Res-SE-ConvNet: A Deep Neural Network for Hypoxemia Severity Prediction for Hospital In-Patients Using Photoplethysmograph Signal by Talha Ibn Mahmud, Sheikh Asif Imran, Celia Shahnaz

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A Disentangled Representation-Based Multimodal Fusion Framework Integrating Pathomics and Radiomics for KRAS Mutation Detection in Colorectal Cancer by Zhilong Lv, Rui Yan, Yuexiao Lin, Lin Gao, Fa Zhang, Ying Wang

“…However, there are still two major problems in existing studies: inadequate single-modal feature learning and lack of multimodal phenotypic feature fusion. …”
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SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders by Samra Siddiqui, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, Dina A. AlHammadi

“…The objective of this research is to develop a robust deep network, called SNet, that offers a solution to complex classification problems. Firstly, the endoscopic images undergo preprocessing before being subjected to feature extraction. …”
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Fuzzy Reliability Optimization for 2-Hub Center Problem with Cluster-Based Policy and Application in Cross-Border Supply Chain Network Design Using TS Algorithm by Chenyi Yan, Xifu Wang, Kai Yang

“…This problem differs from the classical HLP in the sense that (i) the hub-and-spoke (H&S) network is grouped into two clusters in advance based on their cross-border geographic features, and (ii) a fuzzy reliability optimization approach based on the possibility measure is developed. …”
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Cost optimization model for multi-cloud network based on Kubernetes by Ming GAO, Ming LIU, Yangting CHEN, Weiming WANG

“…The cloud-native scheduling system, represented by Kubernetes, is widely used by cloud tenants in a multi-cloud environment.The problem of network observation becomes more and more serious, especially the cost of network traffic across cloud and region.In Kubernetes, the eBPF technology was introduced to collect the network data features of kernel state of operating system to solve the network observation problem, and then the network data features were modeled as QAP, a combination of heuristic and stochastic optimization was used to obtain the best near optimal solution in a real-time computing scenario.This model is superior to the Kubernetes native scheduler in the cost optimization of network resources, which is based on the scheduling strategy of computing resources only, and increases the complexity of scheduling links in a controllable range, effectively reduces the cost of network resources in a multi-cloud area deployment environment.…”
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Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique by T. K. Revathi, B. Sathiyabhama, S Kaliraj, Vidhushavarshini Sureshkumar

“…However, there are many works have been carried out on classification of the disease but they focused less on feature selection and increasing the accuracy of the model. …”
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A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study by Tracy Huang, Chun-Kit Ngan, Yin Ting Cheung, Madelyn Marcotte, Benjamin Cabrera

“…Our approach holistically has higher F1, precision, and recall scores compared to existing feature selection methods. The models in this study select several significant clinical and socioenvironmental variables as risk factors associated with the development of behavioral problems in young survivors of acute lymphoblastic leukemia. …”
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Robustness, Exploitable Relations and History: Assessing Varitel Semantics as a Hybrid Theory of Representation by Nicolás Sebastián Sánchez

“…I will conclude that internal problems beset Shea’s theory of representation. …”
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One-stage uncertain linear optimization by Zeinab Zarea, Alireza Ghaffari-Hadigheh Ghaffari-Hadigheh

“…Uncertainty is one of the intrinsic features of natural phenomenon and optimization is not an exception. …”
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Enhancing classification efficiency in capsule networks through windowed routing: tackling gradient vanishing, dynamic routing, and computational complexity challenges by Gangqi Chen, Zhaoyong Mao, Junge Shen, Dongdong Hou

“…This prevents saturation and mitigates the gradient vanishing problem. In addition, a novel gradient-friendly network structure is developed to facilitate the extraction of complex features with deeper networks. …”
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Features of structural and geometric remodeling of the heart and changes in heart diastolic filling in patients with chronic heart failure of ischemic genesis with reduced left ven... by V. A. Lysenko

“…Despite a substantial range of studies on the features of structural and geometric remodeling of the heart, changes in systolic and diastolic function of the ventricles in CHF patients, this issue still presents a challenge and needs to be improved. …”
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PALM VEIN PATTERN BIOMETRIC AUTHENTICATION TECHNOLOGY by S. O. Baranov, D. B. Abramov

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Method of unknown protocol classification based on autoencoder by Chunxiang GU, Weisen WU, Ya’nan SHI, Guangsong LI

