Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artific...Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artificial intelligence can be used to extract fruit color,shape,or texture data,thus aiding the detection of infections.Recently,the convolutional neural network(CNN)techniques show a massive success for image classification tasks.CNN extracts more detailed features and can work efficiently with large datasets.In this work,we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases.A fine-tuned,pretrained deep learning model(VGG19)was retrained using a plant dataset,from which useful features were extracted.Next,contour features were extracted using pyramid histogram of oriented gradient(PHOG)and combined with the deep features using serial based approach.During the fusion process,a few pieces of redundant information were added in the form of features.Then,a“relevance-based”optimization technique was used to select the best features from the fused vector for the final classifications.With the use of multiple classifiers,an accuracy of up to 99.6%was achieved on the proposed method,which is superior to previous techniques.Moreover,our approach is useful for 5G technology,cloud computing,and the Internet of Things(IoT).展开更多
Phosphorus(P)is a critical nutrient that plays an essential role in improving soil fertility for optimum plant growth and productivity.It is one of the most deficient macro-nutrients in agricultural soils after nitrog...Phosphorus(P)is a critical nutrient that plays an essential role in improving soil fertility for optimum plant growth and productivity.It is one of the most deficient macro-nutrients in agricultural soils after nitrogen and is considered inadequate for plant growth and production.To P availability in soils,the farmers are applying huge amounts of synthetic P fertilizers that adversely affect the wider environment,groundwater,soil fertility and microbial population.Many beneficial microbes are known to release and supply soluble P for improving growth and yield of a variety of plants in a sustainable manner in P deficient soils.Thus,inoculation of these microbes,including arbuscular mycorrhizal fungi(AMF)and phosphate solubilizing bacteria(PSB)to soil to enhance crop production without harming the environment,is an alternative approach to chemical fertilizers.The combined role of AMF and PSB in P solubilization is not well understood and the application and mode of action of these microbial groups are often naive due to variation in the environment.Therefore,the current review article would develop a better understanding of the interactive role and mechanisms of AMF and PSB in improving P availability from both organic and inorganic sources in a sustainable crop production system.Finally,the current review would loop out further avenues for researchers interested to commercially produce effective AMF and PSB-based biofertilizers for sustainable management of phosphorus over a wide range of agricultural crops worldwide.展开更多
Human Action Recognition(HAR)is an active research topic in machine learning for the last few decades.Visual surveillance,robotics,and pedestrian detection are the main applications for action recognition.Computer vis...Human Action Recognition(HAR)is an active research topic in machine learning for the last few decades.Visual surveillance,robotics,and pedestrian detection are the main applications for action recognition.Computer vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of computing.In this article,we proposed a new method for the use of deep learning for HAR.In the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning.The Resnet-50 Pre-Trained Model is used as a deep learning model in this work.Features are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold function.The selected features are finally passed to multiple classifiers for final classification.Experiments are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and KTH.The accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,respectively.Comparison with existing techniques has shown that the proposed method provides improved accuracy for HAR.Also,the proposed method is computationally fast based on the time of execution.展开更多
Wireless Capsule Endoscopy(WCE)is an imaging technology,widely used in medical imaging for stomach infection recognition.However,a one patient procedure takes almost seven to eight minutes and approximately 57,000 fra...Wireless Capsule Endoscopy(WCE)is an imaging technology,widely used in medical imaging for stomach infection recognition.However,a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured.The privacy of patients is very important and manual inspection is time consuming and costly.Therefore,an automated system for recognition of stomach infections from WCE frames is always needed.An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding.Initially,images are normalized in fixed dimension and passed in pre-trained deep models.These architectures are modified at each layer,to make them safer and more secure.Each layer contains an extra block,which stores certain information to avoid possible tempering,modification attacks and layer deletions.Information is stored in multiple blocks,i.e.,block attached to each layer,a ledger block attached with the network,and a cloud ledger block stored in the cloud storage.After that,features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function.The Softmax classifier is applied at the end for final classification.Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%.The statistical analysis and individual model comparison show the proposed method’s authenticity.展开更多
