Functioning of Village Health Sanitation and Nutrition Committees in Punjab: An Appraisal

The present study aims to assess the composition of VHSNCs; to assess the functioning of VHSNCs and find out the deviations, if any, from the prescribed framework of guidelines and, to understand awareness of VHSNC members about their roles. The proposed study is based on primary data collected with the help of structured questionnaire. The data was collected from one hundred Village Health Sanitation and Nutrition Committees in Punjab. Four districts of the Punjab state were selected randomly from each direction i.e North, South, East and West. The districts selected were Gurdaspur, Mansa, Mohali and Firozpur from North, South, East and West direction respectively. The study reveals that sampled VHSNCs in Punjab have 12 members per VHSNC. One-fourth of the chairpersons of the VHSNCs in Punjab were illiterate Only 23 per cent of the VHSNCs claimed to have prepared the village health plan. Meetings were organized on monthly basis in only half of the expected meetings per VHSNC. Large number of members was not attending the meetings organised by VHSNCs in Punjab. Majority of the funds received by VHSNCs was utilized for sanitation and cleanliness of the village. Majority of members were not aware about the components and objectives of VHSNC. All members reported that the untied fund is always helpful in solving the issues and problems of the village and the amount of untied fund given to VHSNCs should be increased

A STUDY ON IMPACT OF FACTORS INFLUENCING EMPLOYEE ATTRITION RATE IN IT SECTOR – WOMEN EMPLOYEES PERSPECTIVE

Management of employee turnover is very essential condition of human resource management. Hiring the right people is the biggest challenge that HR department faces today. Sometimes candidates are not proficient enough for the job but they say yes to impress the interviewer to get the job, but after sometimes they realize that there is no match between them and job, so they start looking for another option. Employee Retention has contributed substantially to the economic power of the company, but they face the problem of employee turnover that affects the organization because if the turnover of employees increases in the organization than it will affect the productivity of the organization. So, to improve the productivity and performance of the organization current study is conducted. The current study is an attempt to identify the impact of factors (Organizational, HR, Personal and Job-related) on the turnover intentions amongst the employee's in Indian IT Sector. Data was collected from 660 women employee's working in Indian IT Sector. Primary data is collected from five major IT companies i.e. Wipro, Infosys, HCL, Accenture and HCL. Various dimensions of both the constructs are available in the literature but selected dimensions of both the constructs are used for drawing inferences that help organizations in identifying factors that affect turnover intentions

A Security Scheme for Textual & Graphical Passwords

Authentication is the process of identifying an individual, usually based on username and password. Authentication merely ensures that the individual is who he or she claims to be. This forestalls the activities against confidentiality and integrity. Shoulder surfing is the main problem of graphical passwords. To overcome the problem of shoulder surfing we introduced a novel Scheme. This scheme provides the login screen to the user at every time the user logs in, this login image consists of set of characters. User with his password clicks some pass characters which are different for different sessions and explained in proposed scheme. To provide better results Neural Network is used for the authenticatio

Broadcasting methods in mobile ad hoc networks: Taxonomy and current state of the art

Flooding also known as broadcasting is one of the most primitive methodologies that focus on investigating searches concerning mobile ad hoc networking due to poorer network procedures which is a main feature in the concept of broadcasting which provides implications to superior applications that includes routing. Broadcasting means in conventional ways transmitting messages from a given branch to all other branches present in a network. The whole grid of the network is manned to ensure that the transmitted data is uniformly ported to the remaining nodes in a decentralized type of network setup. The two issues that renders nodes out of reach all the time are limited radio range and their immovability which assists in concluding that te issue of data transmission covering all networks is assumed to be a multi-objective issue that aims at increasing the count of number of nodules and also decreasing the time taken to reach the specified nodules and also reducing the network overhead which is a crucial characteristic because of the fact that this may direct to congestion also known as broadcast storm issue. This article aims at giving an insight of the taxonomy of transmitting methodologies in MANETS and current state of the art

A Hybrid Ensemble Feature Selection-based Segmentation and Deep Majority Voting Framework on Large Multi-class Diabetes Retinopathy Databases

Diabetic retinopathy is a micro vascular disease that induces a number of changes in the retina. Micro aneurysms, haemorrhage exudates, and the development of new blood vessels all alter the diameter of the blood vessel. Most of the conventional multi-class diabetes retinopathy has different issues such as problem of over-segmentation, classification precision, recall and error rate on high dimensional features space.  Ensemble feature selection measures are used to filter the essential features in the large feature space. In this work, a hybrid ensemble feature selection based multiple classification models are used to improve the classification accuracy on multi-class diabetes retinopathy databases. In this work, a novel image segmentation, ensemble feature extraction measures, and multiple classification approaches are used to find the majority voting in the classification problem. Experimental results show that the proposed ensemble feature extraction-based voting classification model has better efficiency compared to the state of art of conventional approaches

