The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capable...The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.展开更多
Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful ...Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same item.Image fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual perception.Image fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on application.The research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet transform.Image fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient manner.More precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain characteristics.To tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed.The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice.The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images.This study enhances the performance of the eXtreme Gradient Boosting(XGBoost)algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection.The performance of images is improved by segmenting them employing the K-Means algorithm.The segmentation method aids in identifying specific regions of interest,using Particle Swarm Optimization(PCA)for trait selection and XGBoost for data classification.Extensive trials confirm the model’s exceptional visual performance,achieving an accuracy of up to 97.067%and providing good objective indicators.展开更多
Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to chemotherapy.Lung cancer ranks among the most prevalent ...Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to chemotherapy.Lung cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all genders.Timely and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival rates.Screening examinations for lung cancer detection,however,frequently fall short of detecting small polyps and cancers.To address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients alike.This research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological images.The proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell carcinomas.We evaluated the performance of the proposed technique using the histopathological(LC25000)lung dataset.The preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer learning.The dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training dataset.The model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are obtained.The study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.展开更多
This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei.This is crucial for histopathological image analysis,as it involves segmenting cell nuclei.Howeve...This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei.This is crucial for histopathological image analysis,as it involves segmenting cell nuclei.However,challenges exist,such as determining the boundary region of normal and deformed nuclei and identifying small,irregular nuclei structures.Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification,but their complex features limit their practical use in clinical settings.The existing studies have limited accuracy,significant processing costs,and a lack of resilience and generalizability across diverse datasets.We proposed the densely convolutional Breast U-shaped Network(BU-NET)framework to overcome the mentioned issues.The study employs BU-NET’s spatial and channel attention methods to enhance segmentation processes.The inclusion of residual blocks and skip connections in the BU-NEt architecture enhances the process of extracting features and reconstructing the output.This enhances the robustness of training and convergence processes by reducing the occurrence of vanishing gradients.The primary objective of BU-NEt is to enhance the model’s capacity to acquire and analyze more intricate features,all the while preserving an efficient working representation.The BU-NET experiments demonstrate that the framework achieved 88.7%average accuracy,88.8%F1 score for Multi-Organ Nuclei Segmentation Challenge(MoNuSeg),and 91.2%average accuracy,91.8%average F1 for the triple-negative breast cancer(TNBC)dataset.The framework also achieved 93.92 Area under the ROC Curve(AUC)for TNBC.The results demonstrated that the technology surpasses existing techniques in terms of accuracy and effectiveness in segmentation.Furthermore,it showcases the ability to withstand and recover from different tissue types and diseases,indicating possible uses in medical treatments.The research evaluated the efficacy of the proposed method on diverse histopathological imaging datasets,including cancer cells from many organs.The densely connected U-NEt technology offers a promising approach for automating and precisely segmenting cancer cells on histopathology slides,hence assisting pathologists in improving cancer diagnosis and treatment outcomes.展开更多
Comic character detection is becoming an exciting and growing research area in the domain of machine learning.In this regard,recently,many methods are proposed to provide adequate performance.However,most of these met...Comic character detection is becoming an exciting and growing research area in the domain of machine learning.In this regard,recently,many methods are proposed to provide adequate performance.However,most of these methods utilized the custom datasets,containing a few hundred images and fewer classes,to evaluate the performances of their models without comparing it,with some standard datasets.This article takes advantage of utilizing a standard publicly dataset taken from a competition,and proposes a generic data balancing technique for imbalanced dataset to enhance and enable the in-depth training of the CNN.In addition,to classify the superheroes efficiently,a custom 17-layer deep convolutional neural network is also proposed.The computed results achieved overall classification accuracy of 97.9%which is significantly superior to the accuracy of competition’s winner.展开更多
Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maint...Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task.Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last decade.Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect.This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the gearbox.This helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher precision.The results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.展开更多
