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Real-Time Queue Length Estimation With Trajectory Reconstruction Using Surveillance data

Real-Time Queue Length Estimation With Trajectory Reconstruc...

引用

international conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Zihao Sheng Shibei Xue Yunwen Xu Dewei Li Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

This paper presents a new method to estimate real-time queue lengths at a signalized intersection by utilizing limited data extracted from surveillance videos. This method focuses on reconstructing vehicles' trajectories on an entire road segment. The real-time queue length can be derived from these reconstructed trajectories. In order to improve the accuracy of the trajectory reconstruction, a built-up car-following model is proposed to reconstruct the trajectories of vehicles joining and leaving the queue, respectively, which are further corrected by a fusion algorithm. The proposed method was validated in the Next Generation Simulation dataset, and the queue length for each signal cycle can be estimated with a high accuracy. The results show that the proposed method has a higher precision compared to three baseline models.

关键词： Trajectory Surveillance Videos Real-time systems Image reconstruction Cameras Roads

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Prediction of Student Graduation with Naive Bayes Algorithm

Prediction of Student Graduation with Naive Bayes Algorithm

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international conference on Informatics and Computing (ICIC)

作者： Hartatik Kusrini Kusrini Agung Budi Prasetio Magister Teknik Informatika Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9781728180472

ISBN: (纸本)9781728180489

The research carried out in this study is the development and analysis of student performance in the academic field using the Naive Bayes algorithm so that it can help agencies and students see early graduation predictions, and help managers to see the progress and predictions of active student graduation. The purpose of this research is to study student achievement prediction models that have model values. Referring to previous research in reference that getting student prediction results in the form of semester GPA 1,2,3,4, in this study make predictions based on training data and variables that affect the model. The prediction model optimization step by selecting the variable used in the prediction model development is IPS1,2,3,4. The data used in this study are the results of observations from universities. The result of this research is the prediction of Student Achievement Development with Naive Bayes Algorithm based on Ip semester 1.2,3,4 variable and added value are UN rate, Gender, and status stay. From the results of research conducted from the initial stage up to the testing stage the application of the naïve Bayes method for the prediction process of graduate students, it was found that: the application of the naïve Bayes algorithm for model 1 is a model prediction using variable IP students result in an accuracy of 75% and R2 = 68,2%. Model 2 for prediction are used 8 variables namely Nim, Gender, Residence, IPS 1, IPS2, IPS3, IPS4, study period and student status result accuracy of prediction 89% and with a prediction level of R2 = 71,4 %. This certainly improves the performance of the training data efficiency prediction model.

关键词： Computational modeling Training data Predictive models Prediction algorithms data models IP networks Testing

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Application of Exploratory data Analysis to Generate Inferences on the Occurrence of Breast Cancer using a Sample dataset

Application of Exploratory Data Analysis to Generate Inferen...

引用

Intelligent Engineering and Management (ICIEM), international conference on

作者： Sabeel Ashfaq Khan S. Senthil Velan Department of Computer Science and Engineering Amity University Dubai Dubai UAE Amity University Dubai Dubai United Arab Emirates

ISBN: (数字)9781728140971

ISBN: (纸本)9781728140988

Exploratory data Analysis (EDA) is a data analysis technique that can be used to visually represent the knowledge embedded deep in the given data set. The application of this technique can be used in medical data processing for the betterment of the offered services of healthcare providers. In women, breast cancer has become prevalent and requires proper methods to identify the possibility of occurrence at an early stage for treatment and cure. In this context, visualization technique of EDA can be applied to the existing datasets for learning and prediction. In this research work, the analysis aims on finding predominant features which would be helpful in predicting whether the tumor is benign or malignant. To achieve this goal various graphical techniques are used for a better visual understanding. Based on the results obtained it can be found that EDA plays a significant role in describing the data using statistical and visualization analysis without making any speculations about the content. With this dataset and EDA approach we have achieved a clear understanding about the significant features needed to predict if the tumor is benign or malignant.

关键词： data visualization Breast cancer Tumors data models Correlation data analysis

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Research on the Construction of Regional Logistics Network with data Network Chain

Research on the Construction of Regional Logistics Network w...

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IEEE Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Ning Yang Xiaofei Liu Xiao Hu Qingdao Lanzhi Modern Service Industry Digital Engineering Technical Research Center Qingdao China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

In order to comply with the development trend of intelligent logistics under the information background of modern logistics industry, aiming at the problem of the complexity of the logistics data resources and cannot be efficiently utilized, this paper proposes a regional logistics data network chain architecture based on Internet of things and information technology, constructs the core framework of logistics network, which opens up the data chain channel from the collection, processing, fusion to sharing of the logistics information, and improves the efficiency of integration and allocation of logistics data resources. Meanwhile, on the basis of the data network chain, the regional logistics network model based on weighted super-network, which integrates the cargo flow and information flow sub networks is constructed. It is used to quantitatively describe the essential information characteristics of the regional logistics network, which solves the value discount problem of information and logistics data application caused by information asymmetry in the logistics network, and realizes the sharing of logistics data resources in intelligent logistics.

