Antioxidant proteins are essential for protecting cells from free radicals. The accurate identifi... more Antioxidant proteins are essential for protecting cells from free radicals. The accurate identification of antioxidant proteins via biological tests is difficult because of the high time and financial investment required. The potential of peptides produced from natural proteins is demonstrated by the fact that they are generally regarded as secure and may have additional advantageous bioactivities. Antioxidative peptides are typically discovered by analyzing numerous peptides created when a variety of proteases hydrolysis proteins. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. We suggested a machine-learning model that we named AOPs-XGBoost, built on sequence features and Extreme Gradient Boosting (XGBoost). We used 10-fold cross-validation testing was performed on a testing dataset using the proposed AOPs-XGBoost classifier, and the results showed a sensitivity of 67.56%, specificity of 93.87%, average accuracy of 80.72%, mean cross-validation (MCC) of 66.29%), and area under the receiver operating characteristic curve (AUC) of 88.01%. The outcomes demonstrated that the XGBoost model outperformed the other models with accuracy of 80.72% and area under the receiver operating characteristic curve of 88.01% which were better than the other models. Experimental results demonstrate that AOPs-XGBoost is a useful classifier that advances the study of antioxidant proteins.
A developing post-translational modification known as AMPylation involves the formation of a phos... more A developing post-translational modification known as AMPylation involves the formation of a phosphodiester bond on the hydroxyl group of threonine, serine, or tyrosine. Adenosine monophosphate is covalently attached to the side chain of an amino acid in a peptide during this process, which is catalyzed by AMPylation. We used AMPylation peptide sequence data from bacteria, eukaryotes, and archaea to train the models. Then, we compared the results of several feature extraction methods and their combinations in addition to classification algorithms to obtain more accurate prediction models. To prevent additional loss of sequence information, the PseAAC feature is employed to construct a fixedsize descriptor value in vector space. The basic feature set is received from 2nd features extraction method. All of this was accomplished by deriving the protein characteristics from the evolutionary data and sequence of the BLOUSM62 amino acid residue. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. In this research, we proposed framework for AMPylation identification that makes use of the XGBoost algorithm (AMPylation) and sequence-derived functions. XGBoost -Ampy has an accuracy of 86.7%, a sensitivity of 76.1%, a specificity of 97.5%, and a Matthews's correlation coefficient (MCC) of 0.753 for predicting AMylation sites. XGBoost -Amp, the first machine learning model developed, has shown promise and may be able to help with this problem.
The ever-growing number of Internet of Things (IoT) devices increases the amount of data produced... more The ever-growing number of Internet of Things (IoT) devices increases the amount of data produced on daily basis. To handle such a massive amount of data, cloud computing provides storage, processing, and analytical services. Besides this, real-time applications, i.e., online gaming, smart traffic management, and smart healthcare, cannot tolerate the high latency and bandwidth consumption. The fog computing paradigm brings the cloud services closer to the network edge to provide quality of service (QoS) to such applications. However, efficient task scheduling becomes critical for improving the performance due to the heterogeneous nature, resource-constrained, and distributed environment of fog resources. With an efficient task scheduling algorithm, the response time to application requests can be reduced along with bandwidth and cloud resource costs. This paper presents a genetic algorithm-based solution to find an efficient scheduling approach for mapping application modules in a c...
Most data in this Calibration Issue are based on radiocarbon age determinations of tree-ring samp... more Most data in this Calibration Issue are based on radiocarbon age determinations of tree-ring samples with dendrochronologically determined calibrated (cal) ages. For high-precision measurements, substantial sample amounts are needed, and the processed wood usually spans 10 or 20 tree rings. Thus, the calibration curve data points usually have decadal, or bidecadal, spacing. These curves, to be used for the calibration of samples formed over 1 or 2 decades, may not be fully applicable to samples (leaves, twigs,etc.) formed in a single growing season.