Most of the melanoma cases of skin cancer are the life-threatening form of cancer.It is prevalent among the Caucasian group of people due to their light skin tone.Melanoma is the second most common cancer that hits th...Most of the melanoma cases of skin cancer are the life-threatening form of cancer.It is prevalent among the Caucasian group of people due to their light skin tone.Melanoma is the second most common cancer that hits the age group of 15–29 years.The high number of cases has increased the importance of automated systems for diagnosing.The diagnosis should be fast and accurate for the early treatment of melanoma.It should remove the need for biopsies and provide stable diagnostic results.Automation requires large quantities of images.Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma.Three publicly available benchmark skin lesion datasets,ISIC 2017,ISBI 2016,and PH2,are used for the experiments.Currently,the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets.These datasets’pre-analysis is necessary to overcome contrast variations,under or over segmented images boundary extraction,and accurate skin lesion classification.In this paper,we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets.The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images.The two performance measures,processing time and efficiency,are computed for evaluation of the proposed method.Our results showed that the proposed methodology improves the pre-processing efficiency of 77%of ISIC 2017,67%of ISBI 2016,and 92.5%of PH2 datasets.展开更多
This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding...This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size.At each step,the SVD is determined.First,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or not.Finally,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination variation.It is compared with other similarmethods and presents very promised results.展开更多
The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are ...The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy.These traditional methods,namely therapy,need a large number of visits to hospitals and include medication.Neurogames could be used for the effective treatment of ADHD.It could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain signals.This paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial memory.The proposed system consists of a puzzle game where its complexity increases with each level.EEG signals were acquired using the Neurosky headset;then sent the signals to the designed gaming environment.This neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation measures.The results show that this game improves player overall performance from 74%to 98%by playing each game level.展开更多
The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent years.Melanoma is a deadly cancer with a highermortality rate than other skin can...The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent years.Melanoma is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel cell.However,detection and treatment at an early stage can result in a higher chance of survival.The classical methods of detection are expensive and labor-intensive.Also,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of Melanoma.The current improvement in computer-aided systems is providing very encouraging results in terms of precision and effectiveness.In this article,we propose an improved region growing technique for efficient extraction of the lesion boundary.This analysis and detection ofMelanoma are helpful for the expert dermatologist.The CNN features are extracted using the pre-trained VGG-19 deep learning model.In the end,the selected features are classified by SVM.The proposed technique is gauged on openly accessible two datasets ISIC 2017 and PH2.For the evaluation of our proposed framework,qualitative and quantitative experiments are performed.The suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 dataset.These results are notably better than the results of prevalent methods available on the same datasets.The machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of accuracy.The proposed method detects and classifies melanoma far better than other methods.Besides,our framework gained promising results in both segmentation and classification phases.展开更多
To determine the individual circumstances that account for a road traffic accident,it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels.An...To determine the individual circumstances that account for a road traffic accident,it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels.Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model.In this article,we proposed a multi-model hybrid framework of the weighted majority voting(WMV)scheme with parallel structure,which is designed by integrating individually implemented multinomial logistic regression(MLR)and multilayer perceptron(MLP)classifiers using three different accident datasets i.e.,IRTAD,NCDB,and FARS.The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC,RMSE,Kappa rate,classification accuracy,and performs better than state-of-theart approaches for the prediction of casualty severity level.Moreover,the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash.Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.展开更多
Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by...Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal patterns.The traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.展开更多
Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribu...Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribute guided vector that enables high visual quality content generation.This research study proposes automatic face image realism from freehand sketches based on Edge GAN.We propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute details.The density level is projected into non-latent space,having a linear controlled function parameter.This assists the user to appropriately devise the variant densities of facial sketches and image synthesis.Composite data set of Large Scale CelebFaces Attributes(ClebA),Labelled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed approach.The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.展开更多
Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to ...Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.展开更多
In the last few decades,crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems.While human detection in partially crowded scenarios have achiev...In the last few decades,crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems.While human detection in partially crowded scenarios have achieved many reliable works,a highly dense crowdlike situation still is far from being solved.Densely crowded scenes offer patterns that could be used to tackle these challenges.This problem is challenging due to the crowd volume,occlusions,clutter and distortion.Crowd region classification is a precursor to several types of applications.In this paper,we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity.Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.展开更多
基金Princess Nourah bint Abdulrahman University and Researchers Supporting Project Number(PNURSP2024R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.
