform (FFT) algorithms for dose distribution. …”
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MAIN TRENDS OF INVESTMENT AND INNOVATIVE ACTIVITY OF BUSINESS ENTITIES IN THE CONDITIONS OF EUROPEAN INTEGRATION by Olena Iastremska, Hanna Strokovych

“…The following results are obtained: to increase the objectivity and reliability of the analysis, the corresponding integral indicators characterizing each level are combined into a generalized integral index of investment and innovation activity of economic entities, calculated by the method of additive convolution, taking into account the coefficients of significance of each level. …”
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