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Shamimul Qamar

Bio: Shamimul Qamar is an academic researcher from King Khalid University. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 7, co-authored 29 publications receiving 133 citations. Previous affiliations of Shamimul Qamar include SRM University & Noida Institute of Engineering and Technology.


Papers
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Proceedings ArticleDOI
05 May 2019
TL;DR: A comprehensive overview of Big Data, its characteristics, opportunities, issues, and challenges have been explored and described with the help of 51 V's to help in understanding the Big Data in a systematic way.
Abstract: Currently Big Data is the biggest buzzword, and definitely, we believe that Big Data is changing the world. Some researchers say Big Data will be even bigger buzzword than the Internet. With fast-growing computing resources, information and knowledge a new digital globe has emerged. Information is being created and stored at a fast rate and is being accessed by a vast range of applications through scientific computing, commercial workloads, and social media. In 2018, over 28 billion devices globally, are connected to the internet. In 2020, more than 50 billion smart appliances will be connected worldwide and internet traffic flow will be 92 times greater than it was in 2005. The usage of such a massive number of connected devices not only increase the data volume but also the velocity of data addition with speed of light on fiber optic and various wireless networks. This fast generation of enormous data creates numerous threats and challenges. There exist various approaches that are addressing issues and challenges of Big Data with the theory of Vs such as 3 V's, 5 V's, 7 V's etc. The objective of this work is to explore and investigate the status of the current Big Data domain. Further, a comprehensive overview of Big Data, its characteristics, opportunities, issues, and challenges have been explored and described with the help of 51 V's. The outcome of this research will help in understanding the Big Data in a systematic way.

36 citations

Posted Content
TL;DR: The author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.
Abstract: Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

31 citations

Journal ArticleDOI
TL;DR: The author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.
Abstract: Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

23 citations

01 Jan 2012
TL;DR: A prototype model for the breast cancer as well as heart disease prediction using data mining techniques is presented and two decision tree algorithms C4.5 and the C5.0 are compared.
Abstract: In this paper a prototype model for the breast cancer as well as heart disease prediction using data mining techniques is presented. The data used is the Public-Use Data available on web, consisting of 909 records for heart disease and 699 for breast cancer. Two decision tree algorithms C4.5 and the C5.0 have been used on these datasets for prediction and performance of both algorithms is compared. Paper also presents how these rules can be used in evidence based medicine. Evidence Based Medicine (EBM) is a new and important approach which can greatly improve decision making in health care. EBM's task is to prevent, diagnose and medicate diseases using medical evidence [5].Clinical decisions must be based on scientific evidence that demonstrates effectiveness.

22 citations

Journal ArticleDOI
TL;DR: An embedded system prototype has been designed on the concept of an internet of things scenario that uses sensors and actuators around Raspberry Pi board that can screen and control the air contamination with generation of challan from anyplace in the world using IOT.
Abstract: In the automobile sector, the new advancements in technology help the vendors in developing smart vehicles. These smart vehicles are designed to provide all sort of comfort to the society. But still, some improvements are required to make these vehicles smarter in terms of environmental pollution management. The other cause of air pollution is due to the toxic gases released by the industries. The environment pollution is still increasing gradually despite the various efforts of government. A number of solutions are available in the literature to control and monitor environmental pollution. The examination uncovers that there is a necessity of a sensor based embedded system that can screen and control the air contamination with generation of challan from anyplace in the world using IOT. An embedded system prototype has been designed on the concept of an internet of things scenario that uses sensors and actuators around Raspberry Pi board. The system prototype is programmed in python using some standard libraries available on adafruit and github. A web page is also designed to monitor the level of gases remotely at any place in the world. The results demonstrate that the complete system has been successfully tested and implemented.

21 citations


Cited by
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Proceedings Article
01 Jan 1991
TL;DR: It is concluded that properly augmented and power-controlled multiple-cell CDMA (code division multiple access) promises a quantum increase in current cellular capacity.
Abstract: It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

2,951 citations

Proceedings ArticleDOI
30 Jun 2011
TL;DR: This paper discusses and formalizes the issue of cloud service selection in general and proposes a multi-criteria cloudService selection methodology.
Abstract: Cloud computing despite being in an early stage of adoption is becoming a popular choice for businesses to replace in-house IT infrastructure due to its technological advantages such as elastic computing and cost benefits resulting from pay-as-you-go pricing and economy of scale. These factors have led to a rapid increase in both the number of cloud vendors and services on offer. Given that cloud services could be characterized using multiple criteria (cost, pricing policy, performance etc.) it is important to have a methodology for selecting cloud services based on multiple criteria. Additionally, the end user requirements might map to different criteria of the cloud services. This diversity in services and the number of available options have complicated the process of service and vendor selection for prospective cloud users and there is a need for a comprehensive methodology for cloud service selection. The existing research literature in cloud service selection is mostly concerned with comparison between similar services based on cost or performance benchmarks. In this paper we discuss and formalize the issue of cloud service selection in general and propose a multi-criteria cloud service selection methodology.

