Volume 6

The classic 2D SLAM are not good enough in nowadays environment. This report uses virtual machine with Ubuntu based Slam_bot package, based on the RTAB-MAP algorithm and Vision SLAM mapping to simulate the four-wheeled robot to autonomously navigate to the target point in various environments. Also, this report introduces a RGBD-SLAM based algorithm which combines the visual and depth data to process the data collect from the sensors. This robot has many sensors like, lidar sensors, RGB vision camera and odometry sensors. To see how the RTAB-MAP algorithm with RGB-D sensor replace for the 2D SALM. As results, the robot with RGB-D and RTAB-MAP algorithms have very good performance. The results show that the navigation system can complete the navigation and localization in lots of complex situations. However, some problems still exist, the speed of the robot is not fast. This may limit the application of the self-navigation robot to a certain extent, like some emergency occasion.

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Based on the cooperation of artificial intelligence (AI) and unmanned driving technology, finding the best path from the starting node to the target node in the shortest time is a research hotspot. The required path planning algorithms can therefore be classified according to their different approaches to solving the problem. This paper focuses on Dijkstra’s algorithm, A* algorithm, Ant colony optimization, and genetic algorithm. It also discusses the present problems of these algorithms and some improvements made by researchers focusing on automated guided vehicle path planning.

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Since the invention of the memristive device with a nano-scale footprint, a lot of scholars have started to focus on the area of recognition systems based on the Complementary Metal-Oxide Semiconductor chips (CMOS) integrated with memristive devices. This paper’s goal is to compare and analyze the advantage and disadvantage on the near research on the cognitive machine. Start with the construction of a simple dynamic model of neurons in Section 2, the history of the development of the recognitive machine is introduced in Section 3. Section 4 focusing on the comparison and analysis of researches done by different scholars. Through the comparison of multiple scholars’ work, improvement of the memory system would be a potential way to improve the recognition system nowadays, and it is discussed in the conclusion.

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As the popularity of online social networks has increased, so has the manner in which people purchase online. Online reviews on the purchasing of items or the provision of services have become the primary source of user opinion. When a substantial body of knowledge assesses a product or service, that body's effect on the market is significant. Because of this, concerns have been raised among manufacturers and merchants, who frequently compose rubbish reviews to promote or denigrate the quality of specific items or services in order to make a profit or maintain their reputation. You may choose to promote or denigrate certain products or services that have been targeted. This kind of commentary is known as "trash remark." Comment spam violates the interests of enterprises to a significant level, and it possesses a defensive mechanism that combines the "Deepfake" of artificial intelligence technology, which renders conventional security measures inapplicable. Deep learning is the approach that will be most effective in resolving this issue. Researchers assess this research according on how they extract features from the commentary dataset and how they extract features from the commentary dataset, employing a variety of methodologies and strategies to find solutions to the problems. The authors also looked at the key deep learning technologies that have been suggested as a solution to the issue of spam detection in the primary machine, as well as the performance of various deep learning technologies. At the same time, the fact that there is not a lot of spam online review data makes this sort of research a highly significant one. The purpose of this article is to offer an insightful and all-encompassing analysis of comparative research on the topic of spam detection that has recently been conducted.

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Semi-supervised learning is one of the potential research fields in text classification. In this paper, semi-supervised pseudo-label training experiments are conducted using the BERT model that has been pre-trained as a baseline. Only 20% of the original dataset is used for the new training set after segmenting the training set. The raw corpus used for pseudo-label training consists of the remaining 80% of data after labels are removed, while the original test set is still utilized. The results indicate that the key to the semi-supervised pseudo-labelling method is the performance of the original model and reasonable data filtering techniques. Even though the SoftMax value used for data filtering is not precisely equivalent to model prediction accuracy, experimental results show it can somewhat reduce the error propagation problem of the model. This is consistent with earlier research. However, using SoftMax as the threshold for data screening can't bring enough benefits to the model training and make it surpass the training performance of the original data set. As a result, future studies will focus on improving the accuracy of pseudo-labelling with a more suitable data selection method to better the model's performance.