A pot experiment was conducted to determine the potential of AM fungi in phytoremediation of heavy metals and its effect on yield of wheat crop. The experiment was conducted in CR Design with four replications during ...A pot experiment was conducted to determine the potential of AM fungi in phytoremediation of heavy metals and its effect on yield of wheat crop. The experiment was conducted in CR Design with four replications during rabi 2012-13. Data showed no increase in grain and shoot yields by AMF inoculation with Zn, Cu, Fe, Mn at different levels but increased root yield, plant height, spike length and hundred grains weight of wheat as compared with uninoculated crop. Post-harvest soil Zn, Cu, Fe and Mn contents of 2, 4.4, 2.8 and 2.9 mg·kg-1, respectively were maximum in uninoculated plants treated with Zn, Cu, Fe, Mn at triple of recommended level. No increases in plant P, N, Zn, Cu, Fe and Mn uptakes were observed by the inoculation of AMF when compared with uninoculated crop. Maximum plant Zn, Cu, Fe and Mn uptakes of 160.5, 206, 1914.6 and 2653 g·ha-1, respectively were recorded in uninoculated plants applied with Zn, Cu, Fe, Mn at triple of recommended levels. Wheat roots infection intensity by AMF increased with higher AMF soil spores density. Results suggest the potential of phytoremediation of contaminated soil to be improved by the inoculation of crops with AMF.展开更多
In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM1...In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 datasets.Initially,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image gradients.All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image.Later,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected datasets.The concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion images.In the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)technique.To avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)algorithm.The selected features are finally passed to the softmax classifier for the final classification.Three datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively.展开更多
Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due ...Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms.Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant leaf.To solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango leaf.The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques.We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,Pakistan.To evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and Unet.Furthermore,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other models.We have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’identification.Accordingly,an automated approach helps pathologists and mango growers detect and identify those diseases.展开更多
The present study was based on the general hypothesis that boron may affect the accumulation and utilization of other nutrients in plant. For this purpose a field experiment was carried out to find out the influence o...The present study was based on the general hypothesis that boron may affect the accumulation and utilization of other nutrients in plant. For this purpose a field experiment was carried out to find out the influence of boron on the different nutrients content in FCV tobacco (Nicotiana tabacum L.) at TRS Khan Garhi, Mardan, during 2010-2011. Two varieties TM-2008 and Speight G-28 were tested and six levels of boron (0, 0.5, 1, 2, 3 and 5 kg·ha-1) were applied in the form of boric acid, in randomized complete block design in split plot arrangement and replicated thrice. Results indicated that the yield of tobacco crop increased with 1 kg·B·ha-1 and then decreased sequence wise in both varieties. N and P concentrations were significantly affected by applied boron. Similarly, potassium was increased which is a good indication for a better quality of tobacco crop. Application of boron significantly increased the concentrations of boron nutrients ratios such as K/B;Cl/B and Mn/Fe were decreased while K/Cl and Zn/Cu ratios were increased at lower boron concentrations but decreased at higher concentrations of boron. The fertilizer use efficiency of both the cultivars showed similar trend;however, Speight G-28 was more efficient than TM-2008 in boron accumulation. The overall results revealed that the application of boron should be encouraged for balancing nutrients concentration, thus getting higher yield in the prevailing conditions.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
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
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
Coronavirus 19(COVID-19)can cause severe pneumonia that may be fatal.Correct diagnosis is essential.Computed tomography(CT)usefully detects symptoms of COVID-19 infection.In this retrospective study,we present an impr...Coronavirus 19(COVID-19)can cause severe pneumonia that may be fatal.Correct diagnosis is essential.Computed tomography(CT)usefully detects symptoms of COVID-19 infection.In this retrospective study,we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing,segmentation,feature extraction/fusion/selection,and classification.In the pre-processing phase,a Gabor wavelet filter is applied to enhance image intensities.A marker-based,watershed controlled approach with thresholding is used to isolate the lung region.In the segmentation phase,COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification head.DeepLabv3 is an effective decoder that helps to refine segmentation of lesion boundaries.The model was trained using fine-tuned hyperparameters selected after extensive experimentation.Subsequently,the Gray Level Co-occurrence Matrix(GLCM)features and statistical features including circularity,area,and perimeters were computed for each segmented image.The computed features were serially fused and the best features(those that were optimally discriminatory)selected using a Genetic Algorithm(GA)for classification.The performance of the method was evaluated using two benchmark datasets:The COVID-19 Segmentation and the POF Hospital datasets.The results were better than those of existing methods.展开更多
Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image processing.Conventionally,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI ...Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image processing.Conventionally,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract.The procedure is subjective and results in significant inter-/intraobserver variations in disease detection.Moreover,a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination.Consequently,there is a huge demand for a reliable computer-aided diagnostic system(CADx)for diagnosing GI tract diseases.In this work,a CADx was proposed for the diagnosis and classification of GI tract diseases.A novel framework is presented where preprocessing(LAB color space)is performed first;then local binary patterns(LBP)or texture and deep learning(inceptionNet,ResNet50,and VGG-16)features are fused serially to improve the prediction of the abnormalities in the GI tract.Additionally,principal component analysis(PCA),entropy,and minimum redundancy and maximum relevance(mRMR)feature selection methods were analyzed to acquire the optimized characteristics,and various classifiers were trained using the fused features.Open-source color image datasets(KVASIR,NERTHUS,and stomach ULCER)were used for performance evaluation.The study revealed that the subspace discriminant classifier provided an efficient result with 95.02%accuracy on the KVASIR dataset,which proved to be better than the existing state-of-the-art approaches.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
Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the los...Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.展开更多
Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes resea...Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.展开更多
Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer vision.Big breakthroughs in this field have been observed in the last few years;additionally,the ...Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer vision.Big breakthroughs in this field have been observed in the last few years;additionally,the interest in research in this field is evolving,such as understanding of actions and scenes,studying human joints,and human posture recognition.Many HAR techniques are introduced in the literature.Nonetheless,the challenge of redundant and irrelevant features reduces recognition accuracy.They also faced a few other challenges,such as differing perspectives,environmental conditions,and temporal variations,among others.In this work,a deep learning and improved whale optimization algorithm based framework is proposed for HAR.The proposed framework consists of a few core stages i.e.,frames initial preprocessing,fine-tuned pre-trained deep learning models through transfer learning(TL),features fusion using modified serial based approach,and improved whale optimization based best features selection for final classification.Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets.The fusion process increases the length of feature vectors;therefore,improved whale optimization algorithm is proposed and selects the best features.The best selected features are finally classified usingmachine learning(ML)classifiers.Four publicly accessible datasets such as Ut-interaction,Hollywood,Free Viewpoint Action Recognition usingMotion History Volumes(IXMAS),and centre of computer vision(UCF)Sports,are employed and achieved the testing accuracy of 100%,99.9%,99.1%,and 100%respectively.Comparison with state of the art techniques(SOTA),the proposed method showed the improved accuracy.展开更多
Malaria is a critical health condition that affects both sultry and frigid region worldwide,giving rise to millions of cases of disease and thousands of deaths over the years.Malaria is caused by parasites that enter ...Malaria is a critical health condition that affects both sultry and frigid region worldwide,giving rise to millions of cases of disease and thousands of deaths over the years.Malaria is caused by parasites that enter the human red blood cells,grow there,and damage them over time.Therefore,it is diagnosed by a detailed examination of blood cells under the microscope.This is the most extensively used malaria diagnosis technique,but it yields limited and unreliable results due to the manual human involvement.In this work,an automated malaria blood smear classification model is proposed,which takes images of both infected and healthy cells and preprocesses themin the L^(*)a^(*)b^(*)color space by employing several contrast enhancement methods.Feature extraction is performed using two pretrained deep convolutional neural networks,DarkNet-53 and DenseNet-201.The features are subsequently agglutinated to be optimized through a nature-based feature reduction method called the whale optimization algorithm.Several classifiers are effectuated on the reduced features,and the achieved results excel in both accuracy and time compared to previously proposed methods.展开更多
Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in ...Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total cases.Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths.The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis.Manual diagnosis of BC is a complex and challenging task.This work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology images.To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized.The proposed automatic diagnosis of BC detection and classification mainly involves three steps.Initially,a DL model is proposed for feature extraction.Secondly,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best FS.Finally,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are used.Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.展开更多
An experiment was conducted in pots under natural conditions in alkaline calcareous soil to determine wheat (Triticum aestivum L. c.v. Atta Habib) yield and P uptake as influenced by Arbuscular mycorrhizal fungi (AMF)...An experiment was conducted in pots under natural conditions in alkaline calcareous soil to determine wheat (Triticum aestivum L. c.v. Atta Habib) yield and P uptake as influenced by Arbuscular mycorrhizal fungi (AMF) inoculation with compost prepared from fresh animal dung and rock phosphate. Data indicated that wheat grain, shoot and roots yields increased significantly (P ≤ 0.05) by inoculation of commercial mycorrhiza (AMF-II) and half dose of compost. Grain yield increased by 43% and 37%, shoot by 43% and 39% and roots yield by 51% and 45% over control of N and K fertilizers. Straw yield was maximum as 5075 kg·ha-1 in the treatment of AMF-II inoculation with full dose of compost, which was significantly (P ≤ 0.05) higher as 44% and 40% over control of N and K fertilizers. Maximum and significantly (P ≤ 0.05) higher plant N and P uptake by wheat were observed in the treatment inoculated by indigenous mycorrhiza (AMF-I) with full dose of compost followed by the inoculation of AMF-II with full dose of compost and SSP treatment. Maximum and significantly (P ≤ 0.05) increased soil spores’ density of AMF by 26 spores per 20 g soil with maximum roots infection intensity in wheat were observed by the inoculation of AMF-I with full dose of compost. The AMF-II is slightly better than AMF-I regarding grain, shoot and root yield, whereas AMF-I is better in N, P uptake, soil spore density and their root infection intensity than AMF-II. Alone inoculation and compost application increase the yield and nutrients uptake but the highest improvement was observed with inoculation of AMF with compost. Results suggest that inoculation of AMF with compost has potential to improve wheat yields and plants’ P uptake under given soil conditions.展开更多
基金the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2020-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)and the Soonchunhyang University Research Fund.
文摘Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artificial intelligence can be used to extract fruit color,shape,or texture data,thus aiding the detection of infections.Recently,the convolutional neural network(CNN)techniques show a massive success for image classification tasks.CNN extracts more detailed features and can work efficiently with large datasets.In this work,we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases.A fine-tuned,pretrained deep learning model(VGG19)was retrained using a plant dataset,from which useful features were extracted.Next,contour features were extracted using pyramid histogram of oriented gradient(PHOG)and combined with the deep features using serial based approach.During the fusion process,a few pieces of redundant information were added in the form of features.Then,a“relevance-based”optimization technique was used to select the best features from the fused vector for the final classifications.With the use of multiple classifiers,an accuracy of up to 99.6%was achieved on the proposed method,which is superior to previous techniques.Moreover,our approach is useful for 5G technology,cloud computing,and the Internet of Things(IoT).
文摘Phosphorus(P)is a critical nutrient that plays an essential role in improving soil fertility for optimum plant growth and productivity.It is one of the most deficient macro-nutrients in agricultural soils after nitrogen and is considered inadequate for plant growth and production.To P availability in soils,the farmers are applying huge amounts of synthetic P fertilizers that adversely affect the wider environment,groundwater,soil fertility and microbial population.Many beneficial microbes are known to release and supply soluble P for improving growth and yield of a variety of plants in a sustainable manner in P deficient soils.Thus,inoculation of these microbes,including arbuscular mycorrhizal fungi(AMF)and phosphate solubilizing bacteria(PSB)to soil to enhance crop production without harming the environment,is an alternative approach to chemical fertilizers.The combined role of AMF and PSB in P solubilization is not well understood and the application and mode of action of these microbial groups are often naive due to variation in the environment.Therefore,the current review article would develop a better understanding of the interactive role and mechanisms of AMF and PSB in improving P availability from both organic and inorganic sources in a sustainable crop production system.Finally,the current review would loop out further avenues for researchers interested to commercially produce effective AMF and PSB-based biofertilizers for sustainable management of phosphorus over a wide range of agricultural crops worldwide.
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)and the Soonchunhyang University Research Fund.
