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
The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical industry.Doing wet laboratory tests to provide a better persp...The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical industry.Doing wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential muta-tions,computational approaches that can reliably anticipate the consequences of amino acid mutations are critical.This work presents a robust methodology to analyze and identify the effects of mutation on a single protein structure.Initially,the context in a collection of words is determined using a knowledge graph for feature selection purposes.The proposed prediction is based on an easier and sim-pler logistic regression inferred binary classification technique.This approach can able to obtain a classification accuracy(AUC)Area Under the Curve of 87%when randomly validated against experimental energy changes.Moreover,for each cross-fold validation,the precision,recall,and F-Score are presented.These results support the validity of our strategy since it performs the vast majority of prior studies in this domain.展开更多
Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large a...Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.展开更多
In recent years,it has been observed that the disclosure of information increases the risk of terrorism.Without restricting the accessibility of information,providing security is difficult.So,there is a demand for tim...In recent years,it has been observed that the disclosure of information increases the risk of terrorism.Without restricting the accessibility of information,providing security is difficult.So,there is a demand for time tofill the gap between security and accessibility of information.In fact,security tools should be usable for improving the security as well as the accessibility of information.Though security and accessibility are not directly influenced,some of their factors are indirectly influenced by each other.Attributes play an important role in bridging the gap between security and accessibility.In this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and severity.The significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle process.To calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare context.The study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.展开更多
Cryptography is deemed to be the optimum strategy to secure the data privacy in which the data is encoded ahead of time before sharing it.Visual Secret Sharing(VSS)is an encryption method in which the secret message i...Cryptography is deemed to be the optimum strategy to secure the data privacy in which the data is encoded ahead of time before sharing it.Visual Secret Sharing(VSS)is an encryption method in which the secret message is split into at least two trivial images called’shares’to cover it.However,such message are always targeted by hackers or dishonest members who attempt to decrypt the message.This can be avoided by not uncovering the secret message without the universal share when it is presented and is typically taken care of,by the trusted party.Hence,in this paper,an optimal and secure double-layered secret image sharing scheme is proposed.The proposed share creation process contains two layers such as threshold-based secret sharing in the first layer and universal share based secret sharing in the second layer.In first layer,Genetic Algorithm(GA)is applied to find the optimal threshold value based on the randomness of the created shares.Then,in the second layer,a novel design of universal share-based secret share creation method is proposed.Finally,Opposition Whale Optimization Algorithm(OWOA)-based optimal key was generated for rectange block cipher to secure each share.This helped in producing high quality reconstruction images.The researcher achieved average experimental outcomes in terms of PSNR and MSE values equal to 55.154225 and 0.79365625 respectively.The average PSNRwas less(49.134475)and average MSE was high(1)in case of existing methods.展开更多
Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on d...Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.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
The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated ...The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare.Despite the phenomenal advancement in the present healthcare services,the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’data.A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders.Various approaches and methods have been practiced by researchers to secure healthcare digital services.However,there are very few from the Machine learning(ML)domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records(EHRs).The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches.B.A.Kitchenham guidelines have been practiced as a manual to conduct this work.Seven well-known digital libraries namely IEEE Xplore,Science Direct,Springer Link,ACM Digital Library,Willey Online Library,PubMed(Medical and Bio-Science),and MDPI have been included to performan exhaustive search for the existing pertinent studies.Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion,yet the specified area has not been fully explored by the researchers.K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems’log data.Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies.This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively.Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.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
In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vas...In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vast amount of data can be a useful source of knowledge for predicting user behavior. A refined method is required to carry out this task. Web usages mining (WUM) is the tool designed to do this task. WUM system is used to extract the knowledge based on user behavior during the web navigation. The extracted knowledge can be used for predicting the users’ future request when user is browsing the web. In this paper we advanced the online recommender system by using a Longest Common Subsequence (LCS) classification algorithm to classify users’ navigation pattern. Classification using the proposed method can improve the accuracy of recommendation and also proposed an algorithm that uses LCS method to know the user behavior for improvement of design of a website.展开更多
Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unn...Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.展开更多
Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms.Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices.Machinele...Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms.Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices.Machinelearning-based frameworks are effective for active authentication.However,the success of any machine-learningbased techniques depends highly on the relevancy of the data in hand for training.In addition,the training time should be very efficient.Keeping in view both issues,we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate users.We hypothesized that every user has a unique pattern hidden in his/her usage of apps.Motivated by this observation,we’ve designed a way to obtain training data,which can be used by any machine learning model for effective authentication.To achieve better accuracy with reduced training time,we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority list.An information theoretic app ranking scheme was used to prepare a user-targeted apps priority list.Predictability of each instance related to a candidate app was calculated by using a knockout approach.Finally,a weighted rank was calculated for each app specific to every user.Instances with low ranked apps were removed to derive the reduced training set.Two datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.展开更多
The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2 virus or COVID-19) disease was declared pandemic by the WorldHealth Organization (WHO) on March 11, 2020. COVID-19 has already affectedmore th...The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2 virus or COVID-19) disease was declared pandemic by the WorldHealth Organization (WHO) on March 11, 2020. COVID-19 has already affectedmore than 211 nations. In such a bleak scenario, it becomes imperative to analyzeand identify those regions in Saudi Arabia that are at high risk. A preemptivestudy done in the context of predicting the possible COVID-19 hotspots wouldfacilitate in the implementation of prompt and targeted countermeasures againstSARS-CoV-2, thus saving many lives. Working towards this intent, the presentstudy adopts a decision making based methodology of simulation named Analytical Hierarchy Process (AHP), a multi criteria decision making approach, forassessing the risk of COVID-19 in different regions of Saudi Arabia. AHP givesthe ability to measure the risks numerically. Moreover, numerical assessments arealways effective and easy to understand. Hence, this research endeavour employsFuzzy based computational method of decision making for its empirical analysis.Findings in the proposed paper suggest that Riyadh and Makkah are the mostsusceptible regions, implying that if sustained and focused preventive measuresare not introduced at the right juncture, the two cities could be the worst afflictedwith the infection. The results obtained through Fuzzy based computationalmethod of decision making are highly corroborative and would be very usefulfor categorizing and assessing the current COVID-19 situation in the Kingdomof Saudi Arabia. More specifically, identifying the cities that are likely to beCOVID-19 hotspots would help the country’s health and medical fraternity toreinforce intensive containment strategies to counter the ills of the pandemic insuch regions.展开更多
Since the beginning of web applications,security has been a critical study area.There has been a lot of research done to figure out how to define and identify security goals or issues.However,high-security web apps ha...Since the beginning of web applications,security has been a critical study area.There has been a lot of research done to figure out how to define and identify security goals or issues.However,high-security web apps have been found to be less durable in recent years;thus reducing their business continuity.High security features of a web application are worthless unless they provide effective services to the user and meet the standards of commercial viability.Hence,there is a necessity to link in the gap between durability and security of the web application.Indeed,security mechanisms must be used to enhance durability as well as the security of the web application.Although durability and security are not related directly,some of their factors influence each other indirectly.Characteristics play an important role in reducing the void between durability and security.In this respect,the present study identifies key characteristics of security and durability that affect each other indirectly and directly,including confidentiality,integrity availability,human trust and trustworthiness.The importance of all the attributes in terms of their weight is essential for their influence on the whole security during the development procedure of web application.To estimate the efficacy of present study,authors employed the Hesitant Fuzzy Analytic Hierarchy Process(H-Fuzzy AHP).The outcomes of our investigations and conclusions will be a useful reference for the web application developers in achieving a more secure and durable web application.展开更多
Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Thei...Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Their importance lies in their real world application such as economic,ecological,and socioeconomic processes.They are also applied in several biological processes,such as population dynamics of biological species,water balance,temperature regulation in the human body,carbon dioxide control in lungs,blood pressure,immune system,cardiac regulation,etc.Bilinear singular systems naturally represent different physical processes such as the fundamental law of mass action,the DC motor,the induction motor drives,the mechanical brake systems,aerial combat between two aircraft,the missile intercept problem,modeling and control of small furnaces and hydraulic rotary multimotor systems.The current research work discusses the Legendre Neural Network’s implementation to evaluate time-varying singular bilinear systems for finding the exact solution.The results were obtained from two methods namely the RK-Butcher algorithm and the Runge Kutta Arithmetic Mean(RKAM)method.Compared with the results attained from Legendre Neural Network Method for time-varying singular bilinear systems,the output proved to be accurate.As such,this research article established that the proposed Legendre Neural Network could be easily implemented in MATLAB.One can obtain the solution for any length of time from this method in time-varying singular bilinear systems.展开更多
An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimens...An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.展开更多
