Weber Law Based Approach for Multi-Class Image Forgery Detection

Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing forgeries.However,an image can contain one or more types of forgeries.This study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial domain.Firstly,images are divided into equal blocks to get scale-invariant features.Weber law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing forgery.The proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and healthy.Benchmarked(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and testing.On average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.

Binary Oriented Feature Selection for Valid Product Derivation in Software Product Line

Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints(CTC).CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things(IoT)devices because different Internet devices and protocols are communicated.Therefore,managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex,time-consuming,and hard.However,the CTC problem needs to be considered in previously proposed approaches such as Commonality VariabilityModeling of Features(COVAMOF)andGenarch+tool;therefore,invalid products are generated.This research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints(BOFS-CTC),to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the featuremodel of SPL.BOFS-CTC removes the invalid products at the early stage of feature selection for the product configuration.Furthermore,this research developed the BOFS-CTC algorithm and applied it to,IoT-based feature models.The findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product configurations.The accuracy of BOFS-CTC is measured by the integration sampling technique,where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100%correct.Using BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.

A Novel Framework for Multi-Classification of Guava Disease

Guava is one of the most important fruits in Pakistan,and is gradually boosting the economy of Pakistan.Guava production can be interrupted due to different diseases,such as anthracnose,algal spot,fruit fly,styler end rot and canker.These diseases are usually detected and identified by visual observation,thus automatic detection is required to assist formers.In this research,a new technique was created to detect guava plant diseases using image processing techniques and computer vision.An automated system is developed to support farmers to identify major diseases in guava.We collected healthy and unhealthy images of different guava diseases from the field.Then image labeling was done with the help of an expert to differentiate between healthy and unhealthy fruit.The local binary pattern(LBP)was used for the extraction of features,and principal component analysis(PCA)was used for dimensionality reduction.Disease classification was carried out using multiple classifiers,including cubic support vector machine,Fine K-nearest neighbor(F-KNN),Bagged Tree and RUSBoosted Tree algorithms and achieved 100%accuracy for the diagnosis of fruit flies disease using Bagged Tree.However,the findings indicated that cubic support vector machines(C-SVM)was the best classifier for all guava disease mentioned in the dataset.

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

Context Based Adoption of Ranking and Indexing Measures for Cricket Team Ranks

There is an international cricket governing body that ranks the expertise of all the cricket playing nations,known as the International Cricket Council(ICC).The ranking system followed by the ICC relies on the winnings and defeats of the teams.The model used by the ICC to implement rankings is deficient in certain key respects.It ignores key factors like winning margin and strength of the opposition.Various measures of the ranking concept are presented in this research.The proposed methods adopt the concepts of h-Index and PageRank for presenting more comprehensive ranking metrics.The proposed approaches not only rank the teams on their losing/winning stats but also take into consideration the margin of winning and the quality of the opposition.Three cricket team ranking techniques are presented i.e.,(1)Cricket Team-Index(ct-index),(2)Cricket Team Rank(CTR)and(3)Weighted Cricket Team Rank(WCTR).The proposed metrics are validated through the collection of cricket dataset,extracted from Cricinfo,having instances for all the three formats of the game i.e.,T20 International(T20i),One Day International(ODI)and Test matches.The comparative analysis between the proposed and existing techniques,for all the three formats,is presented as well.

Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

Around one in eight women will be diagnosed with breast cancer at some time.Improved patient outcomes necessitate both early detection and an accurate diagnosis.Histological images are routinely utilized in the process of diagnosing breast cancer.Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels.No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer.A strategy for detecting breast cancer is provided in the context of this investigation.Histopathology image texture data is used with the wavelet transform in this technique.The proposed method comprises converting histopathological images from Red Green Blue(RGB)to Chrominance of Blue and Chrominance of Red(YCBCR),utilizing a wavelet transform to extract texture information,and classifying the images with Extreme Gradient Boosting(XGBOOST).Furthermore,SMOTE has been used for resampling as the dataset has imbalanced samples.The suggested method is evaluated using 10-fold cross-validation and achieves an accuracy of 99.27%on the BreakHis 1.040X dataset,98.95%on the BreakHis 1.0100X dataset,98.92%on the BreakHis 1.0200X dataset,98.78%on the BreakHis 1.0400X dataset,and 98.80%on the combined dataset.The findings of this study imply that improved breast cancer detection rates and patient outcomes can be achieved by combining wavelet transformation with textural signals to detect breast cancer in histopathology images.

Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.

Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.