This page has only limited features, please log in for full access.

Unclaimed
Muhammad Fayaz
University of Central Asia

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Preprint content
Published: 29 June 2021
Reads 0
Downloads 0

In past decade, the use of cognitive aids such as textual, visual and audio in 3D-Virtual Learning Environments is increasing day by day as it guide and facilitate both the students and teachers to perform the task with ease in Virtual Environment. In recent studies, it has been observed that the use of cognitive aids in virtual learning environments reduce mental load on learner but at the same time it also minimizes active exploration which negatively affect their performance in non-supervised environment. Therefore, some researchers have shown negative concern about the use of cognitive aids in 3D-Virtual Learning Environments (3D-VLEs). In this paper, we presents the idea of ”Adaptive Repetition” as control strategy for active exploration in 3D-VLEs. At the beginning of experiment in 3D-VLEs , students is given full support to perform the experiment with help of cognitive aids. Using a fuzzy logic based approach , the amount of aids are minimized whenever the experiment is repeated. The adaptive repetition approach put the students in active learning process and enables them to actively explore the learning environment. Ultimately, the negative effects of using cognitive aids in 3D-VLEs is minimized.

ACS Style

Muhammad Fayaz; Aftab Alam; Shah Khalid; Numan Ali; Wali Khan Mashwani. Adaptive Repetition: A Fuzzy Logic based Control Strategy for Active Exploration in 3D-Virtual Learning Environments. 2021, 1 .

AMA Style

Muhammad Fayaz, Aftab Alam, Shah Khalid, Numan Ali, Wali Khan Mashwani. Adaptive Repetition: A Fuzzy Logic based Control Strategy for Active Exploration in 3D-Virtual Learning Environments. . 2021; ():1.

Chicago/Turabian Style

Muhammad Fayaz; Aftab Alam; Shah Khalid; Numan Ali; Wali Khan Mashwani. 2021. "Adaptive Repetition: A Fuzzy Logic based Control Strategy for Active Exploration in 3D-Virtual Learning Environments." , no. : 1.

Research article
Published: 12 June 2021 in Complexity
Reads 0
Downloads 0

Wireless Sensor Network (WSN) is a particular network built from small sensor nodes. These sensor nodes have unique features. That is, it can sense and process data in WSN. WSN has tremendous applications in many fields. Despite the significance of WSN, this kind of network faced several issues. The biggest problems rising in WSN are energy consumption and security. Robust security development is needed to cope with WSN applications. For security purposes in WSN, cryptography techniques are very favorable. However, WSN has resource limitations, which is the main problem in applying any security scheme. Hence, if we are using the cryptography scheme in WSN, we must first guarantee that it must be energy-efficient. Thus, we proposed a secure hybrid session key management scheme for WSN. In this scheme, the major steps of public key cryptography are minimized, and much of the operations are based on symmetric key cryptography. This strategy extensively reduces the energy consumption of WSN and ensures optimum security. The proposed scheme is implemented, and their analysis is performed using different parameters with benchmark schemes. We concluded that the proposed scheme is energy-efficient and outperforms the available benchmark schemes. Furthermore, it provides an effective platform for secure key agreements and management in the WSN environment.

ACS Style

Gulzar Mehmood; Muhammad Sohail Khan; Abdul Waheed; Mahdi Zareei; Muhammad Fayaz; Tariq Sadad; Nazri Kama; Azri Azmi. An Efficient and Secure Session Key Management Scheme in Wireless Sensor Network. Complexity 2021, 2021, 1 -10.

AMA Style

Gulzar Mehmood, Muhammad Sohail Khan, Abdul Waheed, Mahdi Zareei, Muhammad Fayaz, Tariq Sadad, Nazri Kama, Azri Azmi. An Efficient and Secure Session Key Management Scheme in Wireless Sensor Network. Complexity. 2021; 2021 ():1-10.

Chicago/Turabian Style

Gulzar Mehmood; Muhammad Sohail Khan; Abdul Waheed; Mahdi Zareei; Muhammad Fayaz; Tariq Sadad; Nazri Kama; Azri Azmi. 2021. "An Efficient and Secure Session Key Management Scheme in Wireless Sensor Network." Complexity 2021, no. : 1-10.

