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The detection of epileptic seizures by classifying electroencephalography (EEG) signals into ictal and interictal classes is a demanding challenge, because it identifies the seizure and seizure-free states of an epileptic patient. In previous works, several machine learning-based strategies were introduced to investigate and interpret EEG signals for the purpose of their accurate classification. However, non-linear and non-stationary characteristics of EEG signals make it complicated to get complete information about these dynamic biomedical signals. In order to address this issue, this paper focuses on extracting the most discriminating and distinguishing features of seizure EEG recordings to develop an approach that employs both fuzzy-based and traditional machine learning algorithms for epileptic seizure detection. The proposed framework classifies unknown EEG signal segments into ictal and interictal classes. The model is validated using empirical evaluation on two benchmark datasets, namely the Bonn and Children’s Hospital of Boston-Massachusetts Institute of Technology (CHB-MIT) datasets. The obtained results show that in both cases, K-Nearest Neighbor (KNN) and Fuzzy Rough Nearest Neighbor (FRNN) give the highest classification accuracy scores, with improved sensitivity and specificity percentages.
Aayesha; Muhammad Bilal Qureshi; Muhammad Afzaal; Muhammad Fayaz. Machine learning-based EEG signals classification model for epileptic seizure detection. Multimedia Tools and Applications 2021, 80, 17849 -17877.
AMA StyleAayesha, Muhammad Bilal Qureshi, Muhammad Afzaal, Muhammad Fayaz. Machine learning-based EEG signals classification model for epileptic seizure detection. Multimedia Tools and Applications. 2021; 80 (12):17849-17877.
Chicago/Turabian StyleAayesha; Muhammad Bilal Qureshi; Muhammad Afzaal; Muhammad Fayaz. 2021. "Machine learning-based EEG signals classification model for epileptic seizure detection." Multimedia Tools and Applications 80, no. 12: 17849-17877.
Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.
Muhammad Qureshi; Muhammad Qureshi; Muhammad Fayaz; Muhammad Zakarya; Sheraz Aslam; Asadullah Shah. Time and Cost Efficient Cloud Resource Aallocation for Real-Time Data-Intensive Smart Systems. Energies 2020, 13, 5706 .
AMA StyleMuhammad Qureshi, Muhammad Qureshi, Muhammad Fayaz, Muhammad Zakarya, Sheraz Aslam, Asadullah Shah. Time and Cost Efficient Cloud Resource Aallocation for Real-Time Data-Intensive Smart Systems. Energies. 2020; 13 (21):5706.
Chicago/Turabian StyleMuhammad Qureshi; Muhammad Qureshi; Muhammad Fayaz; Muhammad Zakarya; Sheraz Aslam; Asadullah Shah. 2020. "Time and Cost Efficient Cloud Resource Aallocation for Real-Time Data-Intensive Smart Systems." Energies 13, no. 21: 5706.
Among several approaches to privacy-preserving cryptographic schemes, we have concentrated on noise-free homomorphic encryption. It is a symmetric key encryption that supports homomorphic operations on encrypted data. We present a fully homomorphic encryption (FHE) scheme based on sedenion algebra over finite Zn rings. The innovation of the scheme is the compression of a 16-dimensional vector for the application of Frobenius automorphism. For sedenion, we have p16 different possibilities that create a significant bijective mapping over the chosen 16-dimensional vector that adds permutation to our scheme. The security of this scheme is based on the assumption of the hardness of solving a multivariate quadratic equation system over finite Zn rings. The scheme results in 256n multivariate polynomial equations with 256 + 16n unknown variables for n messages. For this reason, the proposed scheme serves as a security basis for potentially post-quantum cryptosystems. Moreover, after sedenion, no newly constructed algebra loses its properties. This scheme would therefore apply as a whole to the following algebras, such as 32-dimensional trigintadunion.
Iqra Mustafa; Hasnain Mustafa; Ahmad Taher Azar; Sheraz Aslam; Syed Muhammad Mohsin; Muhammad Bilal Qureshi; Nouman Ashraf. Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra. IEEE Access 2020, 8, 136524 -136536.