文摘Human Action Recognition(HAR)is an active research topic in machine learning for the last few decades.Visual surveillance,robotics,and pedestrian detection are the main applications for action recognition.Computer vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of computing.In this article,we proposed a new method for the use of deep learning for HAR.In the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning.The Resnet-50 Pre-Trained Model is used as a deep learning model in this work.Features are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold function.The selected features are finally passed to multiple classifiers for final classification.Experiments are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and KTH.The accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,respectively.Comparison with existing techniques has shown that the proposed method provides improved accuracy for HAR.Also,the proposed method is computationally fast based on the time of execution.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Wireless Capsule Endoscopy(WCE)is an imaging technology,widely used in medical imaging for stomach infection recognition.However,a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured.The privacy of patients is very important and manual inspection is time consuming and costly.Therefore,an automated system for recognition of stomach infections from WCE frames is always needed.An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding.Initially,images are normalized in fixed dimension and passed in pre-trained deep models.These architectures are modified at each layer,to make them safer and more secure.Each layer contains an extra block,which stores certain information to avoid possible tempering,modification attacks and layer deletions.Information is stored in multiple blocks,i.e.,block attached to each layer,a ledger block attached with the network,and a cloud ledger block stored in the cloud storage.After that,features are extracted and fused using a Mode value-based approach and optimized using a Genetic Algorithm along with an entropy function.The Softmax classifier is applied at the end for final classification.Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%.The statistical analysis and individual model comparison show the proposed method’s authenticity.
文摘A pot experiment was conducted to determine the potential of AM fungi in phytoremediation of heavy metals and its effect on yield of wheat crop. The experiment was conducted in CR Design with four replications during rabi 2012-13. Data showed no increase in grain and shoot yields by AMF inoculation with Zn, Cu, Fe, Mn at different levels but increased root yield, plant height, spike length and hundred grains weight of wheat as compared with uninoculated crop. Post-harvest soil Zn, Cu, Fe and Mn contents of 2, 4.4, 2.8 and 2.9 mg·kg-1, respectively were maximum in uninoculated plants treated with Zn, Cu, Fe, Mn at triple of recommended level. No increases in plant P, N, Zn, Cu, Fe and Mn uptakes were observed by the inoculation of AMF when compared with uninoculated crop. Maximum plant Zn, Cu, Fe and Mn uptakes of 160.5, 206, 1914.6 and 2653 g·ha-1, respectively were recorded in uninoculated plants applied with Zn, Cu, Fe, Mn at triple of recommended levels. Wheat roots infection intensity by AMF increased with higher AMF soil spores density. Results suggest the potential of phytoremediation of contaminated soil to be improved by the inoculation of crops with AMF.
基金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In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 datasets.Initially,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image gradients.All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image.Later,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected datasets.The concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion images.In the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)technique.To avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)algorithm.The selected features are finally passed to the softmax classifier for the final classification.Three datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively.
文摘Due to the high demand for mango and being the king of all fruits,it is the need of the hour to curb its diseases to fetch high returns.Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms.Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases,i.e.,Anthracnose,apicalnecrosis,etc.,of a mango plant leaf.To solve this issue,we proposed a CNN based Fully-convolutional-network(FrCNnet)model for the segmentation of the diseased part of the mango leaf.The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques.We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute,Multan,Pakistan.To evaluate the proposed model results,we compared the segmentation performance with the available state-of-the-art models,i.e.,Vgg16,Vgg-19,and Unet.Furthermore,the proposed model’s segmentation accuracy is 99.2%with a false negative rate(FNR)of 0.8%,which is much higher than the other models.We have concluded that by using a FrCNnet,the input image could learn better features that are more prominent and much specific,resulting in an improved and better segmentation performance and diseases’identification.Accordingly,an automated approach helps pathologists and mango growers detect and identify those diseases.
文摘The present study was based on the general hypothesis that boron may affect the accumulation and utilization of other nutrients in plant. For this purpose a field experiment was carried out to find out the influence of boron on the different nutrients content in FCV tobacco (Nicotiana tabacum L.) at TRS Khan Garhi, Mardan, during 2010-2011. Two varieties TM-2008 and Speight G-28 were tested and six levels of boron (0, 0.5, 1, 2, 3 and 5 kg·ha-1) were applied in the form of boric acid, in randomized complete block design in split plot arrangement and replicated thrice. Results indicated that the yield of tobacco crop increased with 1 kg·B·ha-1 and then decreased sequence wise in both varieties. N and P concentrations were significantly affected by applied boron. Similarly, potassium was increased which is a good indication for a better quality of tobacco crop. Application of boron significantly increased the concentrations of boron nutrients ratios such as K/B;Cl/B and Mn/Fe were decreased while K/Cl and Zn/Cu ratios were increased at lower boron concentrations but decreased at higher concentrations of boron. The fertilizer use efficiency of both the cultivars showed similar trend;however, Speight G-28 was more efficient than TM-2008 in boron accumulation. The overall results revealed that the application of boron should be encouraged for balancing nutrients concentration, thus getting higher yield in the prevailing conditions.
