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Dr. Muhammad Sardaraz
COMSATS University Islamabad, Attock Campus

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0 Bioinformatics
0 Cloud Computing
0 Compression Algorithms
0 Genome Sequencing
0 DISTRIBUTED COMPUTING

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Journal article
Published: 23 June 2021 in Applied Sciences
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Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

ACS Style

Nimra Malik; Muhammad Sardaraz; Muhammad Tahir; Babar Shah; Gohar Ali; Fernando Moreira. Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds. Applied Sciences 2021, 11, 5849 .

AMA Style

Nimra Malik, Muhammad Sardaraz, Muhammad Tahir, Babar Shah, Gohar Ali, Fernando Moreira. Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds. Applied Sciences. 2021; 11 (13):5849.

Chicago/Turabian Style

Nimra Malik; Muhammad Sardaraz; Muhammad Tahir; Babar Shah; Gohar Ali; Fernando Moreira. 2021. "Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds." Applied Sciences 11, no. 13: 5849.

Article
Published: 09 June 2021 in Cluster Computing
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Cloud computing is a new paradigm of computing. This paradigm delivers services over the internet and eliminates requirements for local data storage. Instead of purchasing hardware and software, cloud computing enables users to use storage or applications as a service. Scheduling is the process of allocating the available resources in cloud environment. Scientific workflows consist of a large number of tasks. Workflow scheduling is a critical issue in cloud computing that targets to complete workflow execution by considering different parameters such as execution time, user deadlines, execution cost, and Quality of Service (QoS), etc. In this article, we present a Multi-resource Load Balancing Algorithm (MrLBA) cloud computing environment. The algorithm is based on Ant Colony Optimization (ACO). The proposed algorithm targets makespan, cost while keeping a well load-balanced system. The algorithm is validated with experimental results on benchmark workflows. The results show that MrLBA reduces both execution time and cost and efficiently utilizes available resources by maintaining balanced load among resources.

ACS Style

Arfa Muteeh; Muhammad Sardaraz; Muhammad Tahir. MrLBA: multi-resource load balancing algorithm for cloud computing using ant colony optimization. Cluster Computing 2021, 1 -11.

AMA Style

Arfa Muteeh, Muhammad Sardaraz, Muhammad Tahir. MrLBA: multi-resource load balancing algorithm for cloud computing using ant colony optimization. Cluster Computing. 2021; ():1-11.

Chicago/Turabian Style

Arfa Muteeh; Muhammad Sardaraz; Muhammad Tahir. 2021. "MrLBA: multi-resource load balancing algorithm for cloud computing using ant colony optimization." Cluster Computing , no. : 1-11.

Research article
Published: 20 May 2021 in International Journal of Distributed Sensor Networks
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A wireless sensor network is the formation of a temporary network of sensor nodes equipped with limited resources working in an ad hoc environment. Routing protocol is one of the key challenges while designing a wireless sensor network, which requires optimum use of limited resources of a sensor node, such as power and so on. Similarly, data security and integrity is another open issue that has emerged as a flash point in research community in the last decade. This article proposes a secure model for routing data from source to destination named as secure and energy-efficient routing. The proposed secure and energy-efficient routing is inherited from authentication and voice encryption scheme developed for Global System for Mobile Communications. Necessary modifications have been carried out in order to fit the Global System for Mobile Communications technology in a wireless sensor network ad hoc environment. Due to its low complexity, the secure and energy-efficient routing consumes lesser battery power both during encryption/decryption and for routing purposes. It is due to the XoR operation used in the proposed scheme which is considered as the most inexpensive process with respect to time and space complexity. It is observed through simulations that secure and energy-efficient routing can work effectively even in critical power level in a sensor network. The article also presents a simulation-based comparative analysis of the proposed secure and energy-efficient routing with two notable existing secure routing protocols. We proved that the proposed secure and energy-efficient routing helps to achieve the desired performance under dynamically changing network conditions with various numbers of malicious nodes. Moreover, in Global System for Mobile Communications, generally three linear feedback shift registers are used to fragment the key in data encryption mechanism. In this article, a mathematical model is proposed to increase the number of possible combinations of shift register in order to make the data encryption mechanism more secure which has never been done before. Due to its liner complexity, lesser power consumption, and more dynamic route updating, the secure and energy-efficient routing can easily find its use in the emerging Internet-of-Things systems.

