In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firear...In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firearms.which is why an automated weapon detection system is needed.Various automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good results.However,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system.These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos.This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter.The proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm.The proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 score.The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved.Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN.It is promising to be used in the field of security and weapon detection.展开更多
Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of...Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of memory,vision,and name.In 2020,approximately 18,020 deaths occurred due to brain tumors.These cases can be minimized if a brain tumor is diagnosed at a very early stage.Computer vision researchers have introduced several techniques for brain tumor detection and classification.However,owing to many factors,this is still a challenging task.These challenges relate to the tumor size,the shape of a tumor,location of the tumor,selection of important features,among others.In this study,we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features.In the proposed framework,initially,a database is normalized in the form of high-grade glioma(HGG)and low-grade glioma(LGG)patients and then two pre-trained deep learning models(ResNet50 and Densenet201)are chosen.The deep learning models were modified and trained using transfer learning.Subsequently,the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models.The selected features are fused using a serial-based approach and classified using a cubic support vector machine.The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8%and 84.6%for HGG and LGG,respectively.The comparison is performed using several classification methods,and it shows the significance of our proposed technique.展开更多
Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis ...Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.展开更多
Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration.Though,the wet-lab experiments for the inve...Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration.Though,the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides(“AIP”)are usually very costly and remain time-consuming.Therefore,before wet-lab investigations,it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development process.Several anti-inflammatory prediction tools have been proposed in the recent past,yet,there is a space to induce enhancement in prediction performance in terms of precision and efficiency.An exceedingly accurate antiinflammatory prediction model is proposed,named AntiFlamPred(“Antiinflammatory Peptide Predictor”),by incorporation of encoded features and probing machine learning algorithms including deep learning.The proposed model performs best in conjunction with deep learning.Rigorous testing and validation were applied including cross-validation,self-consistency,jackknife,and independent set testing.The proposed model yielded 0.919 value for area under the curve(AUC)and revealed Mathew’s correlation coefficient(MCC)equivalent to 0.735 demonstrating its effectiveness and stability.Subsequently,the proposed model was also extensively probed in comparison with other existing models.The performance of the proposed model also out-performs other existing models.These outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based examinations.Subsequently,it has the potential to assiduously support medical and bioinformatics research.展开更多
Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and morbidity.Expanded Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immun...Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and morbidity.Expanded Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immunization activities,however the coverage is quite low despite the accessibility of free vaccination.This study proposes a defaulter prediction model for accurate identification of defaulters.Our proposed framework classifies defaulters at five different stages:defaulter,partially high,partially medium,partially low,and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization schedule.Different machine learning algorithms are applied on Pakistan Demographic and Health Survey(2017–18)dataset.Multilayer Perceptron yielded 98.5%accuracy for correctly identifying children who are likely to default from immunization series at different risk stages of being defaulter.In this paper,the proposed defaulters’prediction framework is a step forward towards a data-driven approach and provides a set of machine learning techniques to take advantage of predictive analytics.Hence,predictive analytics can reinforce immunization programs by expediting targeted action to reduce dropouts.Specially,the accurate predictions support targeted messages sent to at-risk parents’and caretakers’consumer devices(e.g.,smartphones)to maximize healthcare outcomes.展开更多
In the recent decade,the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities.Image segmentation is a key step in digitalization.Segmen...In the recent decade,the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities.Image segmentation is a key step in digitalization.Segmentation plays a key role in almost all areas of image processing,and various approaches have been proposed for image segmentation.In this paper,a novel approach is proposed for image segmentation using a nonuniform adaptive strategy.Region-based image segmentation along with a directional binary pattern generated a better segmented image.An adaptive mask of 8×8 was circulated over the pixels whose bit value was 1 in the generated directional binary pattern.Segmentation was performed in three phases:first,an image was divided into sub-images or image chunks;next,the image patches were taken as input,and an adaptive threshold was generated;and finally the image chunks were processed separately by convolving the adaptive mask on the image chunks.Gradient and Laplacian of Gaussian algorithms along with directional extrema patterns provided a double check for boundary pixels.The proposed approach was tested on chunks of varying sizes,and after multiple iterations,it was found that a block size of 8×8 performs better than other chunks or block sizes.The accuracy of the segmentation technique was measured in terms of the count of ill regions,which were extracted after the segmentation process.展开更多
基金We deeply acknowledge Taif University for Supporting and funding this study through Taif University Researchers Supporting Project Number(TURSP-2020/115),Taif University,Taif,Saudi Arabia.
