Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids i...
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Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.
Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the *** researchers have made progress in correcting and predicting early heart disease,but ...
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Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the *** researchers have made progress in correcting and predicting early heart disease,but more remains to be *** diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional *** using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single *** processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data ***,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the *** reduce the dimensionality,we subsequently used PCA with 95%*** identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble *** model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested *** illustrates how interaction-focu
The National Scholarship Portal in India serves as a one-stop solution for students seeking financial aid for their studies across the country. However, in this digital era, the national-level portal faces challenges ...
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Advancements in digital technologies make it easy to modify the content of digital images. Hence, ensuring digital images' integrity and authenticity is necessary to protect them against various attacks that manip...
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Artificial intelligence (AI) has emerged as a powerful tool in computational biology, where it is being used to analyze large datasets to detect difficult biological patterns. This has enabled the design of new drug m...
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Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...
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Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
Cloud Datacenter Network(CDN)providers usually have the option to scale their network structures to allow for far more resource capacities,though such scaling options may come with exponential costs that contradict th...
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Cloud Datacenter Network(CDN)providers usually have the option to scale their network structures to allow for far more resource capacities,though such scaling options may come with exponential costs that contradict their utility ***,besides the cost of the physical assets and network resources,such scaling may also imposemore loads on the electricity power grids to feed the added nodes with the required energy to run and cool,which comes with extra costs ***,those CDNproviders who utilize their resources better can certainly afford their services at lower price-units when compared to others who simply choose the scaling *** utilization is a quite challenging process;indeed,clients of CDNs usually tend to exaggerate their true resource requirements when they lease their *** providers are committed to their clients with Service Level Agreements(SLAs).Therefore,any amendment to the resource allocations needs to be approved by the clients *** this work,we propose deploying a Stackelberg leadership framework to formulate a negotiation game between the cloud service providers and their client *** this,the providers seek to retrieve those leased unused resources from their *** is not expected from the clients,and they may ask high price units to return their extra resources to the provider’s ***,to motivate cooperation in such a non-cooperative game,as an extension to theVickery auctions,we developed an incentive-compatible pricingmodel for the returned ***,we also proposed building a behavior belief function that shapes the way of negotiation and compensation for each *** to other benchmark models,the assessment results showthat our proposed models provide for timely negotiation schemes,allowing for better resource utilization rates,higher utilities,and grid-friend CDNs.
The intense increase in the installed capacity of wind farms has required a computationally efficient dynamic equivalent model of wind *** types of wind-farm modelling aim to identify the accuracy and simulation time ...
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The intense increase in the installed capacity of wind farms has required a computationally efficient dynamic equivalent model of wind *** types of wind-farm modelling aim to identify the accuracy and simulation time in the presence of the power *** this study,dynamic simulation of equivalent models of a sample wind farm,including single-turbine representation,multiple-turbine representation,quasi-multiple-turbine representation and full-turbine representation models,are performed using a doubly-fed induction generator wind turbine model developed in DIgSILENT *** developed doubly-fed induction generator model in DIgSILENT is intended to simulate inflow wind turbulence for more accurate *** wake effects between wind turbines for the fullturbine representation and multiple-turbine representation models have been considered using the Jensen *** developed model improves the extraction power of the turbine according to the layout of the wind *** accuracy of the mentioned methods is evaluated by calculating the output parameters of the wind farm,including active and reactive powers,voltage and instantaneous flicker *** study was carried out on a sample wind farm,which included 39 wind *** simulation results confirm that the computational loads of the single-turbine representation(STR),the multiple-turbine representation and the quasi-multiple-turbine representation are 1/39,1/8 and 1/8 times the full-turbine representation model,*** the other hand,the error of active power(voltage)with respect to the full-turbine representation model is 74.59%(1.31%),43.29%(0.31%)and 7.19%(0.11%)for the STR,the multiple-turbine representation and the quasi-multiple representation,respectively.
In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification *** approaches often rely on statis...
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In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification *** approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally *** paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved *** classification methods are ill-suited for incomplete medical *** enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete ***,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification *** effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and *** encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.
Atrial fibrillation (A-Fib), a common cardiac arrhythmia, is linked to serious complications like heart failure and stroke, which early diagnosis could prevent. However, its paroxysmal and often asymptomatic nature co...
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