Maximizing the security of the cloud system is most important need of this world. Similarly, the data controllers are also increased stealing the sensitive and personal data from the cloud computing system. A repeated...
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Maximizing the security of the cloud system is most important need of this world. Similarly, the data controllers are also increased stealing the sensitive and personal data from the cloud computing system. A repeated data violation occurs due to a large amount of outsourced and unsecured sensitive data. At present, numerous research works have been performed to secure the data in the cloud, but sometimes they do not succeed in securing the sensitive data. A Multi-Objective Privacy Preservation Model for Cloud Security utilizing hunterpreyoptimization approach is proposed in this paper. Initially, the data is taken from 5 types of dataset like, concrete, Heart disease, Super Conductivity, Air Quality and wholesale customer datasets. The input data is given to the sanitization of data and restoration stage. In sanitization of data and restoration phase, SMA is utilized. After preventing the leakage in the data sanitization and restoration stage, the input data is applied to the key generation phase. Multi-objective functions such as the preservation of information ratio, the ratio of hiding, and the modification degree are performed at the key generation stage with the help of the hunter prey optimization algorithm to improve cloud data security. The proposed MOPP-CS-HPOA method is evaluated under some performances metrics, like modification degree, ratio of hiding, information preservation ratio, key sensitivity and computational time. Then the proposed MOPP-CS-HPOA method attains 35.69%, 38.504% and 31.805% higher information preservation ratio;39.52%, 30.28% and 38.14% higher hiding ratio;analysed with MOPP-CS-JSSO, MOPP-CS-PS-BMFO and MOPP-CS-SVM-KNN-RF-NB-ANN methods.
Cam mechanism is widely used in engineering designs such as engines, presses and gear-cutting machines because of its advantages including high transmission efficiency, high reliability, and the ability to realize com...
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Cam mechanism is widely used in engineering designs such as engines, presses and gear-cutting machines because of its advantages including high transmission efficiency, high reliability, and the ability to realize complex motions. To quantify the negative effects of dimension error, clearance error of kinematic pair and wear error of cam higher pair on the motion accuracy of oscillating follower disk cam mechanism, a computing model of motion reliability and reliability sensitivity of oscillating follower disk cam mechanism was established. By analyzing the reliability sensitivity, we find that the distance error between the camshaft axis and the swing center are two significant factors for the motion reliability of cam mechanism. To maximize the motion reliability of the mechanism, an improved hunterpreyoptimization (IHPO) algorithm was proposed to identify the optimal dimensional parameters of the oscillating follower disk cam mechanism. The simulation results show that the presented method has desirable applicability for improving the motion reliability of disk cam mechanism, and provides a certain reference for the design of other cam mechanisms.
This study presents a novel smart energy management framework for the Indian 28-bus radial distribution system (RDS), optimizing energy consumption across residential, commercial, and industrial sectors. The framework...
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This study presents a novel smart energy management framework for the Indian 28-bus radial distribution system (RDS), optimizing energy consumption across residential, commercial, and industrial sectors. The framework employs the hunter-preyoptimizationalgorithm (HPOA) to enhance appliance scheduling, renewable energy integration (PV, WT, EV, BESS), and dynamic tariff management while addressing uncertainties in electric vehicle (EV) usage and renewable distributed generation (RDG) output. By incorporating photovoltaic (PV) systems, wind turbines (WT), electric vehicles (EVs), and battery energy storage systems (BESS), the system maximizes renewable energy utilization, reducing grid dependency and improving cost-effectiveness. HPOA ensures efficient scheduling, balancing user comfort, cost savings, and revenue generation through real-time pricing (RTP) and feed-in tariffs. The system effectively manages EV and RDG uncertainties, optimizing surplus energy redirection to the grid, thereby enhancing economic viability. A comparative analysis with alternative optimizationalgorithms demonstrates HPOA's superiority in convergence speed, computational efficiency, and energy cost reduction. Additionally, the study evaluates the levelized cost of energy (LCOE), confirming the economic feasibility of the proposed model. The results indicate a significant reduction in electricity costs and grid dependence, yielding a total revenue of Indian Rupee 20,982.00-comprising Indian Rupee 2,042.64 from residential, Indian Rupee 4,780.98 from commercial, and Indian Rupee 7,158.38 from industrial sectors. These findings underscore the financial and sustainability advantages of implementing smart energy management strategies in evolving energy landscapes.
Cybersecurity threat detection in the Internet of Things (IoT) identifies and mitigates risks associated with connected devices. The IoT devices are vulnerable to attacks as they do not contain any robust security mec...
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Cybersecurity threat detection in the Internet of Things (IoT) identifies and mitigates risks associated with connected devices. The IoT devices are vulnerable to attacks as they do not contain any robust security mechanism. Traditional methods for cybersecurity-based threat detection face significant problems such as scalability, privacy concerns, and resource constraints when applied to the dynamic IoT environment. To tackle these challenges, this paper proposed a novel Weighted Variational Autoencoder-based hunterprey Search (WVA-HPS) algorithm for enhancing cybersecurity threat detection in IoT. In this study, a weighted Variational Autoencoder is employed for regulating weight mechanism using weight regularization and the weight average ensemble method. The hunterprey Search optimization (HPSO) algorithm is utilized for minimizing overfitting issues to enhance the efficiency of the WVA method. The proposed WVA-HPS model comprises five different stages such as data collection, data preprocessing, threat detection, model evaluation, and output. The study is validated on diverse datasets namely BoT-IoT, MQTTset, and IoT-23. The WVA-HPS method's performance is analyzed using the metrics namely precision, accuracy, specificity, F-measure, and recall and its performance is compared with existing methods. The experimental results illustrate the performance of the WVA-HPS method for cybersecurity threat detection in an IoT environment.
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