An automated system is proposed for the detection and classification of GI abnormalities.The proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification...An automated system is proposed for the detection and classification of GI abnormalities.The proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep learning.The first bleeding region is segmented using a hybrid approach.The threshold is applied to each channel extracted from the original RGB image.Later,all channels are merged through mutual information and pixel-based techniques.As a result,the image is segmented.Texture and deep learning features are extracted in the proposed classification task.The transfer learning(TL)approach is used for the extraction of deep features.The Local Binary Pattern(LBP)method is used for texture features.Later,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture vectors.The selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning Classifier.The experimental process is evaluated on the basis of two datasets:Private and KVASIR.The accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data set.It can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.展开更多
As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy supply.Plant pathogens will affect its leaves at a certain time during crop cultivat...As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy supply.Plant pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling price.In the agriculture industry,the identification of fungal diseases plays a vital role.However,it requires immense labor,greater planning time,and extensive knowledge of plant pathogens.Computerized approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several times.Therefore,the proposed study presents a new framework for the recognition of fruits and vegetable diseases.This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)model.The localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model.The localized images passed as input to classify the different types of plant diseases.The classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 fully-connected.The proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.展开更多
The incident rate of the Gastrointestinal-Disease(GD)in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image(EI/CI)supported evaluation of the GD is an approved practice.Extract...The incident rate of the Gastrointestinal-Disease(GD)in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image(EI/CI)supported evaluation of the GD is an approved practice.Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity.The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp(GP)with better accuracy.The proposed GP detection system consist;(i)Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy(Fuzzy/Shannon/Kapur)and between-class-variance(Otsu)technique,(ii)Automated(Watershed/Markov-Random-Field)and semi-automated(Chan-Vese/Level-Set/Active-Contour)segmentation of GPfragment,and(iii)Performance evaluation and validation of the proposed scheme.The experimental investigation was performed using four benchmark EI dataset(CVC-ClinicDB,ETIS-Larib,EndoCV2020 and Kvasir).The similarity measures,such as Jaccard,Dice,accuracy,precision,sensitivity and specificity are computed to confirm the clinical significance of the proposed work.The outcome of this research confirms that the fuzzyentropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.展开更多
Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric technique.The main challenges in gait recognition are the change in in-person view angle and covariant factors....Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric technique.The main challenges in gait recognition are the change in in-person view angle and covariant factors.The major covariant factors are walking while carrying a bag and walking while wearing a coat.Deep learning is a new machine learning technique that is gaining popularity.Many techniques for HGR based on deep learning are presented in the literature.The requirement of an efficient framework is always required for correct and quick gait recognition.We proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this work.The proposed framework consists of four primary steps.In the first step,the database is normalized in a video frame.In the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s nature.After that,we trained both modified models using transfer learning and extracted the features.The IACO algorithm is used to improve the extracted features.IACO is used to select the best features,which are then passed to the Cubic SVM for final classification.The cubic SVM employs a multiclass method.The experiment was carried out on three angles(0,18,and 180)of the CASIA B dataset,and the accuracy was 95.2,93.9,and 98.2 percent,respectively.A comparison with existing techniques is also performed,and the proposed method outperforms in terms of accuracy and computational time.展开更多
Globally,Pakistan ranks 4th in cotton production,6th as an importer of raw cotton,and 3rd in cotton consumption.Nearly 10%of GDP and 55%of the country’s foreign exchange earnings depend on cotton products.Approximate...Globally,Pakistan ranks 4th in cotton production,6th as an importer of raw cotton,and 3rd in cotton consumption.Nearly 10%of GDP and 55%of the country’s foreign exchange earnings depend on cotton products.Approximately 1.5 million people in Pakistan are engaged in the cotton value chain.However,several diseases such as Mildew,Leaf Spot,and Soreshine affect cotton production.Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert.Therefore,it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early stages.In this study,a new technique is proposed using deep learning architecture with serially fused features and the best feature selection.The proposed architecture consists of the following steps:(a)a self-collected dataset of cotton diseases is prepared and labeled by an expert;(b)data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step;(c)a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach;(d)features are computed from the third and fourth last layers and serially combined into one matrix;(e)a genetic algorithm is applied to the combined matrix to select the best points for further recognition.For final recognition,a Cubic SVM approach was utilized and validated on a prepared dataset.On the newly prepared dataset,the highest achieved accuracy was 98.8%using Cubic SVM,which shows the perfection of the proposed framework..展开更多
Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s economy.The manual inspection of fruit diseases is a chaotic process that is both time and co...Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s economy.The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert.Hence,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images.According to the literature,many automated methods have been developed for the recognition of fruit diseases at the early stage.However,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant features.Image processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these challenges.Therefore,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based selection.The proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final recognition.The experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.展开更多
Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms.AIDS,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus infection...Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms.AIDS,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus infections.Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods.The National Center for Biotechnology Information(NCBI)promotes science and health by making biomedical and genomic data available to the public.This research aims to classify the different types of rotavirus genome sequences available at the NCBI.First,nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing stage.Based on some significant results,the number of features used for classification is reduced to five.The classification shall be carried out in two phases.The first phase of classification shall select only two features.Unclassified data in the first phase is transferred to the next phase,where the final decision is taken with the remaining three features.Three data sets of animals and human retroviruses are selected;the training data set is used to minimize the classifier’s number and training;the validation data set is used to validate the models.The performance of the classifier is analyzed using the test data set.Also,we use decision tree,naive Bayes,knearest neighbors,and vector support machines to compare results.The results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset.展开更多
:Agriculture has been an important research area in the field of image processing for the last five years.Diseases affect the quality and quantity of fruits,thereby disrupting the economy of a country.Many computerize...:Agriculture has been an important research area in the field of image processing for the last five years.Diseases affect the quality and quantity of fruits,thereby disrupting the economy of a country.Many computerized techniques have been introduced for detecting and recognizing fruit diseases.However,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the system.Herein,we propose an integrated deep learning framework for classifying fruit diseases.We consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and strawberry.The proposed method comprises several important steps.Initially,data increase is applied,and then two different types of features are extracted.In the first feature type,texture and color features,i.e.,classical features,are extracted.In the second type,deep learning characteristics are extracted using a pretrained model.The pretrained model is reused through transfer learning.Subsequently,both types of features are merged using the maximum mean value of the serial approach.Next,the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm.Finally,the selected features are classified using multiple classifiers.An evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.展开更多
Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large distance.Therefore,a method is proposed for the classification and recogni...Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large distance.Therefore,a method is proposed for the classification and recognition of different types of human gait.The proposed approach is consisting of two phases.In phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human gait.In this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information.In phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted scores.The proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark datasets.The proposed method achieved better/improved prediction scores as compared to the recent existing works.展开更多
White blood cells(WBCs)are a vital part of the immune system that protect the body from different types of bacteria and viruses.Abnormal cell growth destroys the body’s immune system,and computerized methods play a v...White blood cells(WBCs)are a vital part of the immune system that protect the body from different types of bacteria and viruses.Abnormal cell growth destroys the body’s immune system,and computerized methods play a vital role in detecting abnormalities at the initial stage.In this research,a deep learning technique is proposed for the detection of leukemia.The proposed methodology consists of three phases.Phase I uses an open neural network exchange(ONNX)and YOLOv2 to localize WBCs.The localized images are passed to Phase II,in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model.The segmented images are used in Phase III,in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs.The proposed methodology is validated on three publically available benchmark datasets,namely ALL-IDB1,ALL-IDB2,and LISC,in terms of different metrics,such as precision,accuracy,sensitivity,and dice scores.The results of the proposed method are comparable to those of recent existing methodologies,thus proving its effectiveness.展开更多
Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases.An important kind of biomedical image is Pap smear image that iswidely employed for cervical cancer diagnosis.C...Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases.An important kind of biomedical image is Pap smear image that iswidely employed for cervical cancer diagnosis.Cervical cancer is a vital reason for increased women’s mortality rate.Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer.Computer-aided systems for cancerous cell detection need to be developed using deep learning(DL)approaches.This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification(IDCNN-CDC)model using biomedical pap smear images.The proposed IDCNN-CDC model involves four major processes such as preprocessing,segmentation,feature extraction,and classification.Initially,the Gaussian filter(GF)technique is applied to enhance data through noise removal process in the Pap smear image.The Tsallis entropy technique with the dragonfly optimization(TE-DFO)algorithm determines the segmentation of an image to identify the diseased portions properly.The cell images are fed into the DL based SqueezeNet model to extract deeplearned features.Finally,the extracted features fromSqueezeNet are applied to the weighted extreme learning machine(ELM)classification model to detect and classify the cervix cells.For experimental validation,the Herlev database is employed.The database was developed at Herlev University Hospital(Denmark).The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity,specificity,accuracy,and F-Score.展开更多
