Diabetic retinopathy(DR)is a disease with an increasing prevalence and the major reason for blindness among working-age population.The possibility of severe vision loss can be extensively reduced by timely diagnosis a...Diabetic retinopathy(DR)is a disease with an increasing prevalence and the major reason for blindness among working-age population.The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment.An automated screening for DR has been identified as an effective method for early DR detection,which can decrease the workload associated to manual grading as well as save diagnosis costs and time.Several studies have been carried out to develop automated detection and classification models for DR.This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy(DR).The proposed model incorporates different processes namely data collection,preprocessing,segmentation,feature extraction and classification.At first,the IoT-based data collection process takes place where the patient wears a head mounted camera to capture the retinal fundus image and send to cloud server.Then,the contrast level of the input DR image gets increased in the preprocessing stage using Contrast Limited Adaptive Histogram Equalization(CLAHE)model.Next,the preprocessed image is segmented using Adaptive Spatial Kernel distance measure-based Fuzzy C-Means clustering(ASKFCM)model.Afterwards,deep Convolution Neural Network(CNN)based Inception v4 model is applied as a feature extractor and the resulting feature vectors undergo classification in line with the Gaussian Naive Bayes(GNB)model.The proposed model was tested using a benchmark DR MESSIDOR image dataset and the obtained results showcased superior performance of the proposed model over other such models compared in the study.展开更多
One of the evolving hand biometric features considered so far is finger knuckle printing,because of its ability towards unique identification of individuals.Despite many attempts have been made in this area of researc...One of the evolving hand biometric features considered so far is finger knuckle printing,because of its ability towards unique identification of individuals.Despite many attempts have been made in this area of research,the accuracy of the recognition model remains a major issue.To overcome this problem,a novel biometric-based method,named fingerknuckle-print(FKP),has been developed for individual verification.The proposed system carries key steps such as preprocessing,segmentation,feature extraction and classification.Initially input FKP image is fed into the preprocessing stage where colour images are converted to gray scale image for augmenting the system performance.Afterwards,segmentation process is carried out with the help of CROI(Circular Region of Interest)and Morphological operation.Then,feature extraction stage is carried out using Gabor-Derivative line approach for extracting intrinsic features.Finally,DCNN(Deep Convolutional Neural Network)is trained for the processed knuckle images to recognize imposter and genuine individuals.Extensive experiments on standard FKP database demonstrates that the proposed method attains considerable improvement compared with state-of-the-art methods.The overall accuracy attained for the proposed methodology is 95.6%which is achieved better than the existing techniques.展开更多
Ensuring software quality in open⁃source environments requires adaptive mechanisms to enhance scalability,optimize service provisioning,and improve reliability.This study presents the dynamic correlation analysis tech...Ensuring software quality in open⁃source environments requires adaptive mechanisms to enhance scalability,optimize service provisioning,and improve reliability.This study presents the dynamic correlation analysis technique to enhance software quality management in open⁃source environments by addressing dynamic scalability,adaptive service provisioning,and software reliability.The proposed methodology integrates a scalability metric,an optimized service provisioning model,and a weighted entropy⁃based reliability assessment to systematically improve key performance parameters.Experimental evaluation conducted on multiple open⁃source software(OSS)versions demonstrates significant improvements:scalability increased by 27.5%,service provisioning time reduced by 18.3%,and software reliability improved by 22.1%compared to baseline methods.A comparative analysis with prior works further highlights the effectiveness of this approach in ensuring adaptability,efficiency,and resilience in dynamic software ecosystems.Future work will focus on real⁃time monitoring and AI⁃driven adaptive provisioning to further enhance software quality management.展开更多
In natural language processing(NLP),managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models,leading to practical inefficiencies.To address thi...In natural language processing(NLP),managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models,leading to practical inefficiencies.To address this challenge,we introduce AdaptForever,a novel approach that enables continuous mastery of NLP tasks through the integration of elastic and mutual learning strategies with a stochastic expert mechanism.Our method freezes the pre-trained model weights while incorporating adapters enhanced with mutual learning capabilities,facilitating effective knowledge transfer from previous tasks to new ones.By combining Elastic Weight Consolidation(EWC)for knowledge preservation with specialized regularization terms,AdaptForever successfully maintains performance on earlier tasks while acquiring new capabilities.Experimental results demonstrate that AdaptForever achieves superior performance across a continuous sequence of NLP tasks compared to existing parameter-efficient methods,while effectively preventing catastrophic forgetting and enabling positive knowledge transfer between tasks.展开更多
