Supporting Efficient Family Joins for Big Data Tables via Multiple Freedom Family Index by Qiang Zhu, Chao Zhu

“…Detailed definitions, practical applications, and processing strategies and algorithms for family joins are provided. Experimental results demonstrate that the index-based join method is quite promising in efficiently processing family joins.…”
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Digital twin based intelligent urban traffic forecasting and guidance strategy by Xiwen LIAO, Supeng LENG, Yujun MING, Tianyang LI

“…As the technology of ubiquitous Internet of things and artificial intelligence improves by leaps and bounds, the transportation system revolution is flourishing and bringing new opportunities and challenges.Considering the defect in the existing navigation system, and the neglect of the temporal and spatial characteristics of traffic flow, the macro traffic network and micro vehicle network were modeled and their coupling relationship was mined.Then, a digital twin based urban traffic forecasting and guidance method was proposed to alleviate the problem of traffic congestion.The spatial-temporal traffic flow information was predicted through the diffusion convolution recurrent neural network, which was explicitly applied to the vehicle path planning decision.On this basis, a spatial-temporal collaborative deep reinforcement learning method was proposed to implement the future-oriented collaborative path planning of vehicles.It also guided the underlying vehicle twins to select the optimal strategy for the real world.With SUMO for simulation verification, the experimental results show that the proposed method is significantly better than the existing algorithms in improving the travel completion ratio and congestion relief, and can improve the efficiency of urban traffic travel.…”
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Classification of diabetic retinopathy stages based on neural networks by M. M. Lukashevich, Y. I. Golub

“…The development of methods and algorithms for the detection and classification of this disease, the automation of this process are the actual and costeffective goals.The article focuses on the classification of the stages of diabetic retinopathy using neural networks based on human retinal images. …”
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Face Recognition using Deep Learning and TensorFlow framework by Makrem Beldi

“…Luckily, in the last couple of years, several organizations and open-source communities have been developing tools and libraries that help abstract the complex mathematical algorithms in order to encourage developers to easily create learning models and train them using any programming languages. …”
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Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM by Yanwei Xu, Weiwei Cai, Liuyang Wang, Tancheng Xie

“…Aiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent diagnosis method of rolling bearing fault based on the improved convolution neural network and light gradient boosting machine is proposed. …”
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The Optimal Replenishment Policy under Trade Credit Financing with Ramp Type Demand and Demand Dependent Production Rate by Juanjuan Qin, Weihua Liu

“…First, the two inventory models are developed under the above situation. Second, the algorithms are given to optimize the replenishment cycle time and the order quantity for the retailer. …”
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Siamese comparative transformer-based network for unsupervised landmark detection. by Can Zhao, Tao Wu, Jianlin Zhang, Zhiyong Xu, Meihui Li, Dongxu Liu

“…Current landmark detection algorithms often train a sophisticated image pose encoder by reconstructing the source image to identify landmarks. …”
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Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review by Andrés L. Suárez-Cetrulo, David Quintana, Alejandro Cervantes

“…The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant.…”
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Role of Artificial Intelligence in Diabetes Mellitus Care: A SWOT Analysis by Priya Kataria, Srivenkata Madhu, Madhu K. Upadhyay

“…Human medicine is a complex field, and it cannot be solely handled by algorithms, especially diabetes, which is a lifelong multisystem disorder. …”
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Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques by Dakhaz Mustafa Abdullah, Adnan Mohsin Abdulazeez, Amira Bibo Sallow

“…The principle point of this paper is to the execution investigation of the classification algorithms accuracy by WEKA Tool. The experimental results show that SVM gives the best result with 95.56%, then CNN with CNN 92.11% and KNN with 88.40%. …”
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Wearable Technologies in Athletic Performance by Tolga Şahin

“…Wearable technology products use different ways such as "Global Positioning System, Local Positioning System, Micro Electromechanical System, Inertial Measurement System" and it should be taken into account that different sensors collect data at different frequencies and process this data with variable algorithms. With the widespread introduction of different commercial products in the last 10 years, using wearable technology can be said to be indispensable helpers for trainers working to improve performance in sports.…”
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The Digipolitical and African Political Thought by Claudia Favarato

“…This understanding rests on three premises: the characteristics of the digital, a sui generis virtual reality; the algorithmic architecture of the cyber socio-political space; and the onto-relational nature of the political subjects, which entails the interplay of the analogue with digital-humans. …”
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Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images by Fenghua Huang, Luming Yan