Malaria is a severe illness triggered by parasites that spreads via mosquito bites.In underdeveloped nations,malaria is one of the top causes of mortality,and it is mainly diagnosed through microscopy.Computer-assiste...Malaria is a severe illness triggered by parasites that spreads via mosquito bites.In underdeveloped nations,malaria is one of the top causes of mortality,and it is mainly diagnosed through microscopy.Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups.Therefore,in this study,we present a new idea based on the ensemble quantum-classical framework for malaria classification.The methods comprise three core steps:localization,segmentation,and classification.In the first core step,an improved FRCNN model is proposed for the localization of the infected malaria cells.Then,the RGB localized images were converted into YCbCr channels to normalize the image intensity values.Subsequently,the actual lesion region was segmented using a histogram-based color thresholding approach.The segmented images were employed for classification in two different ways.In the first method,a CNN model is developed by the selection of optimum layers after extensive experimentation,and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of themicroscopicmalaria images.Second,a quantum-convolutionalmodel is employed for informative feature extraction from microscopicmalaria images,and the extracted feature vectors are supplied to the softmax layer for classification.Finally,classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to CNN.The proposed models attain a precision rate greater than 90%,thereby proving that these models performed better than the existing models.展开更多
Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications...Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.展开更多
Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images ...Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.展开更多
Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous diseases.Detection and diagnosis of SRC carcinoma at earlier stages is a ...Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous diseases.Detection and diagnosis of SRC carcinoma at earlier stages is a challenging,laborious,and costly task.Automatic detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of research.In the presented framework,we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning(DL)technique named Mask Region-based Convolutional Neural Network(Mask-RCNN).In the first step,the input image is fed to Resnet-101 for feature extraction.The extracted feature maps are conveyed to Region Proposal Network(RPN)for the generation of the region of interest(RoI)proposals as well as they are directly conveyed to RoiAlign.Secondly,RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected(FC)network and performs classification along with Bounding Box(bb)generation by using FC layers.The annotations are developed from ground truth(GT)images to perform experimentation on our developed dataset.Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials.We aim to release the employed database soon to assist the improvement in the SRC recognition research area.展开更多
基金This research was financially supported in part by the Ministry of Trade,Industry and Energy(MOTIE)and Korea Institute for Advancement of Technology(KIAT)through the International Cooperative R&D program.(Project No.P0016038)in part by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2016-0-00312)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).
文摘An automated system is proposed for the detection and classification of GI abnormalities.The proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep learning.The first bleeding region is segmented using a hybrid approach.The threshold is applied to each channel extracted from the original RGB image.Later,all channels are merged through mutual information and pixel-based techniques.As a result,the image is segmented.Texture and deep learning features are extracted in the proposed classification task.The transfer learning(TL)approach is used for the extraction of deep features.The Local Binary Pattern(LBP)method is used for texture features.Later,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture vectors.The selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning Classifier.The experimental process is evaluated on the basis of two datasets:Private and KVASIR.The accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data set.It can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.
基金This work was supported by the Soonchunhyang University Research Fund.
文摘As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy supply.Plant pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling price.In the agriculture industry,the identification of fungal diseases plays a vital role.However,it requires immense labor,greater planning time,and extensive knowledge of plant pathogens.Computerized approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several times.Therefore,the proposed study presents a new framework for the recognition of fruits and vegetable diseases.This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)model.The localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model.The localized images passed as input to classify the different types of plant diseases.The classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 fully-connected.The proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.
基金Authors of this research thanks the database contributors of CVC-ClinicDB,ETIS-Larib,EndoCV2020,Kvasir for providing the open access to the dataset for research purpose and thank to Deanship of Scientific Research at Majmaah University for supporting this work under the Project No.155/46683.This research work was partially supported by Chiang Mai University.
文摘The incident rate of the Gastrointestinal-Disease(GD)in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image(EI/CI)supported evaluation of the GD is an approved practice.Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity.The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp(GP)with better accuracy.The proposed GP detection system consist;(i)Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy(Fuzzy/Shannon/Kapur)and between-class-variance(Otsu)technique,(ii)Automated(Watershed/Markov-Random-Field)and semi-automated(Chan-Vese/Level-Set/Active-Contour)segmentation of GPfragment,and(iii)Performance evaluation and validation of the proposed scheme.The experimental investigation was performed using four benchmark EI dataset(CVC-ClinicDB,ETIS-Larib,EndoCV2020 and Kvasir).The similarity measures,such as Jaccard,Dice,accuracy,precision,sensitivity and specificity are computed to confirm the clinical significance of the proposed work.The outcome of this research confirms that the fuzzyentropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2018R1D1A1B07042967)and the Soonchunhyang University Research Fund.
