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Algorithms for Optimizing Fleet Scheduling of Air Ambulances [article]

Joseph Tassone, Salimur Choudhury
2020 arXiv   pre-print
Proper scheduling of air assets can be the difference between life and death for a patient. While poor scheduling can be incredibly problematic during hospital transfers, it can be potentially catastrophic in the case of a disaster. These issues are amplified in the case of an air emergency medical service (EMS) system where populations are dispersed, and resources are limited. There are exact methodologies existing for scheduling missions, although actual calculation times can be quite
more » ... an be quite significant given a large enough problem space. For this research, known coordinates of air and health facilities were used in conjunction with a formulated integer linear programming model. This was the programmed through Gurobi so that performance could be compared against custom algorithmic solutions. Two methods were developed, one based on neighbourhood search and the other on Tabu search. While both were able to achieve results quite close to the Gurobi solution, the Tabu search outperformed the former algorithm. Additionally, it was able to do so in a greatly decreased time, with Gurobi actually being unable to resolve to optimal in larger examples. Parallel variations were also developed with the compute unified device architecture (CUDA), though did not improve the timing given the smaller sample size.
arXiv:2002.11710v1 fatcat:urqkvclp2bh3ff4mcifc6olasy

A Comprehensive Survey on the Ambulance Routing and Location Problems [article]

Joseph Tassone, Salimur Choudhury
2020 arXiv   pre-print
In this research, an extensive literature review was performed on the recent developments of the ambulance routing problem (ARP) and ambulance location problem (ALP). Both are respective modifications of the vehicle routing problem (VRP) and maximum covering problem (MCP), with modifications to objective functions and constraints. Although alike, a key distinction is emergency service systems (EMS) are considered critical and the optimization of these has become all the more important as a
more » ... important as a result. Similar to their parent problems, these are NP-hard and must resort to approximations if the space size is too large. Much of the current work has simply been on modifying existing systems through simulation to achieve a more acceptable result. There has been attempts towards using meta-heuristics, though practical experimentation is lacking when compared to VRP or MCP. The contributions of this work are a comprehensive survey of current methodologies, summarized models, and suggested future improvements.
arXiv:2001.05288v1 fatcat:aqmklf5dlbf3hk5wlxh7nzxf7q

Smart City Response to Homelessness

Pedram Khayyatkhoshnevis, Salimur Choudhury, Eric Latimer, Vijay Mago
2020 IEEE Access  
The smart city is a concept of utilizing digital technologies to improve and enhance the lives of a city's inhabitants. This concept has been the subject of increasing interest over the past few years. However, most studies address improving aspects of a city's infrastructure, such as information security, privacy, communication networks, government, and transportation. Noticeably absent from the subject matter of these studies are social problems, such as poverty and homelessness. In this
more » ... sness. In this paper, we explore how technology can be harnessed to mitigate homelessness. We introduce eight novel heuristic algorithms that create a desirable homeless-to-housing assignment with regards to homeless individuals' characteristics and the nature of services. We discuss the efficiency of each of the algorithms through simulations. Our best performing algorithm obtains 92% accuracy in comparison to the optimal solution and 99.7% fairness. The algorithms are compared in terms of execution time, solution accuracy, fairness, and the relative difference with the optimal solution of this NP-hard problem. INDEX TERMS Smart cities, homelessness, social systems, greedy algorithm, local search algorithm.
doi:10.1109/access.2020.2965557 fatcat:b64wpizhvreolblkeeshszjssi

IoT Big Data Analytics

Salimur Choudhury, Qiang Ye, Mianxiong Dong, Qingchen Zhang
2019 Wireless Communications and Mobile Computing  
Salimur Choudhury Qiang Ye Mianxiong Dong Qingchen Zhang  ... 
doi:10.1155/2019/9245392 fatcat:vri35vm6uvh6rjbf7flahve4qe

