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This paper introduces a novel Energy Efficient Mobility-Based Watchman Algorithm (E2-MBWA) to intensification packet delivery ratio of mitigating the Hotspot issue in Wireless Sensor and Actor Networks (WSAN). Hotspot issue mostly causes of network breakdown and decrease of data packet delivery. Therefore, it is required to design a new technique for data packet forwarding that can resolve these issues in the network. In this study, E2-MBWA has introduced, that cope with the layer-by-layer mechanism for data packet forwarding. The proposed algorithm works with the help of the Data Packet Forwarding Algorithm (DFPA) and Watchman Layer Update Mechanism (WLUM). Furthermore, it also rescues the data storage issues, for this, used secondary nods as substitutes. Moreover, proposed technique is compared with some latest baseline’s approaches, for example, Efficient Traffic Load Reduction Algorithm (ETLRA). The analytical energy model is also described for the best health of the network to measure the accuracy level of the Hotspot issue.
Umar Draz; Tariq Ali; Sana Yasin; Sarah Bukhari; Muhammad Salman Khan; Mohammed Hamdi; Saifur Rahman; Low Tang Jung; Amjad Ali. An Optimal Scheme for UWSAN of Hotspots Issue Based on Energy-Efficient Novel Watchman Nodes. Wireless Personal Communications 2021, 1 -26.
AMA StyleUmar Draz, Tariq Ali, Sana Yasin, Sarah Bukhari, Muhammad Salman Khan, Mohammed Hamdi, Saifur Rahman, Low Tang Jung, Amjad Ali. An Optimal Scheme for UWSAN of Hotspots Issue Based on Energy-Efficient Novel Watchman Nodes. Wireless Personal Communications. 2021; ():1-26.
Chicago/Turabian StyleUmar Draz; Tariq Ali; Sana Yasin; Sarah Bukhari; Muhammad Salman Khan; Mohammed Hamdi; Saifur Rahman; Low Tang Jung; Amjad Ali. 2021. "An Optimal Scheme for UWSAN of Hotspots Issue Based on Energy-Efficient Novel Watchman Nodes." Wireless Personal Communications , no. : 1-26.
In the task of data routing in Internet of Things enabled volatile underwater environments, providing better transmission and maximizing network communication performance are always challenging. Many network issues such as void holes and network isolation occur because of long routing distances between nodes. Void holes usually occur around the sink because nodes die early due to the high energy consumed to forward packets sent and received from other nodes. These void holes are a major challenge for I‐UWSANs and cause high end‐to‐end delay, data packet loss, and energy consumption. They also affect the data delivery ratio. Hence, this paper presents an energy efficient watchman based flooding algorithm to address void holes. First, the proposed technique is formally verified by the Z‐Eves toolbox to ensure its validity and correctness. Second, simulation is used to evaluate the energy consumption, packet loss, packet delivery ratio, and throughput of the network. The results are compared with well‐known algorithms like energy‐aware scalable reliable and void‐hole mitigation routing and angle based flooding. The extensive results show that the proposed algorithm performs better than the benchmark techniques.
Umar Draz; Tariq Ali; Nazir Ahmad Zafar; Abdullah Saeed Alwadie; Muhammad Irfan; Sana Yasin; Amjad Ali; Muazzam A. Khan Khattak. Energy efficient watchman based flooding algorithm for IoT‐enabled underwater wireless sensor and actor networks. ETRI Journal 2021, 43, 414 -426.
AMA StyleUmar Draz, Tariq Ali, Nazir Ahmad Zafar, Abdullah Saeed Alwadie, Muhammad Irfan, Sana Yasin, Amjad Ali, Muazzam A. Khan Khattak. Energy efficient watchman based flooding algorithm for IoT‐enabled underwater wireless sensor and actor networks. ETRI Journal. 2021; 43 (3):414-426.
