Nausheen Saeed | Högskolan Dalarna (original) (raw)

Papers by Nausheen Saeed

Research paper thumbnail of Gravel road classification based on loose gravel using transfer learning

International Journal of Pavement Engineering, Nov 11, 2022

Research paper thumbnail of Gravel road classification based on loose gravel using transfer learning

International Journal of Pavement Engineering

Research paper thumbnail of Performance analysis of microsoft network policy server and freeRADIUS authentication systems in 802.1x based secured wired ethernet using PEAP

Int. Arab J. Inf. Technol., 2019

IEEE 802.1x is an industry standard to implement physical port level security in wired and wirele... more IEEE 802.1x is an industry standard to implement physical port level security in wired and wireless Ethernets by using RADIUS infrastructure. Administrators of corporate networks need secure network admission control for their environment in a way that adds minimum traffic overhead and does not degrade the performance of the network. This research focuses on two widely used RADIUS servers, Microsoft Network Policy Server (NPS) and FreeRADIUS to evaluate their efficiency and network overhead according to a set of pre-defined key performance indicators using Protected Extensible Authentication Protocol (PEAP) in conjunction with Microsoft Challenged Handshake Authentication Protocol version 2 (MSCHAPv2). The key performance indicators – authentication time, reconnection time and protocol overhead were evaluated in real test bed configuration. Results of the experiments explain why the performance of a particular authentications system is better than the other in the given scenario.

Research paper thumbnail of A Review of Intelligent Methods for Unpaved Roads Condition Assessment

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020

Conventional road condition evaluation is an expensive and time-consuming task. Therefore data co... more Conventional road condition evaluation is an expensive and time-consuming task. Therefore data collection from indirect economical methods is desired by road monitoring agencies. Recently intelligent road condition monitoring has become popular. More studies have focused on automated paved road condition monitoring, and minimal research is available to date on automating gravel road condition assessment. Road roughness information gives an overall picture of the road but does not help in identifying the type of defect; therefore, it cannot be helpful in the more specific road maintenance plan. Road monitoring can be automated using data from conventional sensors, vehicles' onboard devices, and audio and video streams from cost-effective devices. This paper reviews classical and intelligent methods for road condition evaluation in general and, more specifically, reviews studies proposing automated solutions targeting gravel or unpaved roads.

Research paper thumbnail of Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

Research paper thumbnail of A multimodal deep learning approach for gravel road condition evaluation through image and audio integration

This study investigates the combination of audio and image data to classify road conditions, part... more This study investigates the combination of audio and image data to classify road conditions, particularly focusing on loose gravel scenarios. The dataset underwent binary categorisation, comprising audio segments capturing gravel sounds and corresponding images. Early feature fusion, utilising a pre-trained Very Deep Convolutional Networks 19 (VGG19) and Principal component analysis (PCA), improved the accuracy of the Random Forest classifier, surpassing other models in accuracy, precision, recall, and F1-score. Late fusion, involving decisionlevel processing with logical disjunction and conjunction gates (AND and OR) in combination with individual classifiers for images and audio based on Densely Connected Convolutional Networks 121 (DenseNet121), demonstrated notable performance, especially with the OR gate, achieving 97 % accuracy. The late fusion method enhances adaptability by compensating for limitations in one modality with information from the other. Adapting maintenance based on identified road conditions minimises unnecessary environmental impact. This method can help to identify loose gravel on gravel roads, substantially improving road safety and implementing a precise maintenance strategy through a data-driven approach.

Research paper thumbnail of Comparison of Pattern Recognition Techniques for Classification of the Acoustics of Loose Gravel

2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI)

Road condition evaluation is a critical part of gravel road maintenance. One of the parameters th... more Road condition evaluation is a critical part of gravel road maintenance. One of the parameters that are assessed is loose Gravel. An expert does this evaluation by subjectively looking at images taken and written text for deciding on the road condition. This method is labor-intensive and subjected to an error of judgment; therefore, it is not reliable. Road management agencies are looking for more efficient and automated objective measurement methods. In this study, acoustic data of gravel hitting the bottom of the car is used, and the relation between these acoustics and the condition of loose gravel on gravel roads is seen. A novel acoustic classification method based on Ensemble bagged tree (EBT) algorithm is proposed in this study for the classification of loose gravel sounds. The accuracy of the EBT algorithm for Gravel and Nongravel sound classification is found to be 97.5. The detection of the negative classes, i.e., non- gravel detection, is preeminent, which is considerably higher than Boosted Trees, RUSBoosted Tree, Support vector machines (SVM), and decision trees.

