Now present years, Cloud computing has emerged as a computational model for High-Performance Computing (HPC) applications, which deals with complex and highly intensive data. Cloud computing is a promising new style of scheduling environment which provides an scalable with on-demand supply methods to the consumers. In a cloud context, job or application scheduling is difficult and NP-complete. The objective of this work is to develop an algorithm to schedule for efficiently managing cloud resources besides finishing jobs with scientific applications applied in the Cloud Resource Broker (CRB) framework in Cloud Data Centre (CDC). The proposed HoneyBee based PSO (HBPSO) scheduling algorithm is deliberated for efficient resource allocation and henceforth the suggested optimization method anticipates to accomplish the objectives in minimizing both execution time and cost required to execute the job. It promotes resource allocation and job scheduling functions by balancing the load and reducing the skew, this in turn will eventually decrease cost, make span and resource utilization. Our goal is to transform the cloud datacenter to an efficient and effective datacenter in terms of time, cost and resource utilization so as to enhance the business values. By reducing completion times and costs while increasing the number of applications completed and user satisfaction, the simulation results utilizing Cloudsim and Cloud analyst demonstrate the efficacy of the suggested research work.

Android has retained its global popularity among cell phone users. Simultaneously, there has been a growth of malware targeting the platform, with larger current lines using unexpectedly cutting-edge detection evasion strategies. Options for timely 0-day detection are necessary as standard signature-based approaches become less effective in detecting unknown threats. This study contributes a strategy based on ensemble learning for detecting malicious apps on Android. To improve the accuracy of Android malware detection, it combines the advantages of hybrid analysis with the efficiency and performance of ensemble device research. Machine learning models are constructed using a large collection of malicious and safe software. The built model can predict all real-world test scenarios. The user has complete access to all program information on their devices. The proposed system employs a variety of machine learning algorithms for ensemble learning, including Decision Tree, Random Forest, and Androguard. The testing module is intended to ensure the efficiency and efficacy of the proposed system. The results show that the suggested method is extremely effective at identifying Android malware, with a detection accuracy of up to 100

Data privacy and security are the primary concerns in today’s era, malware attacks are the prime threats. Though the security systems are strengthened with modern and efficient techniques, they can handle attacks from known malware families. However, the development of new malware is progressing in an exponential curve, keeping the demand for a more efficient algorithm to secure the personal, organizational, and other modes of data. Recently, the malware executable files have been visualized as gray-scale images and transformed into an image classification problem. Literature reports that Deep Learning (DL) algorithms, especially the Convolutional Neural Network (CNN) have been achieving significant accuracy in malware image classification. However, a substantial limitation in Deep Learning algorithms is that they expect the input malware image size should be uniform, which is not possible in practice. Hence, the malware images are resized to fit into uniform dimensions and then fed to the algorithms. The dimension of the malware image is closely related to the lines of code and the block, resizing them to a uniform size would affect the meaning of the binary files. A novel malware image representation based on the Gray-Level Co-occurrence Matrix (GLCM) is proposed here to maintain a stable dimension for all the malware images, hence the image resizing step could be ignored. The sparse nature of the GLCM matrix offers the space for constructing a novel dynamic stride-offset matrix which in turn accelerates the training phase of the CNN classifier. The performance of the proposed Sparse CNN classifier having the GLCM-based images (SCNN + GLCM) as input is investigated with the malware images from three benchmark datasets: MalImg, Malevis, and Microsoft Malware Detection (MMD) dataset. With the set of optimized hyperparameters, the proposed SCNN + GLCM malware image classification is analyzed on various metrics to state its significant performance.

The amount of malware infecting computers and other communication devices and migrating across the internet has greatly increased over the years. Numerous methods and procedures have been put forth up to this point to find and eliminate these hostile agents. However, a lot of malwares is still being developed, which can get past some cutting-edge malware detection algorithms, as new and automated malware production techniques emerge. Consequently, there is a need for the classification and detection of these antagonistic agents that have the potential to compromise the security of individuals, businesses, and a wide range of other digital assets. To develop a new improved approach for efficient zero-day malware detection, there is a compelling need to reduce bias and objectively assess these methods. This study focuses on investigating transfer learning strategies for malware picture classification, including AlexNet, VGG16, VGG19, GoogLeNet, and ResNet. Here, malware binaries are turned into grayscale images before being processed using models for classification based on transfer learning. As transfer learning uses pre-trained models, the primary objective is to save training time. In addition, an effective model for malware classification will be built in order to obtain performance that is unbiased.