Chicago/Turabian StyleUmar Draz; Tariq Ali; Nazir Ahmad Zafar; Abdullah Saeed Alwadie; Muhammad Irfan; Sana Yasin; Amjad Ali; Muazzam A. Khan Khattak. 2021. "Energy efficient watchman based flooding algorithm for IoT‐enabled underwater wireless sensor and actor networks." ETRI Journal 43, no. 3: 414-426.
Digital communication based on the conversion of data which only possible through digital conversion techniques. From the last couple of years, many digital conversion schemes have been introduced in the field of communication. All designed approaches convert data bits into a signal using various line code waveforms. This type of conversion has various kinds of issues such as synchronization between sender and receivers, bandwidth utilization, direct current components and power spectrum density (PSD) that need the attention of researchers. Although all these issues need a proper solution, PSD is a major concern in digital communication. This paper presents a newly designed scheme with the name of Shadow Encoding Scheme (SES) to transmit data bits efficiently by using physical waveform. SES provides a reliable transmission over the physical medium without using extra bandwidth and ideal power spectrum density (PSD) with the help of a shadow copy of the same bitstream which is being transmitted previously over the network. The SES is validated by mathematical equations which are used to calculate PSD and MATLAB simulator is used to simulate SES. The proposed SES is compared with other state-of-the-art line code techniques. The results show that SES performed well in PSD and bandwidth utilization as compared to other benchmark techniques. The coordinates of PSD are also presented in a tabular form which shows a vivid picture of the working condition of various line codes.
Tariq Ali; Abdul Rasheed Razwan; Imran Baig; Muhammad Irfan; Umar Draz. Efficient Shadow Encoding Scheme Towards Power Spectrum Density in Digital Network Communication. Wireless Personal Communications 2021, 119, 3179 -3206.
AMA StyleTariq Ali, Abdul Rasheed Razwan, Imran Baig, Muhammad Irfan, Umar Draz. Efficient Shadow Encoding Scheme Towards Power Spectrum Density in Digital Network Communication. Wireless Personal Communications. 2021; 119 (4):3179-3206.
Chicago/Turabian StyleTariq Ali; Abdul Rasheed Razwan; Imran Baig; Muhammad Irfan; Umar Draz. 2021. "Efficient Shadow Encoding Scheme Towards Power Spectrum Density in Digital Network Communication." Wireless Personal Communications 119, no. 4: 3179-3206.
The amazing fusion of the internet of things (IoT) into traditional health monitoring systems has produced remarkable advances in the field of e-health. Different wireless body area network devices and sensors are providing real-time health monitoring services. As the number of IoT devices is rapidly booming, technological and security challenges are also rising day by day. The data generated from sensor-based devices need confidentiality, integrity, authenticity, and end-to-end security for safe communication over the public network. IoT-based health monitoring systems work in a layered manner, comprising a perception layer, a network layer, and an application layer. Each layer has some security, and privacy concerns that need to be addressed accordingly. A lot of research has been conducted to resolve these security issues in different domains of IoT. Several frameworks for the security of IoT-based e-health systems have also been developed. This paper introduces a security framework for real-time health monitoring systems to ensure data confidentiality, integrity, and authenticity by using two common IoT protocols, namely constrained application protocol (CoAP) and message query telemetry transports (MQTT). This security framework aims to defend sensor data against the security loopholes while it is continuously transmitting over the layers and uses hypertext transfer protocols (HTTPs) for this purpose. As a result, it shields from the breach with a very low ratio of risk. The methodology of this paper focuses on how the security framework of IoT-based real-time health systems is protected under the tiers of CoAP and HTTPs. CoAP works alongside HTTPs and is responsible for providing end-to-end security solutions.
Aamir Hussain; Tariq Ali; Faisal Althobiani; Umar Draz; Muhammad Irfan; Sana Yasin; Saher Shafiq; Zanab Safdar; Adam Glowacz; Grzegorz Nowakowski; Muhammad Khan; Samar Alqhtani. Security Framework for IoT Based Real-Time Health Applications. Electronics 2021, 10, 719 .
