Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Techniques predicting the type of diseases affecting plants in their lifetime will be of immense help to agriculturists. This article throws light upon such techniques in the form of a survey that had been carried out comprehensively covering various image based plant leaf diseases. Such diseases are mostly grievous and they strike at any part of the plant. There are huge accumulated losses due to such diseases that bring down the productivity and increase the economic losses in the agricultural industry. Agriculture industry needs to sustain and evolve from such obstacles to be highly profitable. This can be done by precisely monitoring the health and detecting the diseases at appropriate stages of the plant's life time. Technology has spread its wings in every field of day to day life but still its reach in the field of agriculture is not up to the mark. Agriculture industry is still thriving on outdated technical methodologies. Improper diagnosis of plant disease may lead to huge losses in terms of production, time, cost and product quality. The condition of the plant needs to be tracked throughout its growing stages leading to successful cultivation. As part of technological innovation, researchers had been applying the image processing techniques for monitoring as well as diagnosing the plant diseases in its various stages. Appropriate machine learning algorithms are being designed and applied for precisely identifying the various infections on plants throughout its life cycle and the type of treatment that can be afforded for overcoming loss. © 2020 IEEE.

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Department of Information Security

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Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020

A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases

Wireless Sensor Networks have found their reach in various domains that includes defense, medicine and environmental monitoring. The network is formed by a vast collection of sensor nodes which are stationed separately from one another. The information gauged by the sensors needs to be conveyed to the sink node so as to take the obligatory steps. Routing of the compiled information from the source node to any specific sink node, attaining minimal power consumption and minimizing the presence of congestion are some of the issues encountered in wireless sensor networks. This paper throws light upon a few of the routing techniques engaged in the network for the transmitting the sensed information. © 2020 IEEE.

An Overview of the Routing Techniques Employed in Wireless Sensor Network

Cloud Environments provides affords effective distribution of resource on need, which makes depart from others providing splendid performance, scalability, cost efficient and less maintenance. Task Scheduling increases the dynamic allocation of resource to increase performance and decrease the cost. A solution considering makespan and cost, are used as constraints for the optimization problem. A combination of Gravitational search algorithm (GSA) and Harmony search (HS) is used and created a new hybrid algorithm called Gravitational Harmony Search algorithm (GHSA) which produced enormous improvement over other scheduling algorithms. The simulation is proposed in a cloudsim programming environment and results proved the effectiveness of the cost minimizing and makespan parameters. The proposed algorithm works superior over The simulation is proposed in a cloudsim programming environment and results proved the effectiveness of the cost minimizing and makespan parameters. © 2019, Springer Nature Switzerland AG.

Lecture Notes on Data Engineering and Communications Technologies

GHSA: Task Scheduling in Heterogeneous Cloud

In cloud computing, varied demands are placed on the constantly changing resources. The task scheduling place very vital role in cloud computing environments, this scheduling process needs to schedule the tasks to virtual machine while reducing the makespan and cost. The task scheduling problem comes under NP hard category. Efficient scheduling method makes cloud computing services better and faster. In general, optimization algorithms are used to solve the scheduling issues in cloud. So, in this paper we combined two optimization algorithms namely called as Cuckoo Search (CS) and Particle Swarm Optimization (PSO).The new proposed hybrid algorithm is called as, CS and particle swarm optimization (CPSO). Our main purpose of the proposed paper is to reduce the makespan, cost and deadline violation rate. The performance of the proposed CPSO algorithm is evaluated using cloudsim toolkit. From the simulation results our proposed works minimize the makespan, cost, deadline violation rate, when compared to PBACO, ACO, MIN–MIN, and FCFS. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Personal Communications

A Multi-objective Optimal Task Scheduling in Cloud Environment Using Cuckoo Particle Swarm Optimization

Cloud computing provides increased performance, scalability and is both cost efficient as well as low accounts for maintenance, which makes it a preferred choice when the dynamic allocation of resources is considered. Of the various advantages that cloud computing provides, task scheduling is an essential feature that helps to boost performance and reduce operation cost. In the proposed OLOA, a solution is provided for optimization, taking the makespan and cost as major constraints. This is accomplished using the two algorithms, Lion optimization algorithm (LOA) and the Opposition Based Learning (OBL) algorithm; and creating a hybrid Oppositional Lion optimization algorithm (OLOA). The given solution is simulated and demonstrated in the cloudsim programming environment, where the obtained results show drastic improvement in performance, in comparison to that of the previously used other existing algorithms such as Particle Swarm Optimization (PSO) algorithm, oppositional learning based grey wolf optimizer (OGWO) and the Genetic algorithm (GA), all of which do not match the performance rates of the proposed hybrid algorithm. © 2019 Intelligent Network and Systems Society.

International Journal of Intelligent Engineering and Systems

OLOA: Based task scheduling in heterogeneous clouds

Cloud computing is considered to be a predominant technology in the course of events occurring and has an enchanting contribution in software and hardware setup. In cloud environment the performance improvement is highly dependent on features like load balancing and task scheduling. The major issue in cloud computing is task scheduling which leads to reduction in the performance of the system. Efficient resource scheduling algorithm is required in order to resolve this low performance issue. Through which the clients and users can demand services based on pay-as-you-go basis. There are numerous algorithms proposed especially for explaining load balancing and task scheduling. Since cloud infrastructure is based on huge client's requirement, appropriate decision is required for each and every scheduled job. This paper illustrates a detailed study on huge algorithms which are explained to resolve the common issues taking place in various scheduling of resources. © 2019 IEEE.

