Effectiveness of nurse led intervention on Quality of Life (QoL) among patients with Tuberculosis
Each year about 2.2 million people develop TB in India and an estimated 220,000 die from the disease. According to the World Health Organization (WHO), it’s the most deadly infectious disease in the world, killing 1.5 million people in 2014. TB is most common in developing countries. Tuberculosis is usually preventable and curable under the right conditions. To assess the effectiveness of the nurse-led intervention on Quality of Life (QoL) among patients with tuberculosis. A Quasi-experimental study with time series design with a comparison group was conducted in two selected Hospitals of Kanpur 50 active tuberculosis patients( 25 experimental and 25 in the comparison group) formed the sample for the study The researcher used structured questionnaire to elicit demographic data and WHO QoL BREF scale to assess Quality of life. The nurse-led interventions were individualized teaching on tuberculosis to patients and family education in the prevention and management of tuberculosis. To experimental group the comparison group followed ward routine, The post-intervention and assessment of the quality of life were done at 15 days and 60 days for both the group. There was a significant difference in the quality of life in 15 days and 60 days at p.<001 level The Mean difference was high in all domains of quality of life in the experimental group except physical domain. The highest mean difference in QoL was found in environmental health and the lowest in physical domain with 95% Cl after an intervention that infers the nurse-led intervention was effective whereas the Mean difference in all domains in the comparison group was very low. The Association of pre-intervention level of QOL among subjects with their selected clinical characteristics in the comparison group showed that extension of tuberculosis and the zone affected by tuberculosis in the social domain and symptoms of tuberculosis in environmental Te domain is influenced at p <0.05 level of significance There was no significant association of QoL with demographic variables. The intervention was effective in improving QoL among Tuberculosis patients.
Published by: Taj Mohammed
Author: Taj Mohammed
Paper ID: V4I4-1426
Paper Status: published
Published: August 6, 2018
Business capability meta model an effective conceptual illustration
This article explains how to describe a business using modeling concept to achieve strategic perspective value proposition which would benefit aligning related business disciplines such as case management, BPM, requirement analysis, information management and more importantly helping organization transformation definition and solution deployment.
Published by: Ashok Mahalik
Author: Ashok Mahalik
Paper ID: V4I4-1419
Paper Status: published
Published: August 6, 2018
To study the effect of meditation in mental health promotion
Today stressful and busy life has indulged every individual in the problems related to physical and mental health, social isolation and financial crunch which leads to psychosomatic disturbances. The present study aims to evaluate the efficacy of Meditation in promoting mental health in the professional population. This was a prospective, randomized, case-control study on 55 subjects who were administered for a training module for 4 months. Individual measurements were carried out at the baseline and after 4 months of practice in almost similar conditions. Two standard psychological evaluation tools were used i.e. WHO Quality of Life. Following 4 months practice of PM, there was an improvement in all domains of WHOQOL ranging from 3 points to 5.7 that is physical health (42.1%) There was an improvement in depression in 7 of 12 subjects. Meditation is a cost-effective, non-invasive intervention with minimal risk of adverse effects and can be safely recommended for the promotion of mental health in Individuals.
Published by: Dr. Madhuri Wane
Author: Dr. Madhuri Wane
Paper ID: V4I4-1416
Paper Status: published
Published: August 4, 2018
Advanced detection of spam and email filtering using natural language processing algorithms
Unsolicited bulk emails from random email addresses sent to a user's inbox are generally called junk or spam emails. 45% of all emails sent are spam and 14.5 billion spam emails are sent every single day. Around 36% of spam emails is content related to sales, advertising, and promotions that the recipient explicitly did not opt to receive. However, not all spam emails are used for this purpose. Spam emails are also sent for phishing purposes that deceive users and lead the recipients to malicious websites with unethical intentions. Numerous techniques have been developed to block such spam emails but a majority of users still receive them. This is because of the ability of the spammers to manipulate the filters. Spam costs businesses a whopping $20.5 billion every year. Even worse is that the cost of spam is likely to continue rising. Data indicates that losses to business will grow to $257 billion annually within a few years if the current rate of spam email is not decreased. To curb this problem, we present a method based on Natural Language Processing (NLP) for the filtration of spam emails in order to enhance online security. The technique proposed in this research paper is an approach which stepwise blocks spam mail based on the sender's email address along with the content of the email. This paper presents a proposed NLP system using N-gram model, Word Stemming algorithm and Bayesian Classification algorithm for detection of spam content and effectively filtering it.
Published by: Sujesh Shankar
Author: Sujesh Shankar
Paper ID: V4I4-1417
Paper Status: published
Published: August 4, 2018
Research perspectives in Siddha Varmam therapy
Siddha system is one of the oldest medical systems in the world. Siddhars, the fore-runners of this medical system served the community with many special therapies and Varmam is one among them. Varmam is the vital life energy points located in the human body. 108 main points have been identified by the Siddhars. The term Varmam also indicates the therapeutic manipulation of specific points in which the pranic energy is found concentrated. Even though Varmam therapy has been in existence for centuries, research in Varmam is still in the toddler stage. Central Council for Research in Siddha has the mandate to do research in Varmam. Standardization is the foremost component in any research. There are no specific guidelines to standardize the Varmam therapy. Just as a therapeutic interventional pre-clinical study, the standardization of Varmam is considered a pre-clinical one. Standardization of Varmam is comprised of mapping of Varmam points, the establishment of a relationship with Nadi, standardization of pressure given to Varmam points, standardization of techniques of applying pressure, physiological correlation of Varmam, standardization with respect to bioenergy field and therapeutic grouping of Varmam points. This paper deals with the research perspective of Varmam.
Published by: Natarajan, Ramaswamy R. S
Author: Natarajan
Paper ID: V4I4-1410
Paper Status: published
Published: August 4, 2018
Feature selection in network intrusion detection using metaheuristic algorithms
Network Intrusion Detection (IDS) mechanism is a primary requirement in the current fast growing network systems. Data Mining and Machine Learning (DM-ML) approaches are widely used for network anomaly detection during the past few years. Machine learning based intrusive activity detector is getting popular. However, they produce a high volume of false alarms. One of the main reasons for generating false signals is redundancy in the datasets. To resolve this problem, an efficient feature selection is necessary to improve the intrusion detection system performance. For this purpose, here we use Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The three abovementioned algorithms are used to select the most relevant feature set for identifying network attacks, KNN and SVM algorithms are used as classifiers to evaluate the performance of these feature selection algorithms. The standard NSL-KDD dataset is used for training and testing in this study. We used different metrics to determine which of these algorithms provide a better overall performance when they are used for feature selection in intrusion detection. Our experiments show that PSO, ACO and ABC algorithms perform better than other approaches in feature selection. Feature selection based on ABC provides 98.9% of accuracy rate and 0.78% false alarm with KNN algorithm as the classifier, which is the best result among the examined algorithms.
Published by: Tahira Khorram, Nurdan Akhan Baykan
Author: Tahira Khorram
Paper ID: V4I4-1414
Paper Status: published
Published: August 4, 2018