Regular paper
Published: 11 June 2021 in Computing
Reads 0
Downloads 0

Internet of Things (IoT) has attracted tremendous research attention in the recent past fromindustry and academia. IoT is quite helpful in uplifting living standards by transforming conventional technology into smart systems. Greenhouse production is considered as an ultimate solution for rising global food demands with the growing population. Greenhouse provides a year-round production facility for fresh vegetables with around 50% increased production rate in comparison to open-air cultivation. However, energy consumption and labor cost in greenhouses account for more than 50% of the cost of greenhouse production. In this paper, we have proposed a novel optimization scheme that aims to achieve a trade-off between energy consumption and desired climate setting in greenhouse i.e. temperature, \({\mathrm{CO}}_2\) level, and humidity. For performance evaluation of the proposed system, we have developed an ad-hoc emulator of the greenhouse environment. For the proposed model validation and experimental analysis, we have used 15 days of external environmental data collected in Jeju, South Korea. Proposed optimization scheme results are compared with a baseline scheme. Comparative analysis of experimental results shows that our proposed model maintains desired indoor environment for maximizing crop production with 26.56% reduced energy consumption than the baseline scheme. Furthermore proposed model achieve a 27.76% cost reduction when compared to the baseline scheme. Better optimization results of the proposed scheme give us the confidence to further investigate its effectiveness in a real environment for achieving improved energy efficiency.

ACS Style

Israr Ullah; Muhammad Fayaz; Muhammad Aman; DoHyeun Kim. An optimization scheme for IoT based smart greenhouse climate control with efficient energy consumption. Computing 2021, 1 -25.

AMA Style

Israr Ullah, Muhammad Fayaz, Muhammad Aman, DoHyeun Kim. An optimization scheme for IoT based smart greenhouse climate control with efficient energy consumption. Computing. 2021; ():1-25.

Chicago/Turabian Style

Israr Ullah; Muhammad Fayaz; Muhammad Aman; DoHyeun Kim. 2021. "An optimization scheme for IoT based smart greenhouse climate control with efficient energy consumption." Computing , no. : 1-25.

Review
Published: 07 April 2021 in Electronics
Reads 0
Downloads 0

In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

ACS Style

Imran; Zeba Ghaffar; Abdullah Alshahrani; Muhammad Fayaz; Ahmed Alghamdi; Jeonghwan Gwak. A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges. Electronics 2021, 10, 880 .

AMA Style

Imran, Zeba Ghaffar, Abdullah Alshahrani, Muhammad Fayaz, Ahmed Alghamdi, Jeonghwan Gwak. A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges. Electronics. 2021; 10 (8):880.

Chicago/Turabian Style

Imran; Zeba Ghaffar; Abdullah Alshahrani; Muhammad Fayaz; Ahmed Alghamdi; Jeonghwan Gwak. 2021. "A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges." Electronics 10, no. 8: 880.

Article
Published: 12 February 2021 in Multimedia Tools and Applications
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (12):17849-17877.

Chicago/Turabian Style

Aayesha; 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.

Article
Published: 10 November 2020 in Multimedia Tools and Applications
Reads 0
Downloads 0

For Collaborative Virtual Environments (CVEs), many interaction techniques have been developed. Depending on the purpose of the collaborative work, techniques of interaction and manipulation change from one application to another. There is no general, good and efficient solution for all the collaborative systems. In addition, people in CVEs also use communication channels to share task goals, task decomposition and task progress. Therefore, awareness and communications are usually considered as important instruments to complete collaborative task. In this paper, we have combined different virtual navigation aids i.e. 3DML + audio, 3DML + textual, 3DML + arrows-casting, arrows-casting + audio, arrows-casting + textual and audio + textual; and presented a comparative study of user performance to perform an assembly task in CVEs. We reported the results of a precise experiment containing, 30 virtual teams of 60 individual students. Overall, results showed that students performed task faster using 3DML + arrows-casting while they were slow with audio + textual support in navigation.

