Research Cell: An International Journal of Engineering Sciences

Gagan Jindal ¹, Sikander Singh Cheema ¹

Affiliations
1 Dept. of Computer Engineering, Punjabi University, Patiala, IN

Abstract

Image segmentation is one of the important part in image processing. Image segmentation is a process that divides the image into several parts according to its shape, pixel intensity, region formation or by other features. Image segmentation is the process which comes after the image compression and is followed by the various description parameters. In this paper we discuss algorithm to segment natural image using Hsl color space based on the k-mean clustering method. The basic goal of image segmentation is to the convert the given sample image into somewhat more meaningful and understandable, As well as in understanding high level process such as robotics, face recognition, leaf structure study etc.

Keywords

K-Mean, Hsl, Rgb, Hue, Saturation.

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Arshbeer Kaur ¹, Sikander Singh Cheema ¹

Affiliations
1 Computer Engineering Department, Punjabi University, Patiala, IN

Abstract

This paper is a review paper, in which various techniques have discussed referring from many papers. For efficient detection, data mining, analysis of medical records in less time. Main emphasis is given on how we can maintain medical record in order to get fastest access to the practitioners, doctors and also patients and how we can reach remote area people by the help of technology. Evolutionary Algorithm and technology like telemedicine, equipments, interfaces, and tools have made hand in hand with the healthcare system and medical field to get efficient performance. This will help doctors to assess patient's health condition, who can't tell their condition through their mouth like children with mental disorder, deaf or dumb people.

Keywords

Multi-Agent, Tools, Algorithm.

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Pankaj Goyal ¹, Sikander Singh Cheema ²

Affiliations
1 Computer Engineering Department, Punjabi University, NH 64, Patiala, IN
2 Computer Engineering Department, Punjabi University, Patiala, IN

Abstract

This paper reviews about two crop models named Dssat, AquaCrop. These models give estimate production of different crops based on various input parameters like soil, water, weather conditions, temperature etc. These models are very useful for estimate yield of different crops. One can judge the future production of different crops of any state or country so based on that information they can build their future planning.

Keywords

AquaCrop, DSSAT, Crop Models, Decision Based Models.

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Simranjot Kaur ¹, Sikander Singh Cheema ¹

Affiliations
1 Department of Computer Engineering, Punjabi University, Patiala, IN

Abstract

The rapid growth of Internet and WWW has led to vast amount of data to be captured, processed and analyzed to get correct information. Such type of data can be in structured, unstructured and semi structured form. The following part of the paper is devoted to the characterization of tools, techniques and implementation of Big Data using these techniques. For implementation, this paper focus on some methods to use Grid Computing along with Hadoop. Hadoop is used to analyzed the data and Grid Computing is used to provide large storage capacity and computation power. This paper also listed some open source toolkit used to implement solution such as Hadoop,Globus toolkit.

Keywords

Big Data, Grid Computing, Hadoop Distributed File System, Map Reduce, Processing Capacity, Storage Capacity.

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Parmeet Kaur ¹, Sikander Singh Cheema ¹

Affiliations
1 Department of Computer Engineering, Punjabi University, Patiala, IN

Abstract

Crop Simulation Models helps to estimate crop yield on the basis of some parameters like temperature, soil, weather and crop etc. DSSAT-CSM (Decision Support System for Agro-technology Transfer - Cropping System Model) is an extension of DSSAT crop model. DSSAT-CSM contains a modular structure which has various modules that work along various parameters and give the desired output. CropSyst is a user friendly, multi-year, multi crop, daily time step cropping system simulation models which is used to study the effect of cropping system management on productivity and the environment. It takes the factors such as soil-water budget, crop canopy and ischolar_main growth, soil-plant nitrogen budget, residue production and decomposition and management options like crop rotations, irrigation, nitrogen fertilization and tillage operations into consideration. DSSAT and CropSyst both are used to analyse growth production but based on different input parameters.

Keywords

Cropping System, Crop Simulation, DSSAT, Climgen, CROPGRO Module, Cropsyst, Nitrogen Budget, Decision System.

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