AMA StyleIqra Mustafa, Hasnain Mustafa, Ahmad Taher Azar, Sheraz Aslam, Syed Muhammad Mohsin, Muhammad Bilal Qureshi, Nouman Ashraf. Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra. IEEE Access. 2020; 8 (99):136524-136536.
Chicago/Turabian StyleIqra Mustafa; Hasnain Mustafa; Ahmad Taher Azar; Sheraz Aslam; Syed Muhammad Mohsin; Muhammad Bilal Qureshi; Nouman Ashraf. 2020. "Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra." IEEE Access 8, no. 99: 136524-136536.
Interest in real-time systems has grown considerably over recent years, primarily due to significant increase in the use of smart technologies and latency-sensitive applications such as cloud gaming, audio/video streaming, and smart homes. Significant work has been done on resource mapping in the cloud environment, and a number of promising results have been established accordingly where the focus is mainly on resource provisioning. However, the applicability of cloud computing services for real-time systems generated from smart systems is still in its infancy and remains unexplored, relatively. To address this gap, we propose a model for the smart systems that periodically offload computational workload to the cloud environment where virtual machines are allocated according to rate-monotonic scheduling policy to ensure requests are processed within the associated deadlines. Deadlines of tasks have been relaxed to improve server utilization as well as maintain a level of confidence in the timing constrains. Experimental results are discussed to highlight the applicability of static priority assignment for the workload in the context of virtual machines allocation.
Nasro Min-Allah; Muhammad Bilal Qureshi; FarmanUllah Jan; Saleh Alrashed; Javid Taheri. Deployment of real-time systems in the cloud environment. The Journal of Supercomputing 2020, 77, 2069 -2090.
AMA StyleNasro Min-Allah, Muhammad Bilal Qureshi, FarmanUllah Jan, Saleh Alrashed, Javid Taheri. Deployment of real-time systems in the cloud environment. The Journal of Supercomputing. 2020; 77 (2):2069-2090.
Chicago/Turabian StyleNasro Min-Allah; Muhammad Bilal Qureshi; FarmanUllah Jan; Saleh Alrashed; Javid Taheri. 2020. "Deployment of real-time systems in the cloud environment." The Journal of Supercomputing 77, no. 2: 2069-2090.
Conventional RSA algorithm, being a basis for several proposed cryptosystems, has remarkable security laps with respect to confidentiality and integrity over the internet which can be compromised by state-of-the-art attacks, especially, for different types of data generation, transmission, and analysis by IoT applications. This security threat hindrance is considered to be a hard problem to solve on classical computers. However, bringing quantum mechanics into account, the concept no longer holds true. So, this calls out for the modification of the conventional pre-quantum RSA algorithm into a secure post-quantum cryptographic-based RSA technique. In this research, we propose a post-quantum lattice-based RSA (LBRSA) for IoT applications in order to secure the shared data and information. The proposed work is validated by implementing it in 60-dimensions. The key size is about 1.152 × 105-bits and generation time is 0.8 hours. Furthermore, it has been tested with AVISPA, which confirms security in the presence of an intruder. Moreover, the proposed LB-RSA technique is compared with the existing state-of-the-art techniques. The empirical results advocate that the proposed lattice-based variant is not only safe but beats counterparts in terms of secured data sharing.
Iqra Mustafa; Imran Ullah Khan; Sheraz Aslam; Ahthasham Sajid; Syed Muhammad Mohsin; Muhammad Awais; Muhammad Bilal Qureshi. A Lightweight Post-Quantum Lattice-Based RSA for Secure Communications. IEEE Access 2020, 8, 99273 -99285.
AMA StyleIqra Mustafa, Imran Ullah Khan, Sheraz Aslam, Ahthasham Sajid, Syed Muhammad Mohsin, Muhammad Awais, Muhammad Bilal Qureshi. A Lightweight Post-Quantum Lattice-Based RSA for Secure Communications. IEEE Access. 2020; 8 (99):99273-99285.