ACS Style

M Saud Khan; Noor M Khan; Ahmad Khan; Farhan Aadil; M Tahir; M Sardaraz. A low-complexity, energy-efficient data securing model for wireless sensor network based on linearly complex voice encryption mechanism of GSM technology. International Journal of Distributed Sensor Networks 2021, 17, 1 .

AMA Style

M Saud Khan, Noor M Khan, Ahmad Khan, Farhan Aadil, M Tahir, M Sardaraz. A low-complexity, energy-efficient data securing model for wireless sensor network based on linearly complex voice encryption mechanism of GSM technology. International Journal of Distributed Sensor Networks. 2021; 17 (5):1.

Chicago/Turabian Style

M Saud Khan; Noor M Khan; Ahmad Khan; Farhan Aadil; M Tahir; M Sardaraz. 2021. "A low-complexity, energy-efficient data securing model for wireless sensor network based on linearly complex voice encryption mechanism of GSM technology." International Journal of Distributed Sensor Networks 17, no. 5: 1.

Journal article
Published: 30 April 2021 in Current Bioinformatics
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Aims: To assess the error profile in NGS data, generated from high throughput sequencing machines. Background: Short-read sequencing data from Next Generation Sequencing (NGS) are currently being generated by a number of research projects. Depicting the errors produced by NGS platforms and expressing accurate genetic variation from reads are two inter-dependent phases. It has high significance in various analyses, such as genome sequence assembly, SNPs calling, evolutionary studies, and haplotype inference. The systematic and random errors show incidence profile for each of the sequencing platforms i.e. Illumina sequencing, Pacific Biosciences, 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Ion Torrent sequencing, and Oxford Nanopore sequencing. Advances in NGS deliver galactic data with the addition of errors. Some ratio of these errors may emulate genuine true biological signals i.e., mutation, and may subsequently negate the results. Various independent applications have been proposed to correct the sequencing errors. Systematic analysis of these algorithms shows that state-of-the-art models are missing. Objective: In this paper, an effcient error estimation computational model called ESREEM is proposed to assess the error rates in NGS data. Methods: The proposed model prospects the analysis that there exists a true linear regression association between the number of reads containing errors and the number of reads sequenced. The model is based on a probabilistic error model integrated with the Hidden Markov Model (HMM). Result: The proposed model is evaluated on several benchmark datasets and the results obtained are compared with state-of-the-art algorithms. Conclusions: Experimental results analyses show that the proposed model efficiently estimates errors and runs in less time as compared to others.

ACS Style

Muhammad Tahir; Muhammad Sardaraz; Zahid Mehmood; Muhammad Saud Khan. ESREEM: Efficient Short Reads Error Estimation Computational Model for Next-generation Genome Sequencing. Current Bioinformatics 2021, 16, 339 -349.

AMA Style

Muhammad Tahir, Muhammad Sardaraz, Zahid Mehmood, Muhammad Saud Khan. ESREEM: Efficient Short Reads Error Estimation Computational Model for Next-generation Genome Sequencing. Current Bioinformatics. 2021; 16 (2):339-349.

Chicago/Turabian Style

Muhammad Tahir; Muhammad Sardaraz; Zahid Mehmood; Muhammad Saud Khan. 2021. "ESREEM: Efficient Short Reads Error Estimation Computational Model for Next-generation Genome Sequencing." Current Bioinformatics 16, no. 2: 339-349.

Research article
Published: 01 April 2021 in Science Progress
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Recent advancements in sequencing methods have led to significant increase in sequencing data. Increase in sequencing data leads to research challenges such as storage, transfer, processing, etc. data compression techniques have been opted to cope with the storage of these data. There have been good achievements in compression ratio and execution time. This fast-paced advancement has raised major concerns about the security of data. Confidentiality, integrity, authenticity of data needs to be ensured. This paper presents a novel lossless reference-free algorithm that focuses on data compression along with encryption to achieve security in addition to other parameters. The proposed algorithm uses preprocessing of data before applying general-purpose compression library. Genetic algorithm is used to encrypt the data. The technique is validated with experimental results on benchmark datasets. Comparative analysis with state-of-the-art techniques is presented. The results show that the proposed method achieves better results in comparison to existing methods.

ACS Style

Muhammad Sardaraz; Muhammad Tahir. SCA-NGS: Secure compression algorithm for next generation sequencing data using genetic operators and block sorting. Science Progress 2021, 104, 1 .