文摘In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firearms.which is why an automated weapon detection system is needed.Various automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good results.However,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system.These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos.This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter.The proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm.The proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 score.The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved.Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN.It is promising to be used in the field of security and weapon detection.
基金This study was supported by the grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare(HI18C1216)the grant of the National Research Foundation of Korea(NRF-2020R1I1A1A01074256)the Soonchunhyang University Research Fund.
文摘Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of memory,vision,and name.In 2020,approximately 18,020 deaths occurred due to brain tumors.These cases can be minimized if a brain tumor is diagnosed at a very early stage.Computer vision researchers have introduced several techniques for brain tumor detection and classification.However,owing to many factors,this is still a challenging task.These challenges relate to the tumor size,the shape of a tumor,location of the tumor,selection of important features,among others.In this study,we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features.In the proposed framework,initially,a database is normalized in the form of high-grade glioma(HGG)and low-grade glioma(LGG)patients and then two pre-trained deep learning models(ResNet50 and Densenet201)are chosen.The deep learning models were modified and trained using transfer learning.Subsequently,the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models.The selected features are fused using a serial-based approach and classified using a cubic support vector machine.The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8%and 84.6%for HGG and LGG,respectively.The comparison is performed using several classification methods,and it shows the significance of our proposed technique.
基金Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(2020-0-01592)Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant(2019R1F1A1058548)and Grant(2020R1G1A1013221).
文摘Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results.
基金This project was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University(https://www.kau.edu.sa/),Jeddah,under Grant No.(D-49-611-1441).
文摘Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration.Though,the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides(“AIP”)are usually very costly and remain time-consuming.Therefore,before wet-lab investigations,it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development process.Several anti-inflammatory prediction tools have been proposed in the recent past,yet,there is a space to induce enhancement in prediction performance in terms of precision and efficiency.An exceedingly accurate antiinflammatory prediction model is proposed,named AntiFlamPred(“Antiinflammatory Peptide Predictor”),by incorporation of encoded features and probing machine learning algorithms including deep learning.The proposed model performs best in conjunction with deep learning.Rigorous testing and validation were applied including cross-validation,self-consistency,jackknife,and independent set testing.The proposed model yielded 0.919 value for area under the curve(AUC)and revealed Mathew’s correlation coefficient(MCC)equivalent to 0.735 demonstrating its effectiveness and stability.Subsequently,the proposed model was also extensively probed in comparison with other existing models.The performance of the proposed model also out-performs other existing models.These outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based examinations.Subsequently,it has the potential to assiduously support medical and bioinformatics research.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency DevelopmentProgram for Industry Specialist)the Soonchunhyang University Research Fund.
文摘Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and morbidity.Expanded Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immunization activities,however the coverage is quite low despite the accessibility of free vaccination.This study proposes a defaulter prediction model for accurate identification of defaulters.Our proposed framework classifies defaulters at five different stages:defaulter,partially high,partially medium,partially low,and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization schedule.Different machine learning algorithms are applied on Pakistan Demographic and Health Survey(2017–18)dataset.Multilayer Perceptron yielded 98.5%accuracy for correctly identifying children who are likely to default from immunization series at different risk stages of being defaulter.In this paper,the proposed defaulters’prediction framework is a step forward towards a data-driven approach and provides a set of machine learning techniques to take advantage of predictive analytics.Hence,predictive analytics can reinforce immunization programs by expediting targeted action to reduce dropouts.Specially,the accurate predictions support targeted messages sent to at-risk parents’and caretakers’consumer devices(e.g.,smartphones)to maximize healthcare outcomes.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2021-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)and the Soonchunhyang University Research Fund.
文摘In the recent decade,the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities.Image segmentation is a key step in digitalization.Segmentation plays a key role in almost all areas of image processing,and various approaches have been proposed for image segmentation.In this paper,a novel approach is proposed for image segmentation using a nonuniform adaptive strategy.Region-based image segmentation along with a directional binary pattern generated a better segmented image.An adaptive mask of 8×8 was circulated over the pixels whose bit value was 1 in the generated directional binary pattern.Segmentation was performed in three phases:first,an image was divided into sub-images or image chunks;next,the image patches were taken as input,and an adaptive threshold was generated;and finally the image chunks were processed separately by convolving the adaptive mask on the image chunks.Gradient and Laplacian of Gaussian algorithms along with directional extrema patterns provided a double check for boundary pixels.The proposed approach was tested on chunks of varying sizes,and after multiple iterations,it was found that a block size of 8×8 performs better than other chunks or block sizes.The accuracy of the segmentation technique was measured in terms of the count of ill regions,which were extracted after the segmentation process.