Internet of Things for smart living

Al-Sakib Khan Pathan, Zubair Md. Fadlullah, Salimur Choudhury, Mohamed Guerroumi
2019 Wireless networks  
Fadlullah zfadlullah@ieee.org Salimur Choudhury salimur.choudhury@lakeheadu.ca Mohamed Guerroumi guerroumi@gmail.com 1 Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh  ...  He is a senior member of the IEEE.Salimur Choudhury is an Assistant Professor in the Department of Computer Science at Lakehead University, ON, Canada.  ... 
doi:10.1007/s11276-019-01970-3 fatcat:elyz6jorlzglfignupcxmyoc54

Algorithms for Optimizing Fleet Staging of Air Ambulances [article]

Joseph Tassone, Geoffrey Pond, Salimur Choudhury
2020 arXiv   pre-print
In a disaster situation, air ambulance rapid response will often be the determining factor in patient survival. Obstacles intensify this circumstance, with geographical remoteness and limitations in vehicle placement making it an arduous task. Considering these elements, the arrangement of responders is a critical decision of the utmost importance. Utilizing real mission data, this research structured an optimal coverage problem with integer linear programming. For accurate comparison, the
more » ... omparison, the Gurobi optimizer was programmed with the developed model and timed for performance. A solution implementing base ranking followed by both local and Tabu search-based algorithms was created. The local search algorithm proved insufficient for maximizing coverage, while the Tabu search achieved near-optimal results. In the latter case, the total vehicle travel distance was minimized and the runtime significantly outperformed the one generated by Gurobi. Furthermore, variations utilizing parallel CUDA processing further decreased the algorithmic runtime. These proved superior as the number of test missions increased, while also maintaining the same minimized distance.
arXiv:2001.05291v2 fatcat:2qwksv7osjeedleydhvofbtqty

Utilizing Deep Learning to Identify Drug Use on Twitter Data [article]

Joseph Tassone, Peizhi Yan, Mackenzie Simpson, Chetan Mendhe, Vijay Mago, Salimur Choudhury
2020 arXiv   pre-print
The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of collected Twitter data, models were developed for classifying drug-related tweets. Using topic pertaining keywords, such as slang and methods of drug consumption, a set of tweets was generated. Potential candidates were then preprocessed resulting in a dataset of 3,696,150 rows. The classification power of multiple methods was
more » ... iple methods was compared including support vector machines (SVM), XGBoost, and convolutional neural network (CNN) based classifiers. Rather than simple feature or attribute analysis, a deep learning approach was implemented to screen and analyze the tweets' semantic meaning. The two CNN-based classifiers presented the best result when compared against other methodologies. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Additionally, association rule mining showed that commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of the system. Lastly, the synthetically generated set provided increased scores, improving the classification capability and proving the worth of this methodology.
arXiv:2003.11522v1 fatcat:kxr7pbvvlvdkjhhjhg3zf7prxy

A case of Wilson's disease presenting as acute hepatitis

Nuzhat Choudhury, Mamun Al Mahtab, Salimur Rahman
2012 Khyber Medical University Journal  
This article may be cited as: Choudhury N, Mamun-Al-Mahtab, Rahman S. A case of Wilson's disease presenting as acute hepatitis. KUST Med J 2011;3(2): 64-66.  ...  Nuzhat Choudhury Nuzhat Choudhury Nuzhat Choudhury Nuzhat Choudhury 1 1 1 1 1 , Mamun-Al-Mahtab , Mamun-Al-Mahtab , Mamun-Al-Mahtab , Mamun-Al-Mahtab , Mamun-Al-Mahtab 2 2 2 2 2 , Salimur  ...  Rahman , Salimur Rahman , Salimur Rahman , Salimur Rahman , Salimur Rahman 2 2 2 2 2 KMJ 2011; Vol. 3, No. 2: 64-66 A CASE OF WILSON'S DISEASE PRESENTING AS ACUTE HEPATITIS C K C K C  ... 
doaj:6bd893b7c7ed4827b043f0bf9fe6ea4d fatcat:g7k6pclg6ngrhkniof2byikjl4

A Machine Learning Auxiliary Approach for the Distributed Dense RFID Readers Arrangement Algorithm