AMA Style

Muhammad Sardaraz, Muhammad Tahir. SCA-NGS: Secure compression algorithm for next generation sequencing data using genetic operators and block sorting. Science Progress. 2021; 104 (2):1.

Chicago/Turabian Style

Muhammad Sardaraz; Muhammad Tahir. 2021. "SCA-NGS: Secure compression algorithm for next generation sequencing data using genetic operators and block sorting." Science Progress 104, no. 2: 1.

Journal article
Published: 26 August 2020 in Sustainability
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Blockchain and IoT are being deployed at a large scale in various fields including healthcare for applications such as secure storage, transactions, and process automation. IoT devices are resource-constrained, have no capability of security and self-protection, and can easily be hacked or compromised. Furthermore, Blockchain is an emerging technology with immutability features which provide secure management, authentication, and guaranteed access control to IoT devices. IoT is a cloud-based internet service in which processing and collection of user’s data are accomplished remotely. Smart healthcare also requires the facility to provide the diagnosis of patients located remotely. The smart health framework faces critical issues such as data security, costs, memory, scalability, trust, and transparency between different platforms. Therefore, it is important to handle data integrity and privacy as the user’s authenticity is in question due to an open internet environment. Several techniques are available that primarily focus on resolving security issues i.e., forgery, timing, denial of service and stolen smartcard attacks, etc. Blockchain technology follows the rules of absolute privacy to identify the users associated with transactions. The motivation behind the use of Blockchain in health informatics is the removal of the centralized third party, immutability, improved data sharing, enhanced security, and reduced overhead costs in distributed applications. Healthcare informatics has some specific requirements associated with the security and privacy along with the additional legal requirements. This paper presents a novel authentication and authorization framework for Blockchain-enabled IoT networks using a probabilistic model. The proposed framework makes use of random numbers in the authentication process which is further connected through joint conditional probability. Hence, it establishes a secure connection among IoT devices for further data acquisition. The proposed model is validated and evaluated through extensive simulations using the AVISPA tool and the Cooja simulator, respectively. Experimental results analyses show that the proposed framework provides robust mutual authenticity, enhanced access control, and lowers both the communication and computational overhead cost as compared to others.

ACS Style

Muhammad Tahir; Muhammad Sardaraz; Shakoor Muhammad; Muhammad Saud Khan. A Lightweight Authentication and Authorization Framework for Blockchain-Enabled IoT Network in Health-Informatics. Sustainability 2020, 12, 6960 .

AMA Style

Muhammad Tahir, Muhammad Sardaraz, Shakoor Muhammad, Muhammad Saud Khan. A Lightweight Authentication and Authorization Framework for Blockchain-Enabled IoT Network in Health-Informatics. Sustainability. 2020; 12 (17):6960.

Chicago/Turabian Style

Muhammad Tahir; Muhammad Sardaraz; Shakoor Muhammad; Muhammad Saud Khan. 2020. "A Lightweight Authentication and Authorization Framework for Blockchain-Enabled IoT Network in Health-Informatics." Sustainability 12, no. 17: 6960.

Article
Published: 22 July 2020 in Cluster Computing
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Cloud Computing is referred to as a set of hardware and software that are being combined to deliver various services of computing. The cloud keeps the services for delivery of software, infrastructure, and platform over the Internet based on the user’s demand. In the IT industry, cloud computing plays an important role to access services anywhere in the world. With increasing demand and popularity of cloud computing, several types of threats and vulnerabilities are also increased. Data integrity and privacy are the key issues in cloud computing and are thoughtful as the data is stored in different geographical locations. Therefore, data integrity and privacy protection provisions are the most prominent factors of user’s concerns about the cloud computing environment. In this paper, a new model based on a genetic algorithm (GA) CryptoGA is proposed to cope with data integrity and privacy issues. GA is used to generate keys for encryption and decryption which are integrated with a cryptographic algorithm to ensure privacy and integrity of cloud data. Known and common parameters i.e. execution time, throughput, key size, and avalanche effect are considered for evaluation and comparison. Ten different datasets are used in experiments for testing and validation. Experimental results analysis show that the proposed model ensures the integrity and preserves the privacy of the user’s data against unauthorized parties. Moreover, the CryptoGA is robust and provides better performance on selected parameters as compared to state-of-the-art cryptographic algorithms i.e. DES, 3DES, RSA, Blowfish, and AES.