The present work primarily aims to explore the neuronal calcium(Ca^(2+)),IP_(3),and dopamine(DA)signaling systems through a feedback loop model.To date,there has been no exploration of this feedback model in fractiona...The present work primarily aims to explore the neuronal calcium(Ca^(2+)),IP_(3),and dopamine(DA)signaling systems through a feedback loop model.To date,there has been no exploration of this feedback model in fractional-order dynamical systems.This feedback loop model incorporates several crucial mechanisms like the buffering process,IP_(3)-receptor,ryanodine receptor,plasma membrane Ca^(2+)ATPase and sarcoplasmic/endoplasmic reticulum calcium ATPase(SERCA)pump,leak,sodium-calcium exchanger,voltage-gated Ca^(2+)channel,Orai channels,DA-dependent IP_(3)synthesis,and others.By incorporating these mechanisms,the model aims to provide a more comprehensive and realistic understanding of the system under investigation.The present model incorporates fractional-order dynamics along both spatial and temporal dimensions to examine the impacts of superdiffusion and memory showing Brownian motion of Ca^(2+),IP_(3),and DA signaling molecules.The bidirectional feedback between calcium and IP_(3)signaling systems,unidirectional feedback between calcium and dopamine signaling systems,and unidirectional feedback between IP_(3)and dopamine signaling systems have been incorporated into the present model.These feedback loops establish interactions among calcium,IP_(3),and dopamine signaling systems within neuronal cells.The numerical findings were obtained by using the Crank-Nicholson method with the Grunwald technique for fractional space derivatives and the L1method for fractional time derivatives in conjunction with the Gauss-Seidel Iterations.This research specifically investigates the implications of cell memory as well as superdiffusion on Ca^(2+),IP_(3),and DA dynamics in neuronal cells,which are interactive nonlinear systems.The superdiffusion process results in a reduction in Ca^(2+),IP_(3),and DA concentrations,while cellular memory leads to an increase in ion and molecule concentrations in neuronal cells during the initial time.The disruption of any given process can lead to imbalances in calcium,IP_(3),and DA systems,hence contributing to neurotoxicity and cellular demise.展开更多
AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited...AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.展开更多
In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the...In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the resilience and dependability of software systems.This study presents a novel software defect prediction technique that significantly enhances performance through a hybrid machine learning approach.The innovative methodology integrates a Genetic Algorithm(GA)for precise feature selection,a Decision Tree(DT)for robust classification,and leverages the capabilities of Particle Swarm Optimization(PSO)and Ant Colony Optimization(ACO)algorithms for precision⁃driven optimization.The utilization of datasets from varied sources enriches the predictive prowess of our model.Of particular significance in our pursuit is the unwavering focus on enhancing the prediction process through a highly refined PSO⁃ACO algorithm,thereby optimizing the efficiency and effectiveness of the GA⁃DT hybrid model.The thorough evaluation of our proposed approach unfolds across seven software projects,unveiling a paradigm shift in performance metrics.Results unequivocally demonstrate that the GA⁃DT with PSO⁃ACO algorithm surpasses its counterparts,showcasing unparalleled accuracy and reliability.Furthermore,our hybrid approach demonstrates outstanding performance in terms of F⁃measure,with an impressive increase rate of 78%.展开更多
Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection me...Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.展开更多
Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is availabl...Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.展开更多
In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of ...In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables opti- mization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.展开更多
In this paper, we propose a globally convergent Polak-Ribiere-Polyak (PRP) conjugate gradient method for nonconvex minimization of differentiable functions by employing an Armijo-type line search which is simpler and ...In this paper, we propose a globally convergent Polak-Ribiere-Polyak (PRP) conjugate gradient method for nonconvex minimization of differentiable functions by employing an Armijo-type line search which is simpler and less demanding than those defined in [4,10]. A favorite property of this method is that we can choose the initial stepsize as the one-dimensional minimizer of a quadratic modelΦ(t):= f(xk)+tgkTdk+(1/2) t2dkTQkdk, where Qk is a positive definite matrix that carries some second order information of the objective function f. So, this line search may make the stepsize tk more easily accepted. Preliminary numerical results show that this method is efficient.展开更多