Research paper thumbnail of A Review of Intelligent Methods for Unpaved Roads Condition Assessment

Conventional road condition evaluation is an expensive and time-consuming task. Therefore data co... more Conventional road condition evaluation is an expensive and time-consuming task. Therefore data collection from indirect economical methods is desired by road monitoring agencies. Recently intelligent road condition monitoring has become popular. More studies have focused on automated paved road condition monitoring, and minimal research is available to date on automating gravel road condition assessment. Road roughness information gives an overall picture of the road but does not help in identifying the type of defect; therefore, it cannot be helpful in the more specific road maintenance plan. Road monitoring can be automated using data from conventional sensors, vehicles' onboard devices, and audio and video streams from cost-effective devices. This paper reviews classical and intelligent methods for road condition evaluation in general and, more specifically, reviews studies proposing automated solutions targeting gravel or unpaved roads.

Research paper thumbnail of Classification of the Acoustics of Loose Gravel †

Sensors (Basel, Switzerland), 2021

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed para... more Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-tra...

Research paper thumbnail of Comparison of Pattern Recognition Techniques for Classification of the Acoustics of Loose Gravel

2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2020

Road condition evaluation is a critical part of gravel road maintenance. One of the parameters th... more Road condition evaluation is a critical part of gravel road maintenance. One of the parameters that are assessed is loose Gravel. An expert does this evaluation by subjectively looking at images taken and written text for deciding on the road condition. This method is labor-intensive and subjected to an error of judgment; therefore, it is not reliable. Road management agencies are looking for more efficient and automated objective measurement methods. In this study, acoustic data of gravel hitting the bottom of the car is used, and the relation between these acoustics and the condition of loose gravel on gravel roads is seen. A novel acoustic classification method based on Ensemble bagged tree (EBT) algorithm is proposed in this study for the classification of loose gravel sounds. The accuracy of the EBT algorithm for Gravel and Nongravel sound classification is found to be 97.5. The detection of the negative classes, i.e., non- gravel detection, is preeminent, which is considerably...

Research paper thumbnail of A Review of Intelligent Methods for Unpaved Roads Condition Assessment

Conventional road condition evaluation is an expensive and time-consuming task. Therefore data co... more Conventional road condition evaluation is an expensive and time-consuming task. Therefore data collection from indirect economical methods is desired by road monitoring agencies. Recently intelligent road condition monitoring has become popular. More studies have focused on automated paved road condition monitoring, and minimal research is available to date on automating gravel road condition assessment. Road roughness information gives an overall picture of the road but does not help in identifying the type of defect; therefore, it cannot be helpful in the more specific road maintenance plan. Road monitoring can be automated using data from conventional sensors, vehicles' onboard devices, and audio and video streams from cost-effective devices. This paper reviews classical and intelligent methods for road condition evaluation in general and, more specifically, reviews studies proposing automated solutions targeting gravel or unpaved roads.

Research paper thumbnail of A study on incorporating ICT in teaching methodologies at universities of Quetta

A study on incorporating ICT in teaching methodologies at universities of Quetta, 2015

This paper presents a study on the use of ICT in teaching methodology by the faculty of universit... more This paper presents a study on the use of ICT in teaching methodology by the faculty of universities in Quetta, Pakistan. A Survey about the impact of ICT usage was conducted in three major public universities of Quetta-University Of Balochistan, Sardar Bahadur Khan Women University, and Balochistan University of Information Technology, Engineering and Management Sciences, focusing the faculty and the students. The study shows that ample ICT facilities are available to the faculties in these universities and the teachers incorporate different ICT tools in their teaching methodologies. Teachers and students believe that ICT has played a vital role in improving the quality of education while there remain some areas of improvement like University web portals, access to digital libraries for the students and online student management systems.