This paper proposes a novel method based on statistical tests of hypotheses, such as F-ratio and Welch’s t-tests. The input query image is examined whether it is a textured or structured. If it is structured, the sha...This paper proposes a novel method based on statistical tests of hypotheses, such as F-ratio and Welch’s t-tests. The input query image is examined whether it is a textured or structured. If it is structured, the shapes are segregated into various regions according to its nature;otherwise, it is treated as textured image and considered the entire image as it is for the experiment. The aforesaid tests are applied regions-wise. First, the F-ratio test is applied, if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. means of the two images. If the images pass both tests, then it is concluded that the two images are the same or similar. Otherwise, they differ. Since the proposed system is distribution-based, it is invariant for rotation and scaling. Also, the system facilitates the user to fix the number of images to be retrieved, because the user can fix the level of significance according to their requirements. These are the main advantages of the proposed system.展开更多
In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving...In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.展开更多
Deep learning has reached many successes in Video Processing.Video has become a growing important part of our daily digital interactions.The advancement of better resolution content and the large volume offers serious...Deep learning has reached many successes in Video Processing.Video has become a growing important part of our daily digital interactions.The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving,distributing,compressing and revealing highquality video content.In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask,which creatively combines the Deep Learning Techniques on Convolutional Neural Networks(CNN)and Generative Adversarial Networks(GAN).The video compression method involves the layers are divided into different groups for data processing,using CNN to remove the duplicate frames,repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory(LSTM).Instead of the complete image,the small changes generated using GAN are substituted,which helps with frame-level compression.Pixel wise comparison is performed using K-nearest Neighbours(KNN)over the frame,clustered with K-means and Singular Value Decomposition(SVD)is applied for every frame in the video for all three colour channels[Red,Green,Blue]to decrease the dimension of the utility matrix[R,G,B]by extracting its latent factors.Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video.Repeated experiments on several videos with different sizes,duration,Frames per second(FPS),and quality results demonstrated a significant resampling rate.On normal,the outcome delivered had around a 10%deviation in quality and over half in size when contrasted,and the original video.展开更多
Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mob...Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mobile Adhoc system management,on the other hand,requires further testing and improvements in terms of security.Traditional routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two nodes.The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network.It is achieved by changing the residual energy of each network node.Also,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are proposed.Moreover,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters.The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.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.
文摘The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical industry.Doing wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential muta-tions,computational approaches that can reliably anticipate the consequences of amino acid mutations are critical.This work presents a robust methodology to analyze and identify the effects of mutation on a single protein structure.Initially,the context in a collection of words is determined using a knowledge graph for feature selection purposes.The proposed prediction is based on an easier and sim-pler logistic regression inferred binary classification technique.This approach can able to obtain a classification accuracy(AUC)Area Under the Curve of 87%when randomly validated against experimental energy changes.Moreover,for each cross-fold validation,the precision,recall,and F-Score are presented.These results support the validity of our strategy since it performs the vast majority of prior studies in this domain.
基金funded by the Deanship of Scientific Research at King Khalid University,Kingdom of Saudi Arabia for large group Research Project under grant number:RGP2/249/44.
文摘Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.
基金Funding for this study was received from the Taif University,Taif,Saudi Arabia under the Grant No.TURSP-2020/150.
文摘In recent years,it has been observed that the disclosure of information increases the risk of terrorism.Without restricting the accessibility of information,providing security is difficult.So,there is a demand for time tofill the gap between security and accessibility of information.In fact,security tools should be usable for improving the security as well as the accessibility of information.Though security and accessibility are not directly influenced,some of their factors are indirectly influenced by each other.Attributes play an important role in bridging the gap between security and accessibility.In this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and severity.The significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle process.To calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare context.The study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.
基金supported by RUSA PHASE 2.0,Alagappa University,Karaikudi,India。
文摘Cryptography is deemed to be the optimum strategy to secure the data privacy in which the data is encoded ahead of time before sharing it.Visual Secret Sharing(VSS)is an encryption method in which the secret message is split into at least two trivial images called’shares’to cover it.However,such message are always targeted by hackers or dishonest members who attempt to decrypt the message.This can be avoided by not uncovering the secret message without the universal share when it is presented and is typically taken care of,by the trusted party.Hence,in this paper,an optimal and secure double-layered secret image sharing scheme is proposed.The proposed share creation process contains two layers such as threshold-based secret sharing in the first layer and universal share based secret sharing in the second layer.In first layer,Genetic Algorithm(GA)is applied to find the optimal threshold value based on the randomness of the created shares.Then,in the second layer,a novel design of universal share-based secret share creation method is proposed.Finally,Opposition Whale Optimization Algorithm(OWOA)-based optimal key was generated for rectange block cipher to secure each share.This helped in producing high quality reconstruction images.The researcher achieved average experimental outcomes in terms of PSNR and MSE values equal to 55.154225 and 0.79365625 respectively.The average PSNRwas less(49.134475)and average MSE was high(1)in case of existing methods.