Carrier frequency and symbol rate estimation are the main contents of parameter estimation,which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication.Wit...Carrier frequency and symbol rate estimation are the main contents of parameter estimation,which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication.With the development of wireless communication,the signal transmission environment has become increasingly bad,causing more difficulties in parameter estimation.It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related.In practice,it is impossible to calculate the cyclic spectrum of infinite length data signals.When using finite length data to obtain a cycle spectrum,the truncation noise is induced,resulting in interference.It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines.An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.展开更多
We introduce multilevel augmentation methods for solving operator equations based on direct sum decompositions of the range space of the operator and the solution space of the operator equation and a matrix splitting ...We introduce multilevel augmentation methods for solving operator equations based on direct sum decompositions of the range space of the operator and the solution space of the operator equation and a matrix splitting scheme. We establish a general setting for the analysis of these methods, showing that the methods yield approximate solutions of the same convergence order as the best approximation from the subspace. These augmentation methods allow us to develop fast, accurate and stable nonconventional numerical algorithms for solving operator equations. In particular, for second kind equations, special splitting techniques are proposed to develop such algorithms. These algorithms are then applied to solve the linear systems resulting from matrix compression schemes using wavelet-like functions for solving Fredholm integral equations of the second kind. For this special case, a complete analysis for computational complexity and convergence order is presented. Numerical examples are included to demonstrate the efficiency and accuracy of the methods. In these examples we use the proposed augmentation method to solve large scale linear systems resulting from the recently developed wavelet Galerkin methods and fast collocation methods applied to integral equations of the secondkind. Our numerical results confirm that this augmentation method is particularly efficient for solving large scale linear systems induced from wavelet compression schemes.展开更多
In this paper, a variable-coefficient Benjarnin-Bona-Mahony-Burger （BBMB） equation arising as a math- ematical model of propagation of small-amplitude long waves in nonlinear dispersive media is investigated. The in...In this paper, a variable-coefficient Benjarnin-Bona-Mahony-Burger （BBMB） equation arising as a math- ematical model of propagation of small-amplitude long waves in nonlinear dispersive media is investigated. The inte- grability of such an equation is studied with Painlevd analysis. The Lie symmetry method is performed for the BBMB equation and then similarity reductions and exact solutions are obtained based on the optimal system method. Further- more different types of solitary, periodic and kink waves can be seen with the change of variable coefficients.展开更多
Several software reliability growth models （SRGM） have been developed to monitor the reliability growth during the testing phase of software development. In most of the existing research available in the literatures...Several software reliability growth models （SRGM） have been developed to monitor the reliability growth during the testing phase of software development. In most of the existing research available in the literatures, it is considered that a similar testing effort is required on each debugging effort. However, in practice, different types of faults may require different amounts of testing efforts for their detection and removal. Consequently, faults are classified into three categories on the basis of severity： simple, hard and complex. This categorization may be extended to r type of faults on the basis of severity. Although some existing research in the literatures has incorporated this concept that fault removal rate （FRR） is different for different types of faults, they assume that the FRR remains constant during the overall testing period. On the contrary, it has been observed that as testing progresses, FRR changes due to changing testing strategy, skill, environment and personnel resources. In this paper, a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept. Then, the models are formulated for two particular environments. The models were validated on two real-life data sets. The results show better fit and wider applicability of the proposed models as to different types of failure datasets.展开更多