AMA StyleAamir Hussain, Tariq Ali, Faisal Althobiani, Umar Draz, Muhammad Irfan, Sana Yasin, Saher Shafiq, Zanab Safdar, Adam Glowacz, Grzegorz Nowakowski, Muhammad Khan, Samar Alqhtani. Security Framework for IoT Based Real-Time Health Applications. Electronics. 2021; 10 (6):719.
Chicago/Turabian StyleAamir Hussain; Tariq Ali; Faisal Althobiani; Umar Draz; Muhammad Irfan; Sana Yasin; Saher Shafiq; Zanab Safdar; Adam Glowacz; Grzegorz Nowakowski; Muhammad Khan; Samar Alqhtani. 2021. "Security Framework for IoT Based Real-Time Health Applications." Electronics 10, no. 6: 719.
COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.
Muhammad Irfan; Muhammad Iftikhar; Sana Yasin; Umar Draz; Tariq Ali; Shafiq Hussain; Sarah Bukhari; Abdullah Alwadie; Saifur Rahman; Adam Glowacz; Faisal Althobiani. Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19. International Journal of Environmental Research and Public Health 2021, 18, 3056 .
AMA StyleMuhammad Irfan, Muhammad Iftikhar, Sana Yasin, Umar Draz, Tariq Ali, Shafiq Hussain, Sarah Bukhari, Abdullah Alwadie, Saifur Rahman, Adam Glowacz, Faisal Althobiani. Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19. International Journal of Environmental Research and Public Health. 2021; 18 (6):3056.
Chicago/Turabian StyleMuhammad Irfan; Muhammad Iftikhar; Sana Yasin; Umar Draz; Tariq Ali; Shafiq Hussain; Sarah Bukhari; Abdullah Alwadie; Saifur Rahman; Adam Glowacz; Faisal Althobiani. 2021. "Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19." International Journal of Environmental Research and Public Health 18, no. 6: 3056.
This paper presents a novel approach for prolonging the network lifetime and reducing the energy being consumed within the wireless sensor networks (WSN). The sensor nodes are distributed in the geographical area in a heterogeneous manner and spread in an advanced and normal mode with high and low energy, respectively. All sensor nodes are organized in groups or clusters. Every cluster has a cluster head (CH) node, and every CH gathers the information from all the sensor nodes, which will be communicated to the base station (BS). Sensor nodes are positioned with initial energy. The network's lifetime and consumed energy are both essential parameters in contrast with the existing routing protocols. It has been accomplished by choosing the proper cluster heads. The mathematical model and simulation results obtained on MATLAB 2015b show a significant fall of the total energy being consumed, now standing at 35%, with the lifetime of the network having been enhanced by 80% as compared to LEACH, Modified-LEACH, and SEP protocol.
Nitin Kumar; Vinod Kumar; Tariq Ali; Muhammad Ayaz. Prolong Network Lifetime in the Wireless Sensor Networks: An Improved Approach. Arabian Journal for Science and Engineering 2021, 46, 3631 -3651.
AMA StyleNitin Kumar, Vinod Kumar, Tariq Ali, Muhammad Ayaz. Prolong Network Lifetime in the Wireless Sensor Networks: An Improved Approach. Arabian Journal for Science and Engineering. 2021; 46 (4):3631-3651.
Chicago/Turabian StyleNitin Kumar; Vinod Kumar; Tariq Ali; Muhammad Ayaz. 2021. "Prolong Network Lifetime in the Wireless Sensor Networks: An Improved Approach." Arabian Journal for Science and Engineering 46, no. 4: 3631-3651.