文摘Human gait recognition(HGR)has received a lot of attention in the last decade as an alternative biometric technique.The main challenges in gait recognition are the change in in-person view angle and covariant factors.The major covariant factors are walking while carrying a bag and walking while wearing a coat.Deep learning is a new machine learning technique that is gaining popularity.Many techniques for HGR based on deep learning are presented in the literature.The requirement of an efficient framework is always required for correct and quick gait recognition.We proposed a fully automated deep learning and improved ant colony optimization(IACO)framework for HGR using video sequences in this work.The proposed framework consists of four primary steps.In the first step,the database is normalized in a video frame.In the second step,two pre-trained models named ResNet101 and InceptionV3 are selected andmodified according to the dataset’s nature.After that,we trained both modified models using transfer learning and extracted the features.The IACO algorithm is used to improve the extracted features.IACO is used to select the best features,which are then passed to the Cubic SVM for final classification.The cubic SVM employs a multiclass method.The experiment was carried out on three angles(0,18,and 180)of the CASIA B dataset,and the accuracy was 95.2,93.9,and 98.2 percent,respectively.A comparison with existing techniques is also performed,and the proposed method outperforms in terms of accuracy and computational time.
基金This work was supported by the Soonchunhyang University Research Fund.
文摘Globally,Pakistan ranks 4th in cotton production,6th as an importer of raw cotton,and 3rd in cotton consumption.Nearly 10%of GDP and 55%of the country’s foreign exchange earnings depend on cotton products.Approximately 1.5 million people in Pakistan are engaged in the cotton value chain.However,several diseases such as Mildew,Leaf Spot,and Soreshine affect cotton production.Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert.Therefore,it is essential to develop an automated technique that can accurately detect and recognize these diseases at their early stages.In this study,a new technique is proposed using deep learning architecture with serially fused features and the best feature selection.The proposed architecture consists of the following steps:(a)a self-collected dataset of cotton diseases is prepared and labeled by an expert;(b)data augmentation is performed on the collected dataset to increase the number of images for better training at the earlier step;(c)a pre-trained deep learning model named ResNet101 is employed and trained through a transfer learning approach;(d)features are computed from the third and fourth last layers and serially combined into one matrix;(e)a genetic algorithm is applied to the combined matrix to select the best points for further recognition.For final recognition,a Cubic SVM approach was utilized and validated on a prepared dataset.On the newly prepared dataset,the highest achieved accuracy was 98.8%using Cubic SVM,which shows the perfection of the proposed framework..
基金This research work was partially supported by Chiang Mai University.
文摘Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s economy.The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert.Hence,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images.According to the literature,many automated methods have been developed for the recognition of fruit diseases at the early stage.However,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant features.Image processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these challenges.Therefore,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based selection.The proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final recognition.The experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.
基金This work was supported by the Soonchunhyang University Research Fund.
文摘Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms.AIDS,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus infections.Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods.The National Center for Biotechnology Information(NCBI)promotes science and health by making biomedical and genomic data available to the public.This research aims to classify the different types of rotavirus genome sequences available at the NCBI.First,nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing stage.Based on some significant results,the number of features used for classification is reduced to five.The classification shall be carried out in two phases.The first phase of classification shall select only two features.Unclassified data in the first phase is transferred to the next phase,where the final decision is taken with the remaining three features.Three data sets of animals and human retroviruses are selected;the training data set is used to minimize the classifier’s number and training;the validation data set is used to validate the models.The performance of the classifier is analyzed using the test data set.Also,we use decision tree,naive Bayes,knearest neighbors,and vector support machines to compare results.The results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset.
基金This research was supported by X-mind Corps program of National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(No.2019H1D8A1105622)and the Soonchunhyang University Research Fund.
文摘:Agriculture has been an important research area in the field of image processing for the last five years.Diseases affect the quality and quantity of fruits,thereby disrupting the economy of a country.Many computerized techniques have been introduced for detecting and recognizing fruit diseases.However,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the system.Herein,we propose an integrated deep learning framework for classifying fruit diseases.We consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and strawberry.The proposed method comprises several important steps.Initially,data increase is applied,and then two different types of features are extracted.In the first feature type,texture and color features,i.e.,classical features,are extracted.In the second type,deep learning characteristics are extracted using a pretrained model.The pretrained model is reused through transfer learning.Subsequently,both types of features are merged using the maximum mean value of the serial approach.Next,the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm.Finally,the selected features are classified using multiple classifiers.An evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.
