Cross-Modal Hashing Retrieval Based on Density Clustering by Xiaojun Qi, Xianhua Zeng, Hongmei Tang

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Non-Redundant Feature Extraction in Mobile Edge Computing by Xiaojun Chen, Qi Wang, Chuntao Ding

“…The goal of this paper is to reduce the amount of data uploaded by IoT devices to the cloud/edge server by removing redundant vectors of the feature extractor, thereby reducing the data transmission time and the time it takes for the cloud/edge server to process features, and improving image recognition accuracy. …”
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Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke by Gianluca Brugnara, Michael Baumgartner, Edwin David Scholze, Katerina Deike-Hofmann, Klaus Kades, Jonas Scherer, Stefan Denner, Hagen Meredig, Aditya Rastogi, Mustafa Ahmed Mahmutoglu, Christian Ulfert, Ulf Neuberger, Silvia Schönenberger, Kai Schlamp, Zeynep Bendella, Thomas Pinetz, Carsten Schmeel, Wolfgang Wick, Peter A. Ringleb, Ralf Floca, Markus Möhlenbruch, Alexander Radbruch, Martin Bendszus, Klaus Maier-Hein, Philipp Vollmuth

“…Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25–45% for sensitivity and 4–11% for NPV (p ≤ 0.003 each). We provide an imaging platform ( https://stroke.ccibonn.ai/ ) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.…”
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Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision by Quan Wang, Mengnan Wang, Jiadong Sun, Deji Chen, Pei Shi

“…The detection methods are then categorized into three main groups: traditional image processing, machine learning, and deep learning, with their principles, case studies, limitations, and future development directions analyzed. …”
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Computer Vision-Based Monitoring of Bridge Structural Vibration During Incremental Launching Construction by Hong Shi, Min Zhang, Tao Jin, Xiufeng Shi, Jian Zhang, Yixiang Xu, Xinyi Guo, Xiaoye Cai, Weibing Peng

“…The method utilizes high-definition cameras to capture dynamic images of bridges and incorporates advanced image processing algorithms to automatically identify and track the vibration characteristics of bridge structures, achieving low energy consumption, low cost, and high efficiency in monitoring. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…In one experiment, Alkhatib et al. used Convolutional neural network (CNN) based deep trackers to track the median and sciatic nerve with a surprising accuracy of 0.87.2 Another study employed the same CNN model to locate and discriminate accurate images of sacrum, vertebral levels and intervertebral gaps during percutaneous spinal needle insertion.3 Another study used a different AI model called SVM (support vector machine) classification, image processing, and template matching to locate lumbar level L3-L4 and the ideal puncture site for epidural anaesthesia in real-time. …”
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Nonuniformity Correction Method of Infrared Detector Based on Statistical Properties by Li Dandan, Ding Xiang, Chai Mengyang, Ma Chao, Sun Dexin

“…Then, the linear correlated region based on the recombined data is extracted using a judgment algorithm that processes the linear correlations of multiple regions to simulate the detector's response curve and find the nonuniformity correction coefficient of each pixel. …”
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A Systematic Literature Review on Machine Learning Techniques for Skin Disease Classification by Fadilah Karamun Nisaa Nadiyah, Nayla Nur Alifah, Sri Nurdiati, Elis Khatizah, Mohamad Khoirun Najib

“…The purpose of this research is to review machine learning algorithms that can be utilized to develop image-based skin disease classification systems. …”
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Crowd navigation monitoring during emergencies by Oksana Tymoshchuk, Maksym Tishkov, Victor Bondarenko

“…The second step is the speed estimation method, which is based on calculating the real-world distances and knowing camera parameters and distances in pixels on the resulting image. Implementation of the algorithm was tested on real videos and showed an error of about 0.04 m/s.…”
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Invertible Attention-Guided Adaptive Convolution and Dual-Domain Transformer for Pansharpening by Qun Song, Hangyuan Lu, Chang Xu, Rixian Liu, Weiguo Wan, Wei Tu

“…Pansharpening is the process of fusing a multispectral (MS) image with a panchromatic image to produce a high-resolution MS (HRMS) image. …”
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Spherical Polar Pattern Matching for Star Identification by Jingneng Fu, Ling Lin, Qiang Li