In the notion of communication system resource provision specifically, beam-forming is a concept of proficient utilization of the power of transmission. Network densification and massive MIMO allows us to control the power efficiency and can be effectively distributed among different users by reducing cost. We presented a practical scenario for the performance of massive MIMO and multi-small cell system to analyze the overall performance of the system. Our work is based on the resource allocation with optimal structural constraints to maintain the cost effectiveness while considering economic implications. The base stations located far away from the users receive attenuated signals and give rise to path loss, whereas the problems of inter cell interference also arise due to transmission from a base station to others cells. The performance of the cellular system can be enhanced with the combination of massive Mimo and small cells, where we simulate and also provide an analysis on practical system with optimal and low complexity beam-forming. The proposed scenario illustrates a structure with an optimal linear transmit beamforming regarding an efficient number of parameters to not lose optimality, which is extendable to designate any specific cellular network in consideration. Our approach exploited schemes with low complexity that are facilitating in complete solution formation, and tested them in various and all possible cases and scenarios.
Zahid Qaisar; Muhamamd Irfan; Tariq Ali; Ashfaq Ahmad; Ghulam Ali; Adam Glowacz; Witold Glowacz; Wahyu Caesarendra; Aisha Mashraqi; Umar Draz; Shafiq Hussain. Effective Beamforming Technique Amid Optimal Value for Wireless Communication. Electronics 2020, 9, 1869 .
AMA StyleZahid Qaisar, Muhamamd Irfan, Tariq Ali, Ashfaq Ahmad, Ghulam Ali, Adam Glowacz, Witold Glowacz, Wahyu Caesarendra, Aisha Mashraqi, Umar Draz, Shafiq Hussain. Effective Beamforming Technique Amid Optimal Value for Wireless Communication. Electronics. 2020; 9 (11):1869.
Chicago/Turabian StyleZahid Qaisar; Muhamamd Irfan; Tariq Ali; Ashfaq Ahmad; Ghulam Ali; Adam Glowacz; Witold Glowacz; Wahyu Caesarendra; Aisha Mashraqi; Umar Draz; Shafiq Hussain. 2020. "Effective Beamforming Technique Amid Optimal Value for Wireless Communication." Electronics 9, no. 11: 1869.
The router plays an important role in communication among different processing cores in on-chip networks. Technology scaling on one hand has enabled the designers to integrate multiple processing components on a single chip; on the other hand, it becomes the reason for faults. A generic router consists of the buffers and pipeline stages. A single fault may result in an undesirable situation of degraded performance or a whole chip may stop working. Therefore, it is necessary to provide permanent fault tolerance to all the components of the router. In this paper, we propose a mechanism that can tolerate permanent faults that occur in the router. We exploit the fault-tolerant techniques of resource sharing and paring between components for the input port unit and routing computation (RC) unit, the resource borrowing for virtual channel allocator (VA) and multiple paths for switch allocator (SA) and crossbar (XB). The experimental results and analysis show that the proposed mechanism enhances the reliability of the router architecture towards permanent faults at the cost of 29% area overhead. The proposed router architecture achieves the highest Silicon Protection Factor (SPF) metric, which is 24.8 as compared to the state-of-the-art fault-tolerant architectures. It incurs an increase in latency for SPLASH2 and PARSEC benchmark traffics, which is minimal as compared to the baseline router.
Ayaz Hussain; Muhammad Irfan; Naveed Khan Baloch; Umar Draz; Tariq Ali; Adam Glowacz; Larisa Dunai; Jose Antonino-Daviu. Savior: A Reliable Fault Resilient Router Architecture for Network-on-Chip. Electronics 2020, 9, 1783 .
AMA StyleAyaz Hussain, Muhammad Irfan, Naveed Khan Baloch, Umar Draz, Tariq Ali, Adam Glowacz, Larisa Dunai, Jose Antonino-Daviu. Savior: A Reliable Fault Resilient Router Architecture for Network-on-Chip. Electronics. 2020; 9 (11):1783.
Chicago/Turabian StyleAyaz Hussain; Muhammad Irfan; Naveed Khan Baloch; Umar Draz; Tariq Ali; Adam Glowacz; Larisa Dunai; Jose Antonino-Daviu. 2020. "Savior: A Reliable Fault Resilient Router Architecture for Network-on-Chip." Electronics 9, no. 11: 1783.
Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.