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 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Coronavirus 19(COVID-19)can cause severe pneumonia that may be fatal.Correct diagnosis is essential.Computed tomography(CT)usefully detects symptoms of COVID-19 infection.In this retrospective study,we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing,segmentation,feature extraction/fusion/selection,and classification.In the pre-processing phase,a Gabor wavelet filter is applied to enhance image intensities.A marker-based,watershed controlled approach with thresholding is used to isolate the lung region.In the segmentation phase,COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification head.DeepLabv3 is an effective decoder that helps to refine segmentation of lesion boundaries.The model was trained using fine-tuned hyperparameters selected after extensive experimentation.Subsequently,the Gray Level Co-occurrence Matrix(GLCM)features and statistical features including circularity,area,and perimeters were computed for each segmented image.The computed features were serially fused and the best features(those that were optimally discriminatory)selected using a Genetic Algorithm(GA)for classification.The performance of the method was evaluated using two benchmark datasets:The COVID-19 Segmentation and the POF Hospital datasets.The results were better than those of existing methods.
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)and the Soonchunhyang University Research Fund.
文摘Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image processing.Conventionally,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract.The procedure is subjective and results in significant inter-/intraobserver variations in disease detection.Moreover,a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination.Consequently,there is a huge demand for a reliable computer-aided diagnostic system(CADx)for diagnosing GI tract diseases.In this work,a CADx was proposed for the diagnosis and classification of GI tract diseases.A novel framework is presented where preprocessing(LAB color space)is performed first;then local binary patterns(LBP)or texture and deep learning(inceptionNet,ResNet50,and VGG-16)features are fused serially to improve the prediction of the abnormalities in the GI tract.Additionally,principal component analysis(PCA),entropy,and minimum redundancy and maximum relevance(mRMR)feature selection methods were analyzed to acquire the optimized characteristics,and various classifiers were trained using the fused features.Open-source color image datasets(KVASIR,NERTHUS,and stomach ULCER)were used for performance evaluation.The study revealed that the subspace discriminant classifier provided an efficient result with 95.02%accuracy on the KVASIR dataset,which proved to be better than the existing state-of-the-art approaches.
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.
基金supported by the Technology Development Program of MSS[No.S3033853]by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A4A1031509).
文摘Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.
基金supported in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education under Grant NRF-2019R1A2C1006159 and Grant NRF-2021R1A6A1A03039493in part by the 2022 Yeungnam University Research Grant.
文摘Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians.Consequently,traffic signs have been of great importance for every civilized country,which makes researchers give more focus on the automatic detection of traffic signs.Detecting these traffic signs is challenging due to being in the dark,far away,partially occluded,and affected by the lighting or the presence of similar objects.An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues.This technique aimed to devise an efficient,robust and accurate approach.To attain this,initially,the approach presented a new formula,inspired by existing work,to enhance the image using red and green channels instead of blue,which segmented using a threshold calculated from the correlational property of the image.Next,a new set of features is proposed,motivated by existing features.Texture and color features are fused after getting extracted on the channel of Red,Green,and Blue(RGB),Hue,Saturation,and Value(HSV),and YCbCr color models of images.Later,the set of features is employed on different classification frameworks,from which quadratic support vector machine(SVM)outnumbered the others with an accuracy of 98.5%.The proposed method is tested on German Traffic Sign Detection Benchmark(GTSDB)images.The results are satisfactory when compared to the preceding work.
基金This research work is supported in part by Chiang Mai University and HITEC University.
