For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for...For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic Engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic Engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning(DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.展开更多
This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-S...This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile Range(IQR)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQR and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular health.Collectively,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)techniques.These findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning approaches.This work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization methodologies.Future studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.展开更多
A comprehensive understanding of human intelligence is still an ongoing process,i.e.,human and information security are not yet perfectly matched.By understanding cognitive processes,designers can design humanized cog...A comprehensive understanding of human intelligence is still an ongoing process,i.e.,human and information security are not yet perfectly matched.By understanding cognitive processes,designers can design humanized cognitive information systems(CIS).The need for this research is justified because today’s business decision makers are faced with questions they cannot answer in a given amount of time without the use of cognitive information systems.The researchers aim to better strengthen cognitive information systems with more pronounced cognitive thresholds by demonstrating the resilience of cognitive resonant frequencies to reveal possible responses to improve the efficiency of human-computer interaction(HCI).Apractice-oriented research approach included research analysis and a review of existing articles to pursue a comparative research model;thereafter,amodel development paradigm was used to observe and monitor the progression of CIS during HCI.The scope of our research provides a broader perspective on how different disciplines affect HCI and how human cognitive models can be enhanced to enrich complements.We have identified a significant gap in the current literature on mental processing resulting from a wide range of theory and practice.展开更多
Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence technology.These developments are made to better the standard of med...Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence technology.These developments are made to better the standard of medical decision-making and the standard of treatment given to patients.Fuzzy boundaries,shifting sizes,and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images,slowing down the otherwise efficient process of diagnosing skin cancer.However,these difficulties may be conquered by employing image processing software.To address these issues,the authors of this paper provide a novel intelligent multilevel thresholding with deep learning(IMLT-DL)model for intelligent dermoscopic image processing.Multilevel thresholding and DL are brought together in this model.Top hat filtering and inpainting have been included into IMLT-DL for use in image processing.In addition,mayfly optimization has been used in tandem with multilayer Kapur’s thresholding to identify specific trouble spots.For further investigation,it uses an Inception v3-based feature extractor,and for data classification,it makes use of gradient boosting trees(GBTs).On the International Skin Imaging Collaboration(ISIC)dataset,this model was shown to outperform state-of-the-art alternatives by a margin of 0.992%over the duration of trial iterations.These advances are a major step forward in the quest for faster and more accurate skin lesion detection.展开更多
In this editorial,a commentary on the article by Chang et al has been provided,the course of treatment of anorectal fistulas,especially complex and recurring ones,require accurate diagnostic procedures for determining...In this editorial,a commentary on the article by Chang et al has been provided,the course of treatment of anorectal fistulas,especially complex and recurring ones,require accurate diagnostic procedures for determining ideal surgical procedures.Conventional ways of imaging sometimes fall short,offering insufficient insights in aggravated instances.In this editorial,a novel application of hydrogen peroxide-enhanced magnetic resonance imaging(HP-MRI)that promises significant improvements in the imaging of anorectal fistula.Study is based on a retrospective investigation of 60 patients,contrasts the new HP-MRI with conventional diagnostic techniques such as physical examination,trans-perineal ultrasonography and poor spatial resolution MRI.The findings demonstrate HP-MRI's incredible diagnostic performance,with sensitivity and specificity rates of 96.08%and 90.91%,respectively,and unparalleled interobserver agreement(Kappa values ranging from 0.80 to 0.89).It has been a significant advancement for assessment of anorectal fistulas providing a better roadmap for surgical planning,lowering recurrence rates as well as reduced personal and financial burden on patients by reducing the need for repeated treatment and extended hospital stays.The remaining funds can be utilized for treatment of other medical need.Ultimately HP-MRI provides us a healthier&more efficient society by improvising patients well-being&optimized healthcare infrastructure.展开更多
Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learni...Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.展开更多
