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Vehicle Trajectory Prediction Method based on Deep learning under the background of Internet of Vehicles 2

Vehicle Trajectory Prediction Method based on Deep Learning ...

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2nd international conference on Computing and data science, CONF-CDS 2021

作者： Wang, Rongxia Guangzhou Nanyang Polytechnic College Guangzhou China

With the expansion of the Internet, big data and other industries, mobile devices are almost used in all aspects of life. The location-based service platform provides convenient services for users, but also obtains a large number of user history tracks. Through the trajectory data mining, we can extract valuable user behavior information, help user's better plan routes, and can be applied in urban traffic management and commercial advertising layout and other scenarios. In this paper, a trajectory prediction algorithm based on deep learning is proposed to solve the problem of vehicle trajectory prediction. In this paper, the vehicle passing records are preprocessed and the vehicle trajectory is generated. The trajectory is transformed into the discrete position sequence of the vehicle as the input of the algorithm. Then the space-time multi-dimensional features of vehicle trajectory are extracted. Finally, a trajectory prediction algorithm based on deep learning is constructed. The batch size is 256 and the average track length of training set is 46.45. The convolution neural network and deep bidirectional long-term memory network are fused to learn the local and global information of vehicle trajectory. The research shows that compared with traditional machine learning, deep learning has achieved better results in various fields of computer vision because of its powerful representation ability. The visual perception algorithm based on deep learning can accurately analyze the regularity of vehicle movement and make accurate prediction of vehicle moving trajectory in the future. © Published under licence by IOP Publishing Ltd.

关键词： Trajectories

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An Analysis of Depression Detection Techniques from Online Social Networks 2nd

An Analysis of Depression Detection Techniques from Online S...

引用

2nd international conference on Intelligent Technologies and Applications, INTAP 2019

作者： Bilal, Uffaq Khan, Farhan Hassan Department of Computer Science and Software Engineering IIUI Islamabad Pakistan Islamabad Pakistan

ISBN: (纸本)9789811552311

Mental illness is caused by depression which may have a deep negative impact on individuals or on the society as a whole. It is a growing and severe problem which has the tendency to increase with time due to the extensive use of social networking websites such as Facebook, Twitter, Instagram etc. These social networking websites allow users to share images, videos, expressions and emotions. Depression is a form of mental illness. Patients suffering from depression have mood disorders such as low mood, high mood, lack of interest in things, etc. machine learning techniques on text data and emojis have been applied to automatically classify a user into depressed and non-depressed. State-of-the-art classifiers have been used by the researcher to detect depressed individuals. Benchmark datasets are composed of text and emojis used in the social networking websites where classification is based on four factors Emotional Process, Temporal Process, Linguistic Style and combination of these three factors. In the proposed method, the emotional process is combined with their respective emojis to develop an automatic system for the detection of depressed patients. The features from the emotional process and emojis will be extracted and state-of-the-art classifiers have been proposed to be trained and evaluated using multiple classifiers using different combinations of part-of-speech tags. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Weapon system of systems evaluation method based on Capability Perspective

Weapon system of systems evaluation method based on Capabili...

引用

international conference on applied machine learning (ICAML)

作者： Hao Chen Ruihua Wang Jiajia Wang Zhou Yang Chang Zhao Beijing Jinghang Research Institute of Computing and Communication Beijing China PLA Academy of Military Science Beijing China

ISBN: (纸本)9781728192833

Most of the existing architectures are guided by the architecture framework of the U.S. Department of defense. In order to construct an effective and standardized architecture, the evaluation method based on capability perspective is proposed according to the characteristics of weapon system of systems evaluation. Starting from the combat mission, the method directly extracts data from the constructed architecture, establishes the capability requirement structure chart through the decomposition of capability requirements and the analysis of the relationship between them, and evaluates the weapon equipment system of systems from the perspective of capability requirement satisfaction. Finally, an example is given to illustrate the specific steps and feasibility of the evaluation method. The evaluation results verify the effectiveness of the method.

关键词： Weapons machine learning Numerical models data mining Indexes Optimization System of systems

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Predicting US Elections with Social Media and Neural Networks 2nd

Predicting US Elections with Social Media and Neural Network...

引用

2nd international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Chan, Ellison Krzyzak, Adam Suen, Ching Y. Department of Computer Science and Software Engineering Concordia University MontrealH3G 1M8 Canada

