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Mohammad Shabaz

Bio: Mohammad Shabaz is an academic researcher from Arba Minch University. The author has contributed to research in topics: Cluster analysis & Deep learning. The author has an hindex of 9, co-authored 70 publications receiving 240 citations. Previous affiliations of Mohammad Shabaz include Lovely Professional University & Chitkara University.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: In this article, different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset, which consists of 14 main attributes used for performing the analysis.
Abstract: The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.

197 citations

Journal ArticleDOI
TL;DR: This paper’s main objective was to enhance the functionality of healthcare systems using emerging and innovative computer technologies like IoT and Blockchain in three major areas—drug traceability, remote patient-monitoring, and medical record management.
Abstract: Internet of Things (IoT) is one of the recent innovations in Information Technology, which intends to interconnect the physical and digital worlds. It introduces a vision of smartness by enabling communication between objects and humans through the Internet. IoT has diverse applications in almost all sectors like Smart Health, Smart Transportation, and Smart Cities, etc. In healthcare applications, IoT eases communication between doctors and patients as the latter can be diagnosed remotely in emergency scenarios through body sensor networks and wearable sensors. However, using IoT in healthcare systems can lead to violation of the privacy of patients. Thus, security should be taken into consideration. Blockchain is one of the trending research topics nowadays and can be applied to the majority of IoT scenarios. Few major reasons for using the Blockchain in healthcare systems are its prominent features, i.e., Decentralization, Immutability, Security and Privacy, and Transparency. This paper’s main objective was to enhance the functionality of healthcare systems using emerging and innovative computer technologies like IoT and Blockchain. So, initially, a brief introduction to the basic concepts of IoT and Blockchain is provided. After this, the applicability of IoT and Blockchain in the medical sector is explored in three major areas—drug traceability, remote patient-monitoring, and medical record management. At last, the challenges of deploying IoT and Blockchain in healthcare systems are discussed.

142 citations

Journal ArticleDOI
TL;DR: A smart-engine-based decision has been developed, which further uses classification and regression trees to shift towards decision-making, and a recommendation engine that is powered by deep learning network to suggest the escalation of a farmer from lower to higher category, namely, small to medium to large.
Abstract: Recently, many companies have substituted human labor with robotics. Some farmers are sharing different perspectives on the incorporation of technology into farming techniques. Some are willing to accept the technology, some are hesitant and bemused to adapt modern technology, and others are uncertain and are worried about the potential of technology to cause havoc and decrease yields. The third group prevails the most in the developed world, for lack of know-how, including translation of utility and, most significantly, the expense involved. A special Smart Tillage platform is established to solve the above issues. A smart-engine-based decision has been developed, which further uses classification and regression trees to shift towards decision-making. The decision is focused entirely on different input factors, such as type of crop, time/month of harvest, type of plant required for the crop, type of harvest, and authorised rental budget. Sitting on top of this would be a recommendation engine that is powered by deep learning network to suggest the escalation of a farmer from lower to higher category, namely, small to medium to large. A metaheuristic is one of the best computing techniques that help for solving a problem without the exhaustive application of a procedure. Recommendations will be cost-effective and suitable for an escalating update depending on the use of sufficient amends, practices, and services. We carried out a study of 562 agriculturists. Owing to the failure to buy modern equipment, growers are flooded by debt. We question if customers will be able to rent and exchange appliances. The farmers would be able to use e-marketplace to develop their activities.

62 citations

Journal ArticleDOI
TL;DR: In this article, a novel framework which integrates Spark with a deep learning approach is proposed in order to detect the fraud transactions in the Mastercard data set, which achieves 96% accuracy for both training and testing datasets.
Abstract: In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

53 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a novel collaborative learning-based approach for straggler prediction, the alternate direction method of multipliers (ADMM), which is resource-efficient and learns how to efficiently deal with mitigating straggglers without moving data to a centralized location.
Abstract: Modern big data applications tend to prefer a cluster computing approach as they are linked to the distributed computing framework that serves users jobs as per demand. It performs rapid processing of tasks by subdividing them into tasks that execute in parallel. Because of the complex environment, hardware and software issues, tasks might run slowly leading to delayed job completion, and such phenomena are also known as stragglers. The performance improvement of distributed computing framework is a bottleneck by straggling nodes due to various factors like shared resources, heavy system load, or hardware issues leading to the prolonged job execution time. Many state-of-the-art approaches use independent models per node and workload. With increased nodes and workloads, the number of models would increase, and even with large numbers of nodes. Not every node would be able to capture the stragglers as there might not be sufficient training data available of straggler patterns, yielding suboptimal straggler prediction. To alleviate such problems, we propose a novel collaborative learning-based approach for straggler prediction, the alternate direction method of multipliers (ADMM), which is resource-efficient and learns how to efficiently deal with mitigating stragglers without moving data to a centralized location. The proposed framework shares information among the various models, allowing us to use larger training data and bring training time down by avoiding data transfer. We rigorously evaluate the proposed method on various datasets with high accuracy results.

