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The electric discharge machining (EDM) involves electrons discharged from the electrode and machining progresses due to the removal of the material from the component. This a thermal-based machining process primarily used for hard to machine components with conventional methods. This process is used to make intricate cavities and contours. The fabricated part is the replica of the tool material with high surface finish and good dimensional accuracy. This study aims to evaluate the comprehensive effect of process parameters on electric discharge machining of maraging steel.

Multiple criteria Decision making (MCDM) techniques are used to select the best parameters by comparing several responses to achieve the desired goal. There are different MCDM techniques available for optimization of machining parameters. In the current investigation, multi-objective optimization by data envelopment analysis based ranking (DEAR) approach was used for machining Maraging C300 grade steel.

The Taguchi L9 runs were planned with process parameters such as current (Amp), Tool diameter (mm) and Dielectric pressure (MPa). The effect of process parameters on the responses, namely, material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) were evaluated. High MRR is found at 15 A current, 14 mm tool diameter and dielectric pressure of 0.2 MPa. Optimum process parameters experiment showed reduced crack density.

An effort was made successfully to enhance the responses using the DEAR method and establish the decision making of selecting the optimal parameters by comparing the results obtained by machining maraging steel C300 grade.

This paper deals with numerical studies into combined conduction, convection and radiation from a heated vertical electronic board are provided here.

Here three inbuilt heaters with decrease in their heights were placed in the vertical electronic board. With respect to the non-heat portions, two configurations were studied. The first considers the non-heat portions to be adiabatic, while in the second, they are non-adiabatic. The heat that is produced in three heaters is conducted along the board and is dissipated either from the heater portions alone or from the whole board by convection and radiation. Air is considered as working medium, while the equations of heat transfer and flow of fluid are handled without boundary layer approximations. These equations were further solved using finite volume method with Gauss–Seidel iteration method.

Results of various comparative studies were discussed to bring out the relevance of thermal conductivity, modified Richardson number and surface emissivity on different heat transfer and flow results concerning this problem.

The optimum values of surface emissivity, thermal conductivity and modified Richardson number have also been notionally explored.

Fiber-reinforced polymers (FRP) contain critical electrical conductivity for high-intensity radiated fields such as lightning strike susceptibility, electromagnetic energy from radar, airborne radio frequency transmitter. To provide high-intensity radiated field protection (HIRFP) for the electric and electronic aircraft system and defied the EMI effect on it, metal matrix composite was conquered. To provide the dynamic ever-increasing requirement of industries, it is necessary that Al6061 metal matrix composite assisted with AL₂O₃ and fly ash is used to construct the aircraft to provide HIRFP. The thickness of the material can be maintained as low as possible to use it as a coating material for the aircraft surface. X-band for oblique incidence is used to measure electromagnetic and mechanical safeguarding properties of composites.

Day by day, the applications of aerospace are becoming digital and automated. Proper shielding techniques are required to operate digital electronic devices without electromagnetic interference. It leads to a rapid rise in temperature, thermal ablation, delamination, and adverse effects on the electric and electronic aircraft system. Fly ash, a metal matrix material composite AL6061 with different percentages of reinforcement of Al₂O₃, was contemplated and experimented with for mechanical properties like tensile strength, density and hardness.

The obtained results compared with adjusted values and an improvement of 0.19, 0.18, 0.14 g/cm³ for density of MMC-1, MMC-2, MMC-3.31, 11 MPa for tensile strength of MMC-1, MMC-2. 24, 27, 23 BHN for hardness of MMC-1, MMC-2, MMC-3. With regard to the shielding effectiveness the results compared with adjusted values and obtained 11.36, 14.56, 19.47 dB better value than it. According to the above results, fabricated MMC’s provide superior results for a defined application like HIRFP(Surface material of aircraft).

It can be used to protect electronic devices under a high-intensity radiated field, mainly in aircraft design to protect from lightning effect.

For a better approximation of the signal toward the practical case, the oblique incidence was considered with a different combination of Al₂O₃ and fly ash, reinforced to pure AL6061 to get better shielding and mechanical properties.

A proper understanding of malware characteristics is necessary to protect massive data generated because of the advances in Internet of Things (IoT), big data and the cloud. Because of the encryption techniques used by the attackers, network security experts struggle to develop an efficient malware detection technique. Though few machine learning-based techniques are used by researchers for malware detection, large amounts of data must be processed and detection accuracy needs to be improved for efficient malware detection. Deep learning-based methods have gained significant momentum in recent years for the accurate detection of malware. The purpose of this paper is to create an efficient malware detection system for the IoT using Siamese deep neural networks.

In this work, a novel Siamese deep neural network system with an embedding vector is proposed. Siamese systems have generated significant interest because of their capacity to pick up a significant portion of the input. The proposed method is efficient in malware detection in the IoT because it learns from a few records to improve forecasts. The goal is to determine the evolution of malware similarity in emerging domains of technology.

The cloud platform is used to perform experiments on the Malimg data set. ResNet50 was pretrained as a component of the subsystem that established embedding. Each system reviews a set of input documents to determine whether they belong to the same family. The results of the experiments show that the proposed method outperforms existing techniques in terms of accuracy and efficiency.

The proposed work generates an embedding for each input. Each system examined a collection of data files to determine whether they belonged to the same family. Cosine proximity is also used to estimate the vector similarity in a high-dimensional area.

Hybrid cloud composing of public and private cloud is seen as a solution for storage of health care data characterized by many private and sensitive data. In many hybrid cloud-based solutions, the data are perturbed and kept in public cloud, and the perturbation credentials are kept in private cloud.

Hybrid cloud is a model combing private and public cloud. Security for the data is enforced using this distribution in hybrid clouds. However, these mechanisms are not efficient for range query and retrieval of data from cloud. In this work, a secure and efficient retrieval solution combining K-mean clustering, geometric perturbation and R-Tree indexing is proposed for hybrid clouds.

Compared to existing solution, the proposed indexing on perturbed data is able to achieve 33% reduced retrieval time. The security of indexes as measured using variance of differences was 66% more than existing solutions.

This study is an attempt for efficient retrieval of data with range queries using R-Tree indexing approach.