Wireless sensor networks (WSNs) are normally conveyed in arbitrary regions with no security. The source area uncovers significant data about targets. In this paper, a plan dependent on the cloud utilising data publish...
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Over the past era,subgraph mining from a large collection of graph database is a crucial *** addition,scalability is another big problem due to insufficient *** are several security challenges associated with subgraph...
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Over the past era,subgraph mining from a large collection of graph database is a crucial *** addition,scalability is another big problem due to insufficient *** are several security challenges associated with subgraph mining in today’s on-demand *** address this downside,our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs(BCCA-LSDG).The two-fold process is handled in the proposed BCCA-LSDG:graph indexing and authenticated query search(query processing).A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed *** resolve the issues and provide safe big data transmission,the proposed technique also combines blockchain with a consensus algorithm *** of the big data is ensured by dividing the BC network into distinct networks,each with a restricted number of allowed entities,data kept in the cloud gate server,and data analysis in the *** consensus algorithm is crucial for maintaining the speed,performance and security of the *** Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature ***,the graph index refinement process is undertaken to improve the query *** query error,fuzzy logic is used to refine the index of the graph *** proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems,and the combination of blockchain and subgraph provides a secure communication platform,according to the findings.
To analyse the student’s academic performance, a new prediction model is developed. This proposed model collects the student’s data from standard online sources. At first, these gathered data are pre-processed by ce...
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Diabetic Eye Disease(DED)is a fundamental cause of blindness in human beings in the medical *** techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy(DR).The Machine Lea...
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Diabetic Eye Disease(DED)is a fundamental cause of blindness in human beings in the medical *** techniques are proposed to forecast and examine the stages in Prognostication of Diabetic Retinopathy(DR).The Machine Learning(ML)and the Deep Learning(DL)algorithms are the predomi-nant techniques to project and explore the images of *** though some solu-tions were adapted to challenge the cause of DR disease,still there should be an efficient and accurate DR prediction to be adapted to refine its *** this work,a hybrid technique was proposed for classification and prediction of *** proposed hybrid technique consists of Ensemble Learning(EL),2 Dimensional-Conventional Neural Network(2D-CNN),Transfer Learning(TL)and Correlation ***,the Stochastic Gradient Boosting(SGB)EL method was used to predict the ***,the boosting based EL method was used to predict the DR of *** 2D-CNN was applied to categorize the various stages of DR ***,the TL was adopted to transfer the clas-sification prediction to training *** this TL was applied,a new predic-tion feature was *** the experiment,the proposed technique has achieved 97.8%of accuracy in prophecies of DR images and 98%accuracy in grading of *** experiment was also extended to measure the sensitivity(99.6%)and specificity(97.3%)*** predicted accuracy rate was com-pared with existing methods.
About 170 nations have been affected by the COvid VIrus Disease-19(COVID-19)*** governing bodies across the globe,a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive,...
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About 170 nations have been affected by the COvid VIrus Disease-19(COVID-19)*** governing bodies across the globe,a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive,and they feel challenging to tackle this *** researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these *** the previous works,Long Short-Term Memory(LSTM)was used to predict future COVID-19 *** to LSTM network data,the outbreak is expected tofinish by June ***,there is a chance of an over-fitting problem in LSTM and true positive;it may not produce the required *** COVID-19 dataset has lower accuracy and a higher error rate in the existing *** proposed method has been introduced to overcome the above-mentioned *** COVID-19 prediction,a Linear Decreasing Inertia Weight-based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network(LDIWCSO-HBDCNN)approach is *** this suggested research study,the COVID-19 predicting dataset is employed as an input,and the min-max normalization approach is employed to normalize *** features are selected using Linear Decreasing Inertia Weight-based Cat Swarm Optimization(LDIWCSO)algorithm,enhancing the accuracy of *** Cat Swarm Optimization(CSO)algorithm’s convergence is enhanced using inertia weight in the LDIWCSO *** is used to select the essential features using the bestfitness function *** a specified time across India,death and confirmed cases are predicted using the Half Binomial Distribution based Convolutional Neural Network(HBDCNN)technique based on selected *** demonstrated by empirical observations,the proposed system produces significant performance in terms of f-measure,recall,precision,and accuracy.