Ghulam Ali; Tariq Ali; Muhammad Irfan; Umar Draz; Muhammad Sohail; Adam Glowacz; Maciej Sulowicz; Ryszard Mielnik; Zaid Bin Faheem; Claudia Martis. IoT Based Smart Parking System Using Deep Long Short Memory Network. Electronics 2020, 9, 1696 .
AMA StyleGhulam Ali, Tariq Ali, Muhammad Irfan, Umar Draz, Muhammad Sohail, Adam Glowacz, Maciej Sulowicz, Ryszard Mielnik, Zaid Bin Faheem, Claudia Martis. IoT Based Smart Parking System Using Deep Long Short Memory Network. Electronics. 2020; 9 (10):1696.
Chicago/Turabian StyleGhulam Ali; Tariq Ali; Muhammad Irfan; Umar Draz; Muhammad Sohail; Adam Glowacz; Maciej Sulowicz; Ryszard Mielnik; Zaid Bin Faheem; Claudia Martis. 2020. "IoT Based Smart Parking System Using Deep Long Short Memory Network." Electronics 9, no. 10: 1696.
Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated, and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we propose a framework, in which deep learning and supervised machine learning techniques are implemented for electricity-load forecasting. A three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features’ importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machines (SVM) and a hybrid of Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than the State-Of-The-Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performs well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques.
Nasir Ayub; Muhammad Irfan; Muhammad Awais; Usman Ali; Tariq Ali; Mohammed Hamdi; Abdullah Alghamdi; Fazal Muhammad. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. Energies 2020, 13, 5193 .
AMA StyleNasir Ayub, Muhammad Irfan, Muhammad Awais, Usman Ali, Tariq Ali, Mohammed Hamdi, Abdullah Alghamdi, Fazal Muhammad. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. Energies. 2020; 13 (19):5193.
Chicago/Turabian StyleNasir Ayub; Muhammad Irfan; Muhammad Awais; Usman Ali; Tariq Ali; Mohammed Hamdi; Abdullah Alghamdi; Fazal Muhammad. 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler." Energies 13, no. 19: 5193.
Glaucoma, an eye disease, occurs due to Retinal damages and it is an ordinary cause of blindness. Most of the available examining procedures are too long and require manual instructions to use them. In this work, we proposed a multi-level deep convolutional neural network (ML-DCNN) architecture on retinal fundus images to diagnose glaucoma. We collected a retinal fundus images database from the local hospital. The fundus images are pre-processed by an adaptive histogram equalizer to reduce the noise of images. The ML-DCNN architecture is used for features extraction and classification into two phases, one for glaucoma detection known as detection-net and the second one is classification-net used for classification of affected retinal glaucoma images into three different categories: Advanced, Moderate and Early. The proposed model is tested on 1338 retinal glaucoma images and performance is measured in the form of different statistical terms known as sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRE). On average, SE of 97.04%, SP of 98.99%, ACC of 99.39%, and PRC of 98.2% are achieved. The obtained outcomes are comparable to the state-of-the-art systems and achieved competitive results to solve the glaucoma eye disease problems for complex glaucoma eye disease cases.
Muhammad Aamir; Muhammad Irfan; Tariq Ali; Ghulam Ali; Ahmad Shaf; Alqahtani Saeed S; Ali Al-Beshri; Tariq Alasbali; Mater H. Mahnashi. An Adoptive Threshold-Based Multi-Level Deep Convolutional Neural Network for Glaucoma Eye Disease Detection and Classification. Diagnostics 2020, 10, 602 .
AMA StyleMuhammad Aamir, Muhammad Irfan, Tariq Ali, Ghulam Ali, Ahmad Shaf, Alqahtani Saeed S, Ali Al-Beshri, Tariq Alasbali, Mater H. Mahnashi. An Adoptive Threshold-Based Multi-Level Deep Convolutional Neural Network for Glaucoma Eye Disease Detection and Classification. Diagnostics. 2020; 10 (8):602.