Fuzzy C-Means Algorithm to Diagnose Breast Cancer

The automatic diagnosis of breast cancer is an important, real-world medical problem. A major class of problems in Medical Science involves the diagnosis of disease, based upon various tests performed upon the patient. When several tests are involved, the ultimate diagnosis may be difficult to obtain, even for a medical expert. This has given rise, over the past few decades, to computerized diagnostic tools, intended to aid the Physician in making sense out of the confusion of data. This Paper carried out to generate and evaluate fuzzy model to predict malignancy of breast tumor, using Wisconsin Diagnosis Breast Cancer Database (WDBC). Our objectives in this Paper are: (i) to find the diagnostic performance of fuzzy model in distinction between malignance and benign patterns, (ii) to reduce the number of benign cases sent for biopsy using this model as a supportive tool, and (iii) to validate the capability of this model to recognize new cases

Performance Evaluation of EM and K-Means Clustering Algorithms in Data Mining System

In the Emerging field of Data Mining System there are different techniques namely Clustering, Prediction, Classification, and Association etc. Clustering technique performs by dividing the particular data set into associated groups such that every group does not have anything in common.Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. Actually the main goal is to classify data into clusters such that objects are clustered in the same cluster when they are related according to particular metrics. Classification is the organization of data sets into some predefined sets using various mathematical models. This research discusses the comparison of algorithms K-Means and Expectation-Maximization in clustering. Empirically, we focused on wide experiments where wecompared the best typical algorithm from each of the categories using a large number of real or bigdata sets. The effectiveness of the Expectation-Maximization clustering algorithm is measured through a number of internaland external validity metrics, stability, runtime and scalability tests

Uniqueness Based Records Out Cause With Complete Audit In The Clouds

CRA must retain an arbitrary confidential value for users without affecting the security of the revocable IBE plan. In the Search Engine Optimization and Elmira Plan, for each user, each user creates a secret key by hitting some partial keys, which depend on the partial keys that grandparents use in the hierarchy tree. Another drawback is the lack of scalability, which means that the KU-CSP should have a secret value for each user. In this article, we recommend a new, revocable IBE plan with Cloud Revocation Authority to address each of the shortcomings, i.e. the performance is greatly improved and the CRA maintains system confidentiality for users only. Finally, we expanded the proposed IBE revocable plan to provide a CRA-supported certification plan for a limited time period to manage multiple cloud services. In the current system, bad-behaved users / at-risk in ID-PKS configuration are naturally high. The immediate cancellation method employs a reliable web-authorizing authority to reduce the burden of PKG management and help users decode the encrypted text. For experimental results and reward analysis, our plan is perfect for mobile devices. For security analysis, we make it clear that our plan is completely safe against adaptive recognition attacks according to Daffier-Hellman's two-pronged assumption. Outlines define the structure of an IBE cancellable plan with CRA and define their own security concepts to design potential threats and attacks. CRA Assisted Authentication Plan with Limited Time Rights to Manage Multiple Cloud Services

Image Reordering Based On The Theme Of Diversity

A unique thematic function is proposed to improve the code within the P2P environment, which both displays relevant information as well as balances workload. Therefore, we recommend that you use the updated codebook methodology to improve the information you replace with the resulting codebook and related information, as well as the workload balance between nodes that manage different coding words. An updated version of the Codebook is proposed to be distributed according to the distribution / mixing of personal passwords, which improves target performance at a lower update cost. While most current approaches focus on restricting scalability as well as optimizing high-dimensional visual features, we include content-based images in peer-to-peer systems in this paper to peer-to-peer word models. We recommend using the case. This season's codebook should be updated periodically, instead of the regular time. Within this paper, we offer a unique presenting method to increase mobility around the world, demonstrating both balance and workload. In addition, the peer-to-peer network is dynamically developed, making a stable codebook less efficient for retrieval operations. In order to be able to improve recovery performance and reduce network costs, indexing trimming techniques have been developed. Unlike the central environment, the key challenge is to efficiently acquire an efficient global code book, as images are distributed across peer-to-peer networks

GIHAT: An Efficient Prediction Technique for Measure for Diabetes Mellitus

The medical service industry is a consistently developing field, producing trillions of information consistently. The modernization of the area has an immediate association with this incremental extent. These acquired informational collections are somewhat organized however for the most part unstructured in nature. These acquired information must be prepared with most extreme care to determine finish usable examples for subjective and prescient investigations. These gigantic records of information, in the wake of handling, when utilized, will turn out to be very unpredictable. Diabetes is a lifetime disease marked by elevated levels of sugar in the blood. It is the second leading cause of sightlessness and renal disease worldwide. Sort 2 diabetes mellitus (S2DM) is genuine and expensive metabolic illness that is a developing worries among peoples .S2DM is related with various comorbid conditions that can prompt negative patient results. Comorbid endless torment is extremely basic in S2DM because of the nearness of diabetic neuropathy and musculoskeletal conditions that are related with delayed hyperglycemia. This Paper using General Integrated High Availability Transaction (GIHAT) algorithm concentrates on the causes, sorts, and factors influencing DM (diabetes mellitus), preventive measures, and treatment of diabetes other than those directly associated with Diabetic Patients structured and unstructured data-sets .This algorithm executed in “R” Programming used for statistical analysis which provides the accurate results comparing existing algorithms