In the ever-evolving landscape of optimization algorithms for healthcare datasets, this study introduces an innovative fusion of the gannet optimization algorithm (GOA) with advanced opposition-based learning (OBL) te...
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The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts...
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The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have be...
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The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network *** environments pose significant challenges in maintaining privacy and *** approaches,such as IDS,have been developed to tackle these ***,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional *** fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within *** traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of *** selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)*** this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious *** classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable *** the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive *** experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different *** outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%*** results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.
This study introduces a novel control framework for human-drone interaction (HDI) in industrial warehouses, targeting pick-and-delivery operations. The goals are to enhance operator safety as well as well-being and, a...
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This study introduces a novel control framework for human-drone interaction (HDI) in industrial warehouses, targeting pick-and-delivery operations. The goals are to enhance operator safety as well as well-being and, at the same time, to improve efficiency and reduce production costs. To these aims, the speed and separation monitoring (SSM) operation method is employed for the first time in HDI, drawing an analogy to the safety requirements outlined in collaborative robots' ISO standards. The so-called protective separation distance is used to ensure the safety of operators engaged in collaborative tasks with drones. In addition, we employ the rapid upper limb assessment (RULA) method to evaluate the ergonomic posture of operators during interactions with drones. To validate the proposed approach in a realistic industrial setting, a quadrotor is deployed for pick-and-delivery tasks along a predefined trajectory from the picking bay to the palletizing area, where the interaction between the drone and a moving operator takes place. The drone navigates toward the interaction space while avoiding collisions with shelves and other drones in motion. The control strategy for the drone cruise navigation integrates simultaneously the time-variant artificial potential field (APF) technique for trajectory planning and the iterative linear quadratic regulator (LQR) controller for trajectory tracking. Differently, in the descent phase, the receding horizon LQR algorithm is employed to follow a trajectory planned in accordance with the SSM, which starts from the approach point at the border of the interaction space and ends in the volume with the operator's minimum RULA. The presented control strategy facilitates drone management by adapting the drone's position to changes in the operator's position while satisfying HDI safety requirements. The results of the proposed HDI framework simulations for the case study demonstrate the effectiveness of the method in ensuring a safe and er
Leveraging seawater toilet flushing system in Hong Kong,China,a Seawater-based Urine Phosphorus Recovery(SUPR)process that integrates ureolysis and phosphorus(P)recovery was proposed in our earlier *** this study,a th...
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Leveraging seawater toilet flushing system in Hong Kong,China,a Seawater-based Urine Phosphorus Recovery(SUPR)process that integrates ureolysis and phosphorus(P)recovery was proposed in our earlier *** this study,a thermodynamic model was applied to evaluate the effects of ureolysis and the seawater-to-urine mixing ratio(S/U ratio)on P precipitation in the SUPR *** results suggested that effective P recovery was thermodynamically feasible across a wide range of S/U ratios,with elevated pH levels resulting from ureolysis being critical for P ***,a SUPR reactor was developed to validate this *** the hydraulic retention time(HRT)exceeded 3 h and the S/U ratio was lower than 3:1,more than 98%of P could be recovered without urine storage,chemical dosage,or external *** decrease in the HRT and increase in S/U ratio caused flushing out of fine precipitates,resulting in a relatively low P recovery ***,this could be advantageous when downstream urine nitrification is implemented,as dilution of urine can alleviate the inhibitory effects of free ammonia and free nitrous acid,as well as overcome the P limitation problem,thus facilitating urine ***,there is a trade-off between optimizing P recovery and nitrification efficiencies.
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