To analyze the air quality of any country, a machine learning technique is being developed and an air quality indicator is proposed for a particular area. Air Quality Index is considered to be a basic measure which can indicate the levels of SO 2 , NO 2 . etc. over a particular amount of time. We technologically put forward a model to determine the air quality index in view of historical data of preceding years and computing the same for the forthcoming year considering it as a gradient decent attached boosted multivariable regression problem. We enhance the proposed model's effectiveness by relating cost estimation on behalf of the problem to be a predictive one. Thus this proposed system resolve successfully and work well to envisage the air quality indicator of any entire country or state or any bounded region furnished with enough historical data about contaminants in air. In the proposed model, subsequently machine learning technique is assimilated, upright enactment with performance is accomplished further than the standard regression model. The implementation of envisaging air quality index is prepared for our country India as well as accurateness of 96% is attained via XG Boost Algorithm joined with LightBGM algorithm to find an accurate solution that is in adjacent proximity to the ideal solution.

Cloud enables technologies which are used in healthcare such as mobile apps, electronic medical records, devices with IOT, patient portals, big data analytics. It delivers hassle-free flexibility besides scalability, which further progresses the final decision-making procedure. Using cloud computing, a healthcare system-based hospital finder application enables us to locate hospitals close to our current location. State, city, and hospital name are all search criteria that users can use. It shows us the best route to take to get to the hospital quickly. The proposed work is utilized in the event of a medical emergency (Accident, Cardio, etc.), and regular people frequently struggle to decide which hospital to go to in order to receive the necessary or specialized care. They wander between hospitals looking for healthcare facilities, medications, blood supplies, etc. Hospice Care Taker or Hospice Maestro (an Android App) is designed by means of android based cloud application and this will resolve the problem through permitting people in order to examine neighboring hospitals available with minimum distance on the basis of medical treatments, specialist doctors, medicine/blood availability, ambulance service etc. This android based cloud application offers instruction related to first aid for required/specific treatment and also affords user to show complete details of hospital with its open and close status.

In this paper, a rectangular dielectric resonator antenna (RDRA) with modified microstrip feed is proposed. A pentagon-shaped aperture coupling with a bifurcated modified microstrip-line (MSTL) was used to excite the DRA element on the FR4 substrate's backside, resulting in a 3.8 GHz band. Optimizing the design parameters yields a 10% increase in impedance bandwidth, covering frequencies between 3.08 and 3.8 GHz. Maximum gain and radiation efficiency are shown to be 4.85 dB and 90%, respectively, in the operational band using the proposed DRA. As a result, the proposed antenna may be appropriate for use in 5G NR band systems. Copyright (c) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Latest Developments in Materials & Manufacturing

Nickel phosphorous (Ni-P) coating and co-deposited nickel phosphorous-Al2O3 (Ni-P-Al2O3) composite coatings reinforced with nanoparticles were prepared using electroless deposition process on a mild steel substrate. The coatings were heated treated at 400 degrees C for 1 h. Reasonable increase in micro-hardness is obtained due to the incorporation of Al2O3 nanoparticles in Ni-P. Heat treatment of both coatings increased the hardness by 70.5% and 82.5% compared to as deposited conditions. Improvement in hardness is due to crystallization of amorphous nickel and the formation of nickel phosphide (Ni3P) during heat treatment. The wear behaviour of as deposited and heat treated Ni-P and Ni-P-Al2O3 coatings were investigated in dry sliding conditions. Under these conditions wear involves oxidation and adhesion in Ni-P coating and a combination of oxidation, adhesion and abrasion in Ni-P-Al2O3 coatings. The formation of an oxide film at the surface during the wear test increases wear resistance of Ni-P coating with increase in sliding distance. The incorporation of Al2O3 particles in the Ni-P matrix results in increased wear resistance compared to the Ni-P coating. On heat treated coatings the wear tests induced micro cracks and their propagation is observed as the test proceeds. Delamination and peeling of the coating is observed in Ni-P coating heat treated at 400 degrees C with a relatively low wear resistance. The incorporation of Al2O3 nano particles delays the propagation of micro cracks during the wear test. Thus, the maximum hardness and maximum wear resistance were obtained for Ni-P-Al2O3 heat treated at 400 degrees C. (C) 2020 Elsevier Ltd. All rights reserved.

