Travel time prediction is an indispensable for numerous intelligent transportation systems (ITS) including advanced traveler information systems. The main purpose of this research is to develop a ...dynamic travel time prediction model for road networks. In this paper we propose a new method to predict travel times using Naïve Bayesian Classification (NBC) model because Naïve Bayesian Classification has exhibited high accuracy and speed when applied to large databases. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. In addition, we compare the proposed method with such prediction methods as link-based prediction model and time-varying coefficient linear regression model. It is shown from our experiment that NBC predictor can reduce mean absolute relative error significantly rather than the other predictors. We illustrate the practicability of applying NBC in travel time prediction and prove that NBC is suitable and performs well for traffic data analysis.
Microblogs, especially twitter, has made unprecedented opportunities for users to assert their stance towards various entities, issues, and events. Analyzing user stances from tweets provide ...opportunities to various organizations for decision making. However, it is challenging to identify the stance of a tweet due to its short length characteristics and idiosyncratic nature. Most of the previous studies explore either neural network-based features or hand-crafted features for their learning models. In this paper, we introduce a stance detection method that incorporates both the deep semantic features and hand-crafted features in a unified neural model. We exploit several opinionated lexicons and other textual and twitter-specific characteristics to extract a rich set of hand-crafted features. We use a supervised feature selection method to devise effective features and pass them to train a multilayer perceptron (MLP) network. Besides, we employ a convolutional layer in conjunction with a BiLSTM network (Conv-BiLSTM) to extract the higher-level contextual features. Extensive experiments on the SemEval-2016 stance detection dataset demonstrate the efficiency of our method over several state-of-the-art methods.
Prediction of travel time has major concern in the research domain of Intelligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that ...can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub-classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. With the use of same set of historical travel time estimates, comparison is also made to the forecasting results of other three methods: Successive Moving Average (SMA), Chain Average (CA) and Naïve Bayesian Classification (NBC) method. The results suggest that the travel times for the study periods could be predicted by the proposed method with the minimum Mean Absolute Relative Error (MARE).
Prediction of travel time on road network has emerged as a crucial research issue in intelligent transportation system (ITS). Travel time prediction provides information that may allow travelers to ...change their routes as well as departure time. To provide accurate travel time for travelers is the key challenge in this research area. In this paper, we formulate two new methods which are based on moving average can deal with this kind of challenge. In conventional moving average approach, data may lose at the beginning and end of a series. It may sometimes generate cycles or other movements that are not present in the original data. Our proposed modified method can strongly tackle those kinds of uneven presence of extreme values. We compare the proposed methods with the existing prediction methods like Switching method 10 and NBC method 11. It is also revealed that proposed methods can reduce error significantly in compared with other existing methods.
Automated weather event analysis with machine learning Hasan, Nasimul; Uddin, Md Taufeeq; Chowdhury, Nihad Karim
2016 International Conference on Innovations in Science, Engineering and Technology (ICISET),
2016-Oct.
Conference Proceeding
Weather forecasting has numerous impacts in our daily life from cultivation to event planning. Previous weather forecasting models used the complicated blend of mathematical instruments which was ...insufficient in order to get higher classification rate. In contrast, simple analytical models are well-suited for weather forecasting tasks. In this work, we focus on the weather forecasting by means of classifying different weather events such as normal, rain, and fog by applying comprehensible C4.5 learning algorithm on weather and climate features. The C4.5 classifier classifies weather events by building the decision tree using information entropy from the set of training samples. We conducted experiments on LA weather history dataset; from evaluation results, it is revealed that C4.5 classifier classifies weather events with f-score of around 96.1%. This model also indicates that climate features such as rainfall, visibility, temperature, humidity, and wind speed are highly discriminative toward events classification.