基金supported by the Korea Institute for Advancement of Technology(KIAT)Grant funded by the Korea Government(MOTIE)(P0012724,The Competency,Development Program for Industry Specialist)the Soonchunhyang University Research Fund.
文摘Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large distance.Therefore,a method is proposed for the classification and recognition of different types of human gait.The proposed approach is consisting of two phases.In phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human gait.In this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information.In phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted scores.The proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark datasets.The proposed method achieved better/improved prediction scores as compared to the recent existing works.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the Soonchunhyang University Research Fund.
文摘White blood cells(WBCs)are a vital part of the immune system that protect the body from different types of bacteria and viruses.Abnormal cell growth destroys the body’s immune system,and computerized methods play a vital role in detecting abnormalities at the initial stage.In this research,a deep learning technique is proposed for the detection of leukemia.The proposed methodology consists of three phases.Phase I uses an open neural network exchange(ONNX)and YOLOv2 to localize WBCs.The localized images are passed to Phase II,in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model.The segmented images are used in Phase III,in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs.The proposed methodology is validated on three publically available benchmark datasets,namely ALL-IDB1,ALL-IDB2,and LISC,in terms of different metrics,such as precision,accuracy,sensitivity,and dice scores.The results of the proposed method are comparable to those of recent existing methodologies,thus proving its effectiveness.
文摘Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases.An important kind of biomedical image is Pap smear image that iswidely employed for cervical cancer diagnosis.Cervical cancer is a vital reason for increased women’s mortality rate.Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer.Computer-aided systems for cancerous cell detection need to be developed using deep learning(DL)approaches.This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification(IDCNN-CDC)model using biomedical pap smear images.The proposed IDCNN-CDC model involves four major processes such as preprocessing,segmentation,feature extraction,and classification.Initially,the Gaussian filter(GF)technique is applied to enhance data through noise removal process in the Pap smear image.The Tsallis entropy technique with the dragonfly optimization(TE-DFO)algorithm determines the segmentation of an image to identify the diseased portions properly.The cell images are fed into the DL based SqueezeNet model to extract deeplearned features.Finally,the extracted features fromSqueezeNet are applied to the weighted extreme learning machine(ELM)classification model to detect and classify the cervix cells.For experimental validation,the Herlev database is employed.The database was developed at Herlev University Hospital(Denmark).The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity,specificity,accuracy,and F-Score.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C1010362)and the Soonchunhyang University Research Fund.
文摘Malaria is a severe illness triggered by parasites that spreads via mosquito bites.In underdeveloped nations,malaria is one of the top causes of mortality,and it is mainly diagnosed through microscopy.Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups.Therefore,in this study,we present a new idea based on the ensemble quantum-classical framework for malaria classification.The methods comprise three core steps:localization,segmentation,and classification.In the first core step,an improved FRCNN model is proposed for the localization of the infected malaria cells.Then,the RGB localized images were converted into YCbCr channels to normalize the image intensity values.Subsequently,the actual lesion region was segmented using a histogram-based color thresholding approach.The segmented images were employed for classification in two different ways.In the first method,a CNN model is developed by the selection of optimum layers after extensive experimentation,and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of themicroscopicmalaria images.Second,a quantum-convolutionalmodel is employed for informative feature extraction from microscopicmalaria images,and the extracted feature vectors are supplied to the softmax layer for classification.Finally,classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to CNN.The proposed models attain a precision rate greater than 90%,thereby proving that these models performed better than the existing models.
基金This research was supported by the Ministry of Trade,Industry&Energy(MOTIE),Korea Institute for Advancement of Technology(KIAT)through the Encouragement Program for The Industries of Economic Cooperation Region(P0006082)the Soonchunhyang University Research Fund.
文摘Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.
文摘Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.
文摘Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous diseases.Detection and diagnosis of SRC carcinoma at earlier stages is a challenging,laborious,and costly task.Automatic detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of research.In the presented framework,we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning(DL)technique named Mask Region-based Convolutional Neural Network(Mask-RCNN).In the first step,the input image is fed to Resnet-101 for feature extraction.The extracted feature maps are conveyed to Region Proposal Network(RPN)for the generation of the region of interest(RoI)proposals as well as they are directly conveyed to RoiAlign.Secondly,RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected(FC)network and performs classification along with Bounding Box(bb)generation by using FC layers.The annotations are developed from ground truth(GT)images to perform experimentation on our developed dataset.Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials.We aim to release the employed database soon to assist the improvement in the SRC recognition research area.