ISBN: (纸本)9783030598297

Increasingly, politicians and political parties are engaging their electors using social media. In the US Federal Election of 2016, candidates from both parties made heavy use of social media, particularly Twitter. It is then reasonable to attempt to find a correlation between popularity on Twitter, and eventual popular vote in the election. In this paper, we will focus on using the subscriber ‘location’ field in the profile of each candidate to estimate support in each state. A major challenge is that the Twitter location field in a user profile is not constrained, requiring the application of machine learning techniques to cluster users according to state. In this paper, we will train a Deep Convolutional Neural Network (CNN) to classify place names by state. Then we will apply the model to the Twitter Subscriber ‘location’ field of Twitter subscribers collected from each of the two candidates, Hillary Clinton (D), and Donald Trump (R). Finally, we will compare predicted popular votes in each state, to the actual results from the 2016 Presidential Election. In addition to learning the pattern related to the state names, this additional information may help a machine learning model learn to classify locations by state. The results from our experiments are very promising. Using a dataset containing 695,389 cities, correctly labelled with their state, we partitioned the cities into a training dataset containing 556,311 cities, a validation dataset containing 111,262, and a test dataset containing 27,816. After the trained model was applied to the test dataset. We achieved a correct prediction rate of 84.4365%, a false negative rate of 1.6106%, and a false positive rate of 1.0697%. Applying the trained model on Twitter location data of subscribers of the two candidates, the model achieved an accuracy of 90%. The trained model was able to correctly pick the winner, by popular vote, in 45 out of the 50 states. With another US and Canadian election coming up in 2019, and

关键词： Location

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CO2 Emission Rating by Vehicles using Supervised Algorithms 2

CO2 Emission Rating by Vehicles using Supervised Algorithms

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2nd IEEE international conference on Advances in Computing, Communication and applied Informatics, ACCAI 2023

作者： Ramesh, Sudarshni Shiny Shalynn, I.M. Justus, J. Jean St. Joseph's College of Engineering Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350315905

Personal vehicles play a significant role in contributing to the issue of global warming. Gasoline used in cars emits an estimated 24 pounds of carbon dioxide and other greenhouse gases per gallon, which accounts for roughly 20% of all emissions. Over five pounds of heat-trapping pollutants are produced throughout the fuel's extraction, manufacture, and delivery, while it's important to note that cars release over 19 pounds per gallon directly from their tailpipes. At present, gas-powered vehicles typically achieve a fuel efficiency of around 22.0 miles per gallon, covering an annual distance of 11,500 miles. As a consequence, the combustion of one gallon of gasoline results in approximately 8,887 grams of CO2 being released into the atmosphere. In 1998, the automobile industry pledged to reduce new car emissions by 25% by 2008. At that time, CO2 emissions from new cars were approximately 203g/km. Currently, the average emissions of vehicles stand at approximately 170g/km and it is projected that they will not decrease to 140g/km until after 2020. The amount of CO2 emitted by a standard passenger car is typically around 4.6 metric tons per year, but this can fluctuate depending on factors such as fuel type, fuel efficiency, and mileage. While predicting emissions becomes more challenging as more variables come into play, some experimental designs consider controllable variables and their interactions. To predict gas emissions, we've developed a model that utilizes a car's attributes to determine if it exceeds the CO2 threshold. If it does, the RTA will take action. One of the best techniques for predicting CO2 emissions is supervised machine learning. © 2023 IEEE.

关键词： data visualization

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2nd ACM conference on Information Technology for Social Good, GoodIT 2022

作者： Sultan, Amit Segal, Avi Shani, Guy Gal, Kobi Ben-Gurion University of the Negev Beer-Sheva Israel Ben-Gurion University Beer-Sheva Israel University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9781450392846

Citizen science projects rely on volunteers to contribute time and effort to solve scientific problems, but the majority of citizen science users typically contribute to only one or two projects. Recommender systems have been recently used in citizen science to motivate people to contribute to additional projects. However, these systems often recommend the more popular projects at the expense of less popular projects which need people's contributions more critically. In this work we develop a post processing approach for enhancing "long-tail"item recommendation that can be applied to any recommendation system. We propose a novel re-ranking model, based on the lift measure used in machine learning, which considers item co-occurrence as well as popularity for enhancing long tail recommendations. We demonstrate the efficacy of our approach in the citizen science domain on two data sets and three state of the art recommendation algorithms, comparing hit rate before and after applying lift boosting. Additionally, we compare our approach to two predetermined re-ranking baselines. Results show that our proposed approach significantly improves performance for tail item recommendation without a substantial loss in head item and overall item recommendation performance. Our approach is general and can be naturally applied to existing recommendation systems in citizen science that personalize project suggestions to users, potentially leading to an increase in efficiency and performance. © 2022 ACM.

关键词： Recommender systems

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Deep learning Approach to Load Forecasting: A Survey 2nd

Deep Learning Approach to Load Forecasting: A Survey

引用

2nd international conference on Communication, Networks and Computing, CNC 2020

作者： Akinola, Segun A. Thakur, Prabhat Sharma, Mayank S. Kumar, Krishna Singh, Ghanshyam Centre for Smart Information and Communication Systems Department of Electrical and Electronics Engineering University of Johannesburg Auckland Park Kingsway Campus Johannesburg2006 South Africa Department Electronics and Communication ITM University Gwalior Gwalior India