ACS Style

Muhammad Tahir; Muhammad Sardaraz; Zahid Mehmood; Shakoor Muhammad. CryptoGA: a cryptosystem based on genetic algorithm for cloud data security. Cluster Computing 2020, 1 -14.

AMA Style

Muhammad Tahir, Muhammad Sardaraz, Zahid Mehmood, Shakoor Muhammad. CryptoGA: a cryptosystem based on genetic algorithm for cloud data security. Cluster Computing. 2020; ():1-14.

Chicago/Turabian Style

Muhammad Tahir; Muhammad Sardaraz; Zahid Mehmood; Shakoor Muhammad. 2020. "CryptoGA: a cryptosystem based on genetic algorithm for cloud data security." Cluster Computing , no. : 1-14.

Original research
Published: 18 April 2020 in Journal of Ambient Intelligence and Humanized Computing
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Content-based image retrieval (CBIR) states the procedure of recovering images having similar visual content against a query image from image datasets. In CBIR, the selection of redundant and irrelevant features from images results in the semantic gap issue, which occurs during feature representation and machine learning process. The robust image representation for effective and efficient image retrieval mainly depends upon robust feature selection and classification, which also reduces the semantic gap problem of CBIR. This paper proposed an innovative method for effective and efficient CBIR. The method uses sparse complementary features for vigorous image representation, optimal feature selection based on locality-preserving projection, fuzzy c-means clustering, and soft label support vector machine for robust image classification. In CBIR, smaller and larger sizes of codebook improve the recall and precision (accuracy) of the system, respectively. Due to this reason, the proposed method introduces complementary features based on a larger size codebook, which is assembled using two small sizes of codebooks to increase CBIR performance. The three well-known image datasets (i.e. Corel-1000, Corel-1500, and Holidays) are used to assess the performance of the proposed method. The experimental evaluation highlights promising results as compared to recent methods of CBIR.

ACS Style

Ruqia Bibi; Zahid Mehmood; Rehan Mehmood Yousaf; Tanzila Saba; Muhammad Sardaraz; Amjad Rehman. Query-by-visual-search: multimodal framework for content-based image retrieval. Journal of Ambient Intelligence and Humanized Computing 2020, 11, 5629 -5648.

AMA Style

Ruqia Bibi, Zahid Mehmood, Rehan Mehmood Yousaf, Tanzila Saba, Muhammad Sardaraz, Amjad Rehman. Query-by-visual-search: multimodal framework for content-based image retrieval. Journal of Ambient Intelligence and Humanized Computing. 2020; 11 (11):5629-5648.

Chicago/Turabian Style

Ruqia Bibi; Zahid Mehmood; Rehan Mehmood Yousaf; Tanzila Saba; Muhammad Sardaraz; Amjad Rehman. 2020. "Query-by-visual-search: multimodal framework for content-based image retrieval." Journal of Ambient Intelligence and Humanized Computing 11, no. 11: 5629-5648.

Conference paper
Published: 01 March 2020 in 2020 6th Conference on Data Science and Machine Learning Applications (CDMA)
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An acoustic fingerprint is a condensed and powerful digital signature of an audio signal which is used for audio sample identification. A fingerprint is the pattern of a voice or audio sample. A large number of algorithms have been developed for generating such acoustic fingerprints. These algorithms facilitate systems that perform song searching, song identification, and song duplication detection. In this study, a comprehensive and powerful survey of already developed algorithms is conducted. Four major music fingerprinting algorithms are evaluated for identifying and analyzing the potential hurdles that can affect their results. Since the background and environmental noise reduces the efficiency of music fingerprinting algorithms, behavioral analysis of fingerprinting algorithms is performed using audio samples of different languages and under different environmental conditions. The results of music fingerprint classification are more successful when deep learning techniques for classification are used. The testing of the acoustic feature modeling and music fingerprinting algorithms is performed using the standard dataset of iKala, MusicBrainz and MIR-1K.

ACS Style

Zahid Mehmood; Khurram Ashfaq Qazi; Muhammad Tahir; Rehan Muhammad Yousaf; Muhammad Sardaraz. Potential Barriers to Music Fingerprinting Algorithms in the Presence of Background Noise. 2020 6th Conference on Data Science and Machine Learning Applications (CDMA) 2020, 25 -30.

