Solving aspect-based sentiment analysis task with GNN models and tree reconstruction methods by Peng, Cheng

“…Building the graph structure with dependency parsing algorithm and Tree-Lifting and Tree-Truncating algorithm. 2. Applying three different models: GCN, GAT, and Heterogeneous GCN on the data. 3. …”
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Boosting knowledge distillation and interpretability by Song, Huan

“…The generally accepted interpretable model is the decision tree. Although decision tree classification accuracy is not as good as deep neural networks, it is a more intuitive and interpretable common model. …”
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High-performance CMOS digital multiplier IC design by Chu, Zhuolin

“…Many new design ideas have emerged around how to improve the speed of multipliers, including pipeline structure, Wallace Tree structure, and Booth encoding. The Wallace Tree structure increases the speed of partial product summation by compressing the number of partial products; A Carry-look-ahead Adder can be greatly optimized for the addition of multi-bit numbers, and it skips the lengthy bit-by-bit carry propagation process, dramatically increasing the speed of addition operations. …”
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Design a 16-bit low power delay multiplier by Lun, Yinghui

“…And in the architecture level, Dadda tree architecture tree is used.This kind of architecture can compress the accumulation process, reduce the delay time and save the number of adder cells. …”
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16-bit high speed CMOS multiplier IC design by He, Pengfei

“…Then a 16-bit multiplier was designed based on the modified Booth 2 algorithm and Wallace tree technique, and the RTL code was implemented using Verilog HDL. …”
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16-bit low-power CMOS multiplier IC design by Wang, Jun

“…In conclusion, the optimized Booth encoding Multiplier and Wallace Tree Multiplier were implemented with lower power consumption and shorter delay. …”
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High speed multiplier IC design based on booth algorithm by Chang, Shuming

“…It designed a high speed 32-bit signed multiplier based on Booth algorithm, 4-2 compressor using Wallace Tree structure and Carry-Look-Ahead adder. This design is 47. 03% faster than the original 32-bit signed shift multiplier, according to the length of critical path. …”
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SRAM-based in-memory computing for machine learning applications by Zhao, Yuqin

“…In this paper, a 4k weight-selective digital SRAM design is implemented with improvements on Bitcell and Adder Tree. It uses TG logic in the design to improve the speed and eliminate power consumption. …”
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Face swapping based on machine learning by Zhou, Suxi

“…Firstly use Histogram of Oriented Gradient (HOG) to detect the face in a given image, and use training data to generate a prediction model based on the gradient boosting decision tree (GBDT) algorithm to extract the coordinates of 81 feature points of facial features and facial contours; Then train the Multi-Layer Perceptron (MLP) classifier to predict the gender and race of the face to be recognized and find the reference face image of the same gender race. …”
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Machine learning-based online stability assessment of power systems by Zheng, Hongfei

“…This dissertation uses three main machine learning methods, including DT (decision tree), RF (random forest), and SVM (support vector machine). …”
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Data mining and analysis of lift maintenance records by Ying, Baohui

“…Therefore, to predict the specific location and fault category of the lift failure on a certain day, this dissertation uses the Autoregressive Integrated Moving Average Model(AMIRA) method to predict the lift failure rate after the seasonal adjustment of the fault data.Combined with the chi-squared automatic interaction detector (CHAID) decision tree algorithm, the lift fault position and the lift fault type are successfully predicted. …”
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Low power-delay-product (PDP) CMOS multiplier design by Leng, Xiaoxiang

“…In architecture level, according to the result of modified Booth algorithm, a special Dadda tree is designed for partial product accumulation. …”
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Model development and simulation for robot-based warehouse automation by Zhang, Yijing

“…The high level performs conflict search and constraint addition based on a binary tree. Considering that some of the commonly used warehouse layouts have oblique main aisles, the low level is allowed to adopt hybrid search methods to improve the efficiency of the system. …”
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Machine learning for mixed data by Zhu, Yixuan

“…These models include Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and XGBoost. We will collect the performance metrics data of each model at the optimal result. …”
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Predicting online video popularity using machine learning by Chen, Ziye

“…This study predicts online video popularity using different machine learning techniques such as Random Forest, Decision Tree, and XGBOOST, after comparing their prediction accuracy, standard deviation, and other parameters, I find that the XGBOOST model has the best prediction results, and I improve the Tuned XGBOOST model to get better results. …”
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Sampling-based robot path planning algorithms assessment and improvement by Liu, Xinyu

“…Sampling-based planning algorithms (typically the RRT* family) represent one of the most popular path finding approaches that basically grow a tree or graph structure incrementally through various sampling techniques and try to connect the vertices with collision-free edges. …”
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Computational intelligence algorithm for indoor lighting control systems by Sneha Shrikumar

“…Analysis of the results revealed that Decision Tree algorithm has the best performance with an F1 score of 0.9072. …”
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Ultrahigh-resolution spectral CCD imaging based on machine learning by Xie, Hui

“…Three advanced modeling methods were employed based on the Python library scikit-learn, namely, decision tree, k-nearest neighbor (KNN) and gradient boosting regression (GBR) model. …”
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Using AI and big data to predict stock market by Li, Jing

“…The prediction experiments are conducted by building and training three machine learning models: Decision Tree, Support Vector Machine, and deep learning model Long Short-Term Memory. …”
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Robost SLAM for complex scenes based on RADAR and LIDAR fusion by Li, Zixiao

“…In order to minimise the effect of this error, the initial point cloud data is first processed and reduced by voxel grid sampling, then the processed point cloud data is subjected to KD tree nearest neighbour search with suitable processing method, which gives the result of calculated normal vector and a satisfied evaluation of the quality of plane fitting. …”
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