文摘Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.
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.
基金This research was supported by Taif University Researchers Supporting Project under the Grant No.TURSP-2020/211,Taif University,Taif,Saudi Arabia。
文摘The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare.Despite the phenomenal advancement in the present healthcare services,the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients’data.A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders.Various approaches and methods have been practiced by researchers to secure healthcare digital services.However,there are very few from the Machine learning(ML)domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records(EHRs).The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches.B.A.Kitchenham guidelines have been practiced as a manual to conduct this work.Seven well-known digital libraries namely IEEE Xplore,Science Direct,Springer Link,ACM Digital Library,Willey Online Library,PubMed(Medical and Bio-Science),and MDPI have been included to performan exhaustive search for the existing pertinent studies.Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion,yet the specified area has not been fully explored by the researchers.K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems’log data.Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies.This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively.Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.
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In very short time today web has become an enormously important tool for communicating ideas, conducting business and entertainment. At the time of navigation, web users leave various records of their action. This vast amount of data can be a useful source of knowledge for predicting user behavior. A refined method is required to carry out this task. Web usages mining (WUM) is the tool designed to do this task. WUM system is used to extract the knowledge based on user behavior during the web navigation. The extracted knowledge can be used for predicting the users’ future request when user is browsing the web. In this paper we advanced the online recommender system by using a Longest Common Subsequence (LCS) classification algorithm to classify users’ navigation pattern. Classification using the proposed method can improve the accuracy of recommendation and also proposed an algorithm that uses LCS method to know the user behavior for improvement of design of a website.
文摘Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.
文摘Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms.Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices.Machinelearning-based frameworks are effective for active authentication.However,the success of any machine-learningbased techniques depends highly on the relevancy of the data in hand for training.In addition,the training time should be very efficient.Keeping in view both issues,we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate users.We hypothesized that every user has a unique pattern hidden in his/her usage of apps.Motivated by this observation,we’ve designed a way to obtain training data,which can be used by any machine learning model for effective authentication.To achieve better accuracy with reduced training time,we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority list.An information theoretic app ranking scheme was used to prepare a user-targeted apps priority list.Predictability of each instance related to a candidate app was calculated by using a knockout approach.Finally,a weighted rank was calculated for each app specific to every user.Instances with low ranked apps were removed to derive the reduced training set.Two datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.
基金supported by Taif University Researchers Supporting Project number(TURSP-2020/211),Taif University,Taif,Saudi Arabia.
文摘The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2 virus or COVID-19) disease was declared pandemic by the WorldHealth Organization (WHO) on March 11, 2020. COVID-19 has already affectedmore than 211 nations. In such a bleak scenario, it becomes imperative to analyzeand identify those regions in Saudi Arabia that are at high risk. A preemptivestudy done in the context of predicting the possible COVID-19 hotspots wouldfacilitate in the implementation of prompt and targeted countermeasures againstSARS-CoV-2, thus saving many lives. Working towards this intent, the presentstudy adopts a decision making based methodology of simulation named Analytical Hierarchy Process (AHP), a multi criteria decision making approach, forassessing the risk of COVID-19 in different regions of Saudi Arabia. AHP givesthe ability to measure the risks numerically. Moreover, numerical assessments arealways effective and easy to understand. Hence, this research endeavour employsFuzzy based computational method of decision making for its empirical analysis.Findings in the proposed paper suggest that Riyadh and Makkah are the mostsusceptible regions, implying that if sustained and focused preventive measuresare not introduced at the right juncture, the two cities could be the worst afflictedwith the infection. The results obtained through Fuzzy based computationalmethod of decision making are highly corroborative and would be very usefulfor categorizing and assessing the current COVID-19 situation in the Kingdomof Saudi Arabia. More specifically, identifying the cities that are likely to beCOVID-19 hotspots would help the country’s health and medical fraternity toreinforce intensive containment strategies to counter the ills of the pandemic insuch regions.