AMA Style

Zahid Mehmood, Khurram Ashfaq Qazi, Muhammad Tahir, Rehan Muhammad Yousaf, Muhammad Sardaraz. Potential Barriers to Music Fingerprinting Algorithms in the Presence of Background Noise. 2020 6th Conference on Data Science and Machine Learning Applications (CDMA). 2020; ():25-30.

Chicago/Turabian Style

Zahid Mehmood; Khurram Ashfaq Qazi; Muhammad Tahir; Rehan Muhammad Yousaf; Muhammad Sardaraz. 2020. "Potential Barriers to Music Fingerprinting Algorithms in the Presence of Background Noise." 2020 6th Conference on Data Science and Machine Learning Applications (CDMA) , no. : 25-30.

Journal article
Published: 05 February 2020 in Genes
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Next generation sequencing (NGS) technologies produce a huge amount of biological data, which poses various issues such as requirements of high processing time and large memory. This research focuses on the detection of single nucleotide polymorphism (SNP) in genome sequences. Currently, SNPs detection algorithms face several issues, e.g., computational overhead cost, accuracy, and memory requirements. In this research, we propose a fast and scalable workflow that integrates Bowtie aligner with Hadoop based Heap SNP caller to improve the SNPs detection in genome sequences. The proposed workflow is validated through benchmark datasets obtained from publicly available web-portals, e.g., NCBI and DDBJ DRA. Extensive experiments have been performed and the results obtained are compared with Bowtie and BWA aligner in the alignment phase, while compared with GATK, FaSD, SparkGA, Halvade, and Heap in SNP calling phase. Experimental results analysis shows that the proposed workflow outperforms existing frameworks e.g., GATK, FaSD, Heap integrated with BWA and Bowtie aligners, SparkGA, and Halvade. The proposed framework achieved 22.46% more efficient F-score and 99.80% consistent accuracy on average. More, comparatively 0.21% mean higher accuracy is achieved. Moreover, SNP mining has also been performed to identify specific regions in genome sequences. All the frameworks are implemented with the default configuration of memory management. The observations show that all workflows have approximately same memory requirement. In the future, it is intended to graphically show the mined SNPs for user-friendly interaction, analyze and optimize the memory requirements as well.

ACS Style

Muhammad Tahir; Muhammad Sardaraz. A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce. Genes 2020, 11, 166 .

AMA Style

Muhammad Tahir, Muhammad Sardaraz. A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce. Genes. 2020; 11 (2):166.

Chicago/Turabian Style

Muhammad Tahir; Muhammad Sardaraz. 2020. "A Fast and Scalable Workflow for SNPs Detection in Genome Sequences Using Hadoop Map-Reduce." Genes 11, no. 2: 166.

Research article
Published: 03 January 2020 in Microscopy Research and Technique
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The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.

ACS Style

Amjad Rehman; Muhammad A. Khan; Zahid Mehmood; Tanzila Saba; Muhammad Sardaraz; Muhammad Rashid. Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction. Microscopy Research and Technique 2020, 83, 410 -423.

AMA Style

Amjad Rehman, Muhammad A. Khan, Zahid Mehmood, Tanzila Saba, Muhammad Sardaraz, Muhammad Rashid. Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction. Microscopy Research and Technique. 2020; 83 (4):410-423.

Chicago/Turabian Style

Amjad Rehman; Muhammad A. Khan; Zahid Mehmood; Tanzila Saba; Muhammad Sardaraz; Muhammad Rashid. 2020. "Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction." Microscopy Research and Technique 83, no. 4: 410-423.

Journal article
Published: 20 December 2019 in IEEE Access
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Cloud computing has become the main source for executing scientific experiments. It is an effective technique for distributing and processing tasks on virtual machines. Scientific workflows are complex and demand efficient utilization of cloud resources. Scheduling of scientific workflows is considered as NPcomplete. The problem is constrained by some parameters such as Quality of Service (QoS), dependencies between tasks and users’ deadlines, etc. There exists a strong literature on scheduling scientific workflows in cloud environments. Solutions include standard schedulers, evolutionary optimization techniques, etc. This article presents a hybrid algorithm for scheduling scientific workflows in cloud environments. In the first phase, the algorithm prepares tasks lists for PSO algorithm. Bottleneck tasks are processed on high priority to reduce execution time. In the next phase, tasks are scheduled with the PSO algorithm to reduce both execution time and monetary cost. The algorithm also monitors the load balance to efficiently utilize cloud resources. Benchmark scientific workflows are used to evaluate the proposed algorithm. The proposed algorithm is compared with standard PSO and specialized schedulers to validate the performance. The results show improvement in execution time, monetary cost without affecting the load balance as compared to other techniques.