“…Aiming at the problem that a large number of unknown protocols exist in the Internet,which makes it very difficult to manage and maintain the network security,a classification and identification method of unknown protocols was proposed.Combined with the autoencoder technology and the improved K-means clustering technology,the unknown protocol was classified and identified for the network traffic.The autoencoder was used to reduce dimensionality and select features of network traffic,clustering technology was used to classify the dimensionality reduction data unsupervised,and finally unsupervised recognition and classification of network traffic were realized.Experimental results show that the classification effect is better than the traditional K-means,DBSCAN,GMM algorithm,and has higher efficiency.…”
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Method of unknown protocol classification based on autoencoder by Chunxiang GU, Weisen WU, Ya’nan SHI, Guangsong LI

“…Aiming at the problem that a large number of unknown protocols exist in the Internet,which makes it very difficult to manage and maintain the network security,a classification and identification method of unknown protocols was proposed.Combined with the autoencoder technology and the improved K-means clustering technology,the unknown protocol was classified and identified for the network traffic.The autoencoder was used to reduce dimensionality and select features of network traffic,clustering technology was used to classify the dimensionality reduction data unsupervised,and finally unsupervised recognition and classification of network traffic were realized.Experimental results show that the classification effect is better than the traditional K-means,DBSCAN,GMM algorithm,and has higher efficiency.…”
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基于透视不变二值特征描述子的图像匹配算法 by Li-chuan GENG, Song-zhi SU, Shao-zi LI

“…Current local feature based image matching algorithms are usually less robust to image perspective transformation.Aiming to solve this problem，a new perspective invariant binary code (PIBC) based image matching algorithm is proposed.Firstly，FAST corners are detected on the pyramid images，those corners with non-maximum Harris corner response value and the edge points are further eliminated.And then，by simulating the perspective transformations of images taken from different viewpoints，a single FAST corner is described with binary descriptors under different viewpoint transformations，which makes the descriptor could describe the identical feature point on different perspective transform images.Experimental results show its robustness to image perspective transformation，while its complexity is similar with SURF.…”
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Mobile platform continuous authentication scheme based on gait characteristics by Li YANG, Zhuoru MA, Chenghui ZHANG, Qingqi PEI

“…The popularity of smart phones renders people extremely high requirements for safety.But the traditional one-time authentication method can’t continuously guarantee the security of equipment.To solve the problem,a continuous authentication scheme based on gait characteristics was proposed to realize the identification of current visitors.Moving average filtering,threshold-based useful information interception method and other operations were adopted to reduce noise interference.Template interception was used to maximize the utilization of information,and an optimal combination of time domain features and frequency domain features were proposed to reduce the storage space requirement of users’ information.Finally,the support vector machine realized the identity authentication function.Experiments show that the proposed scheme can effectively authenticate the identities of visitors.…”
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基于透视不变二值特征描述子的图像匹配算法 by Li-chuan GENG, Song-zhi SU, Shao-zi LI

“…Current local feature based image matching algorithms are usually less robust to image perspective transformation.Aiming to solve this problem，a new perspective invariant binary code (PIBC) based image matching algorithm is proposed.Firstly，FAST corners are detected on the pyramid images，those corners with non-maximum Harris corner response value and the edge points are further eliminated.And then，by simulating the perspective transformations of images taken from different viewpoints，a single FAST corner is described with binary descriptors under different viewpoint transformations，which makes the descriptor could describe the identical feature point on different perspective transform images.Experimental results show its robustness to image perspective transformation，while its complexity is similar with SURF.…”
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A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data by Aristos Aristodimou, Athos Antoniades, Efthimios Dardiotis, Eleni Loizidou, George Spyrou, Christina Votsi, Christodoulou Kyproula, Marios Pantzaris, Nikolaos Grigoriadis, Georgios Hadjigeorgiou, Theodoros Kyriakides, Constantinos Pattichi

“…The first two steps of the framework are quality control and feature selection. The next step uses clustering and binary encodes the features. …”
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Double Cost Sensitive Random Forest Algorithm by ZHOU Yan-long, SUN Guang-lu

“…A Double Cost Sensitive Random Forest (DCS-RF) algorithm is proposed to solve the problem that the accuracy of a few classes is not ideal when the classifier identifies unbalanced data. …”
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Technologies and Algorithms for Building the Augmented Reality by I. A. Blagoveshchenskiy, N. A. Demyankov

“…Main attention is payed to the problem of making projection of a 3D model on the marker’s plane. …”
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