An attempt was made to determine the qualitative changes in basmati rice （Pusa Basmati 1121, PBl121） during soaking at 40 ℃ to 80 ℃. Soaking temperature had significant effect （a = 0.01） on chemical composition,...An attempt was made to determine the qualitative changes in basmati rice （Pusa Basmati 1121, PBl121） during soaking at 40 ℃ to 80 ℃. Soaking temperature had significant effect （a = 0.01） on chemical composition, glycemic index and starch characteristics of rice. Starch content, apparent amylose content, crude protein content and crude fat content in un-soaked rice were found to be 73.24%, 27.26%, 8.79% and 2.56%, respectively, but differences in these traits were observed after soaking. Amylose to amylopectin ratio （Am/Ap） decreased from 0.59 to 0.52 （soaked at 80 ℃）. Crude fibre and crude ash contents increased after soaking. The mineral composition （K, P, S, Ca, Mg, Mn, Fe, Cu and Zn） in soaked rice was found to be 16.46% higher than un-soaked rice at the same degree of polishing. Glycemic index of un-soaked rice was found to be 58.41, but decreased to 54.31 after soaking at 80 ℃. Pasting properties, scanning electron microscope images, and X-ray diffractograms suggested partial gelatinization of starch in the temperature range of 60 ℃ to 80 ℃. Based on qualitative changes in rice （apparent amylose content, Am/Ap ratio and crystallinity rate）, it was concluded that intermediate soakincl temperatures （60 ℃ to 70 ℃） would be useful for soaking of PB1121.展开更多
The blockchain technology plays a significant role in the present era of information technology.In the last few years,this technology has been used effectively in several domains.It has already made significant differ...The blockchain technology plays a significant role in the present era of information technology.In the last few years,this technology has been used effectively in several domains.It has already made significant differences in human life,as well as is intended to have noticeable impact in many other domains in the forthcoming years.The rapid growth in blockchain technology has created numerous new possibilities for use,especially for healthcare applications.The digital healthcare services require highly effective security methodologies that can integrate data security with the availablemanagement strategies.To test and understand this goal of security management in Saudi Arabian perspective,the authors performed a numerical analysis and simulation through a multi criteria decision making approach in this study.The authors adopted the fuzzy Analytical Hierarchy Process(AHP)for evaluating the effectiveness and then applied the fuzzy Technique forOrder of Preference by Similarity to Ideal Solution(TOPSIS)technique to simulate the validation of results.For eliciting highly corroborative and conclusive results,the study referred to a real time project of diabetes patients’management application of Kingdom of Saudi Arabia(KSA).The results discussed in this paper are scientifically proven and validated through various analysis approaches.Hence the present study can be a credible basis for other similar endeavours being undertaken in the domain of blockchain research.展开更多
Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless ...Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.展开更多
In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is foun...In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT.In this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data sharing.This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data providers.Then,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process.In this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed ledger.The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts.To examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI repository.The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.展开更多
Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy,with the brim-full horizon yet to unfold.In the absence of effective antiviral a...Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy,with the brim-full horizon yet to unfold.In the absence of effective antiviral and limited medical resources,many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources.Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals.Regardless of discourse on medical resources and diversities in masks,all countries are mandating coverings over nose and mouth in public areas.Towards contribution of public health,the aim of the paper is to devise a real-time technique that can efficiently detect non mask faces in public and thus enforce to wear mask.The proposed technique is ensemble of one stage and two stage detectors to achieve low inference time and high accuracy.We took ResNet50 as a baseline model and applied the concept of transfer learning to fuse high level semantic information in multiple feature maps.In addition,we also propose a bounding box transformation to improve localization performance during mask detection.The experiments are conducted with three popular baseline models namely ResNet50,AlexNet and MobileNet.We explored the possibility of these models to plug-in with the proposed model,so that highly accurate results can be achieved in less inference time.It is observed that the proposed technique can achieve high accuracy(98.2%)when implemented with ResNet50.Besides,the proposed model can generate 11.07%and 6.44%higher precision and recall respectively in mask detection when compared to RetinaFaceMask detector.展开更多
基金RUSA-Phase 2.0 grant sanctioned vide Letter No.F.24-51/2014-U,Policy(TNMulti-Gen)Dept.of Edn.Govt.of India,Dt.09.10.2018.