47 citations


Cited by
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Book
25 Aug 2009
TL;DR: This text is designed to develop an appreciation of KBS and their architecture and to help users understand a broad variety of knowledge based techniques for decision support and planning.
Abstract: Knowledge Based Systems (KBS) are systems that use artificial intelligence techniques in the problem solving process. This text is designed to develop an appreciation of KBS and their architecture and to help users understand a broad variety of knowledge based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters are designed to be modular providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material being presented and to stimulate thought and discussion.

512 citations

01 Jan 2013
TL;DR: This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems and presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research.
Abstract: Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human–machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system’s communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. vi KEywoRDS Spoken dialogue systems, multimodality, evaluation, error-handling, dialogue management, statistical method v MC_Jok nen_FM. ndd Achorn Internat onal 10/10/2009 04:18AM

304 citations

Journal ArticleDOI
TL;DR: This paper’s main objective was to enhance the functionality of healthcare systems using emerging and innovative computer technologies like IoT and Blockchain in three major areas—drug traceability, remote patient-monitoring, and medical record management.
Abstract: Internet of Things (IoT) is one of the recent innovations in Information Technology, which intends to interconnect the physical and digital worlds. It introduces a vision of smartness by enabling communication between objects and humans through the Internet. IoT has diverse applications in almost all sectors like Smart Health, Smart Transportation, and Smart Cities, etc. In healthcare applications, IoT eases communication between doctors and patients as the latter can be diagnosed remotely in emergency scenarios through body sensor networks and wearable sensors. However, using IoT in healthcare systems can lead to violation of the privacy of patients. Thus, security should be taken into consideration. Blockchain is one of the trending research topics nowadays and can be applied to the majority of IoT scenarios. Few major reasons for using the Blockchain in healthcare systems are its prominent features, i.e., Decentralization, Immutability, Security and Privacy, and Transparency. This paper’s main objective was to enhance the functionality of healthcare systems using emerging and innovative computer technologies like IoT and Blockchain. So, initially, a brief introduction to the basic concepts of IoT and Blockchain is provided. After this, the applicability of IoT and Blockchain in the medical sector is explored in three major areas—drug traceability, remote patient-monitoring, and medical record management. At last, the challenges of deploying IoT and Blockchain in healthcare systems are discussed.

142 citations

Journal ArticleDOI
TL;DR: A power-efficient universal asynchronous receiver transmitter (UART) is implemented on 28 nm Artix-7 field-programmable gate array (FPGA) to reduce the power utilization of UART with the FPGA device in industries.
Abstract: In the present scheme of the world, the problem of shortage of power is seen across the world which can be a vulnerability to various communication securities. The scope of proposed research is that it is a step towards completing green communication technology concepts. In order to improve energy efficiency in communication networks, we designed UART using different nanometers of FPGA, which consumes the least amount of energy. This shortage is happening because of expanding of industries across the world and the rapid growth of the population. Therefore, to save the power for our upcoming generation, the globe is moving towards the concept and ideas of green communication and power-/energy-efficient gadget. In this work, a power-efficient universal asynchronous receiver transmitter (UART) is implemented on 28 nm Artix-7 field-programmable gate array (FPGA). The objective of this work is to reduce the power utilization of UART with the FPGA device in industries. To do this, the same authors have used voltage scaling techniques and compared the results with the existing FPGA works.

77 citations

Journal ArticleDOI
TL;DR: In this article, a power-efficient control unit (CU) design and implemented on the Zynq SoC (System on Chip) ultrascale field programmable gate array (FPGA) is presented.
Abstract: The issue of the energy shortage is affecting the entire planet. This is occurring because of massive population and industry growth around the world. As a result, the entire world is attempting to implement green networking systems and manufacture the power/energy efficient products. This research work discusses the green networking system technologies. This work introduces a power-efficient control unit (CU) design and implemented on the Zynq SoC (System on Chip) ultrascale field programmable gate array (FPGA). The VIVADO HLx Design Suite is used to simulate and analyze the CU model which is considered as one of the key components of central processing unit (CPU), used for data communication purposes. The CU is made suitable for the green communication by making it power-efficient. Therefore, the power consumption of the CU is analyzed for the various set frequency value ranging between 100 MHz and 5 GHz, and it is discovered that as the clock frequency rises up, the total power consumption also tends to get increased. The total power of the proposed model is reduced by 77.42%, 21.29%, and 17.93% from three models, respectively, being compared in the present paper. Final results shows that the CU is better suited to run at low frequencies to optimize power consumption.

75 citations