ACS Style

Shah Khalid; Sehat Ullah; Numan Ali; Aftab Alam; Nasir Rasheed; Muhammad Fayaz; Masood Ahmad. The effect of combined aids on users performance in collaborative virtual environments. Multimedia Tools and Applications 2020, 80, 9371 -9391.

AMA Style

Shah Khalid, Sehat Ullah, Numan Ali, Aftab Alam, Nasir Rasheed, Muhammad Fayaz, Masood Ahmad. The effect of combined aids on users performance in collaborative virtual environments. Multimedia Tools and Applications. 2020; 80 (6):9371-9391.

Chicago/Turabian Style

Shah Khalid; Sehat Ullah; Numan Ali; Aftab Alam; Nasir Rasheed; Muhammad Fayaz; Masood Ahmad. 2020. "The effect of combined aids on users performance in collaborative virtual environments." Multimedia Tools and Applications 80, no. 6: 9371-9391.

Journal article
Published: 31 October 2020 in Energies
Reads 0
Downloads 0

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.

ACS Style

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 Style

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 (21):5706.

Chicago/Turabian Style

Muhammad 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.

Journal article
Published: 01 September 2020 in IEEE Access
Reads 0
Downloads 0

Datacentres provide the foundations for cloud computing, but require large amounts of electricity for their operation. Approaches that promise to reduce power use by minimizing execution time, for example using different scheduling and resource management techniques, are discussed in the literature. This paper summarizes some of the most important scheduling techniques in clouds focusing on power consumption, covering VM-level, host-level and task-level scheduling where the most promising approach is task level scheduling, with energy savings by means of load filtering, consolidation, adapted CPU throughput, or host power control. We explore use of the rate monotonic (RM) and backfilling algorithms for real-time task scheduling in cloud environment because RM is the simplest fixed priority scheduling technique, and thus the choice for modern real-time systems, and prior uses of RM in task scheduling have demonstrated power efficiency with optimal results. We specifically consider deadline-based tasks scheduling for real-time clouds which, to the best of our knowledge, has not been employed previously. RM with backfilling is experimentally evaluated and results show that, compared to the classical algorithms, all tasks were scheduled with minimum power consumption (5.5% - 29.3%), on minimum resources (3.9% - 25.2% less) while majority were meeting their deadlines (93.21% - 94.7%). The approach can guarantee deadline oriented Software as a Service (SaaS) in cloud if arrival rate i.e. network transfer time can be estimated in advance. We subsequently provided an extension of the proposed approach to task-based load balancing for almost balanced resource utilization and approximately 1.0% to 1.6% energy efficiency.

ACS Style

Hashim Ali; Muhammad Shuaib Qureshi; Ayaz Ali Khan; Muhammad Zakarya; Muhammad Fayaz. An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres. IEEE Access 2020, 8, 161288 -161303.

AMA Style

Hashim Ali, Muhammad Shuaib Qureshi, Ayaz Ali Khan, Muhammad Zakarya, Muhammad Fayaz. An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres. IEEE Access. 2020; 8 (99):161288-161303.

Chicago/Turabian Style

Hashim Ali; Muhammad Shuaib Qureshi; Ayaz Ali Khan; Muhammad Zakarya; Muhammad Fayaz. 2020. "An Energy and Performance Aware Scheduler for Real-Time Tasks in Cloud Datacentres." IEEE Access 8, no. 99: 161288-161303.

Journal article
Published: 24 August 2020 in Energies
Reads 0
Downloads 0

This research work proposed a hybrid model to maximize energy consumption and maximize user comfort in residential buildings. The proposed model consists of two widely used optimization algorithms named the firefly algorithm (FA) and genetic algorithm (GA). The hybridization of two optimization approaches results in a better optimization process, leading to better performance of the process in terms of minimum power consumption and maximum occupant’s comfort. The inputs of the optimization model are illumination, temperature, and air quality from the user, in addition with the external environment. The outputs of the proposed model are the optimized values of illumination, temperature, and air quality, which are, in turn, used in computing the values of user comfort. After the computation of the comfort index, these values enter the fuzzy controllers, which are used to adjust the cooling/heating system, illumination system, and ventilation system according to the occupant’s requirement. A user-friendly environment for power consumption minimization and user comfort maximization using data from different sensors, user, processes, power control systems, and various actuators is proposed in this work. The results obtained from the hybrid model have been compared with many state-of-the-art optimization algorithms. The final results revealed that the proposed approach performed better as compared to the standard optimization techniques.

