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Danial Alizadeh; Ali Asghar Alesheikh; Mohammad Sharif. Prediction of vessels locations and maritime traffic using similarity measurement of trajectory. Annals of GIS 2020, 1 -12.
AMA StyleDanial Alizadeh, Ali Asghar Alesheikh, Mohammad Sharif. Prediction of vessels locations and maritime traffic using similarity measurement of trajectory. Annals of GIS. 2020; ():1-12.
Chicago/Turabian StyleDanial Alizadeh; Ali Asghar Alesheikh; Mohammad Sharif. 2020. "Prediction of vessels locations and maritime traffic using similarity measurement of trajectory." Annals of GIS , no. : 1-12.
For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40·85% for the second model compared with the first model and 23% for the third model compared with the second model.
Danial Alizadeh; Ali Asghar Alesheikh; Mohammad Sharif. Vessel Trajectory Prediction Using Historical Automatic Identification System Data. Journal of Navigation 2020, 74, 156 -174.
AMA StyleDanial Alizadeh, Ali Asghar Alesheikh, Mohammad Sharif. Vessel Trajectory Prediction Using Historical Automatic Identification System Data. Journal of Navigation. 2020; 74 (1):156-174.
Chicago/Turabian StyleDanial Alizadeh; Ali Asghar Alesheikh; Mohammad Sharif. 2020. "Vessel Trajectory Prediction Using Historical Automatic Identification System Data." Journal of Navigation 74, no. 1: 156-174.
Mohammad Sharif; Ali Asghar Alesheikh; Behnam Tashayo. CaFIRST: A context-aware hybrid fuzzy inference system for the similarity measure of multivariate trajectories. Journal of Intelligent & Fuzzy Systems 2019, 36, 5383 -5395.
AMA StyleMohammad Sharif, Ali Asghar Alesheikh, Behnam Tashayo. CaFIRST: A context-aware hybrid fuzzy inference system for the similarity measure of multivariate trajectories. Journal of Intelligent & Fuzzy Systems. 2019; 36 (6):5383-5395.
Chicago/Turabian StyleMohammad Sharif; Ali Asghar Alesheikh; Behnam Tashayo. 2019. "CaFIRST: A context-aware hybrid fuzzy inference system for the similarity measure of multivariate trajectories." Journal of Intelligent & Fuzzy Systems 36, no. 6: 5383-5395.
Air pollutants and allergens are the main stimuli that have considerable effects on asthmatic patients’ health. Seamless monitoring of patients’ conditions and the surrounding environment, limiting their exposure to allergens and irritants, and reducing the exacerbation of symptoms can aid patients to deal with asthma better. In this context, ubiquitous healthcare monitoring systems can provide any service to any user everywhere and every time through any device and network. In this regard, this research established a GIS-based outdoor asthma monitoring framework in light of ubiquitous systems. The proposed multifaceted model was designed in three layers: (1) pre-processing, for cleaning and interpolating data, (2) reasoning, for deducing knowledge and extract contextual information from data, and (3) prediction, for estimating the asthmatic conditions of patients ubiquitously. The effectiveness of the proposed model is assessed by applying it on a real dataset that comprised of internal context information including patients’ personal information (age, gender, height, medical history), patients’ locations, and their peak expiratory flow (PEF) values, as well as external context information including air pollutant data (O3, SO2, NO2, CO, PM10), meteorological data (temperature, pressure, humidity), and geographic information related to the city of Tehran, Iran. With more than 92% and 93% accuracies in reasoning and estimation mechanism, respectively, the proposed method showed remarkably effective in asthma monitoring and management.
Neda Kaffash-Charandabi; Ali Asghar Alesheikh; Mohammad Sharif. A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals’ contexts. Environmental Science and Pollution Research 2019, 26, 7525 -7539.
AMA StyleNeda Kaffash-Charandabi, Ali Asghar Alesheikh, Mohammad Sharif. A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals’ contexts. Environmental Science and Pollution Research. 2019; 26 (8):7525-7539.
