List of Titles

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Assessing nitrate contamination risks in groundwater : a machine learning approach

- Awais, Muhammad, Aslam, Bilal, Maqsoom, Ahsen, Khalil, Umer, Imran, Muhammad

Authors: Awais, Muhammad , Aslam, Bilal , Maqsoom, Ahsen , Khalil, Umer , Imran, Muhammad
Date: 2021
Type: Text , Journal article
Relation: Applied Sciences (Switzerland) Vol. 11, no. 21 (2021), p.
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Description: Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**

Cloud based secure service providing for IoTs using blockchain

- Rehman, Mubariz, Javaid, Nadeem, Awais, Muhammad, Imran, Muhammad, Naseer, Nidal

Authors: Rehman, Mubariz , Javaid, Nadeem , Awais, Muhammad , Imran, Muhammad , Naseer, Nidal
Date: 2019
Type: Text , Conference paper
Relation: 2019 IEEE Global Communications Conference, GLOBECOM 2019, Waikoloa, 9-13 December 2019
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Description: Internet of Things (IoTs) is widely growing domain of the modern era. With the advancement in technologies, the use of IoTs devices also increases. However, security risks regarding service provisioning and data sharing also increases. There are many existing security approaches. However, these approaches are not suitable for IoTs devices due to their limited storage and computation resources. These secure approaches also require a specific hardware. With the invention of blockchain technologies, many security risks are eliminated. Blockchain also supports data sharing mechanism. In this paper, we proposed a secure service providing mechanism for IoTs using blockchain. We introduced cloud nodes for maintaining the validity states of edge service providers. The edge node reputation is considered as a service rating given by end users. Incentive is given to edge servers after validation of service codes. Incentive is in the form of cryptocurrency. Incentive and edge node reputation values are stored in cloud node and are updated with respect to time. Smart contract is proposed to check the validity state of the edge servers. Smart contract is also used for the comparison and verification of the service codes provided by edge servers. In our proposed system, we perform service authentication at both cloud and edge server layer. Moreover, Proof of Authority (PoA) is used as a consensus mechanism. PoA enhanced overall performance of our proposed system. By experimental analysis, it is shown that our proposed model is suitable for resource constrained devices. © 2019 IEEE.

A blockchain model for fair data sharing in deregulated smart grids

- Samuel, Omaji, Javaid, Nadeem, Awais, Muhammad, Ahmed, Zeeshan, Imran, Muhammad, Guizani, Mohsen

Authors: Samuel, Omaji , Javaid, Nadeem , Awais, Muhammad , Ahmed, Zeeshan , Imran, Muhammad , Guizani, Mohsen
Date: 2019
Type: Text , Conference paper
Relation: 2019 IEEE Global Communications Conference, GLOBECOM 2019, Waikoloa 9-13 December 2019
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Description: The emergence of smart home appliances has generated a high volume of data on smart meters belonging to different customers. However, customers can not share their data in deregulated smart grids due to privacy concern. Although, these data are important for the service provider in order to provide an efficient service. To encourage the customers' participation, this paper proposes an access control mechanism by fairly compensating customers for their participation in data sharing via blockchain using the concept of differential privacy. We addressed the computational issues of existing ethereum blockchain by proposing a proof of authority consensus protocol through the Pagerank mechanism in order to derive the reputation scores. Experimental results show the efficiency of the proposed model to minimize privacy risk, and maximize aggregator's profit. In addition, gas consumption, as well as the cost of the computational resources, is reduced. © 2019 IEEE.

Exploiting energy efficient routing protocols for void hole alleviation in IoT enabled underwater WSN

- Awais, Muhammad, Javaid, Nadeem, Naseer, Nidal, Imran, Muhammad

Authors: Awais, Muhammad , Javaid, Nadeem , Naseer, Nidal , Imran, Muhammad
Date: 2019
Type: Text , Conference paper
Relation: 15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019, Tangier, 24 to 28 June 2019 p. 1797-1802
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Description: In recent times, different routing protocols have been proposed in the Internet of Things enabled Underwater Wireless Sensor Networks (IoT-UWSNs) to explore the underwater environment for different purposes, i.e., scientific and military purposes. However, high Energy Consumption (EC), End to End (E2E) delay, low Packet Delivery Ratio (PDR) and minimum network lifetime make the energy efficient communication a challenging task in Underwater Wireless Sensor Network (UWSN). The high E2E delay, EC and reliable data delivery are the critical issues, which play an important role to enhance the network throughput. So, this paper presents two energy efficient routing protocols namely: Shortest Path-Collision avoidance Based Energy Efficient Routing (SP-CBE2R) protocol and Improved-Collision avoidance Based Energy Efficient Routing (Im-CBE2R) protocol. At this end, both routing protocols minimize the probability of void hole occurrence and in return minimizes the EC and E2E delay. In both routing protocols, courier nodes are positioned at different strategic locations to keep the greedy forwarding continuous. The proposed routing protocols are also analyzed by varying the Packet Size (PS) and Data Rate (DR). Additionally, various simulations have been performed to authenticate the proposed routing protocols. Simulation results show that the proposed routing protocols outperform the baseline routing protocols in counterparts. © 2019 IEEE.