文摘Diabetic retinopathy(DR)is a disease with an increasing prevalence and the major reason for blindness among working-age population.The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment.An automated screening for DR has been identified as an effective method for early DR detection,which can decrease the workload associated to manual grading as well as save diagnosis costs and time.Several studies have been carried out to develop automated detection and classification models for DR.This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy(DR).The proposed model incorporates different processes namely data collection,preprocessing,segmentation,feature extraction and classification.At first,the IoT-based data collection process takes place where the patient wears a head mounted camera to capture the retinal fundus image and send to cloud server.Then,the contrast level of the input DR image gets increased in the preprocessing stage using Contrast Limited Adaptive Histogram Equalization(CLAHE)model.Next,the preprocessed image is segmented using Adaptive Spatial Kernel distance measure-based Fuzzy C-Means clustering(ASKFCM)model.Afterwards,deep Convolution Neural Network(CNN)based Inception v4 model is applied as a feature extractor and the resulting feature vectors undergo classification in line with the Gaussian Naive Bayes(GNB)model.The proposed model was tested using a benchmark DR MESSIDOR image dataset and the obtained results showcased superior performance of the proposed model over other such models compared in the study.
基金RUSA PHASE 2.0, University of Alagappa, Karaikudi has supported this research projectThe UGC-NFSC fellowship has helped support this research
文摘One of the evolving hand biometric features considered so far is finger knuckle printing,because of its ability towards unique identification of individuals.Despite many attempts have been made in this area of research,the accuracy of the recognition model remains a major issue.To overcome this problem,a novel biometric-based method,named fingerknuckle-print(FKP),has been developed for individual verification.The proposed system carries key steps such as preprocessing,segmentation,feature extraction and classification.Initially input FKP image is fed into the preprocessing stage where colour images are converted to gray scale image for augmenting the system performance.Afterwards,segmentation process is carried out with the help of CROI(Circular Region of Interest)and Morphological operation.Then,feature extraction stage is carried out using Gabor-Derivative line approach for extracting intrinsic features.Finally,DCNN(Deep Convolutional Neural Network)is trained for the processed knuckle images to recognize imposter and genuine individuals.Extensive experiments on standard FKP database demonstrates that the proposed method attains considerable improvement compared with state-of-the-art methods.The overall accuracy attained for the proposed methodology is 95.6%which is achieved better than the existing techniques.
文摘Ensuring software quality in open⁃source environments requires adaptive mechanisms to enhance scalability,optimize service provisioning,and improve reliability.This study presents the dynamic correlation analysis technique to enhance software quality management in open⁃source environments by addressing dynamic scalability,adaptive service provisioning,and software reliability.The proposed methodology integrates a scalability metric,an optimized service provisioning model,and a weighted entropy⁃based reliability assessment to systematically improve key performance parameters.Experimental evaluation conducted on multiple open⁃source software(OSS)versions demonstrates significant improvements:scalability increased by 27.5%,service provisioning time reduced by 18.3%,and software reliability improved by 22.1%compared to baseline methods.A comparative analysis with prior works further highlights the effectiveness of this approach in ensuring adaptability,efficiency,and resilience in dynamic software ecosystems.Future work will focus on real⁃time monitoring and AI⁃driven adaptive provisioning to further enhance software quality management.
基金supported by the National Key R&D Program of China(No.2023YFB3308601)Sichuan Science and Technology Program(2024NSFJQ0035,2024NSFSC0004)the Talents by Sichuan provincial Party Committee Organization Department.
文摘In natural language processing(NLP),managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models,leading to practical inefficiencies.To address this challenge,we introduce AdaptForever,a novel approach that enables continuous mastery of NLP tasks through the integration of elastic and mutual learning strategies with a stochastic expert mechanism.Our method freezes the pre-trained model weights while incorporating adapters enhanced with mutual learning capabilities,facilitating effective knowledge transfer from previous tasks to new ones.By combining Elastic Weight Consolidation(EWC)for knowledge preservation with specialized regularization terms,AdaptForever successfully maintains performance on earlier tasks while acquiring new capabilities.Experimental results demonstrate that AdaptForever achieves superior performance across a continuous sequence of NLP tasks compared to existing parameter-efficient methods,while effectively preventing catastrophic forgetting and enabling positive knowledge transfer between tasks.