Research paper thumbnail of Performance Analysis of Microsoft Network Policy Server and FreeRADIUS Authentication Systems in 802.1x based Secured Wired Ethernet using PEAP

Performance A nalysis of Microsoft Network Policy Server and FreeRADIUS Authentication Systems in 802.1x based S ecured W ired Ethernet using PEAP, 2019

IEEE 802.1x is an industry standard to implement physical port level security in wired and wirele... more IEEE 802.1x is an industry standard to implement physical port level security in wired and wireless Ethernets by using RADIUS infrastructure. Administrators of corporate networks need secure network admission control for their environment in a way that adds minimum traffic overhead and does not degrade the performance of the network. This research focuses on two widely used Remote Authentication Dial In User Service (RADIUS) servers, Microsoft Network Policy Server (NPS) and FreeRADIUS to evaluate their efficiency and network overhead according to a set of pre-defined key performance indicators using Protected Extensible Authentication Protocol (PEAP) in conjunction with Microsoft Challenged Handshake Authentication Protocol version 2 (MSCHAPv2). The key performance indicators-authentication time, reconnection time and protocol overhead were evaluated in real test bed configuration. Results of the experiments explain why the performance of a particular authentications system is better than the other in the given scenario.

Research paper thumbnail of -Performance Evaluationof AODVDSDVand DSRRouting Protocolsin Unplanned Areas

Performance Evaluation of AODV, DSDV and DSR Routing Protocols in Unplanned Areas , 2017

Abstract-It is important to explore how VANET Routing protocols behave in unplanned areas which l... more Abstract-It is important to explore how VANET
Routing protocols behave in unplanned areas which
lacks fixed infrastructure such as Road Side Unit to
support vehicular communication. Furthermore, such
areas do not possess vehicular traffic regulatory
structures such as traffic signals, proper road lanes and
traffic planning. These areas show largely
unpredictable traffic pattern in means of vehicle speeds
and density. This paper presents the simulation results
of such scenarios in order to better understand or select
the optimal performing Routing protocol among
DSDV, DSR and AODV against metrics selected:
Packet delivery Ratio, Packet Loss Percentage and
Average End to End delay. The results show that AODV
outperforms DSDV and DSR in unplanned areas. To
the best of our knowledge no such routing protocol
performance evaluations have been performed for
unplanned areas.

Research paper thumbnail of Gravel road classification based on loose gravel using transfer learning

International Journal of Pavement Engineering, Nov 11, 2022

Research paper thumbnail of Gravel road classification based on loose gravel using transfer learning

International Journal of Pavement Engineering

Research paper thumbnail of Performance analysis of microsoft network policy server and freeRADIUS authentication systems in 802.1x based secured wired ethernet using PEAP

Int. Arab J. Inf. Technol., 2019

IEEE 802.1x is an industry standard to implement physical port level security in wired and wirele... more IEEE 802.1x is an industry standard to implement physical port level security in wired and wireless Ethernets by using RADIUS infrastructure. Administrators of corporate networks need secure network admission control for their environment in a way that adds minimum traffic overhead and does not degrade the performance of the network. This research focuses on two widely used RADIUS servers, Microsoft Network Policy Server (NPS) and FreeRADIUS to evaluate their efficiency and network overhead according to a set of pre-defined key performance indicators using Protected Extensible Authentication Protocol (PEAP) in conjunction with Microsoft Challenged Handshake Authentication Protocol version 2 (MSCHAPv2). The key performance indicators – authentication time, reconnection time and protocol overhead were evaluated in real test bed configuration. Results of the experiments explain why the performance of a particular authentications system is better than the other in the given scenario.

Research paper thumbnail of A Review of Intelligent Methods for Unpaved Roads Condition Assessment

2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2020

Conventional road condition evaluation is an expensive and time-consuming task. Therefore data co... more Conventional road condition evaluation is an expensive and time-consuming task. Therefore data collection from indirect economical methods is desired by road monitoring agencies. Recently intelligent road condition monitoring has become popular. More studies have focused on automated paved road condition monitoring, and minimal research is available to date on automating gravel road condition assessment. Road roughness information gives an overall picture of the road but does not help in identifying the type of defect; therefore, it cannot be helpful in the more specific road maintenance plan. Road monitoring can be automated using data from conventional sensors, vehicles' onboard devices, and audio and video streams from cost-effective devices. This paper reviews classical and intelligent methods for road condition evaluation in general and, more specifically, reviews studies proposing automated solutions targeting gravel or unpaved roads.