Activity recognition is a challenging topic in the field of computer vision that has various applications,including surveillance systems,industrial automation,and human-computer interaction.Today,the demand for automa...Activity recognition is a challenging topic in the field of computer vision that has various applications,including surveillance systems,industrial automation,and human-computer interaction.Today,the demand for automation has greatly increased across industries worldwide.Real-time detection requires edge devices with limited computational time.This study proposes a novel hybrid deep learning system for human activity recognition(HAR),aiming to enhance the recognition accuracy and reduce the computational time.The proposed system combines a pretrained image classification model with a sequence analysis model.First,the dataset was divided into a training set(70%),validation set(10%),and test set(20%).Second,all the videos were converted into frames and deep-based features were extracted from each frame using convolutional neural networks(CNNs)with a vision transformer.Following that,bidirectional long short-term memory(BiLSTM)-and temporal convolutional network(TCN)-based models were trained using the training set,and their performances were evaluated using the validation set and test set.Four benchmark datasets(UCF11,UCF50,UCF101,and JHMDB)were used to evaluate the performance of the proposed HAR-based system.The experimental results showed that the combination of ConvNeXt and the TCN-based model achieved a recognition accuracy of 97.73%for UCF11,98.81%for UCF50,98.46%for UCF101,and 83.38%for JHMDB,respectively.This represents improvements in the recognition accuracy of 4%,2.67%,3.67%,and 7.08%for the UCF11,UCF50,UCF101,and JHMDB datasets,respectively,over existing models.Moreover,the proposed HAR-based system obtained superior recognition accuracy,shorter computational times,and minimal memory usage compared to the existing models.展开更多
The recent surge in computer vision and deep learning has attracted significant attention within the galaxy morphology community.Various models have been implemented for galaxy morphology prediction with nearperfect a...The recent surge in computer vision and deep learning has attracted significant attention within the galaxy morphology community.Various models have been implemented for galaxy morphology prediction with nearperfect accuracy for certain classes.However,many studies treat deep learning models as black-box entities,lacking interpretability of their predictions.To address these limitations while ensuring good performance,we introduced an Improved SqueezeNet(I-SqueezeNet)by incorporating unique residual connections to improve the prediction performance,and we utilize Local Interpretable Model-Agnostic Explanations(LIME)to understand the interpretability.We evaluated the simplified SqueezeNet and I-SqueezeNet,with both channel and vertical concatenation,and compared their performances with those of some exiting methods such as Dieleman’s CNN,VGG13,DenseNet121,ResNet50,ResNext50,ResNext101,DSCNN and customized CNN in classifying galaxy objects using a dataset from the publicly available Galaxy Zoo Data Challenge Project.Our experiments showed that I-SqueezeNet with vertical concatenation achieved the highest average accuracy of 94.08%compared to other methods.Beyond achieving high accuracy,the application of LIME for model interpretation sheds light on the machine learning features and reasoning processes behind the model’s predictions.This information provides valuable insight into the galaxy morphology decision-making process,paving the way for further functional enhancements.展开更多
Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.展开更多
Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare infrastructure.This digitization has major advantages,mainly enhanc...Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare infrastructure.This digitization has major advantages,mainly enhancing the security barriers of the green tree infrastructure.In this study,we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems,security vulnerabilities,cyberattacks,and system limitations.In addition,we considered several solutions proposed by thousands of researchers worldwide.Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management,transmission,and processing.Here,we describe 17 security threats that violate the privacy and data integrity of a system,over 21 cyber-attacks on security and QoS,and some system implementation problems such as node compromise,scalability,efficiency,regulatory issues,computation speed,and power consumption.We propose a multi-layered architecture for the future healthcare infrastructure.Second,we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these contingencies.Our thorough theoretical examination of several performance criteria—including confidentiality,access control,interoperability problems,and energy efficiency—as well as mathematical verifications establishes the superiority of security,privacy maintenance,reliability,and efficiency over conventional systems.We conducted in-depth comparative studies on different interoperability parameters in the blockchain models.Our research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising fromlaws and ethics.Determining the theoretical aspects,their scope,and future expectations encourages us to design reliable,secure,and privacy-preserving systems.展开更多
Theill-posed character of haze or fogmakes it difficult to remove froma single image.While most existing methods rely on a transmission map refined through depth estimation and assume a constant scattering coefficient...Theill-posed character of haze or fogmakes it difficult to remove froma single image.While most existing methods rely on a transmission map refined through depth estimation and assume a constant scattering coefficient,this assumption limits their effectiveness.In this paper,we propose an enhanced transmission map that incorporates spatially varying scattering information inherent in hazy images.To improve linearity,the model utilizes the ratio of the difference between intensity and saturation to their sum.Our approach also addresses critical issues such as edge preservation and color fidelity.In terms of qualitative as well as quantitative analysis,experimental outcomes show that the suggested framework is more effective than the currently used haze removal techniques.展开更多