基金funded by the Taif University Researchers Supporting Projects at Taif University,Kingdom of Saudi Arabia,under Grant Number:TURSP-2020/231.
文摘Since the beginning of web applications,security has been a critical study area.There has been a lot of research done to figure out how to define and identify security goals or issues.However,high-security web apps have been found to be less durable in recent years;thus reducing their business continuity.High security features of a web application are worthless unless they provide effective services to the user and meet the standards of commercial viability.Hence,there is a necessity to link in the gap between durability and security of the web application.Indeed,security mechanisms must be used to enhance durability as well as the security of the web application.Although durability and security are not related directly,some of their factors influence each other indirectly.Characteristics play an important role in reducing the void between durability and security.In this respect,the present study identifies key characteristics of security and durability that affect each other indirectly and directly,including confidentiality,integrity availability,human trust and trustworthiness.The importance of all the attributes in terms of their weight is essential for their influence on the whole security during the development procedure of web application.To estimate the efficacy of present study,authors employed the Hesitant Fuzzy Analytic Hierarchy Process(H-Fuzzy AHP).The outcomes of our investigations and conclusions will be a useful reference for the web application developers in achieving a more secure and durable web application.
文摘Bilinear singular systems can be used in the investigation of different types of engineering systems.In the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear systems.Their importance lies in their real world application such as economic,ecological,and socioeconomic processes.They are also applied in several biological processes,such as population dynamics of biological species,water balance,temperature regulation in the human body,carbon dioxide control in lungs,blood pressure,immune system,cardiac regulation,etc.Bilinear singular systems naturally represent different physical processes such as the fundamental law of mass action,the DC motor,the induction motor drives,the mechanical brake systems,aerial combat between two aircraft,the missile intercept problem,modeling and control of small furnaces and hydraulic rotary multimotor systems.The current research work discusses the Legendre Neural Network’s implementation to evaluate time-varying singular bilinear systems for finding the exact solution.The results were obtained from two methods namely the RK-Butcher algorithm and the Runge Kutta Arithmetic Mean(RKAM)method.Compared with the results attained from Legendre Neural Network Method for time-varying singular bilinear systems,the output proved to be accurate.As such,this research article established that the proposed Legendre Neural Network could be easily implemented in MATLAB.One can obtain the solution for any length of time from this method in time-varying singular bilinear systems.
文摘An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.
文摘This paper proposes a novel method based on statistical tests of hypotheses, such as F-ratio and Welch’s t-tests. The input query image is examined whether it is a textured or structured. If it is structured, the shapes are segregated into various regions according to its nature;otherwise, it is treated as textured image and considered the entire image as it is for the experiment. The aforesaid tests are applied regions-wise. First, the F-ratio test is applied, if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. means of the two images. If the images pass both tests, then it is concluded that the two images are the same or similar. Otherwise, they differ. Since the proposed system is distribution-based, it is invariant for rotation and scaling. Also, the system facilitates the user to fix the number of images to be retrieved, because the user can fix the level of significance according to their requirements. These are the main advantages of the proposed system.
文摘In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.
文摘Deep learning has reached many successes in Video Processing.Video has become a growing important part of our daily digital interactions.The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving,distributing,compressing and revealing highquality video content.In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask,which creatively combines the Deep Learning Techniques on Convolutional Neural Networks(CNN)and Generative Adversarial Networks(GAN).The video compression method involves the layers are divided into different groups for data processing,using CNN to remove the duplicate frames,repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory(LSTM).Instead of the complete image,the small changes generated using GAN are substituted,which helps with frame-level compression.Pixel wise comparison is performed using K-nearest Neighbours(KNN)over the frame,clustered with K-means and Singular Value Decomposition(SVD)is applied for every frame in the video for all three colour channels[Red,Green,Blue]to decrease the dimension of the utility matrix[R,G,B]by extracting its latent factors.Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video.Repeated experiments on several videos with different sizes,duration,Frames per second(FPS),and quality results demonstrated a significant resampling rate.On normal,the outcome delivered had around a 10%deviation in quality and over half in size when contrasted,and the original video.
文摘Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mobile Adhoc system management,on the other hand,requires further testing and improvements in terms of security.Traditional routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two nodes.The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network.It is achieved by changing the residual energy of each network node.Also,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are proposed.Moreover,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters.The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.
