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Nadeem Anjum
Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan

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Journal article
Published: 17 August 2021 in Electronics
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The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

ACS Style

Abdullah Lakhan; Qurat-Ul-Ain Mastoi; Mazhar Ali Dootio; Fehaid Alqahtani; Ibrahim R. Alzahrani; Fatmah Baothman; Syed Yaseen Shah; Syed Aziz Shah; Nadeem Anjum; Qammer Hussain Abbasi; Muhammad Saddam Khokhar. Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network. Electronics 2021, 10, 1974 .

AMA Style

Abdullah Lakhan, Qurat-Ul-Ain Mastoi, Mazhar Ali Dootio, Fehaid Alqahtani, Ibrahim R. Alzahrani, Fatmah Baothman, Syed Yaseen Shah, Syed Aziz Shah, Nadeem Anjum, Qammer Hussain Abbasi, Muhammad Saddam Khokhar. Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network. Electronics. 2021; 10 (16):1974.

Chicago/Turabian Style

Abdullah Lakhan; Qurat-Ul-Ain Mastoi; Mazhar Ali Dootio; Fehaid Alqahtani; Ibrahim R. Alzahrani; Fatmah Baothman; Syed Yaseen Shah; Syed Aziz Shah; Nadeem Anjum; Qammer Hussain Abbasi; Muhammad Saddam Khokhar. 2021. "Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network." Electronics 10, no. 16: 1974.

Journal article
Published: 06 July 2021 in Applied Sciences
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Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing.

ACS Style

Rohail Gulbaz; Abdul Siddiqui; Nadeem Anjum; Abdullah Alotaibi; Turke Althobaiti; Naeem Ramzan. Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing. Applied Sciences 2021, 11, 6244 .

AMA Style

Rohail Gulbaz, Abdul Siddiqui, Nadeem Anjum, Abdullah Alotaibi, Turke Althobaiti, Naeem Ramzan. Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing. Applied Sciences. 2021; 11 (14):6244.

Chicago/Turabian Style

Rohail Gulbaz; Abdul Siddiqui; Nadeem Anjum; Abdullah Alotaibi; Turke Althobaiti; Naeem Ramzan. 2021. "Balancer Genetic Algorithm—A Novel Task Scheduling Optimization Approach in Cloud Computing." Applied Sciences 11, no. 14: 6244.

Journal article
Published: 17 March 2021 in PeerJ Computer Science
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As a promising next-generation network architecture, named data networking (NDN) supports name-based routing and in-network caching to retrieve content in an efficient, fast, and reliable manner. Most of the studies on NDN have proposed innovative and efficient caching mechanisms and retrieval of content via efficient routing. However, very few studies have targeted addressing the vulnerabilities in NDN architecture, which a malicious node can exploit to perform a content poisoning attack (CPA). This potentially results in polluting the in-network caches, the routing of content, and consequently isolates the legitimate content in the network. In the past, several efforts have been made to propose the mitigation strategies for the content poisoning attack, but to the best of our knowledge, no specific work has been done to address an emerging attack-surface in NDN, which we call an interest flooding attack. Handling this attack-surface can potentially make content poisoning attack mitigation schemes more effective, secure, and robust. Hence, in this article, we propose the addition of a security mechanism in the CPA mitigation scheme that is, Name-Key Based Forwarding and Multipath Forwarding Based Inband Probe, in which we block the malicious face of compromised consumers by monitoring the Cache-Miss Ratio values and the Queue Capacity at the Edge Routers. The malicious face is blocked when the cache-miss ratio hits the threshold value, which is adjusted dynamically through monitoring the cache-miss ratio and queue capacity values. The experimental results show that we are successful in mitigating the vulnerability of the CPA mitigation scheme by detecting and blocking the flooding interface, at the cost of very little verification overhead at the NDN Routers.

ACS Style

Adnan Mahmood Qureshi; Nadeem Anjum; Rao Naveed Bin Rais; Masood Ur-Rehman; Amir Qayyum. Detection of malicious consumer interest packet with dynamic threshold values. PeerJ Computer Science 2021, 7, e435 .

AMA Style

Adnan Mahmood Qureshi, Nadeem Anjum, Rao Naveed Bin Rais, Masood Ur-Rehman, Amir Qayyum. Detection of malicious consumer interest packet with dynamic threshold values. PeerJ Computer Science. 2021; 7 ():e435.

Chicago/Turabian Style

Adnan Mahmood Qureshi; Nadeem Anjum; Rao Naveed Bin Rais; Masood Ur-Rehman; Amir Qayyum. 2021. "Detection of malicious consumer interest packet with dynamic threshold values." PeerJ Computer Science 7, no. : e435.

Journal article
Published: 07 December 2020 in Applied Sciences
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Salient-object detection is a fundamental and the most challenging problem in computer vision. This paper focuses on the detection of salient objects, especially in low-contrast images. To this end, a hybrid deep-learning architecture is proposed where features are extracted on both the local and global level. These features are then integrated to extract the exact boundary of the object of interest in an image. Experimentation was performed on five standard datasets, and results were compared with state-of-the-art approaches. Both qualitative and quantitative analyses showed the robustness of the proposed architecture.

ACS Style

Wajeeha Sultan; Nadeem Anjum; Mark Stansfield; Naeem Ramzan. Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection. Applied Sciences 2020, 10, 8754 .

AMA Style

Wajeeha Sultan, Nadeem Anjum, Mark Stansfield, Naeem Ramzan. Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection. Applied Sciences. 2020; 10 (23):8754.

Chicago/Turabian Style

Wajeeha Sultan; Nadeem Anjum; Mark Stansfield; Naeem Ramzan. 2020. "Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection." Applied Sciences 10, no. 23: 8754.

Journal article
Published: 28 July 2020 in IEEE Access
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The anticipation of ongoing human interactions is not only highly dynamic and challenging problem but extremely crucial in applications such as remote monitoring, video surveillance, human-robot interaction, anti-terrorists and anti-crime securities. In this work, we address the problem of anticipating the interactions between people monitored by single as well as multiple camera views. To this end, we propose a novel approach that integrates Deep Features with novel hand-crafted features, namely Transformed Optical Flow Components (TOFCs). In order to validate the performance of the proposed approach, we have tested the proposed approach in real outdoor environments, captured using single as well as multiple cameras, having shadow and illumination variations as well as cluttered backgrounds. The results of the proposed approach are also compared with the state-of-the-art approaches. The experimental results show that the proposed approach is promising to anticipate real human interactions.

ACS Style

Shafina Bibi; Nadeem Anjum; Tehmina Amjad; Graeme McRobbie; Naeem Ramzan. Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components. IEEE Access 2020, 8, 137646 -137657.

AMA Style

Shafina Bibi, Nadeem Anjum, Tehmina Amjad, Graeme McRobbie, Naeem Ramzan. Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components. IEEE Access. 2020; 8 (99):137646-137657.

Chicago/Turabian Style

Shafina Bibi; Nadeem Anjum; Tehmina Amjad; Graeme McRobbie; Naeem Ramzan. 2020. "Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components." IEEE Access 8, no. 99: 137646-137657.