Chicago/Turabian StyleMuhammad Aamir; Muhammad Irfan; Tariq Ali; Ghulam Ali; Ahmad Shaf; Alqahtani Saeed S; Ali Al-Beshri; Tariq Alasbali; Mater H. Mahnashi. 2020. "An Adoptive Threshold-Based Multi-Level Deep Convolutional Neural Network for Glaucoma Eye Disease Detection and Classification." Diagnostics 10, no. 8: 602.
Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.
Ayaz Hussain; Umar Draz; Tariq Ali; Saman Tariq; Muhammad Irfan; Adam Glowacz; Jose Alfonso Antonino Daviu; Sana Yasin; Saifur Rahman. Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach. Energies 2020, 13, 3930 .
AMA StyleAyaz Hussain, Umar Draz, Tariq Ali, Saman Tariq, Muhammad Irfan, Adam Glowacz, Jose Alfonso Antonino Daviu, Sana Yasin, Saifur Rahman. Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach. Energies. 2020; 13 (15):3930.
Chicago/Turabian StyleAyaz Hussain; Umar Draz; Tariq Ali; Saman Tariq; Muhammad Irfan; Adam Glowacz; Jose Alfonso Antonino Daviu; Sana Yasin; Saifur Rahman. 2020. "Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach." Energies 13, no. 15: 3930.
The human face has a great accumulation and a diversity of facial expressions. It explores the feelings of a person and can be used to judge the emotional intents of the person to a certain level. By using facial detection and recognition systems, varieties of applications are working in computer vision, surveillance system, security, authentication, or verification of a person and home automation system based on digital image processing with the help of the Internet of Things. The state of the art in these applications is to detect expressions with their intensity level. It is an attention-grabbing problem due to the complex nature of facial features, which is associated with emotions. For that purpose, it is essential to develop an innovative deep learning model to detect and estimate the facial expression intensity level. To do this, a multi-level deep convolutional neural network is proposed to recognize facial expression and their intensity level. At the first level, Expression-Net classifies face expressions, and at the second level, Intensity-Net estimates the intensity of the facial expression. Evaluation of the proposed model for facial expression recognition and intensity estimation is carried out by using the extended Cohn–Kanade and Japanese Female Facial Expression datasets. The proposed method shows an outstanding performance in terms of accuracy of 98.8% and 97.7% for both the datasets as compared to state-of-the-art techniques.
Muhammad Aamir; Tariq Ali; Ahmad Shaf; Muhammad Irfan; Muhammad Qaiser Saleem. ML-DCNNet: Multi-level Deep Convolutional Neural Network for Facial Expression Recognition and Intensity Estimation. Arabian Journal for Science and Engineering 2020, 45, 10605 -10620.
AMA StyleMuhammad Aamir, Tariq Ali, Ahmad Shaf, Muhammad Irfan, Muhammad Qaiser Saleem. ML-DCNNet: Multi-level Deep Convolutional Neural Network for Facial Expression Recognition and Intensity Estimation. Arabian Journal for Science and Engineering. 2020; 45 (12):10605-10620.
Chicago/Turabian StyleMuhammad Aamir; Tariq Ali; Ahmad Shaf; Muhammad Irfan; Muhammad Qaiser Saleem. 2020. "ML-DCNNet: Multi-level Deep Convolutional Neural Network for Facial Expression Recognition and Intensity Estimation." Arabian Journal for Science and Engineering 45, no. 12: 10605-10620.
Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques.
Waqas Ahmad; Nasir Ayub; Tariq Ali; Muhammad Irfan; Muhammad Awais; Muhammad Shiraz; Adam Glowacz. Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine. Energies 2020, 13, 2907 .
AMA StyleWaqas Ahmad, Nasir Ayub, Tariq Ali, Muhammad Irfan, Muhammad Awais, Muhammad Shiraz, Adam Glowacz. Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine. Energies. 2020; 13 (11):2907.
