Research on lightweight malware classification method based on image domain by SUN Jingzhang, CHENG Yinan, ZOU Binghui, QIAO Tonghua, FU Sizheng, ZHANG Qi, CAO Chunjie

“…Firstly, a CBG algorithm was introduced to solve the problems of imbalanced image sizes and excessive noise in malware images. …”
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HR Management Big Data Mining Based on Computational Intelligence and Deep Learning by Genliang Zhao, Zhe Xue

“…To this end, this paper proposes an end-to-end competency-aware job requirement generation framework to automate the job requirement generation, and the prediction based on competency themes can realize the skill prediction in job requirements. …”
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Simulation of the Performance of a Centrifugal Chiller by Luo Yi, Zhang Libiao, Hang Bingbing, Gong Chengcheng

“…The simulation software successfully predicts various parameters, such as chilled water temperatures, cooling water temperatures, and flow rates of the centrifugal chiller under different loads.…”
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A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment. by Tao Yu, Wei Huang, Xin Tang, Duosi Zheng

“…Furthermore, a multi-view combined unsupervised method is designed to thoroughly mine data and enhance the robustness of label predictions. This method mitigates discrepancies in prediction outcomes from three distinct perspectives. …”
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FEATURES OF APPLICATION OF FILTERS KALMAN-BJUSI IN ORIENTATION AND NAVIGATION COMPLEXES by A. S. Benkafo, A. A. Lobaty

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Hand segmentation pipeline from depth map: an integrated approach of histogram threshold selection and shallow CNN classification by Zhengze Xu, Wenjun Zhang

“…We found that MINIMUM, MEAN and MEDIAN are effective ways to separate objects and the threshold in the valley between two maxima similar to MINIMUM algorithm with a minimum error. Then, each segmentation proposal is evaluated by a 3-layers shallow convolutional neural network (CNN) which is trained as a binary classification function to predict whether it is a partition of hand. …”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Application of Finite Mixture of Logistic Regression for Heterogeneous Merging Behavior Analysis by Gen Li

“…This model can automatically provide useful hidden information about the characteristics of the driver population. EM algorithm and Newton-Raphson algorithm were used to estimate the parameters. …”
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Liver Disease Classification using the NAIVE BAYES by Vitra Nurhalisa, Ika Nur Fajri

“…The fast training time and transparent probabilistic predictions of the Naive Bayes algorithm make it a practical solution for developing a prototype of a medical decision support system. …”
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Optimization of Quantitative Financial Data Analysis System Based on Deep Learning by Meiyi Liang

“…In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. …”
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Entropy-Guided Distributional Reinforcement Learning with Controlling Uncertainty in Robotic Tasks by Hyunjin Cho, Hyunseok Kim

“…To address this, we improve the truncated quantile critics algorithm by managing uncertainty in robotic applications. …”
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Design and Implementation of OLAP System for Distributed Data Warehouse by Murtadha Hamad, Abdullah Mahdi

“…Statistical analysis concepts are used from current work to get predictable results which can be used to get suitable result DSS.…”
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Information support for chub mackerel Scomber japonicus fishery in the Pacific waters of the Russian Federation by E. P. Chernienko, I. S. Chernienko

“…The fishery statistics for 2016–2020 and the data on SST with delay of 4–7 days from the date of catch, spatial SST gradients calculated using Belkin algorithm, and day-to-day SST variations were processed using LightGBM machine learning algorithm. …”
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Nove lincidence matrix differential power analysis for resisting ghost peak by Zijing JIANG, Qun DING