关键词： Industries Optimization methods data models Resource management Internet of Things Information technology Logistics

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Mining Indonesia Tourism's Reviews to Evaluate the Services Through Multilabel Classification and LDA

Mining Indonesia Tourism's Reviews to Evaluate the Services ...

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international conference on Electrical Engineering and Informatics (ICELTICs)

作者： Irma Latifatul Laily Indra Budi Aris Budi Santoso Prabu Kresna Putra Faculty of Computer Science University of Indonesia

ISBN: (数字)9781728181998

ISBN: (纸本)9781728182001

The tourism sector is one of the mainstay factors, one of the most significant economic contributors in Lamongan. There are two leading tourism destinations in Lamongan, namely WBL and Mazoola. Evaluation of tourist experience in a tourist destination can use the reviews provided at the end of the trip. Tourists review various aspects of tourism, such as price, services, and location. Classify more than one aspect from reviews is a challenging task. Five labels used, namely: Price, Location, Safety, Services and Facilities, and Environment and Ambiance. This study was conducted to determine the aspects that should be evaluated from the reviews that visitors provide. This research uses five multi-label classifier algorithms commonly used for multi-label classification: NBSVM, Binary Relevance-Naive Bayes, Binary Relevance-Logistic Regression, Classifier Chains-Naive Bayes, and Multilabel kNN. NBSVM was a robust performer. For WBL data in scenario 1, the highest accuracy is BR-LR, which is 92%. Whereas in scenario 2, NBSVM has the highest value of 91,32%. However, in other assessments, NBSVM is still superior. Likewise to Mazola's data, NBSVM has the highest accuracy in both scenarios: 87,38% and 87,28%. This study also extracts three trend topics for each data set, WBL and Mazoola. Trend topics aim to find out what topics are discussed more frequently in each tourist destination review-topic extraction using LDA with the Gensim library.

关键词： Classification algorithms data models Support vector machines Internet Training Market research Libraries

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Priority Based Modeling and Comparative Study of Google Cloud Resources between 2011 and 2019

Priority Based Modeling and Comparative Study of Google Clou...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Dimuth Lasantha Biplob Ray Centre for Intelligent Systems (CIS) School of Engineering and Technology Central Queensland University Rockhampton Australia

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

The cloud resource allocation for jobs must be further optimized and prioritised due to ever increasing demand for cloud computing resources to handle big data. In this research, we have examined the relationship between resource allocation, usages, and priority of tasks to reveal the influence of priority in resource allocation and resource usages. The analysis and modeling of this paper have used the Google cloud public dataset of 2011 and 2019. After processing and cleaning of one month data of Google cloud, we have revealed, the tasks are classified in 12 priorities in the 2011 cluster model whereas 500 priorities in the 2019 cluster model. However, both models have grouped these priorities into five groups. Therefore, we have modeled resource allocation versus usages based on five main priority groups using XGBoost (Extreme Gradient Boosting) and correlation coefficient. The comparative study on the developed models shows, the priority grouping of 2019 has better evenly distribution of resources for jobs but less efficient in most of the priority groups for resource allocation. Based on the performance parameters of the developed models, the resource allocation works more efficiently for most of the 2011 priority groups except `other'. These findings are useful for researchers to develop a balanced priority-based resource allocation-usages model to further optimise resources to reduce the management cost of cloud clusters.

关键词： Cloud computing data privacy Computational modeling data models Resource management Security Task analysis

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A Fuzzy Fan Speed Controller for Smart data processing Device

A Fuzzy Fan Speed Controller for Smart Data Processing Devic...

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IEEE international conference on Green Computing and Communications

作者： Juntao Ding Renyu Zhang Weihong Liu Jing Cao Zongwei Zhu Gangyong Jia Suzhou Insiue for Advnced Sudy Universiy of Science nd Technoogy of ChinSuzhou Insiue for Advnced Sudy Universiy of Science nd Technoogy of ChinSuzhou Insiue for Advnced Sudy Universiy of Science nd Technoogy of ChinSuzhou Insiue for Advnced Sudy Universiy of Science nd Technoogy of ChinSuzhou Insiue for Advnced Sudy Universiy of Science nd Technoogy of ChinSchoo of Couer Science Hngzhou Dinzi Universiy

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176475;9781728176482

Thanks to high computing power brought by new processor designs such as neural network processing unit (NPU), machine learning is widely used for data processing in the field of big data. However, smart data processing requires huge amount of computing, and may cause the NPU to quickly enter a working state at full load and the temperature to rapidly increase, which may lead to a crash or damage the circuit. Although reducing the frequency can prevent the temperature from rising, it will cause the loss of NPU performance. Artificial Intelligence (AI) tasks are usually computationally intensive, as a result, a high performance working mode should be provided. An external fan with suitable control algorithm can meet the above demands. However, traditional fan control algorithms usually increase the fan power consumption, which will result in a substantial increase in the total power. In this paper, we propose a fitness model based on fuzzy control to make better tradeoff between cooling efficiency and power consumption under high workload. The experimental results show that the model can effectively prevent the NPU temperature from being too high during the rapidly rising temperature and can reduce fan power consumption by 10%.