ISBN: (纸本)9789811688959

The power sector has been widely invested-in for many years. There is a need in finding lasting solutions that can ameliorate the ever-dynamic challenges attached to it which makes the researcher looking for techniques in artificial intelligence solving the complication in the power sector. Since when Artificial intelligence came to existence a lot of problems have been solved through the use of its application such as an artificial neural network (ANN), Neural Network (NN), Deep Neural Network (DNN), machine learning (ML) and deep learning (DL). Deep learning has become a very good solving tool which makes research focus more on it to tackle a lot of problems such as forecasting tasks, modeling the non-linearity in data of many fields, computer vision, natural language processing, speech recognition, and signal processing. This updated review paper focuses on the application of deep learning (DL) that applied to solar load forecasting;the common algorithm used was shown in the literature reviews. The main reason for this review is to show the latest updated techniques using DL for forecasting that will help the researcher to select the best methods in DL for forecasting accurately. After the review it shows that deep learning performs better for forecasting showing good accuracy, finding the hidden layer. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Road Accidents Severity Prediction Using a Voting-Based Ensemble ML Model 2nd

Road Accidents Severity Prediction Using a Voting-Based Ens...

引用

2nd international conference on Big data, IoT and machine learning, BIM 2023

作者： Fahad, Kazi Joarder, Md. Foysal Nahid, Md. Tasnim, Tanpia Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

ISBN: (纸本)9789819989362

Nowadays road accidents are becoming a major global challenge, leading to a high rate of injuries, fatalities, and large economic losses annually. The number of collisions in our nation increasing daily. Developing an accurate model for the prediction of the severity of traffic accidents is a crucial task. Many researchers have already predicted different aspects of road accidents. They have applied different machine learning (ML) algorithms for their work. Whereas a minimal number of researchers have used ensemble-based ML algorithms. In this paper, we have used four ML algorithms such as Random Forest, XGBoost, KNN, and LGBM to form a voting-based ensemble ML model for road accident severity prediction. Multiple road accident datasets have been used to predict the best evaluation for this study. Bangladesh (BD) road accident data from 2017 to 2020 have been collected from Accident Research Institute (ARI) in Bangladesh University of Engineering and Technology (BUET). Another dataset was the USA road accident dataset from 2016 to 2020. Furthermore, SelectKBest and ExtraTreeClassifier algorithms have been used for selecting suitable features for this paper. According to the experimental results, the voting demonstrates the best contribution for both binary and multiclass classification in the BD dataset for achieving the highest accuracy of 96% and 71%, respectively. On the other hand, the voting indicates the highest performance for both binary and multiclass classification in the USA dataset, achieving the accuracy of 92.1% and 87%, respectively. Finally, the result of the comparative analysis shows that voting is providing the best performance than others. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Design and Implementation of Network Precision Marketing System Based on Big data Algorithm

Design and Implementation of Network Precision Marketing Sys...

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international conference on applied machine learning (ICAML)

作者： Chuanfang Weng Tangfa Liu Science and Technology College of NCHU Nanchang China Nanchang University of Gongqing College Nanchang China

ISBN: (纸本)9781728192833

With the development of e-commerce, more and more businesses on the road of e-commerce development, commodities to the Internet. As a result, competition for the sale of goods around the world is becoming increasingly fierce. This paper firstly introduces the research status of association rule mining and the description of big data algorithm, and then explores the product information that users are interested in and improves the website of commodity sales, so as to improve customer satisfaction and business relationship with customers. The improved big data algorithm is used to analyze and obtain association rules, and the system application business is designed to enable enterprises to conduct accurate customer positioning through customized services and services, thus reducing their marketing costs. The system excavates product demand through data mining technology, establishes product association library, realizes the rapid response to customer demand, and provides support for positioning target customers, implementing up-selling and cross-selling, and exploring advantageous channels. It has been verified that the improved big data algorithm can mine the valuable association rules hidden in it. After the system is developed, the actual test results are very good.

关键词： Java Roads Customer satisfaction machine learning Big data Libraries Internet

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Design of Doctor-Patient Management Platform Based on Microservice Architecture

Design of Doctor-Patient Management Platform Based on Micros...

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international conference on applied machine learning (ICAML)

作者： Xiaomin Wang Yue Chen Jun Wang Airong Yu Hospital of Eastern Theater Command PLA Nanjing China School of Management Science and Engineering Nanjing University of information science &Technology Nanjing China The Army Engineering University of PLA Nanjing Chin

ISBN: (纸本)9781728192833

For the common medical area in the process of diagnosis and treatment of chronic diseases, the patient's medical history information is not comprehensive, which leads to the practical problems that cannot provide effective medical guidance to patients. This paper proposes to make full use of mobile Internet technology and micro service system to integrate the medical data of patients, and design and implement a hierarchical structure of doctor-patient management platform. Through the construction of the platform, it solves the problem that the primary health care units do not fully obtain the patient diagnosis and treatment data, which leads to insufficient understanding of the disease and poor diagnosis and treatment effect, and closely connects the hospital, doctors and patients. In the process of patients' treatment, doctors can quickly master the patient's history, diagnosis and treatment, medication and physical characteristics, which greatly improves the treatment effect of patients

关键词： Hospitals Process control Medical services machine learning Real-time systems Internet Medical diagnosis

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