“…At present, differential power analysis (DPA) is one of the most important threats to the security of block ciphers in chips.When the collected power trace is insufficient, DPA is vulnerable to ghost peak caused by the difference mean value generated by the wrong key.Based on DPA, a incidence matrix differential power analysis (IMDPA) was proposed which could effectively resist ghost peak.The prediction difference mean matrix was constructed to avoid the influence of the non leaking interval on the key guessing of the leaking interval by using the weak correlation of the guessing key in the non leaking interval.The proposed IMDPA was tested in different leak intervals of AES-128 algorithm.The results show that compared with traditional DPA, IMDPA requires less (up to 85%) power trace to guess the correct key.At the same time, the key guessing efficiency of AES-128 under the implementation of protective measures by IMDPA still has obvious advantages.In order to further verify the universality of IMDPA in block ciphers, experimental verification is conducted on SM4 algorithm.Compared with traditional DPA, IMDPA requires less (up to 87.5%) power traces to guess the correct key.…”
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DPM-based method for tracking maneuvering targets in wireless sensor networks by LIN Jin-zhao1, LI Guo-jun2, ZHOU Xiao-na2, ZHOU Dao-jun 2, JIANG Yong 2

“…Focused on energy efficiency issues under tracking targets within WSN,a new dynamic power management（DPM） method for tracking distributed targets was proposed combining with the network energy consumption model and wake-up mechanism.With precedent location information of maneuvering target,the algorithm involved both cancelling noise by wavelet filter and predicting target state by autoregressive transformation is introduced to awaken wireless sensor nodes so that their sleep time is prolonged and energy consumption is reduced.According to the current location of maneuvering target,related nodes in an appointed cluster of WSN constitute a distributed dynamic tracking unit,and the cluster head is responsible for collecting the measurement information from the nodes in the tracking unit.A locating algorithm based on the relative posi-tion of two circulars formed on the bases of measurement information is adopted to simplify the process of locatiny and tracking target.Simulation results show that the DPM-based method can reduce the nodes’ energy consumption,meet the locating and tracking accuracy,and can be applied to locate and track the maneuvering targets on the earth surface.…”
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Study on Angular Velocity Measurement for Characterizing Viscous Resistance in a Ball Bearing by Kyungmok Kim

“…For accurate detection of the rotating marker, the algorithm employed Multi-Otsu thresholding and the Least Squares Method (LSM). …”
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Research on Passenger Flow Assignment of High-Speed Trains Based on Personalized Itinerary Choice by Jinzi Zheng

“…An equilibrium passenger flow assignment model based on personalized choice is built and an improved Monte-Carlo random simulation algorithm is designed for solving the model. The actual ticket sale data for Beijing-Shanghai high-speed railway are used to verify the feasibility of the proposed model and algorithm. …”
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Temporal dependent rate-distortion optimization based on distortion backward propagation by Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO

“…Rate-distortion optimization (RDO) is a crucial technique in block based hybrid video encoders.However, the widely used independent RDO is far from obtaining optimal coding performance.To improve the rate-distortion (R-D) performance of high efficiency video coding (HEVC), a temporal dependent RDO algorithm was proposed.Firstly, the formula to calculate temporal distortion propagation factor was derived by using an exponential R-D function.Then, the coding distortion and motion compensation predicted error were obtained by pre-encoding, and the temporal distortion propagation factor was estimated by using distortion backward propagation.Finally, the Lagrange multiplier and quantization parameter of coding tree unit were adaptively adjusted to optimize bit resources allocation.Experimental results show that compared with the original RDO method in HEVC under the low-delay configuration, the proposed algorithm achieves an average 4.4% bit rate reduction for all test sequences, and up to 13.0% bit rate reduction for test sequence BasketballDrill, at the same reconstructed video quality.…”
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Low Complexity Mode Decision for 3D-HEVC by Qiuwen Zhang, Nana Li, Yong Gan

“…In this paper, a fast mode decision algorithm based on variable size CU and DE is proposed to reduce 3D-HEVC computational complexity. …”
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BDS Multipath Parameter Estimation in the Presence of Impulsive Noise by Jicheng Ding, Lin Zhao, Chun Jia, Zhibin Luo

“…A modified least mean p-norm (LMP) algorithm is developed to reduce the convergence time with the same steady-state error by predicting the updating trend of weights. …”
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