Browsing by Author "Rawshan, Lamisha"

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A Comparative Study on GA-based Scheduling on Cloud Computing
(Scopus, 2020) Rawshan, Lamisha; Rahman, Tasnim; Begum, Afsana; Hossain, Syeda Sumbul; Bhuiyan, Touhid
Cloud computing provides data storage and computing power based on user demand by assigning tasks to virtual resources. To deliver overall improved performance and meet challenges such as availability, resource utilization and reliability in the cloud, appropriate resource scheduling methods are needed. A number of metaheuristic optimization algorithms are used to solve the problem of resource scheduling. This work lists challenges and analyzes previous scheduling methods based on Genetic Algorithm (GA). It classifies the GA-based scheduling methods with respect to many parameters. At last, it presents the scopes of enhancement for future researchers.
A priority based dynamic resource mapping algorithm for load balancing in cloud
(IEEE, 2018-02-15) Sadia, Farzana; Jahan, Nusrat; Rawshan, Lamisha; Jeba, Madina Tul; Bhuiyan, Touhid
Cloud computing is a rising technology which is responsible for supplying of computing resources on the basis of demand, as and when needed. Cloud provides many facilities due to its vast resources such as sharing resources for different purposes. Cloud computing faces many challenges in respect of performance and efficiency. To increase the cloud computing environment's efficiency, Virtual Machines (VM) has been employed for resource provisioning. In this paper, we proposed an algorithm that performs load distribution of workloads among different VM based on priority. This algorithm is proposed in the aim of load balancing of different nodes by considering maximum throughput with minimum execution time. To achieve that, the VM are sorted according to their processing powers and job requests are assigned to VM based on their instruction numbers and priorities. The proposed algorithm is experimented using CloudSim simulator and the results demonstrated that the performance of the algorithm is better than other conventional algorithms.
Android Malware Detection by Machine Learning Apprehension and Static Feature Characterization
(Springer, 2020-07) Hasan, Md Rashedul; Begum, Afsana; Bin Zamal, Fahad; Rawshan, Lamisha; Bhuiyan, Touhid
The increased usage and popularity of Android devices encourage malware developers to generate newer ways to launch malware in different packaged forms in different applications. These malware causes various information leakage and money lost. For example, only in Canada, McAfee, which surveyed 1,000 Canadians and found 65% of them, had lost more than $100 and almost a third had lost more than $500 to various cyber scams so far this year. Moreover, after identifying software as malware, unethical developer repackages the detected one and again launches the software. Unfortunately, repackaged software remains undetected mostly. In this research three different tasks were done. Comparing to the existing work we have used source code based analysis using bag-of words algorithm in machine learning. By modifying Bag-of-word procedure and adding some additional preprocessing of dataset the evaluation results represent 0.55% better than the existing work in this field. In that case re-packaging was included and this is a new edition in this field of research. Moreover in this research, a vocabulary was also created to identify the malicious code. Here with existing 69 malicious patterns more 12 malicious patterns were added. In addition to these two contributions, we have also implemented our model in a web application to test. This paper represents such a model, which will help the developers or antivirus launcher to detect malware if it is repackaged. This vocabulary will also help to do so.
Assessing the Effectiveness of Topic Modeling Algorithms in Discovering Generic Label with Description
(Springer, 2020-02-13) Rahman, Shadikur; Hossain, Syeda Sumbul; Arman, Md. Shohel; Rawshan, Lamisha; Toma, Tapushe Rabaya; Rafiq, Fatama Binta; Md. Badruzzaman, Khalid Been
Analyzing short text or documents using topic modeling becomes a popular solutions for the increasing number of documents produced in everyday life. For handling the large amount of documents, many topic modeling algorithms are used e.g. LDA, LSI, pLSI, NMF. In this study, we have used LDA, LSI, NMF and also lexical database wordNet synset for candidate labels in our topics labeling. And finally compare the effectiveness of topic modeling algorithms for short documents. Among those LDA gives the better result in terms of WUP similarity. This study will help to select the proper algorithm for labeling topics and can easily identify the meaning of topics.
Customer Feedback Prioritization Technique
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2019-06-29) Hossain, Syeda Sumbul; Jubayer, S. A. M.; Rahman, Shadikur; Bhuiyan, Touhid; Rawshan, Lamisha; Islam, Saiful
Nowadays, a startup is being very popular and entrepreneurs are increasing day by day. Though we are watching many successful startups e.g. Dropbox, Amazon, Viber and so on, the list of unsuccessful startups is very long. Who is being successful they must have their own strategy, which they apply in their startup and get success. In lean startup strategy, the customers give feedbacks about the startup and the owner understands the demand of customers by collecting feedback from customers and provides service according to the feedback. On the other hand, all the feedbacks from the customers are not important for a startup project. So it is needed to separate or prioritize feedbacks which are needed to execute the startup project. But there are not sufficient techniques for prioritizing the feedbacks collected from customers. By conducting a systematic mapping study and a case study (interview and observation is used), we propose a technique which will be used to prioritize customer feedback in lean startup. This technique will be helpful for the startup projects to become successful.
Industry-Academia Collaboration
(Daffodil International University, 2017-07-01) Zamala, MD. Fahad Bin; Begum, Afsana; Rawshan, Lamisha
Industry- Academia relationship is an essential element to generate innovation and employability. Systematic and coordinated approaches are absent in most industry and academy relationships which brings unsuccessful collaboration among them. Current trend of relationship between industry and academia failed to impact revenue generation hence could not create employment opportunities. Identifying the gaps between industry and academy will allow more outcome based research and innovation. Most research collaborations nowadays are ad hoc or opportunistic based which often fail to meet the expectation and are difficult to sustain in a competitive world. Successful collaboration can be established if we are able to recognize potential sources of conflict and ensure that they are addressed properly. In this paper we address those gaps by presenting a systematic review of the literature on industry-academy relationship. Here we investigate areas of conflicts; suggest strategies to overcome those and identify the driving forces that can stimulate the relation between industry and academy.
Industry-Academia Collaboration: Conflicts and Strategies to Stimulate Innovation and Employability
(Daffodil International University, 2017-03-22) Zamal, Md. Fahad Bin; Begum, Afsana; Rawshan, Lamisha
Industry-Academia relationship is an essential element to generate innovation and employability. Systematic and coordinated approaches are absent in most industry and academy relationship which brings unsuccessful collaboration among them. Current trend of relationship between industry and academia failed to impact on revenue generation, hence was not able to create employment opportunity. Identification of the gaps between industry and academy will allow more outcome based research and innovation. Most research collaborations nowadays are ad hoc or opportunistic which often fail to meet the expectation and difficult to sustain in competitive world. Successful collaboration can be established if we are able to recognize potential sources of conflict and ensure that they are addressed properly. In this paper we address those gaps by presenting a systematic review of the literature on industry-academy relationship. Here we investigate on areas of conflicts, suggest strategies to overcome those and identify the driving forces that can stimulate the relation between industry and academy.