ACS Style

Fazli Wahid; Muhammad Fayaz; Ayman AlJarbouh; Masood Mir; Muhammad Aamir; Imran. Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms. Energies 2020, 13, 4363 .

AMA Style

Fazli Wahid, Muhammad Fayaz, Ayman AlJarbouh, Masood Mir, Muhammad Aamir, Imran. Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms. Energies. 2020; 13 (17):4363.

Chicago/Turabian Style

Fazli Wahid; Muhammad Fayaz; Ayman AlJarbouh; Masood Mir; Muhammad Aamir; Imran. 2020. "Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms." Energies 13, no. 17: 4363.

Journal article
Published: 03 August 2020 in Symmetry
Reads 0
Downloads 0

Rigorous model-based design and control for intelligent vehicle suspension systems play an important role in providing better driving characteristics such as passenger comfort and road-holding capability. This paper investigates a new technique for modelling, simulation and control of semi-active suspension systems supporting both ride comfort and road-holding driving characteristics and implements the technique in accordance with the functional mock-up interface standard FMI 2.0. Firstly, we provide a control-oriented hybrid model of a quarter car semi-active suspension system. The resulting quarter car hybrid model is used to develop a sliding mode controller that supports both ride comfort and road-holding capability. Both the hybrid model and controller are then implemented conforming to the functional mock-up interface standard FMI 2.0. The aim of the FMI-based implementation is to serve as a portable test bench for control applications of vehicle suspension systems. It fully supports the exchange of the suspension system components as functional mock-up units (FMUs) among different modelling and simulation platforms, which allows re-usability and facilitates the interoperation and integration of the suspension system components with embedded software components. The concepts are validated with simulation results throughout the paper.

ACS Style

Ayman AlJarbouh; Muhammad Fayaz. Hybrid Modelling and Sliding Mode Control of Semi-Active Suspension Systems for Both Ride Comfort and Road-Holding. Symmetry 2020, 12, 1286 .

AMA Style

Ayman AlJarbouh, Muhammad Fayaz. Hybrid Modelling and Sliding Mode Control of Semi-Active Suspension Systems for Both Ride Comfort and Road-Holding. Symmetry. 2020; 12 (8):1286.

Chicago/Turabian Style

Ayman AlJarbouh; Muhammad Fayaz. 2020. "Hybrid Modelling and Sliding Mode Control of Semi-Active Suspension Systems for Both Ride Comfort and Road-Holding." Symmetry 12, no. 8: 1286.

Journal article
Published: 16 June 2020 in Electronics
Reads 0
Downloads 0

Internet of Things (IoT) is getting more popular day by day, which triggers its adoption for solving domain specific problems. Cities are becoming smart by gathering the context knowledge through sensors and controlling specific parameters through actuators. Dynamically discovering and integrating different data streams from different sensors is a major challenge these days. In this paper, a service matchmaking algorithm is presented for service discovery utilizing IoT devices and services in a particular geographic area. It helps us to identify services based on a variety of parameters (location, query size and processing time, etc.). Customization of service selection and discovery are also explored. The conceptual framework is provided for the proposed model along with a matchmaking algorithm based on IoT devices virtualization. The simulation results elaborate the increased complexity of processing time with respect to the increasing pool of available services. The average processing time varies as the number of conditions are multiplied. Query size and complexity increases with additional number of filters and conditions which results in the reduction of the number of matching services. Moreover, upon decreasing the radius of geographic search area, the number of candidate services decreases for service matching algorithm. This is based on the assumption that IoT devices and services are evenly distributed in a given geographic area. Similarly, the remaining energy of IoT devices is also assumed to be uniformly distributed and, therefore, if we are interested in IoT devices or services with more residual energy, then a limited number of IoT devices or services will fulfill this criterion.