ACS Style

Muhammad Sardaraz; Muhammad Tahir. A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing. IEEE Access 2019, 7, 186137 -186146.

AMA Style

Muhammad Sardaraz, Muhammad Tahir. A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing. IEEE Access. 2019; 7 (99):186137-186146.

Chicago/Turabian Style

Muhammad Sardaraz; Muhammad Tahir. 2019. "A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing." IEEE Access 7, no. 99: 186137-186146.

Journal article
Published: 07 January 2019 in Current Bioinformatics
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Background: Biological sequence data have increased at a rapid rate due to the advancements in sequencing technologies and reduction in the cost of sequencing data. The huge increase in these data presents significant research challenges to researchers. In addition to meaningful analysis, data storage is also a challenge, an increase in data production is outpacing the storage capacity. Data compression is used to reduce the size of data and thus reduces storage requirements as well as transmission cost over the internet. Objective: This article presents a novel compression algorithm (FCompress) for Next Generation Sequencing (NGS) data in FASTQ format. Method: The proposed algorithm uses bits manipulation and dictionary-based compression for bases compression. Headers are compressed with reference-based compression, whereas quality scores are compressed with Huffman coding. Results: The proposed algorithm is validated with experimental results on real datasets. The results are compared with both general purpose and specialized compression programs. Conclusion: The proposed algorithm produces better compression ratio in a comparable time to other algorithms.

ACS Style

Muhammad Sardaraz. FCompress: An Algorithm for FASTQ Sequence Data Compression. Current Bioinformatics 2019, 14, 123 -129.

AMA Style

Muhammad Sardaraz. FCompress: An Algorithm for FASTQ Sequence Data Compression. Current Bioinformatics. 2019; 14 (2):123-129.

Chicago/Turabian Style

Muhammad Sardaraz. 2019. "FCompress: An Algorithm for FASTQ Sequence Data Compression." Current Bioinformatics 14, no. 2: 123-129.

Review
Published: 01 November 2018 in 2018 14th International Conference on Emerging Technologies (ICET)
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Cloud Computing provides utility-based IT services. The services are available as pay per use. Cloud gives advantage to organizations in setting up fundamental hardware and software requirements i.e. instead of purchasing hardware or software cloud services can be used. The availability of cloud services any time and anywhere makes it a feasible solution for many applications. cloud services are constrained by some parameters such as Quality of Service (QoS), efficient utilization of cloud resources, user budget, user deadlines, energy consumption etc. In this article, we present a comprehensive review of techniques or algorithms designed to reduce energy consumption in cloud data centers. The review covers Evolutionary Algorithms (EA) such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Genetic Algorithms (GA). We discuss each technique with strengths and weaknesses. Target objectives of each algorithm are also compared. The article is concluded with future research directions.

ACS Style

Khola Maryam; Muhammad Sardaraz; Muhammad Tahir. Evolutionary Algorithms in Cloud Computing from the Perspective of Energy Consumption: A Review. 2018 14th International Conference on Emerging Technologies (ICET) 2018, 1 -6.

AMA Style

Khola Maryam, Muhammad Sardaraz, Muhammad Tahir. Evolutionary Algorithms in Cloud Computing from the Perspective of Energy Consumption: A Review. 2018 14th International Conference on Emerging Technologies (ICET). 2018; ():1-6.

Chicago/Turabian Style

Khola Maryam; Muhammad Sardaraz; Muhammad Tahir. 2018. "Evolutionary Algorithms in Cloud Computing from the Perspective of Energy Consumption: A Review." 2018 14th International Conference on Emerging Technologies (ICET) , no. : 1-6.