Chicago/Turabian StyleNeda Kaffash-Charandabi; Ali Asghar Alesheikh; Mohammad Sharif. 2019. "A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals’ contexts." Environmental Science and Pollution Research 26, no. 8: 7525-7539.
Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW) method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.
Mohammad Sharif; Ali Asghar Alesheikh; Neda Kaffash Charandabi. Context-aware pattern discovery for moving object trajectories. Proceedings of the ICA 2018, 1, 1 -6.
AMA StyleMohammad Sharif, Ali Asghar Alesheikh, Neda Kaffash Charandabi. Context-aware pattern discovery for moving object trajectories. Proceedings of the ICA. 2018; 1 ():1-6.
Chicago/Turabian StyleMohammad Sharif; Ali Asghar Alesheikh; Neda Kaffash Charandabi. 2018. "Context-aware pattern discovery for moving object trajectories." Proceedings of the ICA 1, no. : 1-6.
Movement of an entity is greatly affected by its internal and external contexts. Such consequential influence has created new paradigms for context-aware movement data mining and analysis. The significance of incorporating contextual information and movement data is becoming quite evident because of the growing interest in context-aware movement analysis. Despite such importance, there is limited consensus among researchers on the definition of context and context-aware system design in movement studies. Therefore, this paper comprehensively reviews current concepts of context and provides a definition and a taxonomy for context in movement analysis. The paper proceeds by providing a definition of context-aware systems in the movement area after a complete review and comparison of the present definitions present in the literature. Inspired by related works, the paper further suggests a holistic three-layer design framework tailored to context-aware systems in movement studies to examine in greater depth the techniques applied during the design stages. The paper outlines the challenges and emergent issues in future research directions in context-aware movement analysis. The present study is an attempt to bridge the gap between solely using context and developing context-aware systems, thus paving the way for further research in movement applications. WIREs Data Mining Knowl Discov 2018, 8:e1233. doi: 10.1002/widm.1233 This article is categorized under:
Mohammad Sharif; Ali Asghar Alesheikh. Context-aware movement analytics: implications, taxonomy, and design framework. WIREs Data Mining and Knowledge Discovery 2017, 8, e1233 .
AMA StyleMohammad Sharif, Ali Asghar Alesheikh. Context-aware movement analytics: implications, taxonomy, and design framework. WIREs Data Mining and Knowledge Discovery. 2017; 8 (1):e1233.
Chicago/Turabian StyleMohammad Sharif; Ali Asghar Alesheikh. 2017. "Context-aware movement analytics: implications, taxonomy, and design framework." WIREs Data Mining and Knowledge Discovery 8, no. 1: e1233.
Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW) method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.
M. Sharif; A. A. Alesheikh. MULTI-DIMENSIONAL PATTERN DISCOVERY OF TRAJECTORIES USING CONTEXTUAL INFORMATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-4/W7, 31 -36.
AMA StyleM. Sharif, A. A. Alesheikh. MULTI-DIMENSIONAL PATTERN DISCOVERY OF TRAJECTORIES USING CONTEXTUAL INFORMATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-4/W7 ():31-36.
Chicago/Turabian StyleM. Sharif; A. A. Alesheikh. 2017. "MULTI-DIMENSIONAL PATTERN DISCOVERY OF TRAJECTORIES USING CONTEXTUAL INFORMATION." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W7, no. : 31-36.