“…To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1) different radial basis kernel functions (RBFs) are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF) is proposed by combining them in a weighted manner; (2) the binary decision tree-based multiclass SMO (BDT-SMO) is used in the classification of hyperspectral fused images; (3) experiments are carried out, where the single radial basis function- (SRBF-) based BDT-SMO classifier and the CRBF-based BDT-SMO classifier are used, respectively, to classify the land usages of hyperspectral fused images, and genetic algorithms (GA) are used to optimize the kernel parameters of the classifiers. …”
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Approximation properties relative to continuous scale space for hybrid discretisations of Gaussian derivative operators by Tony Lindeberg

“…The presented results are intended as a guide, when designing as well as interpreting the experimental results of scale-space algorithms that operate at very fine scale levels.…”
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Action recognition method based on fusion of skeleton and apparent features by Hongyan WANG, Hai YUAN

“…Focusing on the issue that traditional skeletal feature-based action recognition algorithms were not easy to distinguish similar actions, an action recognition method based on the fusion of deep joints and manual apparent features was considered.The joint spatial position and constraints was firstly input into the long short-term memory (LSTM) model equipped with spatio-temporal attention mechanism to acquire spatio-temporal weighted and highly separable deep joint features.After that, heat maps were introduced to locate the key frames and joints, and manually extract the apparent features around the key joints that could be considered as an effective complement to the deep joint features.Finally, the apparent features and the deep skeleton features could be fused frame by frame to achieve effectively discriminating similar actions.Simulation results show that, compared with the state-of-the-art action recognition methods, the proposed method can distinguish similar actions effectively and then the accuracy of action recognition is promoted rather obviously.…”
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Secure and efficient batch authentication scheme based on dynamic revocation mechanism in space information network by Yinghui ZHANG, Lingyun HU, Yixin LI, Jianting NING, Dong ZHENG

“…A secure and efficient batch authentication scheme based on dynamic revocation mechanism was proposed for the problem of cross-domain authentication of a large number of mobile users in space information networks.Early key negotiation was achieved by predicting the satellite trajectory and updating the session key in real time.Algorithms were designed for a single as well as a large number of mobile terminals to perform signing and verification, which effectively reduce the computational burden of satellites.Cuckoo filters were adopted by the new scheme to achieve dynamic revocation and malicious access control of mobile terminals.Finally, under the Diffie-Hellman assumption, the proposed scheme was proved to be resistant to replay and man-in-the-middle attacks based on a random oracle model and automated validation of internet security protocols and applications.Security goals such as traceability and revocability were achieved by the scheme, thus improving the efficiency of transmission and computation by more than 80% and 20%, respectively, compared with the existing optimal scheme.…”
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Ultra-low-power media access control protocol based on clock drift characteristics in wireless sensor networks by Wooguil Pak

“…To track the reception node’s wakeup time for synchronization, WideMAC uses two estimation algorithms that estimate the wakeup time and the upper bound of the total timing error. …”
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HTTP malicious traffic detection method based on hybrid structure deep neural network by Jia LI, Xiaochun YUN, Shuhao LI, Yongzheng ZHANG, Jiang XIE, Fang FANG

“…In response to the HTTP malicious traffic detection problem,a preprocessing method based on cutting mechanism and statistical association was proposed to perform statistical information correlation as well as normalization processing of traffic.Then,a hybrid neural network was proposed based on the combination of raw data and empirical feature engineering.It combined convolutional neural network (CNN) and multilayer perceptron (MLP) to process text and statistical information.The effect of the model was significantly improved compared with traditional machine learning algorithms (e.g.,SVM).The F₁value reached 99.38% and had a lower time complexity.At the same time,a data set consisting of more than 450 000 malicious traffic and more than 20 million non-malicious traffic was created.In addition,prototype system based on model was designed with detection precision of 98.1%~99.99% and recall rate of 97.2%~99.5%.The application is excellent in real network environment.…”
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Improvement of Image Compression by Changing the Mathematical Equation Style in Communication Systems by Riyadh M. Al-Saleem, Yasameen A. Ghani, Shihab A. Shawkat

“…In this paper, it is demonstrated that there is a quality improvement by applying anisotropic interpolation to edge enhancement for its ability to satisfy the dispersed data of the propagation process, which leads to faster compression due to concern for optimum quality rather than fast algorithms. The test images for these patterns showed a discrepancy in the image resolution when the compression coefficient was increased, as the results using three types of image compression methods proved a clear superiority when using “partial differential equations (PDE)”.…”
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Deepfake Dilemmas: Imagine Tomorrow’s Surveillance Society through Three Scenarios by Fatma Doğan Akkaya

“…Moving beyond technical considerations, the research employs social prediction methods to explore three potential future scenarios involving deepfake avatars: seamless integration that facilitates self-expression but complicates digital identity control; pervasive algorithmic surveillance that exacerbates social control and privacy issues; and the concept of ghost avatars, that is, digital representations that function as commodities or tools of resistance. …”
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