Journal article
Published: 01 September 2017 in Expert Systems with Applications
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We present a brief introduction to the applications of pattern matching.We present a novel pattern matching algorithm for DNA sequences.We present multithreading in pattern matching.We use Turing machine for pattern matching.We present comparative results with significance improvements. To solve, manage and analyze biological problems using computer technology is called bioinformatics. With the emergent evolution in computing era, the volume of biological data has increased significantly. These large amounts of data have increased the need to analyze it in reasonable space and time. DNA sequences contain basic information of species, and pattern matching between different species is an important and challenging issue to cope with. There exist generalized string matching and some specialized DNA pattern matching algorithms in the literature. There is still need to develop fast and space efficient pattern matching algorithms that consider new hardware development. In this paper, we present a novel DNA sequences pattern matching algorithm called EPMA. The proposed algorithm utilizes fixed length 2-bits binary encoding, segmentation and multi-threading. The idea is to find the pattern with multiple searcher agents concurrently. The proposed algorithm is validated with comparative experimental results. The results show that the new algorithm is a good candidate for DNA sequence pattern matching applications. The algorithm effectively utilizes modern hardware and will help researchers in the sequence alignment, short read error correction, phylogenetic inference etc. Furthermore, the proposed method can be extended to generalized string matching and their applications.

ACS Style

Muhammad Tahir; Muhammad Sardaraz; Ataul Aziz Ikram. EPMA: Efficient pattern matching algorithm for DNA sequences. Expert Systems with Applications 2017, 80, 162 -170.

AMA Style

Muhammad Tahir, Muhammad Sardaraz, Ataul Aziz Ikram. EPMA: Efficient pattern matching algorithm for DNA sequences. Expert Systems with Applications. 2017; 80 ():162-170.

Chicago/Turabian Style

Muhammad Tahir; Muhammad Sardaraz; Ataul Aziz Ikram. 2017. "EPMA: Efficient pattern matching algorithm for DNA sequences." Expert Systems with Applications 80, no. : 162-170.

Just accepted article
Published: 14 June 2016 in Journal of Bioinformatics and Computational Biology
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Advances in high throughput sequencing technologies and reduction in cost of sequencing have led to exponential growth in high throughput DNA sequence data. This growth has posed challenges such as storage, retrieval, and transmission of sequencing data. Data compression is used to cope with these challenges. Various methods have been developed to compress genomic and sequencing data. In this article, we present a comprehensive review of compression methods for genome and reads compression. Algorithms are categorized as referential or reference free. Experimental results and comparative analysis of various methods for data compression are presented. Finally, key challenges and research directions in DNA sequence data compression are highlighted.

ACS Style

Muhammad Sardaraz; Muhammad Tahir; Ataul Aziz Ikram. Advances in high throughput DNA sequence data compression. Journal of Bioinformatics and Computational Biology 2016, 14, 1 -18.

AMA Style

Muhammad Sardaraz, Muhammad Tahir, Ataul Aziz Ikram. Advances in high throughput DNA sequence data compression. Journal of Bioinformatics and Computational Biology. 2016; 14 (3):1-18.

Chicago/Turabian Style

Muhammad Sardaraz; Muhammad Tahir; Ataul Aziz Ikram. 2016. "Advances in high throughput DNA sequence data compression." Journal of Bioinformatics and Computational Biology 14, no. 3: 1-18.

Journal article
Published: 22 September 2015 in Current Bioinformatics
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ACS Style

Muhammad Tahir; Muhammad Sardaraz; Ataul Ikram; Hassan Bajwa. HaShRECA: Hadoop Based Short Read Error Correction Algorithm for Genome Assembly. Current Bioinformatics 2015, 10, 469 -475.

AMA Style

Muhammad Tahir, Muhammad Sardaraz, Ataul Ikram, Hassan Bajwa. HaShRECA: Hadoop Based Short Read Error Correction Algorithm for Genome Assembly. Current Bioinformatics. 2015; 10 (4):469-475.

Chicago/Turabian Style

Muhammad Tahir; Muhammad Sardaraz; Ataul Ikram; Hassan Bajwa. 2015. "HaShRECA: Hadoop Based Short Read Error Correction Algorithm for Genome Assembly." Current Bioinformatics 10, no. 4: 469-475.

Comparative study
Published: 01 October 2014 in Genomics
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The growth of Next Generation Sequencing technologies presents significant research challenges, specifically to design bioinformatics tools that handle massive amount of data efficiently. Biological sequence data storage cost has become a noticeable proportion of total cost in the generation and analysis. Particularly increase in DNA sequencing rate is significantly outstripping the rate of increase in disk storage capacity, which may go beyond the limit of storage capacity. It is essential to develop algorithms that handle large data sets via better memory management. This article presents a DNA sequence compression algorithm SeqCompress that copes with the space complexity of biological sequences. The algorithm is based on lossless data compression and uses statistical model as well as arithmetic coding to compress DNA sequences. The proposed algorithm is compared with recent specialized compression tools for biological sequences. Experimental results show that proposed algorithm has better compression gain as compared to other existing algorithms.

