In many industries inclusive of automotive vehicle industry, predictive maintenance has become mo... more In many industries inclusive of automotive vehicle industry, predictive maintenance has become more important. It is hard to diagnose failure in advance in the vehicle industry because of the limited availability of sensors and some of the designing exertions. However with the great development in automotive industry, it looks feasible today to analyze sensor’s data along with machine learning techniques for failure prediction. In this article, an approach is presented for fault prediction of four main subsystems of vehicle, fuel system, ignition system, exhaust system, and cooling system. Sensor is collected when vehicle is on the move, both in faulty condition (when any failure in specific system has occurred) and in normal condition. The data is transmitted to the server which analyzes the data. Interesting patterns are learned using four classifiers, Decision Tree, Support Vector Machine, K Nearest Neighbor, and Random Forest. These patterns are later used to detect future failu...
Wheat yellow rust is a common agricultural disease that affects the crop every year across the wo... more Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to impro...
Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes ... more Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20–30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Pattern...
2018 15th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT), 2018
The ubiquitous use of IEEE 802.11 has aggravated the need to make efficient use of available band... more The ubiquitous use of IEEE 802.11 has aggravated the need to make efficient use of available bandwidth. Currently handoff decision in IEEE 802.11 is made based on the received signal strength but these results in poor connectivity specifically when an access point is overloaded. Overlapping regions where users can be connected to multiple access points, switching to less loaded access point can improve overall network capacity. In this article, we propose a decentralized approach for best access point selection which also prevents an access point to get overloaded. We propose an algorithm for handover strategy to improve network capacity via load balancing and it also minimizes switching overhead. We perform detail analysis on publically available dataset which consists of millions of Wi-Fi sessions with multiple access points.
IoT Architectures, Models, and Platforms for Smart City Applications
Agriculture holds paramount significance in Pakistan due to its high impact on gross domestic pro... more Agriculture holds paramount significance in Pakistan due to its high impact on gross domestic product (GDP). However, there is huge gap between actual production and estimated production in agriculture due to manual farming system, which is time-consuming, inefficient, and labor-intensive. As of today, ultra-modern technology such as Internet of Things (IoT) can assist in acquiring timely and accurate crop information essential for the success of precision agriculture technology. Towards such ends, the authors propose an IoT-based crop health monitoring system comprised of different sensors used in agricultural fields. Additionally, low altitude remote sensing platforms, such as drones, are used to capture the spectral imagery of the entire crop field of the study region. The development of such a system can be instrumental for crop status monitoring and localizing the areas under stress to maximize the agricultural output by leveraging the IoT technology.
Indoor air quality typically encompasses the ambient conditions inside buildings and public facil... more Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for fu...
Crop classification in early phenological stages has been a difficult task due to spectrum simila... more Crop classification in early phenological stages has been a difficult task due to spectrum similarity of different crops. For this purpose, low altitude platforms such as drones have great potential to provide high resolution optical imagery where Machine Learning (ML) applied to classify different types of crops. In this research work, crop classification is performed at different phenological stages using optical images which are obtained from drone. For this purpose, gray level co-occurrence matrix (GLCM) based features are extracted from underlying gray scale images collected by the drone. To classify the different types of crops, different ML algorithms including Random Forest (RF), Naive Bayes (NB), Neural Network (NN) and Support Vector Machine (SVM) are applied. The results showed that the ML algorithms performed much better on GLCM features as compared to gray scale images with a margin of 13.65% in overall accuracy.
The International Arab Journal of Information Technology
Central Processing Unit (CPU) is the most significant resource and its scheduling is one of the m... more Central Processing Unit (CPU) is the most significant resource and its scheduling is one of the main functions of an operating system. In timeshared systems, Round Robin (RR) is most widely used scheduling algorithm. The efficiency of RR algorithm is influenced by the quantum time, if quantum is small, there will be overheads of more context switches and if quantum time is large, then given algorithm will perform as First Come First Served (FCFS) in which there is more risk of starvation. In this paper, a new CPU scheduling algorithm is proposed named as Amended Dynamic Round Robin (ADRR) based on CPU burst time. The primary goal of ADRR is to improve the conventional RR scheduling algorithm using the active quantum time notion. Quantum time is cyclically adjusted based on CPU burst time. We evaluate and compare the performance of our proposed ADRR algorithm based on certain parameters such as, waiting time, turnaround time etc. and compare the performance of our proposed algorithm....