Data from RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
The quality of human life increases with increased usage of the Internet of Things (IoT). However... more The quality of human life increases with increased usage of the Internet of Things (IoT). However, this massive use of IoT produces a large amount of data which creates a problem for data storage and analysis. Cloud computing is used to solve such issues like storage and processing requirements for data generated by IoT. However, several applications like traffic control, health observation, and games, etc., are time-sensitive and need a quick response. The delay created by sending data to the Cloud and then returning the Server response to the user of such programs has an unintended impact. To overwhelmed these limitations, the Fog computing concept was launched in 2012. Fog computing is an elongation of Cloud computing in which user services are extended to networking devices. In recent times the Fog computing is considered as a favorable approach to cope with the delay-sensitive applications. Moreover, Fog computing in combination with the Cloud computing model can also provide p...
Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the fra... more Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field (MAP-MRF). It developed its own maximization step to be used within this framework. This research has proposed an improvement in the SCMM's maximization step for segmenting simulated brain Magnetic Resonance Images (MRIs). The improved model is named as the Weighted Spatially Constrained Finite Mixture Model (WSCFMM). To compare the performance of SCMM and WSCFMM, simulated T1-Weighted normal MRIs were segmented. A region of interest (ROI) was extracted from segmented images. The similarity level between the extracted ROI and the ground truth (GT) was found by using the Jaccard and Dice similarity measuring method. According to the Jaccard similarity measuring method, WSCFMM showed an overall improvement of 4.72%, whereas the Dice similarity measuring method provided an overall improvement of 2.65% against the SCMM. Besides, WSCFMM signi cantly stabilized and reduced the execution time by showing an improvement of 83.71%. The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.
In the past few years, mobile data traffic has seen exponential growth due to the emergence of sm... more In the past few years, mobile data traffic has seen exponential growth due to the emergence of smart applications. Although throughput enhancement techniques such as macro- and femtocells reduce cell size, they are relatively expensive to implement. Mobile device-to-device (D2D) communication has emerged as a solution to support the growing popularity of multimedia content for local service in next-generation 5G cellular networks. Content sharing is the prominent feature, which helps D2D communication in reducing offload traffic on the network, improving the energy efficiency of the device, and reducing backhaul connectivity costs. In traditional mapping approaches such as one to one or one to many, a massive amount of traffic is distributed among the devices resulting in high-energy consumption. In this paper, we propose a novel energy-efficient content sharing scheme called Energy-Efficient Collaboration-based Content (EECC) sharing strategy in D2D communication that shares conten...
Evaluation of features for social contact prediction
2017 13th International Conference on Emerging Technologies (ICET)
Human contact prediction is considered a challenging problem in the context of social networks. W... more Human contact prediction is considered a challenging problem in the context of social networks. We have approached this problem as link prediction problem and analyzed social profiles and their impact on predicting links in a social network. In this work, we have argued that not all social features share the same importance for link prediction. The experiments in this paper are performed using varying weights for the available features in the dataset. Our results show that we can gain significant improvement in link prediction while using the suggested weighted features of social profiles.
Object Recognition Using Simulated Evolution on Fourier Descriptors
Advances in Intelligent and Soft Computing, 2010
This work presents a study on object recognition of isolated objects. Fourier descriptors have be... more This work presents a study on object recognition of isolated objects. Fourier descriptors have been used as features of the outlines of the objects under discussion. The problem of selecting the best descriptors has been formulated as an optimization problem and solved ...
Fog Computing is one of the new computing structures which takes the Cloud to the verge of the ne... more Fog Computing is one of the new computing structures which takes the Cloud to the verge of the network. The structure is formulated for applications that need low latency. Fog Computing has been projected to improve the disadvantages of Cloud Computing. The system is confronted with the variability of dynamic resources that are heterogeneous and distributed. Hence, efficient scheduling and resource allocation are necessary to maximize the use of these resources and the satisfaction of users. In this paper, a resource-aware scheduler RACE (Resource Aware Cost-Efficient Scheduler) is proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications and minimum bandwidth usage. This RACE comprises of two algorithms. The ModuleScheduler in RACE categorizes the incoming application modules according to their computation and bandwidth requirements which are then placed by CompareModule. Comprehensive experimental results obtained from the simulation by using ifogsim simulator show that our approach performs better in most of the cases as compared to the Traditional Cloud placement and the baseline algorithm. INDEX TERMS Cloud/fog environment, scheduling, Internet of Things (IoTs).