규칙-기반 분류화 기법을 이용한 도로 네트워크 상에서의 주행 시간 예측 알고리즘 이현조(Hyun-jo Lee); 니하드 카림 초우더리(Nihad Karim Chowdhur); 장재우(Jae-Woo Chang)
한국콘텐츠학회 논문지, 8(10),
2008, Letnik:
8, Številka:
10
Journal Article
Odprti dostop
Prediction of travel time on road network is one of crucial research issue in dynamic route guidance system. A new approach based on Rule-Based classification is proposed for predicting travel time. ...This approach departs from many existing prediction models in that it explicitly consider traffic patterns during day time as well as week day. We can predict travel time accurately by considering both traffic condition of time range in a day and traffic patterns of vehicles in a week. We compare the proposed method with the existing prediction models like Link-based, Micro-T* and Switching model. It is also revealed that proposed method can reduce MARE (mean absolute relative error) significantly, compared with the existing predictors. 동적 경로 안내 시스템과 같은 첨단 여행 정보 시스템(ATIS)의 발전에 따라 도로 네트워크 상에서 보다 정확한 주행 시간 예측 기법에 대한 연구가 활발히 진행되고 있다. 그러나 기존 대부분의 연구들은 주어진 경로 상의 평균 주행 속도만을 기반으로 주행 시간을 예측한다. 이는 러시아워 시간대의 혼잡한 도로, 주말에 교외로 나가는 대규모의 차량 등과 같은 일별 혹은 주별 도로 교통 상황을 반영하지 못하기 때문에, 주행 시간 예측의 정확도가 저하된다. 이를 해결하기 위해 본 연구에서는 규칙-기반 분류화 기법을 이용한 주행 시간 예측 알고리즘을 제안한다. 제안된 알고리즘은 데이터마이닝 기법인 규칙-기반 분류화 기법을 사용하여, 과거 차량의 궤적 데이터로부터 하루의 시간대별 교통량과 주별 차량의 운행 양식 등 도로 교통 상황을 추출하고, 이를 통해 차량의 주행 시간을 보다 정확하게 예측한다. 제안된 알고리즘 기존의 링크-기반 예측(link-based prediction) 알고리즘, Micro T* 알고리즘3, 그리고 스위칭 (switching) 알고리즘10과 예측 정확도 측면에서 성능 비교를 수행한다. 예측 정확도 성능 비교 결과, 제안된 기법이 타 예측 기법에 비해 MARE (mean absolute relative error) 가 크게 감소하여 성능이 향상됨을 보인다. 그 밖에 다른 기법들과 장단점을 비교하여, 제안된 기법의 유용성을 나타낸다.
In this paper, we propose new query processing algorithms for typical spatial queries in SNDB, such as range search and k nearest neighbors (k-NN) search. Our two query processing algorithms can ...reduce the computation time of network distance between a pair of nodes and the number of disk I/Os required for accessing nodes by using a materialization-based technique with the shortest network distances of all the nodes in the spatial network. Thus, our query processing algorithms improve the existing efficient k-NN (INE) and range search (RNE) algorithms proposed by 1. It is shown that our range query processing algorithm achieves about up to one of magnitude better performance than the RNE and our k- NN query processing algorithm achieves about up to 150% performance improvements over INE.
There is an increasing focus on researching children admitted to hospital with new variants of COVID-19, combined with concerns with hyperinflammatory syndromes and the overuse of antimicrobials. ...Paediatric guidelines have been produced in Bangladesh to improve their care. Consequently, the objective is to document the management of children with COVID-19 among 24 hospitals in Bangladesh. Key outcome measures included the percentage prescribed different antimicrobials, adherence to paediatric guidelines and mortality rates using purposely developed report forms. The majority of 146 admitted children were aged 5 years or under (62.3%) and were boys (58.9%). Reasons for admission included fever, respiratory distress and coughing; 86.3% were prescribed antibiotics, typically parenterally, on the WHO 'Watch' list, and empirically (98.4%). There were no differences in antibiotic use whether hospitals followed paediatric guidance or not. There was no prescribing of antimalarials and limited prescribing of antivirals (5.5% of children) and antiparasitic medicines (0.7%). The majority of children (92.5%) made a full recovery. It was encouraging to see the low hospitalisation rates and limited use of antimalarials, antivirals and antiparasitic medicines. However, the high empiric use of antibiotics, alongside limited switching to oral formulations, is a concern that can be addressed by instigating the appropriate programmes.
Objective: The focus on COVID-19 in children in low- and middle-income countries including Bangladesh has been on addressing key issues including poor vaccination rates as well as mental health ...issues, domestic violence and child labour. However, the focus on optimally managing children in hospitals is changing with new variants and concerns with the development of hyperinflammatory syndromes. There are also concerns with the overuse of antimicrobials to treat patients with COVID-19 in hospitals enhancing resistance rates. The Bangladesh Paediatric Association have developed guidelines to improve patient care building on national guidance. Consequently, there is a need to document the current management of children with COVID-19 in Bangladesh and use the findings for future guidance.
Methods: Rapid analysis of the management of children with COVID-19 among eight private and public hospitals in Bangladesh with varying numbers of in-patient beds using purposely developed case report forms (CRFs). The CRFs were piloted before full roll-out.
Results: Overall low numbers of children in hospital with COVID-19 (4.3% of in-patient beds). The majority were male (59.6%) and aged 5 years or under (63.5%). Reasons for admission included respiratory distress/ breathing difficulties with 94.2% of COVID-19 cases confirmed. All children were prescribed antibiotics empirically, typically those on the Watch list of antibiotics and administered parenterally, with only a small minority switched to oral therapy before discharge. There was appreciable prescribing of Vitamins (C and D) and zinc and encouragingly limited prescribing of other antimicrobials (antivirals, antimalarials and antiparasitic medicines). Length of stay was typically 5 to 10 days.
Conclusion: Encouraging to see low hospitalisation rates and limited use of antimicrobials apart from antibiotics. Concerns with high empiric use of antibiotics and limited switching to oral formulations can be addressed by instigating antimicrobial stewardship programmes. We will be monitoring this.
Bangladesh Journal of Medical Science Vol.20(5) 2021 p.188-198
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