ACS Style

Zulfiqar Ali Khan; Israr Ullah; Muhammad Ibrahim; Muhammad Fayaz; Ayman AlJarbouh; Muhammad Shuaib Qureshi. Virtualization Based Efficient Service Matching and Discovery in Internet of Things. Electronics 2020, 9, 1 .

AMA Style

Zulfiqar Ali Khan, Israr Ullah, Muhammad Ibrahim, Muhammad Fayaz, Ayman AlJarbouh, Muhammad Shuaib Qureshi. Virtualization Based Efficient Service Matching and Discovery in Internet of Things. Electronics. 2020; 9 (6):1.

Chicago/Turabian Style

Zulfiqar Ali Khan; Israr Ullah; Muhammad Ibrahim; Muhammad Fayaz; Ayman AlJarbouh; Muhammad Shuaib Qureshi. 2020. "Virtualization Based Efficient Service Matching and Discovery in Internet of Things." Electronics 9, no. 6: 1.

Journal article
Published: 21 May 2020 in Systems
Reads 0
Downloads 0

Geometric-Zeno behaviour is a highly challenging problem in the analysis (including simulation) of hybrid systems. Geometric-Zeno can be defined as an infinite number of discrete mode switches in a finite time interval. Typically, for hybrid models exhibiting geometric-Zeno, the numerical simulation either halts or produces false results, because an infinite number of discrete events occur in a given simulation time-step. In this paper, we provide formal methods for regularization of geometric-Zeno behaviour by using a non-standard analysis. In particular, we provide formal conditions for the existence of geometric-Zeno in hybrid systems, and we propose methods to allow geometric-Zeno executions to be continued beyond geometric-Zeno limit points. The concepts are illustrated with a case study throughout the paper.

ACS Style

Ayman AlJarbouh; Muhammad Fayaz; Muhammad Shuaib Qureshi. Non-Standard Analysis for Regularization of Geometric-Zeno Behaviour in Hybrid Systems. Systems 2020, 8, 1 .

AMA Style

Ayman AlJarbouh, Muhammad Fayaz, Muhammad Shuaib Qureshi. Non-Standard Analysis for Regularization of Geometric-Zeno Behaviour in Hybrid Systems. Systems. 2020; 8 (2):1.

Chicago/Turabian Style

Ayman AlJarbouh; Muhammad Fayaz; Muhammad Shuaib Qureshi. 2020. "Non-Standard Analysis for Regularization of Geometric-Zeno Behaviour in Hybrid Systems." Systems 8, no. 2: 1.

Journal article
Published: 30 April 2020 in Applied Sciences
Reads 0
Downloads 0

The purpose of this research is to demonstrate the ability of machine-learning (ML) methods for liver cancer classification using a fused dataset of two-dimensional (2D) computed tomography (CT) scans and magnetic resonance imaging (MRI). Datasets of benign (hepatocellular adenoma, hemangioma, cyst) and malignant (hepatocellular carcinoma, hepatoblastoma, metastasis) liver cancer were acquired at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. The final dataset was generated by fusion of 1200 (100 × 6 × 2) MR and CT-scan images, 200 (100 MRI and 100 CT-scan) images size 512 × 512 for each class of cancer. The acquired dataset was preprocessed by employing the Gabor filters to reduce the noise and taking an automated region of interest (ROIs) using an Otsu thresholding-based segmentation approach. The preprocessed dataset was used to acquire 254 hybrid-feature data for each ROI, which is the combination of the histogram, wavelet, co-occurrence, and run-length features, while 10 optimized hybrid features were selected by employing (probability of error plus average correlation) feature selection technique. For classification, we deployed this optimized hybrid-feature dataset to four ML classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48, using a ten fold cross-validation method. MLP showed an overall accuracy of (95.78% on MRI and 97.44% on CT). Unfortunately, the obtained results were not promising, and there were some limitations due to the different modalities of the dataset. Thereafter, a fusion of MRI and CT-scan datasets generated the fused optimized hybrid-feature dataset. The MLP has shown a promising accuracy of 99% among all the deployed classifiers.