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Treating class imbalance in non-technical loss detection : an exploratory analysis of a real dataset

- Ghori, Khawaja, Awais, Muhammad, Khattak, Akmal, Imran, Muhammad, Amin, Fazal, Szathmary, Laszlo

Authors: Ghori, Khawaja , Awais, Muhammad , Khattak, Akmal , Imran, Muhammad , Amin, Fazal , Szathmary, Laszlo
Date: 2021
Type: Text , Journal article
Relation: IEEE Access Vol. 9, no. (2021), p. 98928-98938
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Description: Non-Technical Loss (NTL) is a significant concern for many electric supply companies due to the financial impact caused as a result of suspect consumption activities. A range of machine learning classifiers have been tested across multiple synthesized and real datasets to combat NTL. An important characteristic that exists in these datasets is the imbalance distribution of the classes. When the focus is on predicting the minority class of suspect activities, the classifiers' sensitivity to the class imbalance becomes more important. In this paper, we evaluate the performance of a range of classifiers with under-sampling and over-sampling techniques. The results are compared with the untreated imbalanced dataset. In addition, we compare the performance of the classifiers using penalized classification model. Lastly, the paper presents an exploratory analysis of using different sampling techniques on NTL detection in a real dataset and identify the best performing classifiers. We conclude that logistic regression is the most sensitive to the sampling techniques as the change of its recall is measured around 50% for all sampling techniques. While the random forest is the least sensitive to the sampling technique, the difference in its precision is observed between 1% - 6% for all sampling techniques. © 2013 IEEE.

Impact of feature selection on non-technical loss detection

- Ghori, Khawaja, Rabeeh Ayaz, Abbasi, Awais, Muhammad, Imran, Muhammad, Ullah, Atta, Szathmary, Laszlo

Authors: Ghori, Khawaja , Rabeeh Ayaz, Abbasi , Awais, Muhammad , Imran, Muhammad , Ullah, Atta , Szathmary, Laszlo
Date: 2020
Type: Text , Conference paper
Relation: 6th Conference on Data Science and Machine Learning Applications, CDMA 2020, Riyadh, Saudi Arabia, 4 to 5 March 2020, Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020 p. 19-24
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Description: Over the years, many countries have faced huge financial deficits due to Non-Technical Loss (NTL) in power sector. There are many ways of attempting to illegal use of electricity like by-passing and reversing meters. There have been many attempts to bring down NTL using manual and automated techniques. Manual NTL detection is not proving fruitful as it incurs heavy costs and has a low hit ratio. Due to the shortcoming of manual NTL detection, automated detection of NTL using machine learning classifiers is gaining attention in the research community. The datasets containing NTL belong to the class imbalance domain where regular consumers (negative class) out weight the representation of irregular consumers (positive class). To identify the right number of representative records, many techniques are proposed but selecting the right features in deciding NTL is equally an important task where not much has been contributed to the literature. In this paper, we propose the Incremental Feature Selection (IFS) algorithm which first uses feature importance to identify the most relevant features for NTL detection and then these features are used to test three classifiers namely CatBoost, Decision Tree (DT) Classifier and K-Nearest Neighbors (KNN) for NTL detection. This way, we have not only identified the most relevant features for NTL detection in a real dataset but also have brought down the overall computation time of the classifiers. Moreover, our proposed framework is tested on three performance evaluation metrics used in imbalance domain. The results show that using the most relevant features identified by the IFS algorithm, the three classifiers have the same or slightly better efficiency as compared to using all features. © 2020 IEEE.

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Performance analysis of different types of machine learning classifiers for non-technical loss detection

- Ghori, Khawaja, Abbasi, Rabeeh, Awais, Muhammad, Imran, Muhammad, Ullah, Ata, Szathmary, Laszlo

Authors: Ghori, Khawaja , Abbasi, Rabeeh , Awais, Muhammad , Imran, Muhammad , Ullah, Ata , Szathmary, Laszlo
Date: 2020
Type: Text , Journal article
Relation: IEEE Access Vol. 8, no. (2020), p. 16033-16048
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Description: With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection. © 2013 IEEE.

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Establishing effective communications in disaster affected areas and artificial intelligence based detection using social media platform

- Raza, Mohsin, Awais, Muhammad, Ali, Kamran, Aslam, Nauman, Paranthaman, Vishnu, Imran, Muhammad, Ali, Farman

Authors: Raza, Mohsin , Awais, Muhammad , Ali, Kamran , Aslam, Nauman , Paranthaman, Vishnu , Imran, Muhammad , Ali, Farman
Date: 2020
Type: Text , Journal article
Relation: Future Generation Computer Systems Vol. 112, no. (2020), p. 1057-1069
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Description: Floods, earthquakes, storm surges and other natural disasters severely affect the communication infrastructure and thus compromise the effectiveness of communications dependent rescue and warning services. In this paper, a user centric approach is proposed to establish communications in disaster affected and communication outage areas. The proposed scheme forms ad hoc clusters to facilitate emergency communications and connect end-users/ User Equipment (UE) to the core network. A novel cluster formation with single and multi-hop communication framework is proposed. The overall throughput in the formed clusters is maximized using convex optimization. In addition, an intelligent system is designed to label different clusters and their localities into affected and non-affected areas. As a proof of concept, the labeling is achieved on flooding dataset where region specific social media information is used in proposed machine learning techniques to classify the disaster-prone areas as flooded or unflooded. The suitable results of the proposed machine learning schemes suggest its use along with proposed clustering techniques to revive communications in disaster affected areas and to classify the impact of disaster for different locations in disaster-prone areas. © 2020 Elsevier B.V.

Intelligent IoT framework for indoor healthcare monitoring of Parkinson's disease patient

- Raza, Mohsina, Awais, Muhammad, Singh, Nishant, Imran, Muhammad, Hussain, Sajjad

Authors: Raza, Mohsina , Awais, Muhammad , Singh, Nishant , Imran, Muhammad , Hussain, Sajjad
Date: 2021
Type: Text , Journal article
Relation: IEEE Journal on Selected Areas in Communications Vol. 39, no. 2 (2021), p. 593-602
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Description: Parkinson's disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson's patients to be $21,482, with an additional $29,695 burden to society. Due to the high stakes and rapidly rising Parkinson's patients' count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient's conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson's over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson's progression. © 1983-2012 IEEE.

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