Recent human effort has been directed at expanding pervasive smart environments. For this, ubiquitous computing technology is introduced to provide all users with any service, anytime, anywhere, with any device, and under any network. However, high cost, long time consumption, extensive effort, and in some cases irrevocability are the main challenges and difficulties for developing ubiquitous systems. Therefore, one solution is to initially simulate, analyze, and validate practices prior to deploying sensing and computational devices in the real world. Simulation, as a performance evaluation technique, has attracted attentions due to its speed, cost-effectiveness, repeatability, scalability, flexibility, and ease of implementation. Moreover, emulation, as a hybrid method, not only offers most simulation advantages but also benefits from tight control of implementation, as well as a certain degree of realistic results. Both simulators and emulators are significant tools for enhancing the understanding of ubiquitous sensor networks (USNs) through testing and analyzing several scenarios prior to actual sensor placements. In this regard, this paper surveys 130 simulation and emulation environments and frameworks, which were originally designed and adapted for USN. Of these 130, the 22 that have been widely used, regularly updated, and well supported by their developers are compared based on multifarious criteria. Finally, several studies that had favorably compared the performance of simulators and/or emulators are examined. We believe the present research findings will be helpful for students and researchers to pick an appropriate simulator/emulator, and for software developers and those who are keen on producing their own environment
Mohammad Sharif; Abolghasem Sadeghi-Niaraki. Ubiquitous sensor network simulation and emulation environments: A survey. Journal of Network and Computer Applications 2017, 93, 150 -181.
AMA StyleMohammad Sharif, Abolghasem Sadeghi-Niaraki. Ubiquitous sensor network simulation and emulation environments: A survey. Journal of Network and Computer Applications. 2017; 93 ():150-181.
Chicago/Turabian StyleMohammad Sharif; Abolghasem Sadeghi-Niaraki. 2017. "Ubiquitous sensor network simulation and emulation environments: A survey." Journal of Network and Computer Applications 93, no. : 150-181.
Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, employing spatial autocorrelation indices as important tools is inevitable. In this study, general and local indices of Moran’s I and Getis-Ord statistics were assessed in their representation of the structural characteristics of carbon monoxide (CO) and fine particulate matter (PM2.5) polluted areas in Tehran, Iran, which is one of the most polluted cities in the world. For this purpose, a grid (200 m × 200 m) was applied across the city, and the inverse distance weighted (IDW) interpolation method was used to allocate a value to each pixel. To compare the methods of detecting clusters meaningfully and quantitatively, the pollution cleanliness index (PCI) was established. The results ascertained a high clustering level of the pollutants in the study area (with 99% confidence level). PM2.5 clusters separated the city into northern and southern parts, as most of the cold spots were situated in the north half and the hotspots were in the south. However, the CO hotspots also covered an area from the northeast to southwest of the city and the cold spots were spread over the rest of the city. The Getis-Ord’s PCI suggested a more polluted air quality than the Moran’s I PCI. The study provides a feasible methodology for urban planners and decision makers to effectively investigate and govern contaminated sites with the aim of reducing the harmful effects of air pollution on public health and the environment.
Roya Habibi; Ali Asghar Alesheikh; Ali Mohammadinia; Mohammad Sharif. An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran. ISPRS International Journal of Geo-Information 2017, 6, 270 .
AMA StyleRoya Habibi, Ali Asghar Alesheikh, Ali Mohammadinia, Mohammad Sharif. An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran. ISPRS International Journal of Geo-Information. 2017; 6 (9):270.
Chicago/Turabian StyleRoya Habibi; Ali Asghar Alesheikh; Ali Mohammadinia; Mohammad Sharif. 2017. "An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran." ISPRS International Journal of Geo-Information 6, no. 9: 270.
Technological advances have led to an increasing development of data sources. Since the introduction of social networks, numerous studies on the relationships between users and their behaviors have been conducted. In this context, trip behavior is an interesting topic that can be explored via Location-Based Social Networks (LBSN). Due to the wide availability of various spatial data sources, the long-standing field of collective human mobility prediction has been revived and new models have been introduced. Recently, a parameterized model of predicting human mobility in cities, known as rank-based model, has been introduced. The model predicts the flow from an origin toward a destination using “rank” concept. However, the notion of rank has not yet been well explored. In this study, we investigate the potential of LBSN data alongside the rank concept in predicting human mobility patterns in Manhattan, New York City. For this purpose, we propose three scenarios, including: rank-distance, the number of venues between origin and destination, and a check-in weighted venue schema to compute the ranks. When trip distribution patterns are considered as a whole, applying a check-in weighting schema results in patterns that are approximately 10 percent more similar to the ground truth data. From the accuracy perspective, as the predicted numbers of trips are closer to real number of trips, the trip distribution is also enhanced by about 50 percent.