Cloud computing is the highly demanded technology nowadays.Due to the service oriented architecture,seamless accessibility and other advantages of this advent technology,many transaction rich applications are making u...Cloud computing is the highly demanded technology nowadays.Due to the service oriented architecture,seamless accessibility and other advantages of this advent technology,many transaction rich applications are making use of it.At the same time,it is vulnerable to hacks and threats.Hence securing this environment is of at most important and many research works are being reported focusing on it.This paper proposes a safe storage mechanism using Elliptic curve cryptography(ECC)for the Transaction Rich Applications(TRA).With ECC based security scheme,the security level of the protected system will be increased and it is more suitable to secure the delivered data in the portable devices.The proposed scheme shields the aligning of different kind of data elements to each provider using an ECC algorithm.Analysis,comparison and simulation prove that the proposed system is more effective and secure for the Transaction rich applications in Cloud.展开更多
Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizati...Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.展开更多
The(3+1)-dimensional Zakharov–Kuznetsov(ZK) and the new extended quantum ZK equations are functional to decipher the dense quantum plasma, ion-acoustic waves, electron thermal energy,ion plasma, quantum acoustic wave...The(3+1)-dimensional Zakharov–Kuznetsov(ZK) and the new extended quantum ZK equations are functional to decipher the dense quantum plasma, ion-acoustic waves, electron thermal energy,ion plasma, quantum acoustic waves, and quantum Langmuir waves. The enhanced modified simple equation(EMSE) method is a substantial approach to determine competent solutions and in this article, we have constructed standard, illustrative, rich structured and further comprehensive soliton solutions via this method. The solutions are ascertained as the integration of exponential, hyperbolic,trigonometric and rational functions and formulate the bright solitons, periodic, compacton, bellshape, parabolic shape, singular periodic, plane shape and some new type of solitons. It is worth noting that the wave profile varies as the physical and subsidiary parameters change. The standard and advanced soliton solutions may be useful to assist in describing the physical phenomena previously mentioned. To open out the inward structure of the tangible incidents, we have portrayed the three-dimensional, contour plot, and two-dimensional graphs for different parametric values. The attained results demonstrate the EMSE technique for extracting soliton solutions to nonlinear evolution equations is efficient, compatible and reliable in nonlinear science and engineering.展开更多
There has been an explosion of cloud services as organizations take advantage of their continuity,predictability,as well as quality of service and it raises the concern about latency,energy-efficiency,and security.Thi...There has been an explosion of cloud services as organizations take advantage of their continuity,predictability,as well as quality of service and it raises the concern about latency,energy-efficiency,and security.This increase in demand requires new configurations of networks,products,and service operators.For this purpose,the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet optimization.This work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing requests.Performance is evaluated in terms of reducing bandwidth,task execution times and latencies,and increasing throughput.A minimum execution time algorithm is used to compute the completion time of all the available resources which are allocated to the virtual machine and lion optimization algorithm is applied to packets in a cloud environment.The proposed work is shown to improve the throughput and latency rate.展开更多
Deep learning has risen in popularity as a face recognition technology in recent years.Facenet,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per face.It also claims to ha...Deep learning has risen in popularity as a face recognition technology in recent years.Facenet,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per face.It also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)dataset.How-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the images.This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images.The proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye gaze.It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face images.The system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.展开更多