2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019

A study on QoS based task scheduling using meta heuristic algorithms in cloud environment

Cloud computing, with its huge potential has started evolving as a new age technology. The varied applications of cloud that includes QoS, Charging based on usage, virtualization, offering self-services that are in demand, elasticity etc. make it a viable option for businesses in today's IT industry. Allocating the optimal task over the available set of resources is termed to be task scheduling. The task scheduling must be done effectively for the sake of maximizing the usage of cloud computing. For this purpose various task scheduling strategies have been adopted. In this paper, a systematic survey on various task scheduling approaches in cloud computing has been carried out and the same has been presented herewith. © 2019 IEEE.

Scheduling of task in cloud environment using optimization algorithms: Survey

Cloud Computing is a gathering of physical and virtualized assets gave to the clients according to request and pay per uses bases via internet. Basically, the task scheduling and resource allocation two features are considered such as cost and makespan. In order to achieve better performance in task scheduling, resource allocation and task scheduling must be precisely organized and optimized jointly. Several works have been published in the literature to do the scheduling in cloud. In this paper, for enhancing the scheduling process cuckoo search (CS) and harmony search (HS) algorithm is hybrid as CHSA to improve the optimization problem. These two algorithms are effectively combined to do intelligent process scheduling. According to this, a new multi-objective function is proposed by combining cost, energy consumption, memory usage, credit and penalty. Finally, the performance of the CHSA algorithm is compared with different algorithms such as existing hybrid cuckoo gravitational search algorithm, individual CS and HS algorithm with various multi-objective parameters. By analyzing the result our proposed CHSA algorithm attain minimum cost, minimum memory usage, minimum energy consumption, minimum penalty and maximum credit compared to existing techniques. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

A Hybrid Approach for Task Scheduling Using the Cuckoo and Harmony Search in Cloud Computing Environment

In cloud computing task scheduling is one of the important processes. The key problem of scheduling is how to allocate the entire task to a corresponding virtual machine while maximizing profit. The main objective of this paper is to execute the entire task with low cost, less resource use, and less energy consumption. To obtain the multi-objective function for scheduling, in this paper we propose a hybridization of cuckoo search and gravitational search algorithm (CGSA). The vital design of our approach is to exploit the merits of both cuckoo search (CS) and gravitational search algorithms (GSA) while avoiding their drawbacks. The performance of the algorithm is analyzed based on the different evaluation measures. The algorithms like GSA, CS, Particle swarm optimization (PSO), and genetic algorithm (GA) are used as a comparative analysis. The experimental results show that our proposed algorithm achieves the better result compare to the existing approaches. © 2018 Taylor & Francis Group, LLC.

Information Security Journal

CGSA scheduler: A multi-objective-based hybrid approach for task scheduling in cloud environment

Cloud computing offers dynamic allocation of resources on demand, the feature which makes it to stand apart providing great performance, scalability, cost efficient and less maintenance, thus making it an apt choice. Task scheduling becomes the essential factor in increasing the performance for the dynamic allocation of resources which is most essential in the cloud environment to increase performance and decrease the cost. In this work, a solution is proposed using makespan and cost, taking them as important constraints for the optimization problem. We have merged two algorithms namely, cuckoo search algorithm (CSA) and oppositional based learning (OBL) and created a new hybrid algorithm called oppositional cuckoo search algorithm (OCSA) to provide solution to the above stated issue. Our proposed OCSA algorithm showed noticeable improvement over the other task scheduling algorithms. The proposed work is simulated in cloudsim programming environment and the simulation results show the effectiveness of the proposed work by minimizing cost and makespan parameters. The obtained results are better in comparison to other existing algorithms like particle swarm optimization (PSO), Improved Differential Evolution Algorithm (IDEA) and genetic algorithm (GA). © 2018 Intelligent Network and Systems Society.

OCSA: Task scheduling algorithm in cloud computing environment

In current scenario cloud computing is the leading technology and it is amazing in software and hardware setup. Cloud computing is a pre-eminent technique and users are in need of more service and greater efficiency load balancing and scheduling plays the most important role in cloud computing. In cloud efforts are minimized and also provide service interaction to the user. By this approach users can avail to pay for what they use. Lots of algorithms are proposed for describing the various scheduling tasks and load balancing. Since Cloud computing deals with large user's appropriate decisions are required for every scheduled job. This paper gives detailed survey of various studies on many algorithms that are explained to overcome the common problems identified in different scheduling tasks. Additionally, this paper focuses on comparative on the basis of Quality of Service to project clear ideas on scheduling algorithms. © 2016 IEEE.

2016 International Conference on Control Instrumentation Communication and Computational Technologies, ICCICCT 2016

Comparative analysis of scheduling and load balancing algorithms in cloud environment

At present scenario Cloud computing is an ultimate model and it is remarkable in software and hardware environments. In cloud efforts are minimized and also provide service interaction to user. The above approach users has a beneficial that pay for what they use. Scheduling is one of the most important criteria in cloud environment. Many algorithms are proposed for explaining the various scheduling tasks. Since cloud computing deals with large users’ appropriate decisions are required for all scheduling tasks. This paper provides elaborate studies on many algorithms are explained to overcome the common problems identified in different scheduling tasks. This study also focuses on relative on the basis of Quality of service (QOS) to project clear ideas on scheduling algorithms. © Research India Publications.