基金Princess Nourah bint Abdulrahman University and Researchers Supporting Project Number(PNURSP2024R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recently,there have been several uses for digital image processing.Image fusion has become a prominent application in the domain of imaging processing.To create one final image that provesmore informative and helpful compared to the original input images,image fusion merges two or more initial images of the same item.Image fusion aims to produce,enhance,and transform significant elements of the source images into combined images for the sake of human visual perception.Image fusion is commonly employed for feature extraction in smart robots,clinical imaging,audiovisual camera integration,manufacturing process monitoring,electronic circuit design,advanced device diagnostics,and intelligent assembly line robots,with image quality varying depending on application.The research paper presents various methods for merging images in spatial and frequency domains,including a blend of stable and curvelet transformations,everageMax-Min,weighted principal component analysis(PCA),HIS(Hue,Intensity,Saturation),wavelet transform,discrete cosine transform(DCT),dual-tree Complex Wavelet Transform(CWT),and multiple wavelet transform.Image fusion methods integrate data from several source images of an identical target,thereby enhancing information in an extremely efficient manner.More precisely,in imaging techniques,the depth of field constraint precludes images from focusing on every object,leading to the exclusion of certain characteristics.To tackle thess challanges,a very efficient multi-focus wavelet decomposition and recompositionmethod is proposed.The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice.The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images.This study enhances the performance of the eXtreme Gradient Boosting(XGBoost)algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection.The performance of images is improved by segmenting them employing the K-Means algorithm.The segmentation method aids in identifying specific regions of interest,using Particle Swarm Optimization(PCA)for trait selection and XGBoost for data classification.Extensive trials confirm the model’s exceptional visual performance,achieving an accuracy of up to 97.067%and providing good objective indicators.
文摘Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to chemotherapy.Lung cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all genders.Timely and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival rates.Screening examinations for lung cancer detection,however,frequently fall short of detecting small polyps and cancers.To address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients alike.This research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological images.The proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell carcinomas.We evaluated the performance of the proposed technique using the histopathological(LC25000)lung dataset.The preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer learning.The dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training dataset.The model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are obtained.The study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.
基金funded by Princess Nourah bint Abdulrahman University and Researchers supporting Project number (PNURSP2024R346),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei.This is crucial for histopathological image analysis,as it involves segmenting cell nuclei.However,challenges exist,such as determining the boundary region of normal and deformed nuclei and identifying small,irregular nuclei structures.Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification,but their complex features limit their practical use in clinical settings.The existing studies have limited accuracy,significant processing costs,and a lack of resilience and generalizability across diverse datasets.We proposed the densely convolutional Breast U-shaped Network(BU-NET)framework to overcome the mentioned issues.The study employs BU-NET’s spatial and channel attention methods to enhance segmentation processes.The inclusion of residual blocks and skip connections in the BU-NEt architecture enhances the process of extracting features and reconstructing the output.This enhances the robustness of training and convergence processes by reducing the occurrence of vanishing gradients.The primary objective of BU-NEt is to enhance the model’s capacity to acquire and analyze more intricate features,all the while preserving an efficient working representation.The BU-NET experiments demonstrate that the framework achieved 88.7%average accuracy,88.8%F1 score for Multi-Organ Nuclei Segmentation Challenge(MoNuSeg),and 91.2%average accuracy,91.8%average F1 for the triple-negative breast cancer(TNBC)dataset.The framework also achieved 93.92 Area under the ROC Curve(AUC)for TNBC.The results demonstrated that the technology surpasses existing techniques in terms of accuracy and effectiveness in segmentation.Furthermore,it showcases the ability to withstand and recover from different tissue types and diseases,indicating possible uses in medical treatments.The research evaluated the efficacy of the proposed method on diverse histopathological imaging datasets,including cancer cells from many organs.The densely connected U-NEt technology offers a promising approach for automating and precisely segmenting cancer cells on histopathology slides,hence assisting pathologists in improving cancer diagnosis and treatment outcomes.
文摘Comic character detection is becoming an exciting and growing research area in the domain of machine learning.In this regard,recently,many methods are proposed to provide adequate performance.However,most of these methods utilized the custom datasets,containing a few hundred images and fewer classes,to evaluate the performances of their models without comparing it,with some standard datasets.This article takes advantage of utilizing a standard publicly dataset taken from a competition,and proposes a generic data balancing technique for imbalanced dataset to enhance and enable the in-depth training of the CNN.In addition,to classify the superheroes efficiently,a custom 17-layer deep convolutional neural network is also proposed.The computed results achieved overall classification accuracy of 97.9%which is significantly superior to the accuracy of competition’s winner.
文摘Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs.Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task.Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last decade.Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect.This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the gearbox.This helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher precision.The results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.
基金supported by the School of Computing,Faculty of Engineering,Universiti Teknologi Malaysia,Johor Bahru,81310 Skudai,Malaysia.