Chicago/Turabian StyleWaqas Ahmad; Nasir Ayub; Tariq Ali; Muhammad Irfan; Muhammad Awais; Muhammad Shiraz; Adam Glowacz. 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine." Energies 13, no. 11: 2907.
Increasing waste generation has become a significant challenge in developing countries due to unprecedented population growth and urbanization. From the literature, many issues have been investigated that signify direct connection with the increase in waste material generation and related difficulties to handle it in a smart city. These issues are the resultants of an improper collection and disposal mechanism used for waste material, the increase in moving trends of peoples toward big cities and lack of intelligent technology used to support the municipal solid waste management system. Consequently, the management of waste material has become a challenge due to a large amount of waste littered everywhere. Furthermore, various problems also occur due to the existing systems that are not only inadequate and inefficient but also their non-scientific procedures involved in the solid waste management. In this paper, an IoT-based smart waste bin monitoring and municipal solid waste management system is proposed. This system helps to solve the problems associated with management of waste material and the IoT-based waste collection for the smart city as discussed above. The proposed system is capable in the collection of waste effectively, detection of fire in waste material and forecasting of the future waste generation. The IoT-based device performs the controlling and monitoring of the electric bins. These devices are wirelessly connected with the central hub to transmit the information about the bins filling level with the existing location. The significant advantage of the system is to collect waste material on time in order to avoid the overflow of bins that would help in saving the environment from pollution.
Tariq Ali; Muhammad Irfan; Abdullah Saeed Alwadie; Adam Glowacz. IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities. Arabian Journal for Science and Engineering 2020, 45, 10185 -10198.
AMA StyleTariq Ali, Muhammad Irfan, Abdullah Saeed Alwadie, Adam Glowacz. IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities. Arabian Journal for Science and Engineering. 2020; 45 (12):10185-10198.
Chicago/Turabian StyleTariq Ali; Muhammad Irfan; Abdullah Saeed Alwadie; Adam Glowacz. 2020. "IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities." Arabian Journal for Science and Engineering 45, no. 12: 10185-10198.
Timely partition of the whole network is extremely difficult task in dynamic large-scale wireless sensor network (WSN). A lot of existing technique that solved this issue with maintaining the network status and relevant information, but these techniques do not provide the proper validation and verification and completely depend upon the simulation. Due to the distributed and heterogeneous nature of WSN, management of such environment is highly complex. The dynamic self-configuring behavior of the nodes and scalable nature of WSN may cause critical issues, like hotspot, power consumption, unnecessary delays, throughput and network lifetime. This paper, therefore, presents the Subnet Based Hotspot Algorithm (SBHA) that not only discus the strategy of network division in the form of subnets but also provide the detail verification proof of correctness. By doing so, routing path towards sink nodes become small in size that reduces the traffic load at the neighboring nodes of the sink. As a result, nodes around the sink will not early depreciate hence the chances of hotspot occurrence will be reduced, ultimately network lifetime will be increased. Firstly, we analyze SBHA with detail formal specifications in order to validate and verify the performance of proposed algorithm with VDM-SL tool box, after this we simulate the SBHA to demonstrate its accuracy and efficiency. The results analysis shows that the E2E delay and network lifetime of SBHA is comparatively 50% and 75% higher than the EE-CBA, while the energy consumption ration for 600 number of nodes consumed 750J by SBHA and 850J by EE-CBA.
Tariq Ali; Sana Yasin; Umar Draz; Muhammad Ayaz. Towards Formal Modeling of Subnet Based Hotspot Algorithm in Wireless Sensor Networks. Wireless Personal Communications 2019, 107, 1573 -1606.
AMA StyleTariq Ali, Sana Yasin, Umar Draz, Muhammad Ayaz. Towards Formal Modeling of Subnet Based Hotspot Algorithm in Wireless Sensor Networks. Wireless Personal Communications. 2019; 107 (4):1573-1606.