In many image analysis and computer vision applications, object recognition is the ultimate goal.... more In many image analysis and computer vision applications, object recognition is the ultimate goal. This work presents study and experimentation for object recognition when isolated objects are under discussion. The circumstances of similarity transformations, presence of noise, and occlusion have been included as the part of the study. For simplicity, instead of objects, outline of the objects have been used for the whole process of the recognition. Hu’s moments and their extended counterparts have been used as features of the objects. Genetic Algorithm is applied on these moments to get optimum descriptors. These optimum descriptors are then used in process of recognition. Various similarity measures have been used and compared for recognition. The test objects are matched with the model objects in database and the object with the least similarity measure is taken as the recognized object. A detailed experimental study has been made under different conditions and circumstances inclu...
Use of ICT in Education: Perceptions of College Teachers in Remote Region of Pakistan
Use of Information and Communication Technology (ICT) has become a sine qua non in the education ... more Use of Information and Communication Technology (ICT) has become a sine qua non in the education set up. It has a huge impact on the learning of both students and teachers. Researchers world around have focused on the very concern. In Pakistan, too, many researchers and educationist have worked on several aspects of the use of technology in education. However, the area is still grey in the country and there is a need of further studies with respect to teachers' beliefs and perceptions of using the technology, ICT curriculum as well as teacher development in the area. This study therefore focused on the perceptions of college teachers about the usage of ICT in education. The study particularly focused on the college teachers in a remote and mountainous region of Pakistan, which gives uniqueness to the study. Data were collected through a simple questionnaire survey and were analyzed using descriptive statistics. For the study 100 questioners were administered to college teachers....
Ontology is a philosophical concept originally, and it describes natures of objects [1]. In 1980&... more Ontology is a philosophical concept originally, and it describes natures of objects [1]. In 1980's, scientific researchers introduced ontology into the field of artificial intelligence (AI), so ontology has new meanings. In the fields of computer science, ontology is a formal ...
Antioxidant proteins are essential for protecting cells from free radicals. The accurate identifi... more Antioxidant proteins are essential for protecting cells from free radicals. The accurate identification of antioxidant proteins via biological tests is difficult because of the high time and financial investment required. The potential of peptides produced from natural proteins is demonstrated by the fact that they are generally regarded as secure and may have additional advantageous bioactivities. Antioxidative peptides are typically discovered by analyzing numerous peptides created when a variety of proteases hydrolysis proteins. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. We suggested a machine-learning model that we named AOPs-XGBoost, built on sequence features and Extreme Gradient Boosting (XGBoost). We used 10-fold cross-validation testing was performed on a testing dataset using the proposed AOPs-XGBoost classifier, and the results showed a sensitivity of 67.56%, specificity of 93.87%, average accuracy of 80.72%, mean cross-validation (MCC) of 66.29%), and area under the receiver operating characteristic curve (AUC) of 88.01%. The outcomes demonstrated that the XGBoost model outperformed the other models with accuracy of 80.72% and area under the receiver operating characteristic curve of 88.01% which were better than the other models. Experimental results demonstrate that AOPs-XGBoost is a useful classifier that advances the study of antioxidant proteins.
A developing post-translational modification known as AMPylation involves the formation of a phos... more A developing post-translational modification known as AMPylation involves the formation of a phosphodiester bond on the hydroxyl group of threonine, serine, or tyrosine. Adenosine monophosphate is covalently attached to the side chain of an amino acid in a peptide during this process, which is catalyzed by AMPylation. We used AMPylation peptide sequence data from bacteria, eukaryotes, and archaea to train the models. Then, we compared the results of several feature extraction methods and their combinations in addition to classification algorithms to obtain more accurate prediction models. To prevent additional loss of sequence information, the PseAAC feature is employed to construct a fixedsize descriptor value in vector space. The basic feature set is received from 2nd features extraction method. All of this was accomplished by deriving the protein characteristics from the evolutionary data and sequence of the BLOUSM62 amino acid residue. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. In this research, we proposed framework for AMPylation identification that makes use of the XGBoost algorithm (AMPylation) and sequence-derived functions. XGBoost -Ampy has an accuracy of 86.7%, a sensitivity of 76.1%, a specificity of 97.5%, and a Matthews's correlation coefficient (MCC) of 0.753 for predicting AMylation sites. XGBoost -Amp, the first machine learning model developed, has shown promise and may be able to help with this problem.