ACS Style

Samreen Naeem; Aqib Ali; Salman Qadri; Wali Khan Mashwani; Nasser Tairan; Habib Shah; Muhammad Fayaz; Farrukh Jamal; Christophe Chesneau; Sania Anam. Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images. Applied Sciences 2020, 10, 3134 .

AMA Style

Samreen Naeem, Aqib Ali, Salman Qadri, Wali Khan Mashwani, Nasser Tairan, Habib Shah, Muhammad Fayaz, Farrukh Jamal, Christophe Chesneau, Sania Anam. Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images. Applied Sciences. 2020; 10 (9):3134.

Chicago/Turabian Style

Samreen Naeem; Aqib Ali; Salman Qadri; Wali Khan Mashwani; Nasser Tairan; Habib Shah; Muhammad Fayaz; Farrukh Jamal; Christophe Chesneau; Sania Anam. 2020. "Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images." Applied Sciences 10, no. 9: 3134.

Journal article
Published: 25 December 2019 in Symmetry
Reads 0
Downloads 0

The standard manufacturing organizations follow certain rules. The highest ubiquitous organizing principles in infrastructure design are modular idea and symmetry, both of which are of the utmost importance. Symmetry is a substantial principle in the manufacturing industry. Symmetrical procedures act as the structural apparatus for manufacturing design. The rapid growth of population needs outstrip infrastructure such as roads, bridges, railway lines, commercial, residential buildings, etc. Numerous underground facilities are also installed to fulfill different requirements of the people. In these facilities one of the most important facility is water supply pipelines. Therefore, it is essential to regularly analyze the water supply pipelines’ risk index in order to escape from economic and human losses. In this paper, we proposed a simplified hierarchical fuzzy logic (SHFL) model to reduce the set of rules. To this end, we have considered four essential factors of water supply pipelines as input to the proposed SHFL model that are: leakage, depth, length and age. Different numbers of membership functions are defined for each factor according to its distribution. The proposed SHFL model takes only 95 rules as compared to the traditional mamdani fuzzy logic method that requires 1225 rules. It is very hard and time consuming for experts to design 1225 rules accurately and precisely. Further, we proposed a Do-it-Yourself (DIY) system for the proposed SHFL method. The purpose of the DIY system is that one can design the FIS model according to his or her need.

ACS Style

Muhammad Fayaz; Quoc Bao Pham; Nguyen Thi Thuy Linh; Pham Thi Thao Nhi; Dao Nguyen Khoi; Muhammad Shuaib Qureshi; Abdul Salam Shah; Shah Khalid; Nguyen-Khoi Dao. A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference. Symmetry 2019, 12, 44 .

AMA Style

Muhammad Fayaz, Quoc Bao Pham, Nguyen Thi Thuy Linh, Pham Thi Thao Nhi, Dao Nguyen Khoi, Muhammad Shuaib Qureshi, Abdul Salam Shah, Shah Khalid, Nguyen-Khoi Dao. A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference. Symmetry. 2019; 12 (1):44.

Chicago/Turabian Style

Muhammad Fayaz; Quoc Bao Pham; Nguyen Thi Thuy Linh; Pham Thi Thao Nhi; Dao Nguyen Khoi; Muhammad Shuaib Qureshi; Abdul Salam Shah; Shah Khalid; Nguyen-Khoi Dao. 2019. "A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference." Symmetry 12, no. 1: 44.

Journal article
Published: 08 November 2019 in International Journal of Modern Education and Computer Science
Reads 0
Downloads 0
ACS Style

Zahid Ullah; Muhammad Fayaz; Su-Hyeon Lee. An Efficient Technique for Optimality Measurement of Approximation Algorithms. International Journal of Modern Education and Computer Science 2019, 11, 13 -21.

AMA Style

Zahid Ullah, Muhammad Fayaz, Su-Hyeon Lee. An Efficient Technique for Optimality Measurement of Approximation Algorithms. International Journal of Modern Education and Computer Science. 2019; 11 (11):13-21.

Chicago/Turabian Style

Zahid Ullah; Muhammad Fayaz; Su-Hyeon Lee. 2019. "An Efficient Technique for Optimality Measurement of Approximation Algorithms." International Journal of Modern Education and Computer Science 11, no. 11: 13-21.