In many industries inclusive of automotive vehicle industry, predictive maintenance has become mo... more In many industries inclusive of automotive vehicle industry, predictive maintenance has become more important. It is hard to diagnose failure in advance in the vehicle industry because of the limited availability of sensors and some of the designing exertions. However with the great development in automotive industry, it looks feasible today to analyze sensor’s data along with machine learning techniques for failure prediction. In this article, an approach is presented for fault prediction of four main subsystems of vehicle, fuel system, ignition system, exhaust system, and cooling system. Sensor is collected when vehicle is on the move, both in faulty condition (when any failure in specific system has occurred) and in normal condition. The data is transmitted to the server which analyzes the data. Interesting patterns are learned using four classifiers, Decision Tree, Support Vector Machine, K Nearest Neighbor, and Random Forest. These patterns are later used to detect future failu...
Wheat yellow rust is a common agricultural disease that affects the crop every year across the wo... more Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to impro...
Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes ... more Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20–30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Pattern...
2018 15th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT (HONET-ICT), 2018
The ubiquitous use of IEEE 802.11 has aggravated the need to make efficient use of available band... more The ubiquitous use of IEEE 802.11 has aggravated the need to make efficient use of available bandwidth. Currently handoff decision in IEEE 802.11 is made based on the received signal strength but these results in poor connectivity specifically when an access point is overloaded. Overlapping regions where users can be connected to multiple access points, switching to less loaded access point can improve overall network capacity. In this article, we propose a decentralized approach for best access point selection which also prevents an access point to get overloaded. We propose an algorithm for handover strategy to improve network capacity via load balancing and it also minimizes switching overhead. We perform detail analysis on publically available dataset which consists of millions of Wi-Fi sessions with multiple access points.
IoT Architectures, Models, and Platforms for Smart City Applications
Agriculture holds paramount significance in Pakistan due to its high impact on gross domestic pro... more Agriculture holds paramount significance in Pakistan due to its high impact on gross domestic product (GDP). However, there is huge gap between actual production and estimated production in agriculture due to manual farming system, which is time-consuming, inefficient, and labor-intensive. As of today, ultra-modern technology such as Internet of Things (IoT) can assist in acquiring timely and accurate crop information essential for the success of precision agriculture technology. Towards such ends, the authors propose an IoT-based crop health monitoring system comprised of different sensors used in agricultural fields. Additionally, low altitude remote sensing platforms, such as drones, are used to capture the spectral imagery of the entire crop field of the study region. The development of such a system can be instrumental for crop status monitoring and localizing the areas under stress to maximize the agricultural output by leveraging the IoT technology.
Indoor air quality typically encompasses the ambient conditions inside buildings and public facil... more Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for fu...
Crop classification in early phenological stages has been a difficult task due to spectrum simila... more Crop classification in early phenological stages has been a difficult task due to spectrum similarity of different crops. For this purpose, low altitude platforms such as drones have great potential to provide high resolution optical imagery where Machine Learning (ML) applied to classify different types of crops. In this research work, crop classification is performed at different phenological stages using optical images which are obtained from drone. For this purpose, gray level co-occurrence matrix (GLCM) based features are extracted from underlying gray scale images collected by the drone. To classify the different types of crops, different ML algorithms including Random Forest (RF), Naive Bayes (NB), Neural Network (NN) and Support Vector Machine (SVM) are applied. The results showed that the ML algorithms performed much better on GLCM features as compared to gray scale images with a margin of 13.65% in overall accuracy.
The International Arab Journal of Information Technology
Central Processing Unit (CPU) is the most significant resource and its scheduling is one of the m... more Central Processing Unit (CPU) is the most significant resource and its scheduling is one of the main functions of an operating system. In timeshared systems, Round Robin (RR) is most widely used scheduling algorithm. The efficiency of RR algorithm is influenced by the quantum time, if quantum is small, there will be overheads of more context switches and if quantum time is large, then given algorithm will perform as First Come First Served (FCFS) in which there is more risk of starvation. In this paper, a new CPU scheduling algorithm is proposed named as Amended Dynamic Round Robin (ADRR) based on CPU burst time. The primary goal of ADRR is to improve the conventional RR scheduling algorithm using the active quantum time notion. Quantum time is cyclically adjusted based on CPU burst time. We evaluate and compare the performance of our proposed ADRR algorithm based on certain parameters such as, waiting time, turnaround time etc. and compare the performance of our proposed algorithm....
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