With the increasing demand for doctors in chest related diseases,there is a 15%performance gap every five years.If this gap is not filled with effective chest disease detection automation,the healthcare industry may f...With the increasing demand for doctors in chest related diseases,there is a 15%performance gap every five years.If this gap is not filled with effective chest disease detection automation,the healthcare industry may face unfavorable consequences.There are only several studies that targeted X-ray images of cardiothoracic diseases.Most of the studies only targeted a single disease,which is inadequate.Although some related studies have provided an identification framework for all classes,the results are not encouraging due to a lack of data and imbalanced data issues.This research provides a significant contribution to Generative Adversarial Network(GAN)based synthetic data and four different types of deep learning-based models that provided comparable results.The models include a ResNet-152 model with image augmentation with an accuracy of 67%,a ResNet-152 model without image augmentation with an accuracy of 62%,transfer learning with Inception-V3 with an accuracy of 68%,and finally ResNet-152 model with image augmentation but targeted only six classes with an accuracy of 83%.展开更多
This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hun...This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network(DHO-ANN).The noisy crowdsourced cough datasets were collected from the public domain.This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora.The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures,F1 score,confusion matrix,specificity,and sensitivity parameters.Besides,it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset,COUGHVID.Moreover,the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5%compared to the existing methods.展开更多
Graph colouring is the system of assigning a colour to each vertex of a graph.It is done in such a way that adjacent vertices do not have equal colour.It is fundamental in graph theory.It is often used to solve real-w...Graph colouring is the system of assigning a colour to each vertex of a graph.It is done in such a way that adjacent vertices do not have equal colour.It is fundamental in graph theory.It is often used to solve real-world problems like traffic light signalling,map colouring,scheduling,etc.Nowadays,social networks are prevalent systems in our life.Here,the users are considered as vertices,and their connections/interactions are taken as edges.Some users follow other popular users’profiles in these networks,and some don’t,but those non-followers are connected directly to the popular profiles.That means,along with traditional relationship(information flowing),there is another relation among them.It depends on the domination of the relationship between the nodes.This type of situation can be modelled as a directed fuzzy graph.In the colouring of fuzzy graph theory,edge membership plays a vital role.Edge membership is a representation of flowing information between end nodes of the edge.Apart from the communication relationship,there may be some other factors like domination in relation.This influence of power is captured here.In this article,the colouring of directed fuzzy graphs is defined based on the influence of relationship.Along with this,the chromatic number and strong chromatic number are provided,and related properties are investigated.An application regarding COVID-19 infection is presented using the colouring of directed fuzzy graphs.展开更多
Amajor recent development in computer networking is the emergence of Software Defined Networking(SDN),whose goal is to provide a centralized,programmable control plane that is decoupled from the distributed data plane...Amajor recent development in computer networking is the emergence of Software Defined Networking(SDN),whose goal is to provide a centralized,programmable control plane that is decoupled from the distributed data planes on individual network devices.In particular,the development of OpenFlow has demonstrated many potential benefits of SDN,and multiple vendors have started to offer commercial switches supporting the OpenFlow standard.Researchers have also made progress on SDN components including SDN controllers,展开更多
文摘For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic Engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic Engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning(DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.
文摘This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile Range(IQR)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQR and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular health.Collectively,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)techniques.These findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning approaches.This work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization methodologies.Future studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.
基金This work was supported by King Saud University through Researchers Supporting Project Number(RSP2022R426),King Saud University,Riyadh,Saudi Arabia.
文摘A comprehensive understanding of human intelligence is still an ongoing process,i.e.,human and information security are not yet perfectly matched.By understanding cognitive processes,designers can design humanized cognitive information systems(CIS).The need for this research is justified because today’s business decision makers are faced with questions they cannot answer in a given amount of time without the use of cognitive information systems.The researchers aim to better strengthen cognitive information systems with more pronounced cognitive thresholds by demonstrating the resilience of cognitive resonant frequencies to reveal possible responses to improve the efficiency of human-computer interaction(HCI).Apractice-oriented research approach included research analysis and a review of existing articles to pursue a comparative research model;thereafter,amodel development paradigm was used to observe and monitor the progression of CIS during HCI.The scope of our research provides a broader perspective on how different disciplines affect HCI and how human cognitive models can be enhanced to enrich complements.We have identified a significant gap in the current literature on mental processing resulting from a wide range of theory and practice.