Peizhi Yan, Salimur Choudhury, Ruizhong Wei
2020 IEEE Access  
This paper is an extended version of the work published. Radio-frequency identification (RFID) is widespread in industries such as supply-chain management and logistics due to its low-cost feature. In many real-world problems, one often needs to leverage a considerable amount of RFID readers to cover a large area. Many graph-based dense RFID readers system anti-collision algorithms were proposed to address the collision problems. However, state-of-the-art collision avoidance algorithms are
more » ... algorithms are centralized algorithms. In a dense RFID system, the graphs generated by the centralized algorithms could be very complicated. Therefore, a centralized algorithm increases the computational workload of the central server. We proposed a distributed anti-collision algorithm based on the idea of a centralized collision avoidance algorithm called MWISBAII. In our later research, we found that due to the lack of global information, there is a gap between the performance of our distributed algorithm and the centralized MWISBAII. To narrow this gap, we introduced machine learning into the proposed algorithm. The machine learning model is an empirical model that mitigates the deficiency of the lack of global information. The experimental results show that the proposed distributed algorithm with machine learning can get almost the same performance as the centralized MWISBAII in different experimental settings. INDEX TERMS Large scale RFID network optimization, reader coverage collision avoidance (RCCA), maximum weight independent set (MWIS), machine learning (ML).
doi:10.1109/access.2020.2977683 fatcat:2g4no2adrfeutedq3n74czvnwu

A Resource Allocation Model Based on Trust Evaluation in Multi-Cloud Environments

A B M Bodrul Alam, Zubair Md Fadlullah, Salimur Choudhury
2021 IEEE Access  
doi:10.1109/access.2021.3100316 fatcat:drkp5hn2rjbtxhwbsgkegwh3hq

Near-Optimal Resource Allocation Algorithms for 5G+ Cellular networks

Huda Yousef Alsheyab, Salimur Choudhury, Ebrahim Bedeer, Salama S. Ikki
2019 IEEE Transactions on Vehicular Technology  
Fifth generation and beyond (5G+) systems will support novel cases, and hence, require new network architecture. In this work, network flying platforms (NFPs) as aerial hubs are considered in future 5G+ networks to provide fronthaul connectivity to small cells (SCs). We aim to find the optimal association between the NFPs and SCs to maximize the total sum rate subject to quality-of-service (QoS), bandwidth, and supported number of links constraints. The formulated optimization problem is an
more » ... n problem is an integer linear program and the optimal association between the NFPs and SCs is found using numerical solvers at the expense of high computational complexity. We propose two algorithms (centralized and distributed) to reach a sub-optimal association at reduced complexity. Simulation results show that the performance of the proposed algorithms approaches the counterpart of its optimal solution and outperforms the stateof-the-art techniques from the literature.
doi:10.1109/tvt.2019.2914908 fatcat:rc2z3ox7vvd6xdhnpjnsey726i

Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services

Mahzabeen Emu, Salimur Choudhury
2021 IEEE Open Journal of the Computer Society  
She 99 is the recipient of Vector Institute AI Scholarship 100 (2019), OGS 2020-21, Mitacs accelerate grant, and 101 the 2021 Governor General Gold Medal Award, Canada. 102 SALIMUR CHOUDHURY (schoudh1@  ...  Choudhury, and Y. Abdulsalam.  ... 
doi:10.1109/ojcs.2021.3098462 fatcat:mycl3xng55hnjkhczkb46axa5u

SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest

Gaurav Rao, Salimur Choudhury, Pawan Lingras, David Savage, Vijay Mago
2020 BMC Medical Informatics and Decision Making  
Background When an Out-of-Hospital Cardiac Arrest (OHCA) incident is reported to emergency services, the 911 agent dispatches Emergency Medical Services to the location and activates responder network system (RNS), if the option is available. The RNS notifies all the registered users in the vicinity of the cardiac arrest patient by sending alerts to their mobile devices, which contains the location of the emergency. The main objective of this research is to find the best match between the user
more » ... h between the user who could support the OHCA patient. Methods For performing matching among the user and the AEDs, we used Bipartite Matching and Integer Linear Programming. However, these approaches take a longer processing time; therefore, a new method Preprocessed Integer Linear Programming is proposed that solves the problem faster than the other two techniques. Results The average processing time for the experimentation data was 1850 s using Bipartite matching, 32 s using the Integer Linear Programming and 2 s when using the Preprocessed Integer Linear Programming method. The proposed algorithm performs matching among users and AEDs faster than the existing matching algorithm and thus allowing it to be used in the real world. Conclusion: This research proposes an efficient algorithm that will allow matching of users with AED in real-time during cardiac emergency. Implementation of this system can help in reducing the time to resuscitate the patient.
doi:10.1186/s12911-020-01334-4 pmid:33380330 fatcat:35ssk7ehfbflnlqosl6fweqfya

Prioritizing restoration of fragmented landscapes for wildlife conservation: A graph-theoretic approach

Denys Yemshanov, Robert G. Haight, Frank H. Koch, Marc-André Parisien, Tom Swystun, Quinn Barber, A. Cole Burton, Salimur Choudhury, Ning Liu
2019 Biological Conservation  
A B S T R A C T Anthropogenic disturbances fragmenting wildlife habitat greatly contribute to extinction risk for many species. In western Canada, four decades of oil and gas exploration have created a network of seismic lines, which are linear disturbances where seismic equipment operates. Seismic lines cause habitat fragmentation and increase predator access to intact forest, leading to declines of some wildlife populations, particularly the threatened woodland caribou, Rangifer tarandus
more » ... gifer tarandus caribou. Restoration of forests within seismic lines is an important activity to reduce habitat fragmentation and recovery caribou. We present an optimization model with the objective of guiding landscape restoration strategies that maximize the area of connected habitat for a caribou population in a fragmented landscape. We use our model to find optimal strategies for seismic line restoration in the Cold Lake Area of Alberta, Canada, a 6726-km 2 expanse of boreal forest that represents prime caribou habitat. We formulate mixed integer programming models that depict the landscape as a network of interconnected habitat patches. We develop and compare formulations that emphasize the population's local or long-distance access to habitat. Optimal restoration involves a mix of two strategies: the first establishes short-distance connections between forest patches with large areas of intact habitat and the second establishes corridors between areas with known species locations and large amounts of suitable habitat. Our approach reveals the trade-offs between these strategies and finds the optimal restoration solutions under a limited budget. The approach is generalizable and applicable to other regions and species sensitive to changes in landscape-level habitat connectivity. 2011a , 2011b Schneider et al., 2010; Wilson and Demars, 2015; Wittmer et al., 2005) . In particular, the creation of linear corridors allows predators to travel more quickly and further into caribou habitat https://doi.
doi:10.1016/j.biocon.2019.02.003 fatcat:pcdhfoglwzambcmbqqaju7h4jm

Utilizing deep learning and graph mining to identify drug use on Twitter data

Joseph Tassone, Peizhi Yan, Mackenzie Simpson, Chetan Mendhe, Vijay Mago, Salimur Choudhury
2020 BMC Medical Informatics and Decision Making  
Background The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. Methods Social media data (tweets and attributes) were collected and processed using topic pertaining keywords, such as drug slang and
more » ... g and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset of 3,696,150 rows. The predictive classification power of multiple methods was compared including SVM, XGBoost, BERT and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. Results To test the predictive capability of the model, SVM and XGBoost were first employed. The results calculated from the models respectively displayed an accuracy of 59.33% and 54.90%, with AUC's of 0.87 and 0.71. The values show a low predictive capability with little discrimination. Conversely, the CNN-based classifiers presented a significant improvement, between the two models tested. The first was trained with 2661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as "smoke", "cocaine", and "marijuana" triggering a drug-positive classification. Conclusion Predictive analysis with a CNN is promising, whereas attribute-based models presented little predictive capability and were not suitable for analyzing text of data. This research found that the commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased accuracy scores and improves the predictive capability.
doi:10.1186/s12911-020-01335-3 pmid:33380324 fatcat:nqnt3q2glvgwhlmxvzletp2bga
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