Research paper thumbnail of Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

Research paper thumbnail of A multimodal deep learning approach for gravel road condition evaluation through image and audio integration

This study investigates the combination of audio and image data to classify road conditions, part... more This study investigates the combination of audio and image data to classify road conditions, particularly focusing on loose gravel scenarios. The dataset underwent binary categorisation, comprising audio segments capturing gravel sounds and corresponding images. Early feature fusion, utilising a pre-trained Very Deep Convolutional Networks 19 (VGG19) and Principal component analysis (PCA), improved the accuracy of the Random Forest classifier, surpassing other models in accuracy, precision, recall, and F1-score. Late fusion, involving decisionlevel processing with logical disjunction and conjunction gates (AND and OR) in combination with individual classifiers for images and audio based on Densely Connected Convolutional Networks 121 (DenseNet121), demonstrated notable performance, especially with the OR gate, achieving 97 % accuracy. The late fusion method enhances adaptability by compensating for limitations in one modality with information from the other. Adapting maintenance based on identified road conditions minimises unnecessary environmental impact. This method can help to identify loose gravel on gravel roads, substantially improving road safety and implementing a precise maintenance strategy through a data-driven approach.

Research paper thumbnail of Comparison of Pattern Recognition Techniques for Classification of the Acoustics of Loose Gravel

2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI)

Road condition evaluation is a critical part of gravel road maintenance. One of the parameters th... more Road condition evaluation is a critical part of gravel road maintenance. One of the parameters that are assessed is loose Gravel. An expert does this evaluation by subjectively looking at images taken and written text for deciding on the road condition. This method is labor-intensive and subjected to an error of judgment; therefore, it is not reliable. Road management agencies are looking for more efficient and automated objective measurement methods. In this study, acoustic data of gravel hitting the bottom of the car is used, and the relation between these acoustics and the condition of loose gravel on gravel roads is seen. A novel acoustic classification method based on Ensemble bagged tree (EBT) algorithm is proposed in this study for the classification of loose gravel sounds. The accuracy of the EBT algorithm for Gravel and Nongravel sound classification is found to be 97.5. The detection of the negative classes, i.e., non- gravel detection, is preeminent, which is considerably higher than Boosted Trees, RUSBoosted Tree, Support vector machines (SVM), and decision trees.

Research paper thumbnail of A Review of Intelligent Methods for Unpaved Roads Condition Assessment

Conventional road condition evaluation is an expensive and time-consuming task. Therefore data co... more Conventional road condition evaluation is an expensive and time-consuming task. Therefore data collection from indirect economical methods is desired by road monitoring agencies. Recently intelligent road condition monitoring has become popular. More studies have focused on automated paved road condition monitoring, and minimal research is available to date on automating gravel road condition assessment. Road roughness information gives an overall picture of the road but does not help in identifying the type of defect; therefore, it cannot be helpful in the more specific road maintenance plan. Road monitoring can be automated using data from conventional sensors, vehicles' onboard devices, and audio and video streams from cost-effective devices. This paper reviews classical and intelligent methods for road condition evaluation in general and, more specifically, reviews studies proposing automated solutions targeting gravel or unpaved roads.

Research paper thumbnail of Classification of the Acoustics of Loose Gravel †

Sensors (Basel, Switzerland), 2021

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed para... more Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-tra...

Research paper thumbnail of Comparison of Pattern Recognition Techniques for Classification of the Acoustics of Loose Gravel

2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2020

Road condition evaluation is a critical part of gravel road maintenance. One of the parameters th... more Road condition evaluation is a critical part of gravel road maintenance. One of the parameters that are assessed is loose Gravel. An expert does this evaluation by subjectively looking at images taken and written text for deciding on the road condition. This method is labor-intensive and subjected to an error of judgment; therefore, it is not reliable. Road management agencies are looking for more efficient and automated objective measurement methods. In this study, acoustic data of gravel hitting the bottom of the car is used, and the relation between these acoustics and the condition of loose gravel on gravel roads is seen. A novel acoustic classification method based on Ensemble bagged tree (EBT) algorithm is proposed in this study for the classification of loose gravel sounds. The accuracy of the EBT algorithm for Gravel and Nongravel sound classification is found to be 97.5. The detection of the negative classes, i.e., non- gravel detection, is preeminent, which is considerably...