文摘Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence technology.These developments are made to better the standard of medical decision-making and the standard of treatment given to patients.Fuzzy boundaries,shifting sizes,and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images,slowing down the otherwise efficient process of diagnosing skin cancer.However,these difficulties may be conquered by employing image processing software.To address these issues,the authors of this paper provide a novel intelligent multilevel thresholding with deep learning(IMLT-DL)model for intelligent dermoscopic image processing.Multilevel thresholding and DL are brought together in this model.Top hat filtering and inpainting have been included into IMLT-DL for use in image processing.In addition,mayfly optimization has been used in tandem with multilayer Kapur’s thresholding to identify specific trouble spots.For further investigation,it uses an Inception v3-based feature extractor,and for data classification,it makes use of gradient boosting trees(GBTs).On the International Skin Imaging Collaboration(ISIC)dataset,this model was shown to outperform state-of-the-art alternatives by a margin of 0.992%over the duration of trial iterations.These advances are a major step forward in the quest for faster and more accurate skin lesion detection.
文摘In this editorial,a commentary on the article by Chang et al has been provided,the course of treatment of anorectal fistulas,especially complex and recurring ones,require accurate diagnostic procedures for determining ideal surgical procedures.Conventional ways of imaging sometimes fall short,offering insufficient insights in aggravated instances.In this editorial,a novel application of hydrogen peroxide-enhanced magnetic resonance imaging(HP-MRI)that promises significant improvements in the imaging of anorectal fistula.Study is based on a retrospective investigation of 60 patients,contrasts the new HP-MRI with conventional diagnostic techniques such as physical examination,trans-perineal ultrasonography and poor spatial resolution MRI.The findings demonstrate HP-MRI's incredible diagnostic performance,with sensitivity and specificity rates of 96.08%and 90.91%,respectively,and unparalleled interobserver agreement(Kappa values ranging from 0.80 to 0.89).It has been a significant advancement for assessment of anorectal fistulas providing a better roadmap for surgical planning,lowering recurrence rates as well as reduced personal and financial burden on patients by reducing the need for repeated treatment and extended hospital stays.The remaining funds can be utilized for treatment of other medical need.Ultimately HP-MRI provides us a healthier&more efficient society by improvising patients well-being&optimized healthcare infrastructure.
文摘Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.
基金funded by the Ongoing Research Funding Program(ORF-2025-890),King Saud University,Riyadh,Saudi Arabia.
文摘Activity recognition is a challenging topic in the field of computer vision that has various applications,including surveillance systems,industrial automation,and human-computer interaction.Today,the demand for automation has greatly increased across industries worldwide.Real-time detection requires edge devices with limited computational time.This study proposes a novel hybrid deep learning system for human activity recognition(HAR),aiming to enhance the recognition accuracy and reduce the computational time.The proposed system combines a pretrained image classification model with a sequence analysis model.First,the dataset was divided into a training set(70%),validation set(10%),and test set(20%).Second,all the videos were converted into frames and deep-based features were extracted from each frame using convolutional neural networks(CNNs)with a vision transformer.Following that,bidirectional long short-term memory(BiLSTM)-and temporal convolutional network(TCN)-based models were trained using the training set,and their performances were evaluated using the validation set and test set.Four benchmark datasets(UCF11,UCF50,UCF101,and JHMDB)were used to evaluate the performance of the proposed HAR-based system.The experimental results showed that the combination of ConvNeXt and the TCN-based model achieved a recognition accuracy of 97.73%for UCF11,98.81%for UCF50,98.46%for UCF101,and 83.38%for JHMDB,respectively.This represents improvements in the recognition accuracy of 4%,2.67%,3.67%,and 7.08%for the UCF11,UCF50,UCF101,and JHMDB datasets,respectively,over existing models.Moreover,the proposed HAR-based system obtained superior recognition accuracy,shorter computational times,and minimal memory usage compared to the existing models.