文摘The present work primarily aims to explore the neuronal calcium(Ca^(2+)),IP_(3),and dopamine(DA)signaling systems through a feedback loop model.To date,there has been no exploration of this feedback model in fractional-order dynamical systems.This feedback loop model incorporates several crucial mechanisms like the buffering process,IP_(3)-receptor,ryanodine receptor,plasma membrane Ca^(2+)ATPase and sarcoplasmic/endoplasmic reticulum calcium ATPase(SERCA)pump,leak,sodium-calcium exchanger,voltage-gated Ca^(2+)channel,Orai channels,DA-dependent IP_(3)synthesis,and others.By incorporating these mechanisms,the model aims to provide a more comprehensive and realistic understanding of the system under investigation.The present model incorporates fractional-order dynamics along both spatial and temporal dimensions to examine the impacts of superdiffusion and memory showing Brownian motion of Ca^(2+),IP_(3),and DA signaling molecules.The bidirectional feedback between calcium and IP_(3)signaling systems,unidirectional feedback between calcium and dopamine signaling systems,and unidirectional feedback between IP_(3)and dopamine signaling systems have been incorporated into the present model.These feedback loops establish interactions among calcium,IP_(3),and dopamine signaling systems within neuronal cells.The numerical findings were obtained by using the Crank-Nicholson method with the Grunwald technique for fractional space derivatives and the L1method for fractional time derivatives in conjunction with the Gauss-Seidel Iterations.This research specifically investigates the implications of cell memory as well as superdiffusion on Ca^(2+),IP_(3),and DA dynamics in neuronal cells,which are interactive nonlinear systems.The superdiffusion process results in a reduction in Ca^(2+),IP_(3),and DA concentrations,while cellular memory leads to an increase in ion and molecule concentrations in neuronal cells during the initial time.The disruption of any given process can lead to imbalances in calcium,IP_(3),and DA systems,hence contributing to neurotoxicity and cellular demise.
文摘AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.
文摘In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the resilience and dependability of software systems.This study presents a novel software defect prediction technique that significantly enhances performance through a hybrid machine learning approach.The innovative methodology integrates a Genetic Algorithm(GA)for precise feature selection,a Decision Tree(DT)for robust classification,and leverages the capabilities of Particle Swarm Optimization(PSO)and Ant Colony Optimization(ACO)algorithms for precision⁃driven optimization.The utilization of datasets from varied sources enriches the predictive prowess of our model.Of particular significance in our pursuit is the unwavering focus on enhancing the prediction process through a highly refined PSO⁃ACO algorithm,thereby optimizing the efficiency and effectiveness of the GA⁃DT hybrid model.The thorough evaluation of our proposed approach unfolds across seven software projects,unveiling a paradigm shift in performance metrics.Results unequivocally demonstrate that the GA⁃DT with PSO⁃ACO algorithm surpasses its counterparts,showcasing unparalleled accuracy and reliability.Furthermore,our hybrid approach demonstrates outstanding performance in terms of F⁃measure,with an impressive increase rate of 78%.
基金the Deanship of Scientific Research at King Khalid University for funding this work through large group under grant number(GRP.2/663/46).
文摘Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Wheat fungal infections pose a danger to the grain quality and crop productivity.Thus,prompt and precise diagnosis is essential for efficient crop management.This study used the WFD2020 image dataset,which is available to everyone,to look into howdeep learningmodels could be used to find powdery mildew,leaf rust,and yellow rust,which are three common fungal diseases in Punjab,India.We changed a few hyperparameters to test TensorFlowbased models,such as SSD and Faster R-CNN with ResNet50,ResNet101,and ResNet152 as backbones.Faster R-CNN with ResNet50 achieved amean average precision(mAP)of 0.68 among these models.We then used the PyTorch-based YOLOv8 model,which significantly outperformed the previous methods with an impressive mAP of 0.99.YOLOv8 proved to be a beneficial approach for the early-stage diagnosis of fungal diseases,especially when it comes to precisely identifying diseased areas and various object sizes in images.Problems,such as class imbalance and possible model overfitting,persisted despite these developments.The results show that YOLOv8 is a good automated disease diagnosis tool that helps farmers quickly find and treat fungal infections using image-based systems.