“…Finally, a reference star image is generated from the identified star pair to complete the matching process of all guide stars in the field of view. …”
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Measuring the Intracluster Light Fraction with Machine Learning by Louisa Canepa, Sarah Brough, Francois Lanusse, Mireia Montes, Nina Hatch

“…Our model can be directly applied to Hyper Suprime-Cam images, processing up to 500 images in a matter of seconds on a single GPU, or fine-tuned for other imaging surveys such as LSST, with the fine-tuning process taking just 3 minutes. …”
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Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning by Ersin Elbasi, Ahmet E. Topcu, Elda Cina, Aymen I. Zreikat, Ahmed Shdefat, Chamseddine Zaki, Wiem Abdelbaki

“…The Cope Leaf Dataset offers a comprehensive collection of leaf images from various plant species, enabling the development and evaluation of advanced classification algorithms. …”
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Parkinson’s Disease Detection Based on Transfer Learning by Mohammad Talal Ghazal

“…In this research, a method was developed to detect Parkinson's disease using machine learning,  learning transfer techniques were relied upon to extract features from handwriting images that we obtained from the NewHandPD database, and then these images were classified into two categories (Parkinson's disease and non-Parkinson's disease) by KNN classification algorithm, for being accurate and fast in calculations, the results  of the  training of the INCEPTION-V4 model showed  a detection accuracy of up to 93%, as well as an area under the curve of 0.89 with a loss of only 0.2 , where this model can be relied on to diagnose and detect Parkinson's disease with high accuracy. …”
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Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning by Abdul Razzaq, Sharaiz Shahid, Muhammad Akram, Muhammad Ashraf, Shahid Iqbal, Aamir Hussain, M. Azam Zia, Sulman Qadri, Najia Saher, Faisal Shahzad, Ali Nawaz Shah, Aziz-ur Rehman, Sven-Erik Jacobsen

“…In leaf imprint, the state of stomata has been determined by measuring the correlation between the area of stomata and the aperture of each detected stoma in the image. The stomata states have been classified through the Support Vector Machine (SVM) algorithm. …”
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Small parallel residual convolutional neural network and traffic congestion detection by Shan Jiang, Yuming Feng

“…At the same time, in order to reduce the time complexity and space complexity of the algorithm, this paper reduces the scale of large convolutional neural network models and proposes a small parallel residual convolutional neural network (SPRCNN) as an image classification model and applied it to traffic congestion detection. …”
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Hyperspectral Simultaneous Anomaly Detection and Denoising: Insights From Integrative Perspective by Minghua Wang, Lianru Gao, Longfei Ren, Xian Sun, Jocelyn Chanussot

“…Conversely, with the assistance of the antinoise dictionary conduction and the subspace domain-based low-rankness, the identification of anomalies with different features from the background can provide effective feedback to the denoising process. The proposed algorithm is efficiently solved by the well-designed linearized alternating direction method of multipliers with an adaptive penalty. …”
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Interpretation of myocardial perfusion SPECT with attenuation correction. Part 2 by A. A. Ansheles, V. В. Sergienko

“…Special emphasis is placed on the interpretation of apical and septal perfusion defects and diffuse perfusion irregularity, on the demonstration of the role of current reconstruction algorithms in obtaining highest-quality myocardial images, and on the assessment of self-potentialities of lowdose CT findings at myocardial SPECT/CT, as well as on proposals to standardize quantitative parameters for perfusion assessment at myocardial SPECT/CT.Results. …”
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Improved Convolutional Neural Networks for Course Teaching Quality Assessment by Yun Liu

“…The extracted features of video sequences were input into long short-team memory (LSTM) to process temporal features. Experimental results show that SLRCN algorithm has the best performance in training set and test set. …”
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Divide-and-conquer routing for learning heterogeneous individualized capsules. by Hailei Yuan, Qiang Ren

“…Extensive experiments on benchmark image classification datasets demonstrate that our approach consistently outperforms the original dynamic routing algorithm as well as other state-of-the-art routing strategies, resulting in improved feature learning and classification accuracy. …”
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