The ever-growing number of Internet of Things (IoT) devices increases the amount of data produced... more The ever-growing number of Internet of Things (IoT) devices increases the amount of data produced on daily basis. To handle such a massive amount of data, cloud computing provides storage, processing, and analytical services. Besides this, real-time applications, i.e., online gaming, smart traffic management, and smart healthcare, cannot tolerate the high latency and bandwidth consumption. The fog computing paradigm brings the cloud services closer to the network edge to provide quality of service (QoS) to such applications. However, efficient task scheduling becomes critical for improving the performance due to the heterogeneous nature, resource-constrained, and distributed environment of fog resources. With an efficient task scheduling algorithm, the response time to application requests can be reduced along with bandwidth and cloud resource costs. This paper presents a genetic algorithm-based solution to find an efficient scheduling approach for mapping application modules in a c...
Most data in this Calibration Issue are based on radiocarbon age determinations of tree-ring samp... more Most data in this Calibration Issue are based on radiocarbon age determinations of tree-ring samples with dendrochronologically determined calibrated (cal) ages. For high-precision measurements, substantial sample amounts are needed, and the processed wood usually spans 10 or 20 tree rings. Thus, the calibration curve data points usually have decadal, or bidecadal, spacing. These curves, to be used for the calibration of samples formed over 1 or 2 decades, may not be fully applicable to samples (leaves, twigs,etc.) formed in a single growing season.
Data from RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
The quality of human life increases with increased usage of the Internet of Things (IoT). However... more The quality of human life increases with increased usage of the Internet of Things (IoT). However, this massive use of IoT produces a large amount of data which creates a problem for data storage and analysis. Cloud computing is used to solve such issues like storage and processing requirements for data generated by IoT. However, several applications like traffic control, health observation, and games, etc., are time-sensitive and need a quick response. The delay created by sending data to the Cloud and then returning the Server response to the user of such programs has an unintended impact. To overwhelmed these limitations, the Fog computing concept was launched in 2012. Fog computing is an elongation of Cloud computing in which user services are extended to networking devices. In recent times the Fog computing is considered as a favorable approach to cope with the delay-sensitive applications. Moreover, Fog computing in combination with the Cloud computing model can also provide p...
Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the fra... more Spatially Constrained Mixture Model (SCMM) is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field (MAP-MRF). It developed its own maximization step to be used within this framework. This research has proposed an improvement in the SCMM's maximization step for segmenting simulated brain Magnetic Resonance Images (MRIs). The improved model is named as the Weighted Spatially Constrained Finite Mixture Model (WSCFMM). To compare the performance of SCMM and WSCFMM, simulated T1-Weighted normal MRIs were segmented. A region of interest (ROI) was extracted from segmented images. The similarity level between the extracted ROI and the ground truth (GT) was found by using the Jaccard and Dice similarity measuring method. According to the Jaccard similarity measuring method, WSCFMM showed an overall improvement of 4.72%, whereas the Dice similarity measuring method provided an overall improvement of 2.65% against the SCMM. Besides, WSCFMM signi cantly stabilized and reduced the execution time by showing an improvement of 83.71%. The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.
In the past few years, mobile data traffic has seen exponential growth due to the emergence of sm... more In the past few years, mobile data traffic has seen exponential growth due to the emergence of smart applications. Although throughput enhancement techniques such as macro- and femtocells reduce cell size, they are relatively expensive to implement. Mobile device-to-device (D2D) communication has emerged as a solution to support the growing popularity of multimedia content for local service in next-generation 5G cellular networks. Content sharing is the prominent feature, which helps D2D communication in reducing offload traffic on the network, improving the energy efficiency of the device, and reducing backhaul connectivity costs. In traditional mapping approaches such as one to one or one to many, a massive amount of traffic is distributed among the devices resulting in high-energy consumption. In this paper, we propose a novel energy-efficient content sharing scheme called Energy-Efficient Collaboration-based Content (EECC) sharing strategy in D2D communication that shares conten...
