Manas Kumar Sanyal

Employee Motivation from Performance Appraisal Implications: Test of a Theory in the Software Industry in West Bengal (India)

Abstract The Software industry and its operation have attracted the concentration of a considerable number of researchers in the last decade, as the industry has proven its uniqueness in nature, operation, contribution to GDP and HR practice. The software industry is distinguished from other industries, by fast changing technology, quality conscious and multinational clients, stiff international competition and shortage as well as fast obsolesce of skill, although the success of IT companies depends on its’ talented and skilled workforce. As a result, measurement and management of performance of individual, team and the organization as a whole is a requisite. The paper tried to study the attitude of the employees’ of the software companies in West Bengal (India) towards performance appraisal. The paper has identified main consequences of appraisal from the previous literatures and tried to find out the effects of those end results on employee motivation towards performance appraisal. The exploratory paper, based on primary survey of 506 employee data collected from 19 software companies of Kolkata, the tier I region for the software industry in West Bengal. Data are analyzed using factor analysis, to find out the applications of appraisal, followed by a binary regression to understand their implications on the employee motivation. The study has found the importance of the line managers in the practice of the appraisal process also reviewed different dilemmas regarding appraisal practice and employee issues depending on companys size, business focus. The practice of appraising and its implications are also diverse in different companies throughout the industry.

A Conceptual Framework for Big Data Implementation to Handle Large Volume of Complex Data

Globally industries, businesses, people, government are producing and consuming vast amounts of data on daily basis. Now-a-days, it’s become challenging to the IT world to deal with the variety and velocity of large volume of data. To overcome these bottlenecks, Big Data is taking a big role for catering data capturing, organizing and analyzing process in innovative and faster way. Big Data software and services foster organizational growth by generating values and ideas out of the voluminous, fast moving and heterogeneous data and by enabling completely a new innovative Information Technology (IT) eco-system that have not been possible before. The ideas and values are derived from the IT eco-system based on advanced data-analysis on top of the IT Servers, System Architecture or Network and Physical objects virtualization. In this research paper, authors have presented a conceptual framework for providing solution of the problem where required huge volume of data processing using different BIG data technology stack. The proposed model have given solution through data capturing, organizing data, analyzing data, finally making value and decision for the concern stakeholders.

AISLDr: Artificial Intelligent Self-learning Doctor

In recent decades, observation is that there are numerous corruptions in medical diagnosis; instead of proper diagnosis, some corrupted practitioners follow the money-earning-diagnosis-path by trapping the patient at critical stage in some countries. The common people are suffering from lack of diagnosis due to high diagnostic cost and lack of certified practitioners. This paper analyzes this shortcoming and ‘design and implement’ an intelligent system (AISLDr) which can perform the same without any corruption in a cost effective manner as like honest human doctor. In this work, the disease Tuberculosis has taken as a prototype because many people of our country are suffering from this disease and they know it at critical stage as India is highest Tuberculosis (TB) burden country. Here, our AISLDr performs diagnosis as well as draws awareness in the society to serve the nation in sustainable manner using fuzzy logic, probabilistic reasoning, and artificial intelligence (AI).

Load Balancing of Unbalanced Matrix with Hungarian Method

We know that cloud computing is an online-based servicing. So there are more than a million number of web servers, who are connected to online cloud computing to offer various types of online web services to cloud customers. Limited numbers of web servers connected to the cloud networks have to execute more than a million number of tasks at the same time. So, it is not simple to execute all tasks at a particular moment. Some machines execute all tasks, so there is a need to balance all loads at a time. Load balance minimizes the completion time as well as executes all tasks in a particular way. It is not possible to have an equal number of servers to execute equal tasks. Tasks to be completed in cloud environment system or environment will be greater than the connected components. Hence, a less number of servers have to execute a greater numbers of jobs. We propose a new algorithm in which some machines complete the jobs, where a number of jobs are greater than the number of machines and balance every machine to maximize the excellence of services in the cloud system.