ACS Style

Muhammad Sardaraz; Muhammad Tahir; Ataul Aziz Ikram; Hassan Bajwa. SeqCompress: An algorithm for biological sequence compression. Genomics 2014, 104, 225 -228.

AMA Style

Muhammad Sardaraz, Muhammad Tahir, Ataul Aziz Ikram, Hassan Bajwa. SeqCompress: An algorithm for biological sequence compression. Genomics. 2014; 104 (4):225-228.

Chicago/Turabian Style

Muhammad Sardaraz; Muhammad Tahir; Ataul Aziz Ikram; Hassan Bajwa. 2014. "SeqCompress: An algorithm for biological sequence compression." Genomics 104, no. 4: 225-228.

Conference paper
Published: 01 May 2013 in 2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT)
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Advances in genomics, proteomics, and bioinformatics have revolutionized the drug discovery and drug development. Computational systems biology, computational bioinformatics, and many biomedical applications are also growing at a rapid pace with an increasing demand for processing power. Hardware clusters and grid computing solutions are approached to fulfill the high demand for the processing power. The grid clusters approach proved success but introduced the need of frameworks to hide the complexity of parallel programming and enable the programmer to focus on the application logic. In this paper we present a novel cloud computing based neural network framework. We will further present results of implementation of Multiple Sequence Alignment (MSA) algorithms in cloud architecture. The experiments show optimal results in terms of computational complexity and preserve accuracy as well.

ACS Style

Ataul Aziz Ikram; Salma Ibrahim; Muhammad Sardaraz; Muhammad Tahir; Hassan Bajwa; Christian Bach. Neural network based cloud computing platform for bioinformatics. 2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT) 2013, 1 -6.

AMA Style

Ataul Aziz Ikram, Salma Ibrahim, Muhammad Sardaraz, Muhammad Tahir, Hassan Bajwa, Christian Bach. Neural network based cloud computing platform for bioinformatics. 2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT). 2013; ():1-6.

Chicago/Turabian Style

Ataul Aziz Ikram; Salma Ibrahim; Muhammad Sardaraz; Muhammad Tahir; Hassan Bajwa; Christian Bach. 2013. "Neural network based cloud computing platform for bioinformatics." 2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT) , no. : 1-6.

Journal article
Published: 01 January 2013 in International Journal of Computer and Electrical Engineering
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Wireless sensor networks (WSNs) are constrained in terms of memory, computation, communication, and energy. To reduce communication overhead and energy expenditure in (WSNs), data aggregation is used. Data aggregation is a very important technique, but it gives extra opportunity to the adversary to attack the network, inject false messages into the network and trick the base station to accept false aggregation results. This paper presents a secure data aggregation framework (SDAF) for (WSNs). The goal of the framework is to ensure data integrity and data confidentiality. SDAF uses two types of keys. Base station shares a unique key with each sensor node that is used for integrity and the aggregator shares a unique key with each sensor node (within that cluster) that is used for data confidentiality. Sensor nodes calculate a message authentication code (MAC) of the sensed data using shared key with base station, which verifies the MAC for message integrity. Sensor nodes encrypt the sensed data using shared key with aggregator, which ensures data confidentiality. Proposed framework has low communication overhead as the redundant packets are dropped at the aggregators

ACS Style

M. Sardaraz; Muhammad Tahir; Ataul Aziz Ikram. SDAF: A Secure Data Aggregation Framework for Wireless Sensor Networks. International Journal of Computer and Electrical Engineering 2013, 447 -450.

AMA Style

M. Sardaraz, Muhammad Tahir, Ataul Aziz Ikram. SDAF: A Secure Data Aggregation Framework for Wireless Sensor Networks. International Journal of Computer and Electrical Engineering. 2013; ():447-450.

Chicago/Turabian Style

M. Sardaraz; Muhammad Tahir; Ataul Aziz Ikram. 2013. "SDAF: A Secure Data Aggregation Framework for Wireless Sensor Networks." International Journal of Computer and Electrical Engineering , no. : 447-450.