Cloud computing permits users to request on demand fundamentally unrestricted quantity of computing power. But the computing power is billed as pay per use basis based on the use of computing units called Virtual Machines (VMs). Many real time applications like drug discovery, protein folding, data analysis, climate modelling and energy research etc need more computational power in cloud. Hence these applications ingest huge volume of energy which will result in more price. So in this work, an effective scheduling algorithm that takes into account both energy and price are considered called PPS(Price-proficient Scheduling Algorithm).This algorithm is proficiently implemented to allocate VMs to all the tasks involved in solving large complex applications. CloudSim Toolkit framework is used for this proposed work and it is justified that the demonstrated results provide substantial decrease in financial price and energy during execution of complex as well as simple tasks.

The number of cloud users and their aspiration for completion of tasks at less energy consumption and operating cost are rapidly increasing. Hence, the authors of this paper aim to minimize the makespan and operating cost by optimally scheduling the tasks and allocating the resources of cloud service. The optimum task scheduling and resource allocation are obtained for each objective function using the simple genetic algorithm. Further, the non-dominated solutions of the dual objectives are obtained using the non-dominated sorting genetic algorithm-II, the most successful multi-objective optimization technique. A complex cloud service problem consisting of ten tasks, fifteen subtasks and fifteen heterogeneous resources is considered to investigate the proposed method. The numerical results obtained in the single objective and multi objective optimization problems show that the makespan and the operating cost are significantly reduced using the simple genetic algorithm and a wide range of non-dominated solutions are obtained in the multi-objective optimization problem, by which the cloud users shall be benefitted to choose the most appropriate solution based on the other design constraints they have.

Fiber-reinforced laminated composite structures are extensively used in aircraft and aerospace industries for their high specific strength and stiffness. In such applications, they are generally subjected to nonuniform thermal loads due to change in thermal conditions. Therefore, the composite structures used in the applications in which they are subjected to nonuniform thermal loads must also be designed to withstand thermal loads. As mechanical and thermal properties of fiber-reinforced laminated composites are greatly influenced by the direction of fibers and stacking sequences, they are optimally varied in this article to maximize the critical buckling temperature of the composite plate. The ply angle and stacking sequence of the laminated composite plate are optimized using genetic algorithm to maximize the thermal buckling temperature. As the plate is subjected to different kinds of nonuniform thermal load cases, finite element technique is used to analyze the plate during the optimization process. As geometry and supporting conditions of the plate also have great influence on its thermal buckling strength, the investigation is further widened by carrying out the optimization process for the plate model constructed with various types of support conditions, aspect ratio, and nonuniform load cases. The numerical results clearly show the necessities that the optimum ply angle and stacking sequences are greatly varying based on the aspect ratio, support conditions, and nonuniform loading cases.

The utilization of cloud services has significantly increased due to the easiness in accessibility, better performance, and decrease in the high initial cost. In general, cloud users anticipate completing their tasks without any delay, whereas cloud providers yearn for reducing the energy cost, which is one of the major costs in the cloud service environment. However, reducing energy consumption increases the makespan and leads to customer dissatisfaction. So, it is essential to obtain a set of non-domination solutions for these multiple and conflicting objectives (makespan and energy consumption). In order to control the energy consumption efficaciously, the Dynamic Voltage Frequency Scaling system is incorporated in the optimization procedure and a set of non-domination solutions are obtained using Non-dominated Sorting Genetic Algorithm (NSGA-II). Further, the Artificial Neural Network (ANN), which is one of the most successful machine learning algorithms, is used to predict the virtual machines based on the characteristics of tasks and features of the resources. The optimum solutions obtained using the optimization process with the support of ANN and without the support of ANN are presented and discussed.

The research on green cloud computing is indispensable to reduce the emission of carbon as well as to rescue the environment from the hands of global warming, as the climatic change due to the emission of carbon is a critical issue now a days. The green cloud computing mainly concentrates on reducing the amount of cloud resources to condense the emission of carbon. So an effective and efficient scheduling algorithm, Green Task Scheduling (GTS) Algorithm, is proposed in this paper to bring down the amount of cloud resources in use. The proposed algorithm also reduces the hardware cost significantly. As green cloud computing also concentrates to save the energy consumption by providing only the required amount of voltage based on the frequency of work, a technique called as Dynamic Voltage Frequency Scaling (DVFS) is also used in this paper to control the voltage as well as the frequency of the processor without affecting the working performance. The numerical results prove that the makes pan, energy and cost are optimally minimized by implementing GTS along with DVFS in cloud computing environment.