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With the widening gap between the rich and the poor, social inequality has emerged in all aspects. The issue of inequity in education needs more attention, because it is related to the development of national quality. There have been many studies on the use of online housing advertisements for feature extraction and semantic analysis, and the use of machine learning methods to construct models to predict socioeconomic status. This study considers the influence factors of education, and conducts Bayesian classification and LDA model analysis on all reviews of New York schools on the largest school rating and recording website in the United States to explore the primary factors associated with educational imbalance in a society. Results show that various requirements for teachers, such as teaching ability and student management ability, were the most important factors that appeared in the reviews. Gender issues are also very important in education. In terms of the overall parts of speech, the emotions are all positive, indicating that the current level of education can satisfy parents as a whole. However, there are still many potential problems of educational inequality that need to be discovered and solved, and the methods of inquiry need to be expanded and upgraded.

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Urban crime poses a serious challenge to urban sustainability and livability. Many studies have been conducted to explore the patterns and causes of urban crime, as well as prevention techniques. Studies have found that neighborhood socioeconomic status affects the incidence of urban crime, but studies on this topic are limited due to data limitations. To fill this gap, this study designed an approach for Brooklyn, USA, that collects publicly available data from housing advertising sites and the Open Street Map and trains a machine learning model to predict fine-grained neighborhood socioeconomic status. The experimental results show that the gradient boosting decision tree regression model has the best prediction accuracy. Then, we verified the predicted significant correlation between fine-grained neighborhood socioeconomic status and criminal activity in the precinct by using a geographically weighted regression model, that is, criminal activity has a higher incidence in disadvantaged neighborhoods. It was also found that neighbourhood socioeconomic status was the best predictor of harassment and burglary.

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The universal issue for authorities is to plan land use more effectively and efficiently, and to provide more sustainable mobility in urban areas. The sustainable prism model is proposed to achieve the requirements for sustainable development. Transit-oriented Development (TOD) is a city planning method that coordinates the mass transit system and the land use pattern. This article analyzes the sustainable prism and discusses the effect of TOD on sustainabilities in ecology, economy and equality to provide the city planner insight into the urban development pattern. Generally, TOD can reduce the energy consumption in the transport sector and related infrastructure, contribute to the air quality from the environmental aspect, facilitate employment and attract investment in the economic aspect and improve discrepancy in employment availability and transport burden among different groups from the social equality aspect. However, the noise pollution in the environment, the value capture effect in the economic aspect and the TOD efficiency in the social justice aspect are insufficiently discussed.

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Urban vitality is defined as the zing of cities providing citizens with the ability to live, and it is important to offer an essential basis for estimating urban growth and spatial balance. Shenzhen’s rapid development has made remarkable achievements in a brief period of forty years as a special economic zone since reform and openness. This study takes Shenzhen as an example, uses 21 POI data obtained from Baidu Maps, and discusses the relationship between urban vitality and time and space and the influence factors of urban vitality. Results show that urban vitality has a close relationship with time and space and high-vitality areas are judged based on the production activities of urban people. Besides, amusement activities do not have a clear influence on urban vitality. Furthermore, convenient transportation and ordered life-supporting services play a stressful role in pushing urban vitality. Based on the above three elements, it is necessary to pay attention to taking production activities, creating convenient transportation, and providing ordered life-supporting services to increase a city’s urban vitality.

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Diabetes is one of the most diseases in the world. In the last 40 years, the number of persons worldwide with diabetes has tripled. There were 108 million patients over the age of 18 in 1980 and 422 million in 2014, accounting for 8.5% of the entire population at that time. Diabetes directly caused 1.5 million fatalities worldwide in 2012, with hyperglycemia-related illnesses accounting for 2.2 million deaths. Diabetes is expected to be the 7th greatest cause of death by 2030 according to the World Health Organization. As the risk of diabetes increases, machine learning algorithms are used to improve early diagnosis of diabetes, and various researchers have also done some corresponding algorithms for predicting diabetes machine learning. As a commonly used machine learning algorithm, AdaBoost integrated learning algorithm is superior in the diagnosis and prediction of diabetes mellitus. In this paper, it is proposed that a hybrid model to detect the risk of diabetes. This hybrid model is detected and eliminated by K-means-based outliers, synthesizing the distribution of minority data oversampling techniques (SMOTE), and Adaboost to classify diabetes. According to the final experimental result, the model prediction accuracy is 0.950 after using the hybrid model in the PIMA dataset. In the future, if a larger number of sample training data are utilized for training, the model's accuracy will improve.

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