文摘The recent surge in computer vision and deep learning has attracted significant attention within the galaxy morphology community.Various models have been implemented for galaxy morphology prediction with nearperfect accuracy for certain classes.However,many studies treat deep learning models as black-box entities,lacking interpretability of their predictions.To address these limitations while ensuring good performance,we introduced an Improved SqueezeNet(I-SqueezeNet)by incorporating unique residual connections to improve the prediction performance,and we utilize Local Interpretable Model-Agnostic Explanations(LIME)to understand the interpretability.We evaluated the simplified SqueezeNet and I-SqueezeNet,with both channel and vertical concatenation,and compared their performances with those of some exiting methods such as Dieleman’s CNN,VGG13,DenseNet121,ResNet50,ResNext50,ResNext101,DSCNN and customized CNN in classifying galaxy objects using a dataset from the publicly available Galaxy Zoo Data Challenge Project.Our experiments showed that I-SqueezeNet with vertical concatenation achieved the highest average accuracy of 94.08%compared to other methods.Beyond achieving high accuracy,the application of LIME for model interpretation sheds light on the machine learning features and reasoning processes behind the model’s predictions.This information provides valuable insight into the galaxy morphology decision-making process,paving the way for further functional enhancements.
基金supported by King Saud University,Riyadh,Saudi Arabia,through the Researchers Supporting Project under Grant RSPD2025R697.
文摘Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
文摘Cloud-based setups are intertwined with the Internet of Things and advanced,and technologies such as blockchain revolutionize conventional healthcare infrastructure.This digitization has major advantages,mainly enhancing the security barriers of the green tree infrastructure.In this study,we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems,security vulnerabilities,cyberattacks,and system limitations.In addition,we considered several solutions proposed by thousands of researchers worldwide.Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management,transmission,and processing.Here,we describe 17 security threats that violate the privacy and data integrity of a system,over 21 cyber-attacks on security and QoS,and some system implementation problems such as node compromise,scalability,efficiency,regulatory issues,computation speed,and power consumption.We propose a multi-layered architecture for the future healthcare infrastructure.Second,we classify all threats and security concerns based on these layers and assess suggested solutions in terms of these contingencies.Our thorough theoretical examination of several performance criteria—including confidentiality,access control,interoperability problems,and energy efficiency—as well as mathematical verifications establishes the superiority of security,privacy maintenance,reliability,and efficiency over conventional systems.We conducted in-depth comparative studies on different interoperability parameters in the blockchain models.Our research justifies the use of various positive protocols and optimization methods to improve the quality of services in e-healthcare and overcome problems arising fromlaws and ethics.Determining the theoretical aspects,their scope,and future expectations encourages us to design reliable,secure,and privacy-preserving systems.
基金The Deanship of Research and Graduate Studies at King Khalid University funded this work through a Large Research Project under grant number RGP2/274/46.
文摘Theill-posed character of haze or fogmakes it difficult to remove froma single image.While most existing methods rely on a transmission map refined through depth estimation and assume a constant scattering coefficient,this assumption limits their effectiveness.In this paper,we propose an enhanced transmission map that incorporates spatially varying scattering information inherent in hazy images.To improve linearity,the model utilizes the ratio of the difference between intensity and saturation to their sum.Our approach also addresses critical issues such as edge preservation and color fidelity.In terms of qualitative as well as quantitative analysis,experimental outcomes show that the suggested framework is more effective than the currently used haze removal techniques.
文摘Cloud computing is the highly demanded technology nowadays.Due to the service oriented architecture,seamless accessibility and other advantages of this advent technology,many transaction rich applications are making use of it.At the same time,it is vulnerable to hacks and threats.Hence securing this environment is of at most important and many research works are being reported focusing on it.This paper proposes a safe storage mechanism using Elliptic curve cryptography(ECC)for the Transaction Rich Applications(TRA).With ECC based security scheme,the security level of the protected system will be increased and it is more suitable to secure the delivered data in the portable devices.The proposed scheme shields the aligning of different kind of data elements to each provider using an ECC algorithm.Analysis,comparison and simulation prove that the proposed system is more effective and secure for the Transaction rich applications in Cloud.
文摘Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.