Research paper thumbnail of A Review of Intelligent Methods for Unpaved Roads Condition Assessment

Conventional road condition evaluation is an expensive and time-consuming task. Therefore data co... more Conventional road condition evaluation is an expensive and time-consuming task. Therefore data collection from indirect economical methods is desired by road monitoring agencies. Recently intelligent road condition monitoring has become popular. More studies have focused on automated paved road condition monitoring, and minimal research is available to date on automating gravel road condition assessment. Road roughness information gives an overall picture of the road but does not help in identifying the type of defect; therefore, it cannot be helpful in the more specific road maintenance plan. Road monitoring can be automated using data from conventional sensors, vehicles' onboard devices, and audio and video streams from cost-effective devices. This paper reviews classical and intelligent methods for road condition evaluation in general and, more specifically, reviews studies proposing automated solutions targeting gravel or unpaved roads.

Research paper thumbnail of A study on incorporating ICT in teaching methodologies at universities of Quetta

A study on incorporating ICT in teaching methodologies at universities of Quetta, 2015

This paper presents a study on the use of ICT in teaching methodology by the faculty of universit... more This paper presents a study on the use of ICT in teaching methodology by the faculty of universities in Quetta, Pakistan. A Survey about the impact of ICT usage was conducted in three major public universities of Quetta-University Of Balochistan, Sardar Bahadur Khan Women University, and Balochistan University of Information Technology, Engineering and Management Sciences, focusing the faculty and the students. The study shows that ample ICT facilities are available to the faculties in these universities and the teachers incorporate different ICT tools in their teaching methodologies. Teachers and students believe that ICT has played a vital role in improving the quality of education while there remain some areas of improvement like University web portals, access to digital libraries for the students and online student management systems.

Research paper thumbnail of Performance Analysis of Microsoft Network Policy Server and FreeRADIUS Authentication Systems in 802.1x based Secured Wired Ethernet using PEAP

Performance A nalysis of Microsoft Network Policy Server and FreeRADIUS Authentication Systems in 802.1x based S ecured W ired Ethernet using PEAP, 2019

IEEE 802.1x is an industry standard to implement physical port level security in wired and wirele... more IEEE 802.1x is an industry standard to implement physical port level security in wired and wireless Ethernets by using RADIUS infrastructure. Administrators of corporate networks need secure network admission control for their environment in a way that adds minimum traffic overhead and does not degrade the performance of the network. This research focuses on two widely used Remote Authentication Dial In User Service (RADIUS) servers, Microsoft Network Policy Server (NPS) and FreeRADIUS to evaluate their efficiency and network overhead according to a set of pre-defined key performance indicators using Protected Extensible Authentication Protocol (PEAP) in conjunction with Microsoft Challenged Handshake Authentication Protocol version 2 (MSCHAPv2). The key performance indicators-authentication time, reconnection time and protocol overhead were evaluated in real test bed configuration. Results of the experiments explain why the performance of a particular authentications system is better than the other in the given scenario.

Research paper thumbnail of -Performance Evaluationof AODVDSDVand DSRRouting Protocolsin Unplanned Areas

Performance Evaluation of AODV, DSDV and DSR Routing Protocols in Unplanned Areas , 2017

Abstract-It is important to explore how VANET Routing protocols behave in unplanned areas which l... more Abstract-It is important to explore how VANET
Routing protocols behave in unplanned areas which
lacks fixed infrastructure such as Road Side Unit to
support vehicular communication. Furthermore, such
areas do not possess vehicular traffic regulatory
structures such as traffic signals, proper road lanes and
traffic planning. These areas show largely
unpredictable traffic pattern in means of vehicle speeds
and density. This paper presents the simulation results
of such scenarios in order to better understand or select
the optimal performing Routing protocol among
DSDV, DSR and AODV against metrics selected:
Packet delivery Ratio, Packet Loss Percentage and
Average End to End delay. The results show that AODV
outperforms DSDV and DSR in unplanned areas. To
the best of our knowledge no such routing protocol
performance evaluations have been performed for
unplanned areas.