Chicago/Turabian StyleIqra Mustafa; Imran Ullah Khan; Sheraz Aslam; Ahthasham Sajid; Syed Muhammad Mohsin; Muhammad Awais; Muhammad Bilal Qureshi. 2020. "A Lightweight Post-Quantum Lattice-Based RSA for Secure Communications." IEEE Access 8, no. 99: 99273-99285.
Grid resource allocation mechanism maps tasks to the available grid resources according to some predefined criterion, such as minimizing makespan or execution cost, load balancing, energy efficiency, maintaining user-defined task deadlines, and efficiently using resource memory. The minimization of the makespan is a dominant criterion and is more challenging when computationally intensive tasks have realtime deadlines and data requirements. Such tasks require data files for processing that are transferred from data storage resources to the computing resources, which consume network bandwidth. Resource allocation mechanism for these tasks takes into account the data files transfer time and processing power of the computing resources to complete execution within deadlines. The problem of allocating real-time data-intensive tasks to the grid heterogeneous computing resources with the assumption that the data resources are decoupled from the computing resources, remain challenging. This paper addresses the aforementioned problem as the global optimization problem by considering heterogeneous computing resources of various processing capabilities connected to the data storage resources by network links of various bandwidths. We have analytically formulated the resources with the aim to maximize total number of mapped tasks while possibly minimizing the makespan subject to the time QoS constraints of deadlines, execution time, and data files transfer time. The experimental results reveal that the proposed technique outperforms the other alternatives when real-time tasks are considered.
Muhammad Bilal Qureshi; Mohammed Abdulrahman Alqahtani; Nasro Min-Allah. Grid Resource Allocation for Real-Time Data-Intensive Tasks. IEEE Access 2017, 5, 22724 -22734.
AMA StyleMuhammad Bilal Qureshi, Mohammed Abdulrahman Alqahtani, Nasro Min-Allah. Grid Resource Allocation for Real-Time Data-Intensive Tasks. IEEE Access. 2017; 5 ():22724-22734.
Chicago/Turabian StyleMuhammad Bilal Qureshi; Mohammed Abdulrahman Alqahtani; Nasro Min-Allah. 2017. "Grid Resource Allocation for Real-Time Data-Intensive Tasks." IEEE Access 5, no. : 22724-22734.
When there is a mismatch between the cardinality of a periodic task set and the priority levels supported by the underlying hardware systems, multiple tasks are grouped into one class so as to maintain a specific level of confidence in their accuracy. However, such a transformation is achieved at the expense of the loss of schedulability of the original task set. We further investigate the aforementioned problem and report the following contributions: (i) a novel technique for mapping unlimited priority tasks into a reduced number of classes that do not violate the schedulability of the original task set and (ii) an efficient feasibility test that eliminates insufficient points during the feasibility analysis. The theoretical correctness of both contributions is checked through formal verifications. Moreover, the experimental results reveal the superiority of our work over the existing feasibility tests by reducing the number of scheduling points that are needed otherwise.
Muhammad Bilal Qureshi; Saleh Alrashed; Nasro Min-Allah; Joanna Kołodziej; Piotr Arabas. Maintaining the Feasibility of Hard Real–Time Systems with a Reduced Number of Priority Levels. International Journal of Applied Mathematics and Computer Science 2015, 25, 709 -722.
AMA StyleMuhammad Bilal Qureshi, Saleh Alrashed, Nasro Min-Allah, Joanna Kołodziej, Piotr Arabas. Maintaining the Feasibility of Hard Real–Time Systems with a Reduced Number of Priority Levels. International Journal of Applied Mathematics and Computer Science. 2015; 25 (4):709-722.
Chicago/Turabian StyleMuhammad Bilal Qureshi; Saleh Alrashed; Nasro Min-Allah; Joanna Kołodziej; Piotr Arabas. 2015. "Maintaining the Feasibility of Hard Real–Time Systems with a Reduced Number of Priority Levels." International Journal of Applied Mathematics and Computer Science 25, no. 4: 709-722.