基金Project supported by the National Natural Science Foundation of China (No. 60474064), and the Natural Science Foundation of Zhejiang Province (No. Y105694), China
文摘In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave’s search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate. In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables opti- mization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.
基金This work is supported by the Chinese NSF grants 60475042 Guangxi NSF grants 0542043the Foundation of Advanced Research Center of Zhongshan University and Hong Kong
文摘In this paper, we propose a globally convergent Polak-Ribiere-Polyak (PRP) conjugate gradient method for nonconvex minimization of differentiable functions by employing an Armijo-type line search which is simpler and less demanding than those defined in [4,10]. A favorite property of this method is that we can choose the initial stepsize as the one-dimensional minimizer of a quadratic modelΦ(t):= f(xk)+tgkTdk+(1/2) t2dkTQkdk, where Qk is a positive definite matrix that carries some second order information of the objective function f. So, this line search may make the stepsize tk more easily accepted. Preliminary numerical results show that this method is efficient.
基金supported by the National Key R&D Program of China(2016YFB0800203)
文摘Carrier frequency and symbol rate estimation are the main contents of parameter estimation,which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication.With the development of wireless communication,the signal transmission environment has become increasingly bad,causing more difficulties in parameter estimation.It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related.In practice,it is impossible to calculate the cyclic spectrum of infinite length data signals.When using finite length data to obtain a cycle spectrum,the truncation noise is induced,resulting in interference.It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines.An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.
基金Supported in part by the Natural Science Foundation of China under grants 10371137and 10201034Foundation of Doctoral Program of National Higher Education of China under under grant 20030558008Guangdong Provincial Natural Science Foundation of China u
文摘We introduce multilevel augmentation methods for solving operator equations based on direct sum decompositions of the range space of the operator and the solution space of the operator equation and a matrix splitting scheme. We establish a general setting for the analysis of these methods, showing that the methods yield approximate solutions of the same convergence order as the best approximation from the subspace. These augmentation methods allow us to develop fast, accurate and stable nonconventional numerical algorithms for solving operator equations. In particular, for second kind equations, special splitting techniques are proposed to develop such algorithms. These algorithms are then applied to solve the linear systems resulting from matrix compression schemes using wavelet-like functions for solving Fredholm integral equations of the second kind. For this special case, a complete analysis for computational complexity and convergence order is presented. Numerical examples are included to demonstrate the efficiency and accuracy of the methods. In these examples we use the proposed augmentation method to solve large scale linear systems resulting from the recently developed wavelet Galerkin methods and fast collocation methods applied to integral equations of the secondkind. Our numerical results confirm that this augmentation method is particularly efficient for solving large scale linear systems induced from wavelet compression schemes.
文摘In this paper, a variable-coefficient Benjarnin-Bona-Mahony-Burger （BBMB） equation arising as a math- ematical model of propagation of small-amplitude long waves in nonlinear dispersive media is investigated. The inte- grability of such an equation is studied with Painlevd analysis. The Lie symmetry method is performed for the BBMB equation and then similarity reductions and exact solutions are obtained based on the optimal system method. Further- more different types of solitary, periodic and kink waves can be seen with the change of variable coefficients.
文摘Several software reliability growth models （SRGM） have been developed to monitor the reliability growth during the testing phase of software development. In most of the existing research available in the literatures, it is considered that a similar testing effort is required on each debugging effort. However, in practice, different types of faults may require different amounts of testing efforts for their detection and removal. Consequently, faults are classified into three categories on the basis of severity： simple, hard and complex. This categorization may be extended to r type of faults on the basis of severity. Although some existing research in the literatures has incorporated this concept that fault removal rate （FRR） is different for different types of faults, they assume that the FRR remains constant during the overall testing period. On the contrary, it has been observed that as testing progresses, FRR changes due to changing testing strategy, skill, environment and personnel resources. In this paper, a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept. Then, the models are formulated for two particular environments. The models were validated on two real-life data sets. The results show better fit and wider applicability of the proposed models as to different types of failure datasets.