Evaluation of features for social contact prediction
2017 13th International Conference on Emerging Technologies (ICET)
Human contact prediction is considered a challenging problem in the context of social networks. W... more Human contact prediction is considered a challenging problem in the context of social networks. We have approached this problem as link prediction problem and analyzed social profiles and their impact on predicting links in a social network. In this work, we have argued that not all social features share the same importance for link prediction. The experiments in this paper are performed using varying weights for the available features in the dataset. Our results show that we can gain significant improvement in link prediction while using the suggested weighted features of social profiles.
Object Recognition Using Simulated Evolution on Fourier Descriptors
Advances in Intelligent and Soft Computing, 2010
This work presents a study on object recognition of isolated objects. Fourier descriptors have be... more This work presents a study on object recognition of isolated objects. Fourier descriptors have been used as features of the outlines of the objects under discussion. The problem of selecting the best descriptors has been formulated as an optimization problem and solved ...
Fog Computing is one of the new computing structures which takes the Cloud to the verge of the ne... more Fog Computing is one of the new computing structures which takes the Cloud to the verge of the network. The structure is formulated for applications that need low latency. Fog Computing has been projected to improve the disadvantages of Cloud Computing. The system is confronted with the variability of dynamic resources that are heterogeneous and distributed. Hence, efficient scheduling and resource allocation are necessary to maximize the use of these resources and the satisfaction of users. In this paper, a resource-aware scheduler RACE (Resource Aware Cost-Efficient Scheduler) is proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications and minimum bandwidth usage. This RACE comprises of two algorithms. The ModuleScheduler in RACE categorizes the incoming application modules according to their computation and bandwidth requirements which are then placed by CompareModule. Comprehensive experimental results obtained from the simulation by using ifogsim simulator show that our approach performs better in most of the cases as compared to the Traditional Cloud placement and the baseline algorithm. INDEX TERMS Cloud/fog environment, scheduling, Internet of Things (IoTs).
In many image analysis and computer vision applications, object recognition is the ultimate goal.... more In many image analysis and computer vision applications, object recognition is the ultimate goal. This work presents study and experimentation for object recognition when isolated objects are under discussion. The circumstances of similarity transformations, presence of noise, and occlusion have been included as the part of the study. For simplicity, instead of objects, outline of the objects have been used for the whole process of the recognition. Hu’s moments and their extended counterparts have been used as features of the objects. Genetic Algorithm is applied on these moments to get optimum descriptors. These optimum descriptors are then used in process of recognition. Various similarity measures have been used and compared for recognition. The test objects are matched with the model objects in database and the object with the least similarity measure is taken as the recognized object. A detailed experimental study has been made under different conditions and circumstances inclu...
Use of ICT in Education: Perceptions of College Teachers in Remote Region of Pakistan
Use of Information and Communication Technology (ICT) has become a sine qua non in the education ... more Use of Information and Communication Technology (ICT) has become a sine qua non in the education set up. It has a huge impact on the learning of both students and teachers. Researchers world around have focused on the very concern. In Pakistan, too, many researchers and educationist have worked on several aspects of the use of technology in education. However, the area is still grey in the country and there is a need of further studies with respect to teachers' beliefs and perceptions of using the technology, ICT curriculum as well as teacher development in the area. This study therefore focused on the perceptions of college teachers about the usage of ICT in education. The study particularly focused on the college teachers in a remote and mountainous region of Pakistan, which gives uniqueness to the study. Data were collected through a simple questionnaire survey and were analyzed using descriptive statistics. For the study 100 questioners were administered to college teachers....
Ontology is a philosophical concept originally, and it describes natures of objects [1]. In 1980&... more Ontology is a philosophical concept originally, and it describes natures of objects [1]. In 1980's, scientific researchers introduced ontology into the field of artificial intelligence (AI), so ontology has new meanings. In the fields of computer science, ontology is a formal ...
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