Load Balancing with Job Switching in Cloud Computing Network

Cloud computing, described as distributed online computing, is a kind of Internet-based computing that provides pooled web resources and applications to connected servers and other machines on user’s demand. It is a web system for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources which can be rapidly provisioned and released with minimal management effort. Load balancing is an important issue in the cloud computing. Cloud computing comprises of many web resources and managing. This plays a vital role in executing a user’s request. In this present condition the load balancing algorithms should be very efficient in allocating the user request and also ensuring the usage of the resources in an intelligent way so that underutilization of the resources will not occur and preemptive based resource management be there in the cloud environment. Cloud computing services different types of nodes connected to cloud to assist the execution of great number of tasks. As a result, to select suitable node or machine for executing a task is able to develop the performance of cloud computing system. A job switching is the switching of the job from one machine to another machine to minimize the overall completion time. In this paper, we propose a load balancing algorithm combining minimum completion time as well as load balancing strategies with job switching.

Cloud Adoption: A Future Road Map for Indian SMEs

The Cloud Environment has taken an important role in present Indian organizations mainly for small and medium-scaled enterprises (SME). The Software as Service (SAAS) model of cloud implementation has enabled them pay-per-use strategy to leverage their business needs with minimum investments. The various initiatives, like moving toward cashless transactions, taken by Government of India in recent past, forced them to adapt software system for their daily business requirements. It has created a huge business opportunity for the cloud solution providers. In this paper, authors have surveyed and outlined the current scenario of cloud adoption by Indian enterprises. The authors also tried to focus on various facets of cloud adoption along with the factors that are blockers for cloud adoption for Indian enterprises.

Load Balancing of Unbalanced Matrix Problem of the Sufficient Machines with Min-Min Algorithm

Nowadays, cloud computing as a developing Internet accommodation concept has been propagating to provide different Internet resources to users. Cloud computing occupies a variety of computing Internet applications for facilitating the execution of sizable voluminous-scale tasks. Cloud computing is a web predicated distributed computing. There is more than a million number of servers connected to the Internet to provide several types of accommodations to provide cloud users. Constrained numbers of servers execute fewer numbers tasks at a time. So it is not too easy to execute all functions at a time. Some systems run all functions, so there are needed to balance all loads. Load balance reduces the completion time as well as performs all tasks in a particular way. There are not possible to remain an equal number of servers to execute equal tasks. Tasks to be executed in cloud computing would be less than the connected servers sometime. Excess servers have to execute a fewer number of tasks. Here, we are going to present an algorithm for load balancing and performance with minimization completion time and throughput. We apply here a very famous Hungarian method to balance all loads in distributing computing. Hungarian Technique helps us to minimize the cost matrix problem.

Improved Cost-Effective Technique for Resource Allocation in Mobile Cloud Computing

Mobile cloud computing (MCC) is a big research topic in this modern technology-based era. This technology combines cloud computing with mobile computing in an innovative way to give better performance and cost-effective service to mobile users. MCC gives opportunities to execute different applications on the mobile devices by transferring the compute-intensive job to the cloud, but there are some problem arising in case of connectivity with mobile devices and cloud servers. To satisfy the user’s demand and accessing cloud server to offload, the task from mobile device to cloud in mobile cloud computing is a difficult job. According to our knowledge we know that the cloud computing has been built upon the growth of distributing computing and virtualization concept. Thus, efficient mapping of tasks to available resource in cost-effective way in the mobile cloud environment is a challenging issue. Our main aim is to allocate nodes to their respective resource at cloud server by maintaining optimal response time and increase the quality of service by maintaining both resource cost and computation performances in mobile cloud environment.

Image Encryption Based on Arnold Cat Map and GA Operator

In this paper, three steps of encryption technique are used to encrypt image. At first, crossover and mutation operators of genetic algorithm are applied on each pixel of image to make modified image. The crossover points are decided from a random sequence generated by logistic map. Second, pixels of modified image are shuffled like transposition cipher based on Arnold cat map. Finally, the encryption image is obtained by performing logical operation between modified image and random sequence produced by logistic map. Some important tests have been performed on encrypted images to check security level of proposed algorithm. It has been observed that the proposed algorithm is able to resist various attacks and provide sufficient security, and it is giving better result among some of the published algorithms.

Load Balancing of Unbalanced Matrix Problem with More Machines

In nowadays cloud computing as a developing web accommodation model has been propagating to offer different Internet resources to users. Cloud computing occupies a range of computing Internet applications for facilitating the finishing of sizable voluminous-scale tasks. Cloud computing is a web predicated distributed computing. There is more than a million number of servers connected to the Internet to provide several types of accommodations to provide cloud users. Constrained numbers of servers execute fewer numbers tasks at a time. So it is not too easy to execute all tasks at a time. Some systems execute all tasks, so there are needed to balance all loads. Load balance minimizes the completion time as well as executes all tasks a particular way.