文摘Human action recognition(HAR)based on Artificial intelligence reasoning is the most important research area in computer vision.Big breakthroughs in this field have been observed in the last few years;additionally,the interest in research in this field is evolving,such as understanding of actions and scenes,studying human joints,and human posture recognition.Many HAR techniques are introduced in the literature.Nonetheless,the challenge of redundant and irrelevant features reduces recognition accuracy.They also faced a few other challenges,such as differing perspectives,environmental conditions,and temporal variations,among others.In this work,a deep learning and improved whale optimization algorithm based framework is proposed for HAR.The proposed framework consists of a few core stages i.e.,frames initial preprocessing,fine-tuned pre-trained deep learning models through transfer learning(TL),features fusion using modified serial based approach,and improved whale optimization based best features selection for final classification.Two pre-trained deep learning models such as InceptionV3 and Resnet101 are fine-tuned and TL is employed to train on action recognition datasets.The fusion process increases the length of feature vectors;therefore,improved whale optimization algorithm is proposed and selects the best features.The best selected features are finally classified usingmachine learning(ML)classifiers.Four publicly accessible datasets such as Ut-interaction,Hollywood,Free Viewpoint Action Recognition usingMotion History Volumes(IXMAS),and centre of computer vision(UCF)Sports,are employed and achieved the testing accuracy of 100%,99.9%,99.1%,and 100%respectively.Comparison with state of the art techniques(SOTA),the proposed method showed the improved accuracy.
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Malaria is a critical health condition that affects both sultry and frigid region worldwide,giving rise to millions of cases of disease and thousands of deaths over the years.Malaria is caused by parasites that enter the human red blood cells,grow there,and damage them over time.Therefore,it is diagnosed by a detailed examination of blood cells under the microscope.This is the most extensively used malaria diagnosis technique,but it yields limited and unreliable results due to the manual human involvement.In this work,an automated malaria blood smear classification model is proposed,which takes images of both infected and healthy cells and preprocesses themin the L^(*)a^(*)b^(*)color space by employing several contrast enhancement methods.Feature extraction is performed using two pretrained deep convolutional neural networks,DarkNet-53 and DenseNet-201.The features are subsequently agglutinated to be optimized through a nature-based feature reduction method called the whale optimization algorithm.Several classifiers are effectuated on the reduced features,and the achieved results excel in both accuracy and time compared to previously proposed methods.
基金This work was supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total cases.Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths.The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis.Manual diagnosis of BC is a complex and challenging task.This work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology images.To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized.The proposed automatic diagnosis of BC detection and classification mainly involves three steps.Initially,a DL model is proposed for feature extraction.Secondly,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best FS.Finally,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are used.Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.
文摘An experiment was conducted in pots under natural conditions in alkaline calcareous soil to determine wheat (Triticum aestivum L. c.v. Atta Habib) yield and P uptake as influenced by Arbuscular mycorrhizal fungi (AMF) inoculation with compost prepared from fresh animal dung and rock phosphate. Data indicated that wheat grain, shoot and roots yields increased significantly (P ≤ 0.05) by inoculation of commercial mycorrhiza (AMF-II) and half dose of compost. Grain yield increased by 43% and 37%, shoot by 43% and 39% and roots yield by 51% and 45% over control of N and K fertilizers. Straw yield was maximum as 5075 kg·ha-1 in the treatment of AMF-II inoculation with full dose of compost, which was significantly (P ≤ 0.05) higher as 44% and 40% over control of N and K fertilizers. Maximum and significantly (P ≤ 0.05) higher plant N and P uptake by wheat were observed in the treatment inoculated by indigenous mycorrhiza (AMF-I) with full dose of compost followed by the inoculation of AMF-II with full dose of compost and SSP treatment. Maximum and significantly (P ≤ 0.05) increased soil spores’ density of AMF by 26 spores per 20 g soil with maximum roots infection intensity in wheat were observed by the inoculation of AMF-I with full dose of compost. The AMF-II is slightly better than AMF-I regarding grain, shoot and root yield, whereas AMF-I is better in N, P uptake, soil spore density and their root infection intensity than AMF-II. Alone inoculation and compost application increase the yield and nutrients uptake but the highest improvement was observed with inoculation of AMF with compost. Results suggest that inoculation of AMF with compost has potential to improve wheat yields and plants’ P uptake under given soil conditions.