文摘The(3+1)-dimensional Zakharov–Kuznetsov(ZK) and the new extended quantum ZK equations are functional to decipher the dense quantum plasma, ion-acoustic waves, electron thermal energy,ion plasma, quantum acoustic waves, and quantum Langmuir waves. The enhanced modified simple equation(EMSE) method is a substantial approach to determine competent solutions and in this article, we have constructed standard, illustrative, rich structured and further comprehensive soliton solutions via this method. The solutions are ascertained as the integration of exponential, hyperbolic,trigonometric and rational functions and formulate the bright solitons, periodic, compacton, bellshape, parabolic shape, singular periodic, plane shape and some new type of solitons. It is worth noting that the wave profile varies as the physical and subsidiary parameters change. The standard and advanced soliton solutions may be useful to assist in describing the physical phenomena previously mentioned. To open out the inward structure of the tangible incidents, we have portrayed the three-dimensional, contour plot, and two-dimensional graphs for different parametric values. The attained results demonstrate the EMSE technique for extracting soliton solutions to nonlinear evolution equations is efficient, compatible and reliable in nonlinear science and engineering.
基金This research was supported by the Sejong University Research Fund Korea and University of Shaqra,Saudi Arabia.
文摘There has been an explosion of cloud services as organizations take advantage of their continuity,predictability,as well as quality of service and it raises the concern about latency,energy-efficiency,and security.This increase in demand requires new configurations of networks,products,and service operators.For this purpose,the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet optimization.This work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing requests.Performance is evaluated in terms of reducing bandwidth,task execution times and latencies,and increasing throughput.A minimum execution time algorithm is used to compute the completion time of all the available resources which are allocated to the virtual machine and lion optimization algorithm is applied to packets in a cloud environment.The proposed work is shown to improve the throughput and latency rate.
文摘Deep learning has risen in popularity as a face recognition technology in recent years.Facenet,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per face.It also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)dataset.How-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the images.This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images.The proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye gaze.It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face images.The system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.
文摘With the increasing demand for doctors in chest related diseases,there is a 15%performance gap every five years.If this gap is not filled with effective chest disease detection automation,the healthcare industry may face unfavorable consequences.There are only several studies that targeted X-ray images of cardiothoracic diseases.Most of the studies only targeted a single disease,which is inadequate.Although some related studies have provided an identification framework for all classes,the results are not encouraging due to a lack of data and imbalanced data issues.This research provides a significant contribution to Generative Adversarial Network(GAN)based synthetic data and four different types of deep learning-based models that provided comparable results.The models include a ResNet-152 model with image augmentation with an accuracy of 67%,a ResNet-152 model without image augmentation with an accuracy of 62%,transfer learning with Inception-V3 with an accuracy of 68%,and finally ResNet-152 model with image augmentation but targeted only six classes with an accuracy of 83%.
文摘This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network(DHO-ANN).The noisy crowdsourced cough datasets were collected from the public domain.This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora.The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures,F1 score,confusion matrix,specificity,and sensitivity parameters.Besides,it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset,COUGHVID.Moreover,the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5%compared to the existing methods.
基金supported and funded by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2018R1D1A1B07049321).
文摘Graph colouring is the system of assigning a colour to each vertex of a graph.It is done in such a way that adjacent vertices do not have equal colour.It is fundamental in graph theory.It is often used to solve real-world problems like traffic light signalling,map colouring,scheduling,etc.Nowadays,social networks are prevalent systems in our life.Here,the users are considered as vertices,and their connections/interactions are taken as edges.Some users follow other popular users’profiles in these networks,and some don’t,but those non-followers are connected directly to the popular profiles.That means,along with traditional relationship(information flowing),there is another relation among them.It depends on the domination of the relationship between the nodes.This type of situation can be modelled as a directed fuzzy graph.In the colouring of fuzzy graph theory,edge membership plays a vital role.Edge membership is a representation of flowing information between end nodes of the edge.Apart from the communication relationship,there may be some other factors like domination in relation.This influence of power is captured here.In this article,the colouring of directed fuzzy graphs is defined based on the influence of relationship.Along with this,the chromatic number and strong chromatic number are provided,and related properties are investigated.An application regarding COVID-19 infection is presented using the colouring of directed fuzzy graphs.
文摘Amajor recent development in computer networking is the emergence of Software Defined Networking(SDN),whose goal is to provide a centralized,programmable control plane that is decoupled from the distributed data planes on individual network devices.In particular,the development of OpenFlow has demonstrated many potential benefits of SDN,and multiple vendors have started to offer commercial switches supporting the OpenFlow standard.Researchers have also made progress on SDN components including SDN controllers,