Omid Reza Abbasi; Ali Asghar Alesheikh; Mohammad Sharif. Ranking the City: The Role of Location-Based Social Media Check-Ins in Collective Human Mobility Prediction. ISPRS International Journal of Geo-Information 2017, 6, 136 .
AMA StyleOmid Reza Abbasi, Ali Asghar Alesheikh, Mohammad Sharif. Ranking the City: The Role of Location-Based Social Media Check-Ins in Collective Human Mobility Prediction. ISPRS International Journal of Geo-Information. 2017; 6 (5):136.
Chicago/Turabian StyleOmid Reza Abbasi; Ali Asghar Alesheikh; Mohammad Sharif. 2017. "Ranking the City: The Role of Location-Based Social Media Check-Ins in Collective Human Mobility Prediction." ISPRS International Journal of Geo-Information 6, no. 5: 136.
Characterizing the spatial variation of traffic-related air pollution has been and is a long-standing challenge in quantitative environmental health impact assessment of urban transportation planning. Advanced approaches are required for modeling complex relationships among traffic, air pollution, and adverse health outcomes by considering uncertainties in the available data. A new hybrid fuzzy model is developed and implemented through hierarchical fuzzy inference system (HFIS). This model is integrated with a dispersion model in order to model the effect of transportation system on the PM2.5 concentration. An improved health metric is developed as well based on a HFIS to model the impact of traffic-related PM2.5 on health. Two solutions are applied to improve the performance of both the models: the topologies of HFISs are selected according to the problem and used variables, membership functions, and rule set are determined through learning in a simultaneous manner. The capabilities of this proposed approach is examined by assessing the impacts of three traffic scenarios involved in air pollution in the city of Isfahan, Iran, and the model accuracy compared to the results of available models from literature. The advantages here are modeling the spatial variation of PM2.5 with high resolution, appropriate processing requirements, and considering the interaction between emissions and meteorological processes. These models are capable of using the available qualitative and uncertain data. These models are of appropriate accuracy, and can provide better understanding of the phenomena in addition to assess the impact of each parameter for the planners.
Behnam Tashayo; Abbas Alimohammadi; Mohammad Sharif. A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning. Sustainability 2017, 9, 134 .
AMA StyleBehnam Tashayo, Abbas Alimohammadi, Mohammad Sharif. A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning. Sustainability. 2017; 9 (1):134.
Chicago/Turabian StyleBehnam Tashayo; Abbas Alimohammadi; Mohammad Sharif. 2017. "A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning." Sustainability 9, no. 1: 134.
Analyzing the spatial behaviors of moving-point objects (MPOs) and discovering their movement patterns have been of great interest to the geographic information science community recently. These interests can be explored through analyzing similarities in the MPO trajectories. Because movements of objects take place in various contexts, their trajectories are also highly influenced by such contexts. Therefore, it is essential to fully understand the contexts and to realize how they can be incorporated into movement analysis. This article first proposes a taxonomy for contexts. Then, a modified version of dynamic time warping called context-based dynamic time warping (CDTW) is introduced, to contextually assess the multidimensional weighted similarities of trajectories. Ultimately, the results of similarity searches are utilized in discovering the relative movement patterns of the MPOs. To evaluate the performance and effectiveness of our proposed CDTW method, we run several experiments on real datasets that were obtained from commercial airplanes in a constrained Euclidean space, taking into account contextual information. Specifically, these experiments were conducted to explore the role of contexts and their interactions in similarity measures of trajectories. The results yielded the robustness of CDTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80% accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.
Mohammad Sharif; Ali Asghar Alesheikh. Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method. GIScience & Remote Sensing 2017, 54, 426 -452.
AMA StyleMohammad Sharif, Ali Asghar Alesheikh. Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method. GIScience & Remote Sensing. 2017; 54 (3):426-452.
Chicago/Turabian StyleMohammad Sharif; Ali Asghar Alesheikh. 2017. "Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method." GIScience & Remote Sensing 54, no. 3: 426-452.