Chicago/Turabian StyleTariq Ali; Sana Yasin; Umar Draz; Muhammad Ayaz. 2019. "Towards Formal Modeling of Subnet Based Hotspot Algorithm in Wireless Sensor Networks." Wireless Personal Communications 107, no. 4: 1573-1606.
This paper deals with the relationship between pre-learning stress, long term memory, and EEG signals in the brain. Studying the effect of stress is very important especially in academic life for the students. Nowadays; there have been many recent methods evaluating the relationship between stress, learning and memory performance based on different techniques. The most common methods are conducted based on the biological response. Some of these methods have assessed the impact of stress based on biochemical effects by measuring specific hormones such as cortisol, adrenalin and glucocorticoids, or based on physiological effects such as blood pressure, heart rate, skin temperature. However, in all these methods, there are inconsistent findings due to the instability of hormones and a large number of related factors. The aim of this research is to discover the impact of pre-learning stress on long-term memory retrieval using EEG signals. The results indicate that there is a relationship between theta rhythm in the temporal lobe and long-term memory retrieval.
Omar Alshorman; Tariq Ali; Muhammad Irfan. EEG Analysis for Pre-learning Stress in the Brain. Communications in Computer and Information Science 2017, 447 -455.
AMA StyleOmar Alshorman, Tariq Ali, Muhammad Irfan. EEG Analysis for Pre-learning Stress in the Brain. Communications in Computer and Information Science. 2017; ():447-455.
Chicago/Turabian StyleOmar Alshorman; Tariq Ali; Muhammad Irfan. 2017. "EEG Analysis for Pre-learning Stress in the Brain." Communications in Computer and Information Science , no. : 447-455.
Providing better communication and maximising the communication performance in a Underwater Wireless Sensor Network (UWSN) is always challenging due to the volatile characteristics of the underwater environment. Radio signals cannot properly propagate underwater, so there is a need for acoustic technology that can support better data rates and reliable underwater wireless communications. Node mobility, 3-D spaces and horizontal communication links are some critical challenges to the researcher in designing new routing protocols for UWSNs. In this paper, we have proposed a novel routing protocol called Layer by layer Angle-Based Flooding (L2-ABF) to address the issues of continuous node movements, end-to-end delays and energy consumption. In L2-ABF, every node can calculate its flooding angle to forward data packets toward the sinks without using any explicit configuration or location information. The simulation results show that L2-ABF has some advantages over some existing flooding-based techniques and also can easily manage quick routing changes where node movements are frequent.
Tariq Ali; Low Tang Jung; Ibrahima Faye. End-to-End Delay and Energy Efficient Routing Protocol for Underwater Wireless Sensor Networks. Wireless Personal Communications 2014, 79, 339 -361.
AMA StyleTariq Ali, Low Tang Jung, Ibrahima Faye. End-to-End Delay and Energy Efficient Routing Protocol for Underwater Wireless Sensor Networks. Wireless Personal Communications. 2014; 79 (1):339-361.
Chicago/Turabian StyleTariq Ali; Low Tang Jung; Ibrahima Faye. 2014. "End-to-End Delay and Energy Efficient Routing Protocol for Underwater Wireless Sensor Networks." Wireless Personal Communications 79, no. 1: 339-361.
Tariq Ali; Low Tang Jung; Ibrahima Faye; Sadia Ameer. Bi-directional Flooding Protocol for Underwater Wireless Sensor Networks. Research Journal of Applied Sciences, Engineering and Technology 2014, 7, 3775 -3785.
AMA StyleTariq Ali, Low Tang Jung, Ibrahima Faye, Sadia Ameer. Bi-directional Flooding Protocol for Underwater Wireless Sensor Networks. Research Journal of Applied Sciences, Engineering and Technology. 2014; 7 (18):3775-3785.
Chicago/Turabian StyleTariq Ali; Low Tang Jung; Ibrahima Faye; Sadia Ameer. 2014. "Bi-directional Flooding Protocol for Underwater Wireless Sensor Networks." Research Journal of Applied Sciences, Engineering and Technology 7, no. 18: 3775-3785.