关键词： Fan speed controller Fuzzy control Smart data processing device

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Machine-Learning-based Alarm Prediction with GANs-based Self-Optimizing data Augmentation in Large-Scale Optical Transport Networks

Machine-Learning-based Alarm Prediction with GANs-based Self...

引用

international conference on Computing, Networking and Communications (ICNC)

作者： Haotao Zhuang Yongli Zhao Xiaosong Yu Yajie Li Ying Wang Jie Zhang State Key Laboratory of IPOC Beijing University of Posts and Telecommunications Beijing China State Grid Information &Telecommunication Company Beijing China

ISBN: (数字)9781728149059

ISBN: (纸本)9781728149066

Machine learning (ML) is an effective solution to deal with some networking issues in large scale optical transport networks. Alarm (caused by failure, disaster, etc.) prediction is a typical and important use-case, where ML algorithm can work to predict events in advance. By accurate prediction, network administrators can be guided to undertake preventive measures ahead of time, thus avoiding the adverse effect. For training and testing of such event prediction models, it's first and foremost to build a high-quality data set from real networks. However, the data collected from large-scale optical transport networks are inferior in quality and demand prompt pre-processing, for they are sometimes invalid, incomplete, inconsistent, and even inaccurate. After data pre-processing, there is only a small amount of available data left to build an ML data set, which extremely limits the performance of the alarm prediction model. This paper proposes a machine-learning-based alarm prediction method using self-optimizing data augmentation based on generative adversarial networks (GANs) for optical transport networks. Experimental results on a commercial backbone synchronous digital hierarchy (SDH) network with 274 nodes and 487 links demonstrate that the proposed method can achieve high accuracy for alarm prediction.

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Sketches: Fast Membership Scans for Continuous Variable Predicate Workloads

Sketches: Fast Membership Scans for Continuous Variable Pred...

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IEEE international conference on Big data

作者： Edward Schwalb

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

We consider workloads reducible to membership checks against predicates over continuous variables, for which a scan is required. We explore trading-off storage of re-usable components to avoid repeated computation for each entry of a full scan. Our method renders effective the storage of reusable results in smaller faster memory. Upon receipt of data, a build step produces a compressed representation. Subsequently, upon receipt of a query, a compilation step constructs lookup tables which are used to determine membership, with "one-sided" error guarantees; when misses occur, the full predicate evaluation is performed. We mitigate the exponential complexity by providing a recursive decomposition generating compressed representation reusable across numerous queries, useful e.g. for quantile membership checks. We experiment with a number of knobs, distributions and selectivity rates. We develop an analytic performance model that relies on metrics measurable on a workload sample, specify analytically the conditions for which speedups are achievable, and demonstrate concordance with the empirical evaluation. The key advantages of the proposed methods are: (1) Unlocking the value of GPU massive parallelism; (2) Lossy compression which enables loading into memory a much larger number of entries as compared to using the raw data; (3) The size and speed are superior to Bloom Filters with similar miss-rates; and (4) Predictable query latency regardless of the width of the raw data or predicate computation cost or workload distribution or selectivity.

关键词： Analytical models Instruction sets Loading Graphics processing units Parallel processing Big data Probes

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Toward Universal data Interoperability in Networked Belief models

Toward Universal Data Interoperability in Networked Belief M...

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conference on Human System Interactions, HSI

作者： Aaron L Bramson UNC Charlotte Charlotte USA

ISBN: (数字)9781728173924

ISBN: (纸本)9781728173931

An increasing number of applications in the sensor fusion, internet of things, knowledge processing, graph database, and networked information fields (e.g. Markov models, Bayesian networks, semantic webs) require (1) integrating confidence levels with probability processing and/or (2) belief representations other than the dominant probabilistic approach. Although alternatives such as fuzzy sets and Dempster-Shafer theory exist, the techniques to update belief levels, combine beliefs, and losslessly convert one form of belief into others are currently fragmented and inadequate. Our goal is to develop the capabilities to (1) translate among multiple belief representations and (2) update beliefs and confidence levels in a consistent manner across representations. The proposed solution utilizes an integrated belief meta-structure into which, and from which, all types of belief models can be converted. This method tracks the conceptually distinct components separately, but fosters their interaction where appropriate. In this way, even measures which are conceptually incommensurable will become interoperable in practice. We first describe the various types of belief representations divided into measures of credence and measures of confidence, then we discuss the challenges of translating and combining them, and finally outline the belief meta-structure used for interoperability.

关键词： Uncertainty Bayes methods Interoperability Probabilistic logic Measurement uncertainty Knowledge engineering Markov processes

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