文摘Most of the melanoma cases of skin cancer are the life-threatening form of cancer.It is prevalent among the Caucasian group of people due to their light skin tone.Melanoma is the second most common cancer that hits the age group of 15–29 years.The high number of cases has increased the importance of automated systems for diagnosing.The diagnosis should be fast and accurate for the early treatment of melanoma.It should remove the need for biopsies and provide stable diagnostic results.Automation requires large quantities of images.Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma.Three publicly available benchmark skin lesion datasets,ISIC 2017,ISBI 2016,and PH2,are used for the experiments.Currently,the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets.These datasets’pre-analysis is necessary to overcome contrast variations,under or over segmented images boundary extraction,and accurate skin lesion classification.In this paper,we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets.The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images.The two performance measures,processing time and efficiency,are computed for evaluation of the proposed method.Our results showed that the proposed methodology improves the pre-processing efficiency of 77%of ISIC 2017,67%of ISBI 2016,and 92.5%of PH2 datasets.
文摘This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size.At each step,the SVD is determined.First,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or not.Finally,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination variation.It is compared with other similarmethods and presents very promised results.
基金funding for this study under Technology Development Fund(TDF-02-228).supported by AIDA Lab CCIS Prince Sultan University Riyadh Saudi Arabia and authors would also like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges(APC)of this publication.
文摘The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy.These traditional methods,namely therapy,need a large number of visits to hospitals and include medication.Neurogames could be used for the effective treatment of ADHD.It could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain signals.This paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial memory.The proposed system consists of a puzzle game where its complexity increases with each level.EEG signals were acquired using the Neurosky headset;then sent the signals to the designed gaming environment.This neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation measures.The results show that this game improves player overall performance from 74%to 98%by playing each game level.
文摘The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent years.Melanoma is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel cell.However,detection and treatment at an early stage can result in a higher chance of survival.The classical methods of detection are expensive and labor-intensive.Also,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of Melanoma.The current improvement in computer-aided systems is providing very encouraging results in terms of precision and effectiveness.In this article,we propose an improved region growing technique for efficient extraction of the lesion boundary.This analysis and detection ofMelanoma are helpful for the expert dermatologist.The CNN features are extracted using the pre-trained VGG-19 deep learning model.In the end,the selected features are classified by SVM.The proposed technique is gauged on openly accessible two datasets ISIC 2017 and PH2.For the evaluation of our proposed framework,qualitative and quantitative experiments are performed.The suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 dataset.These results are notably better than the results of prevalent methods available on the same datasets.The machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of accuracy.The proposed method detects and classifies melanoma far better than other methods.Besides,our framework gained promising results in both segmentation and classification phases.
文摘To determine the individual circumstances that account for a road traffic accident,it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels.Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model.In this article,we proposed a multi-model hybrid framework of the weighted majority voting(WMV)scheme with parallel structure,which is designed by integrating individually implemented multinomial logistic regression(MLR)and multilayer perceptron(MLP)classifiers using three different accident datasets i.e.,IRTAD,NCDB,and FARS.The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC,RMSE,Kappa rate,classification accuracy,and performs better than state-of-theart approaches for the prediction of casualty severity level.Moreover,the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash.Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.
文摘Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment;it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear,nonlinear,and seasonal patterns.The traditional model,like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival inNew Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.
基金The authors received no specific funding for this study.
文摘Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal identification.In Edge GAN,the major contribution is attribute guided vector that enables high visual quality content generation.This research study proposes automatic face image realism from freehand sketches based on Edge GAN.We propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute details.The density level is projected into non-latent space,having a linear controlled function parameter.This assists the user to appropriately devise the variant densities of facial sketches and image synthesis.Composite data set of Large Scale CelebFaces Attributes(ClebA),Labelled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed approach.The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.
文摘Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.
基金the Ministry of Higher Education Malaysia through Fundamental Research Grant Scheme(FRGS)and managed by Universiti Teknologi Malaysia under Vot No.Q.J130000.2508.13491the Machine Learning Research Group+1 种基金Prince Sultan University RiyadhSaudi Arabia[RG-CCIS-2017-06-16].
文摘In the last few decades,crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems.While human detection in partially crowded scenarios have achieved many reliable works,a highly dense crowdlike situation still is far from being solved.Densely crowded scenes offer patterns that could be used to tackle these challenges.This problem is challenging due to the crowd volume,occlusions,clutter and distortion.Crowd region classification is a precursor to several types of applications.In this paper,we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity.Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.