基金Post Graduate School, Indian Agriculture Research Institute, New Delhi, Indiathe Council of Scientific and Industrial Research, New Delhi, India
文摘An attempt was made to determine the qualitative changes in basmati rice （Pusa Basmati 1121, PBl121） during soaking at 40 ℃ to 80 ℃. Soaking temperature had significant effect （a = 0.01） on chemical composition, glycemic index and starch characteristics of rice. Starch content, apparent amylose content, crude protein content and crude fat content in un-soaked rice were found to be 73.24%, 27.26%, 8.79% and 2.56%, respectively, but differences in these traits were observed after soaking. Amylose to amylopectin ratio （Am/Ap） decreased from 0.59 to 0.52 （soaked at 80 ℃）. Crude fibre and crude ash contents increased after soaking. The mineral composition （K, P, S, Ca, Mg, Mn, Fe, Cu and Zn） in soaked rice was found to be 16.46% higher than un-soaked rice at the same degree of polishing. Glycemic index of un-soaked rice was found to be 58.41, but decreased to 54.31 after soaking at 80 ℃. Pasting properties, scanning electron microscope images, and X-ray diffractograms suggested partial gelatinization of starch in the temperature range of 60 ℃ to 80 ℃. Based on qualitative changes in rice （apparent amylose content, Am/Ap ratio and crystallinity rate）, it was concluded that intermediate soakincl temperatures （60 ℃ to 70 ℃） would be useful for soaking of PB1121.
基金Funding for this study was received from the Ministry of Education and Deanship of Scientific Research at King Abdulaziz University,Kingdom of Saudi Arabia under the Grant No.IFPHI-264-611-2020.
文摘The blockchain technology plays a significant role in the present era of information technology.In the last few years,this technology has been used effectively in several domains.It has already made significant differences in human life,as well as is intended to have noticeable impact in many other domains in the forthcoming years.The rapid growth in blockchain technology has created numerous new possibilities for use,especially for healthcare applications.The digital healthcare services require highly effective security methodologies that can integrate data security with the availablemanagement strategies.To test and understand this goal of security management in Saudi Arabian perspective,the authors performed a numerical analysis and simulation through a multi criteria decision making approach in this study.The authors adopted the fuzzy Analytical Hierarchy Process(AHP)for evaluating the effectiveness and then applied the fuzzy Technique forOrder of Preference by Similarity to Ideal Solution(TOPSIS)technique to simulate the validation of results.For eliciting highly corroborative and conclusive results,the study referred to a real time project of diabetes patients’management application of Kingdom of Saudi Arabia(KSA).The results discussed in this paper are scientifically proven and validated through various analysis approaches.Hence the present study can be a credible basis for other similar endeavours being undertaken in the domain of blockchain research.
文摘Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.
文摘In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT.In this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data sharing.This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data providers.Then,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process.In this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed ledger.The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts.To examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI repository.The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.
文摘Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy,with the brim-full horizon yet to unfold.In the absence of effective antiviral and limited medical resources,many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources.Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals.Regardless of discourse on medical resources and diversities in masks,all countries are mandating coverings over nose and mouth in public areas.Towards contribution of public health,the aim of the paper is to devise a real-time technique that can efficiently detect non mask faces in public and thus enforce to wear mask.The proposed technique is ensemble of one stage and two stage detectors to achieve low inference time and high accuracy.We took ResNet50 as a baseline model and applied the concept of transfer learning to fuse high level semantic information in multiple feature maps.In addition,we also propose a bounding box transformation to improve localization performance during mask detection.The experiments are conducted with three popular baseline models namely ResNet50,AlexNet and MobileNet.We explored the possibility of these models to plug-in with the proposed model,so that highly accurate results can be achieved in less inference time.It is observed that the proposed technique can achieve high accuracy(98.2%)when implemented with ResNet50.Besides,the proposed model can generate 11.07%and 6.44%higher precision and recall respectively in mask detection when compared to RetinaFaceMask detector.
