Dynamically Tunable Multidimensional Feature Focusing and Diffusion Networks for Water Surface Debris Detection by Chong Zhang, Jie Yue, Jianglong Fu

“…First, a Self-moving Point Convolutional Gating Network (SPCG-Net) was designed, which integrated an adaptive point-moving mechanism with a convolutional gating linear unit to enhance the flexibility and accuracy of feature extraction. …”
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Method of Non-Destructive Control of Single-Phase and Three-Phase Transformers's Condition on the Basis of Frequency Characteristics by I. L. Hramyka, V. N. Galushko

“…Nowadays, there are many different methods of transformer diagnostics. The analysis of used methods and diagnostic systems indicates that a certain complexity of further development of existing methods and diagnostic systems has been achieved. …”
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Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning by Xiaoyun Lei, Zhian Zhang, Peifang Dong

“…The reward and punishment function and the training method are designed for the instability of the training stage and the sparsity of the environment state space. In different training stages, we dynamically adjust the starting position and target position. …”
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Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper by Kiranmayee Janardhan, Vinay Martin D’Sa Prabhu, T. Christy Bobby

“…Classification of brain gliomas is also essential because different types require different treatment approaches. …”
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A Hybrid Deep Learning Paradigm for Robust Feature Extraction and Classification for Cataracts by Akshay Bhuvaneswari Ramakrishnan, Mukunth Madavan, R. Manikandan, Amir H. Gandomi

“…ABSTRACT The study suggests using a hybrid convolutional neural networks‐support vector machines architecture to extract reliable characteristics from medical images and classify them as an ensemble using four different models. …”
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Multimode Flex-Interleaver Core for Baseband Processor Platform by Rizwan Asghar, Dake Liu

“…Algorithmic level optimizations like 2D transformation and realization of recursive computation are applied, which appear to be the key to reach to an efficient hardware multiplexing among different interleaver implementations. The presented hardware enables the mapping of vital types of interleavers including multiple block interleavers and convolutional interleaver onto a single architecture. …”
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Transferable Deep Learning Models for Accurate Ankle Joint Moment Estimation during Gait Using Electromyography by Amged Elsheikh Abdelgadir Ali, Dai Owaki, Mitsuhiro Hayashibe

“…Transferable prediction across different subjects is advantageous for calibration-free, practical clinical applications. …”
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Influence of Target Surface BRDF on Non-Line-of-Sight Imaging by Yufeng Yang, Kailei Yang, Ao Zhang

“…The reconstructed NLOS images were classified via a convolutional neural network to assess how different surface materials impacted imaging quality. …”
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Scene Text Detection Based on Multi-scale Feature Extraction and Bidirectional Feature Fusion by LIAN Zhe, YIN Yanjun, ZHI Min, XU Qiaozhi

“…However, single-scale convolution methods are usually difficult to take into account the feature representation of text targets with different shapes and scales. …”
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A cross-stage features fusion network for building extraction from remote sensing images by Xiaolong Zuo, Zhenfeng Shao, Jiaming Wang, Xiao Huang, Yu Wang

“…The deep learning-based building extraction methods produce different feature maps at different stages of the network, which contain different information features. …”
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Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China by Zhuo Chen, Tao Liu

“…A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background.…”
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WISP: Workframe for Interferogram Signal Phase-Unwrapping by Timofey F. Khirianov, Aleksandra I. Khirianova, Egor V. Parkevich, Ilya Makarov

“…Iterations continue until the difference between the reconstructed and experimental phase distributions reaches an asymptotic minimum. …”
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Long-Term Neonatal EEG Modeling with DSP and ML for Grading Hypoxic–Ischemic Encephalopathy Injury by Leah Twomey, Sergi Gomez, Emanuel Popovici, Andriy Temko

“…First, the EEG signal is transformed into an amplitude and frequency modulated audio spectrogram, which enhances its relevant signal properties. The difference between EEG Grades 1 and 2 is enhanced. A convolutional neural network is then designed as a regressor to map the input image into an EEG grade, by utilizing an optimized rounding module to leverage the monotonic relationship among the grades. …”
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Infrared object detection for robot vision based on multiple focus diffusion and task interaction alignment by Jixu Zhang, Li Wang, Hung-Wei Li, Meng-Yen Hsieh, Shunxiang Zhang, Hua Wen, Meng Chen

“…However, the small gray-scale difference between the object and the background region in the infrared grayscale image and the single gray-scale information lead to the blurring of the semantic information of the image, which makes the robot unable to detect the object effectively. …”
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Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning by Qinglei Zhang, Laifeng Tang, Jiyun Qin, Jianguo Duan, Ying Zhou

“…This method combines a one-dimensional convolutional neural network (1DCNN) and a channel attention mechanism (CAM). 1DCNN can effectively extract local features of time series data, while CAM assigns different weights to each channel to highlight key features. …”
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Radar signal recognition exploiting information geometry and support vector machine by Yuqing Cheng, Muran Guo, Limin Guo

“…Specifically, the time‐frequency images of different LPI radar signals are obtained via the Choi‐Williams distribution (CWD) transform, and the AlexNet network, one improved convolutional neural network (CNN), is used to extract time‐frequency features. …”
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Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System by Ahmet Bozdag, Muhammed Yildirim, Mucahit Karaduman, Hursit Burak Mutlu, Gulsah Karaduman, Aziz Aksoy

“…<b>Results:</b> The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature—the developed model combines features obtained from three different pre-trained architectures for feature extraction. …”
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A Comprehensive Evaluation of Machine Learning and Deep Learning Models for Churn Prediction by Nabil M. AbdelAziz, Mostafa Bekheet, Ahmad Salah, Nissreen El-Saber, Wafaa T. AbdelMoneim

“…Therefore, this study attempts to analyze the effectiveness of the advanced machine learning and deep learning models for churn prediction in the evaluation of the models’ performance across different sectors. This would help conclude whether the varied patterns of the churn throughout different sectors to the level that affects the model performance and to what extent. …”
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Method to generate cyber deception traffic based on adversarial sample by Yongjin HU, Yuanbo GUO, Jun MA, Han ZHANG, Xiuqing MAO

“…In order to prevent attacker traffic classification attacks,a method for generating deception traffic based on adversarial samples from the perspective of the defender was proposed.By adding perturbation to the normal network traffic,an adversarial sample of deception traffic was formed,so that an attacker could make a misclassification when implementing a traffic analysis attack based on a deep learning model,achieving deception effect by causing the attacker to consume time and energy.Several different methods for crafting perturbation were used to generate adversarial samples of deception traffic,and the LeNet-5 deep convolutional neural network was selected as a traffic classification model for attackers to deceive.The effectiveness of the proposed method is verified by experiments,which provides a new method for network traffic obfuscation and deception.…”
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A modified deep neural network enables identification of foliage under complex background by Xiaolong Zhu, Junhao Zuo, Honge Ren

“…Experimental results show that the modified approach can identify out different leaves with similar characteristics in one scene, and demonstrate the superiority of our proposed approach over some state-of-the-art deep neural networks, when it comes to recognise foliage in complicated environments.…”
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