Laminated composites are highly in demand for the applications where high strength and stiffness are required at less weight. They generally fail due to buckling, as they are modeled as thin plates and are loaded compressively. Therefore, the design parameters of the laminated composite plates are to be optimized for the multiple-conflicting objectives buckling strength and weight. However, the composite plates, which are used in real world applications, are to be made with cut-outs and finite element analysis is required to analyze them. As it makes the optimization process more complex, a methodology is proposed in this paper to carry out a multiobjective optimization for the rectangular composite plate made with a central elliptical cut-out. The nondominated solutions are obtained using nondominated sorting genetic algorithm (NSGA-II) in which the multilayer feed-forward neural network is used to replace the time consuming finite element analysis. The numerical results show that the proposed method finds the nondominated solutions efficiently and reduces the computational cost prominently.

Cloud computing is a new exemplar for data sharing and storing in cloud centers and it achieves the phenomenal growth for remote accessing resources via networks. But, succeeding the power consumption controls, concurrently achieving the performance oriented tasks are the most crucial issues for cloud services. For that, the system is implemented with three important energy saving schemes for monitoring cloud services and also to reduce the server idle energy consumptions. In existing, the optimization of energy is done in cloud servers only when the arrival rate is low. By using this EGCM the problems of server wake up, and cloud system congestion is overcomed, but it cannot able to eliminate the unnecessary idle energy saving when arrival rate increases and also it cannot allocate resources or switch the resources between idle and sleep status during the execution of process.. Here, the main objective of the proposed work is to reduce idle energy consumption without sacrificing performance and without violating (SLA) for that, the system introduces new examining methodology called Artificial Association Bee [AAB]. The new method is used to solve the constrained optimized problem and support the cloud service providers or server for energy saving and optimization. The new method reduces unwanted idle energy consumption by switching idle to sleep modes in an iterative manner, when more tasks are performed in the cloud service execution process. Such as the new AAB methodology provides an effective server performance for loading or sharing or providing the services to the cloud clients, and also it achieves energy consumption, with a help of iterative modes. The Simulation results show that efficient energy reduction is verified by applying energy saving schemes.

Clouds are rapidly becoming an important platform for scientific applications. For executing large programs in cloud, the class of programs can be decomposed into multiple sequences of tasks that can be executed in different VMs. Users have to pay for the resources they use according to some pricing model. Cloud applications consume huge amount of energy, this also cause high operational cost. So this work introduces green computing in cloud called green cloud. Green computing is the study of efficient and eco-friendly computing resources in order to reduce energy consumption, carbon emission, etc. In recent years, companies in the IT industry have come to realise that going green is in their best interest that is reduced costs. Energy consumption at different levels in cloud computing system was discussed. This work discuses several techniques for reducing energy consumption in cloud and evaluates the CPU energy with power consumption. It also works on memory consumption techniques and presents the results for that. It compares the memory consumption with power consumption and present results for the same.

Cloud computing is a general term used to describe a new class of network based computing that takes place over the internet. The primary benefit of moving to Clouds is application scalability. Cloud computing is very beneficial for the application which are sharing their resources on different nodes. Scheduling the task is quite a challenging in cloud environment. Usually tasks are scheduled by user requirements. New scheduling strategies need to be proposed to overcome the problems proposed by network properties between user and resources. New scheduling strategies may use some of the conventional scheduling concepts to merge them together with some network aware strategies to provide solutions for better and more efficient job scheduling. Scheduling strategy is the key technology in cloud computing. This paper provides the survey on scheduling algorithms. There working with respect to the resource sharing. We systemize the scheduling problem in cloud computing, and present a cloud scheduling hierarchy.

In this study, an optimization procedure is proposed to maximize the safety factor of laminated composite plates subject to in-plane loading. Fiber orientation angles and stacking sequence are chosen as design variables. Genetic Algorithm (GA), which is a reliable global search algorithm, is used to search the optimal design. Static failure criteria are used to determine whether load bearing capacity for a configuration generated during the optimization process. In order to avoid spurious optimal designs, both the Tsai–Wu and the maximum stress criteria are employed to check static failure. Numerical results are obtained and presented for different loading cases.