177 citations

01 Jan 2013
TL;DR: How Big Data is becoming a growing force in the changing healthcare landscape is explored and how to generate, capture, analyze and make use of the streams of new kinds of data that are about to flood healthcare is explored.
Abstract: The potential of Big Data allows us to hope to slow the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine. Yet, as with the Internet, social media, and cloud computing, early enthusiasts are creating hyperbolic expectations about how and how quickly Big Data will transform healthcare. A number of issues challenge the adoption and success of healthcare Big Data, including privacy and security, who owns the data, and the regulatory labyrinth. Furthermore, real advances depend on better ways to exploit the disconnected puddles and lakes of existing data as well as better ways to generate, capture, analyze and make use of the streams of new kinds of data that are about to flood healthcare. This paper will introduce to Big Data and explore how it is becoming a growing force in the changing healthcare landscape.

95 citations

Journal ArticleDOI
TL;DR: In this paper, the authors reviewed the functionalities and effectiveness of the free mobile health applications available in the Google Play and App stores used in Saudi Arabia, Italy, Singapore, United Kingdom, USA, and India during the COVID-19 outbreak.
Abstract: Purpose The objective of this paper was to review the functionalities and effectiveness of the free mobile health applications available in the Google Play and App stores used in Saudi Arabia, Italy, Singapore, the United Kingdom, USA, and India during the COVID-19 outbreak. Methods This study adopted a systematic search strategy to identify the free mobile applications available in the App and Google Play stores related to the COVID-19 outbreak. According to the PRISMA flowchart of the search, only 12 applications met the inclusion criterion. Results The 12 mobile applications that met the inclusion criterion were: Mawid, Tabaud, Tawakkalna, Sehha, Aarogya setu, TraceTogether, COVID safe, Immuni, COVID symptom study, COVID watch, NHS COVID-19, and PathCheck. The following features and functionalities of the apps were described: app overview (price, ratings, android, iOS, developer/owner, country, status), health tools (user status-risk assessment, self-assessment, E-pass integration, test results reporting, online consultation, contact tracing), learning options (personalized notes, educational resources, COVID-19 information), communication tools (query resolution, appointments, social network, notifications), app design (data visualization, program plan), networking tools (location mapping - GPS, connectivity with other devices), and safety and security options (alerts, data protection). Also, the effectiveness of the apps was analyzed. Conclusion The analysis revealed that various applications have been developed for different functions like contact tracing, awareness building, appointment booking, online consultation, etc. However, only a few applications have integrated various functions and features such as self-assessment, consultation, support and access to information. Also, most of the apps are focused on contact tracing, while very few are dedicated to raising awareness and sharing information about the COVID-19 pandemic. Likewise, the majority of applications rely on GPS and Bluetooth technologies for relevant functions. No apps were identified that had built-in social media features. It is suggested to design and develop an integrated mobile health application with most of the features and functionalities analyzed in this study.

83 citations

01 Jan 2014
TL;DR: This paper surveys different papers in which one or more algorithms of data mining used for the prediction of heart disease and results from using neural networks show that the prediction by using data mining algorithm given efficient results.
Abstract: Data mining is the computer based process of analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally taken much time consuming to resolve. The huge amounts of data generated for prediction of heart disease are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. Result from using neural networks is nearly 100% in one paper (10) and in (6). So that the prediction by using data mining algorithm given efficient results. Applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease. Cholesterol: - abnormal levels of lipids (fats) in the blood are risk factor of heart diseases. Cholesterol is a soft, waxy substance found among the lipids in the bloodstream and in all the body's cells. High level of triglyceride (most common type of fat in body) combined with high levels of LDL (low density lipoprotein) cholesterol speed up atherosclerosis increasing the risk of heart diseases. High blood pressure: - High blood pressure also known as HBP or hypertension is a widely misunderstood medical condition. High blood pressure increase the risk of the walls of our blood vessels walls becoming overstretched and injured. Also increase the risk of having heart attack or stroke and of developing heart failure, kidney failure and peripheral vascular disease.

65 citations