Showing papers by "SNDT Women's University published in 2020"

Nutrition and the Gut Microbiota in 10- to 18-Month-Old Children Living in Urban Slums of Mumbai, India.

Abstract: In this cross-sectional study, we describe the composition and diversity of the gut microbiota among undernourished children living in urban slums of Mumbai, India, and determine how nutritional status, including anthropometric measurements, dietary intakes from complementary foods, feeding practices, and micronutrient concentrations, is associated with their gut microbiota. We collected rectal swabs from children aged 10 to 18 months living in urban slums of Mumbai participating in a randomized controlled feeding trial and conducted 16S rRNA sequencing to determine the composition of the gut microbiota. Across the study cohort, Proteobacteria dominated the gut microbiota at over 80% relative abundance, with Actinobacteria representation at <4%, suggesting immaturity of the gut. Increased microbial α-diversity was associated with current breastfeeding, greater head circumference, higher fat intake, and lower hemoglobin concentration and weight-for-length Z-score. In redundancy analyses, 47% of the variation in Faith's phylogenetic diversity (Faith's PD) could be accounted for by age and by iron and polyunsaturated fatty acid intakes. Differences in community structure (β-diversity) of the microbiota were observed among those consuming fats and oils the previous day compared to those not consuming fats and oils the previous day. Our findings suggest that growth, diet, and feeding practices are associated with gut microbiota metrics in undernourished children, whose gut microbiota were comprised mainly of Proteobacteria, a phylum containing many potentially pathogenic taxa.IMPORTANCE The impact of comprehensive nutritional status, defined as growth, nutritional blood biomarkers, dietary intakes, and feeding practices, on the gut microbiome in children living in low-resource settings has remained underreported in microbiome research. Among undernourished children living in urban slums of Mumbai, India, we observed a high relative abundance of Proteobacteria, a phylum including many potentially pathogenic species similar to the composition in preterm infants, suggesting immaturity of the gut, or potentially a high inflammatory burden. We found head circumference, fat and iron intake, and current breastfeeding were positively associated with microbial diversity, while hemoglobin and weight for length were associated with lower diversity. Findings suggest that examining comprehensive nutrition is critical to gain more understanding of how nutrition and the gut microbiota are linked, particularly in vulnerable populations such as children in urban slum settings.

Energy Efficient Opportunistic Routing with Sleep Scheduling in Wireless Sensor Networks

Abstract: The network life of wireless sensor networks (WSNs) relies on the limited energy of non-rechargeable batteries used at the sensor node. Hence, maximum energy saving is essential in the research area while designing a routing algorithm for the WSNs. An energy-saving opportunistic routing (ENS_OR) uses an opportunistic routing concept to improve network performance while relaying data. In this paper, the ENS_OR is further revised with a sleep scheduling algorithm to reduce energy dissipation in one dimensional topology. The proposed sleep scheduling algorithm is designed to enhance network performance by minimizing energy dissipation due to the idle listening of nodes. Sleep interval is adaptive, and it is made proportional to the residual energy of nodes as well as the flow rate of the network. The results of the proposed algorithm are analyzed and compared with ENS_OR without sleep mode and other routing protocols used in WSNs. The results prove that ENS_OR with sleep mode is beneficial to conserve energy for a prolonged lifetime.

Eating habits and sleep patterns of adolescents with depression symptoms in Mumbai, India.

Abstract: Adolescents with depression engage in unhealthy eating habits and irregular sleep patterns and are often at an increased risk for weight-related problems. Improvement in these lifestyle behaviours may help to prevent depression, but knowledge about the associations between depression, sleep, eating habits and body weight among adolescents in India is limited. This cross-sectional study investigated the prevalence of depression and its association with sleep patterns, eating habits and body weight status among a convenience sample of 527 adolescents, ages 10-17 years in Mumbai, India. Participants completed a survey on sleep patterns such as sleep duration, daytime sleepiness and sleep problems and eating habits such as frequency of breakfast consumption, eating family meals and eating out. Depression was assessed using the Patient Health Questionnaire modified for Adolescents (PHQ-A). Anthropometric measurements were also taken. Within this sample, 25% had moderate to severe depression (PHQ-A ≥ 10) and 46% reported sleeping less than 6 h > thrice a week. Adolescents with moderate to severe depression had significantly higher body mass index than those with minimal depression (26.2 ± 6.6 vs. 20.2 ± 4.8 kg/m2 ). The odds of having clinically significant depression (PHQ-A ≥ 10) was 4.5 times higher in adolescents who had family meals ≤ once a week, 1.6 times higher among those who were sleeping <6 h and 2.3 times higher among participants having trouble falling to sleep more than thrice a week. The findings indicated that a significant proportion of adolescents had depression symptoms; improving sleep and eating habits may present potential targets for interventions.

Feature Extraction using Wavelet Transform and Euclidean Distance for speaker recognition system

Abstract: The current speaker recognition system has many issues. i.e., channel mismatching, noises, emotions, idioms, etc. To build a reliable and robust speaker recognition system, it’s essential to implement the precise feature extraction technique. A first major step in speaker recognition system is feature extraction from the speech signal for that speaker. The speaker model can be build using these features, So that one can extract correct and useful features. The traditional method for feature extraction is MFCC. Here, we have proposed a new technique to obtain the features. We are going to use the wavelet transform technique along with Euclidean Distance computational Technique, which gives multidimensional features for the speaker.

Mobile media exposure and use in children aged zero to five years with diagnosed neurodevelopmental disability.

Abstract: OBJECTIVE The present study was conducted to determine the extent of exposure to and use of mobile devices by children (aged 0-60 months) with a diagnosed neurodevelopmental disability. DESIGN A self-report survey-based design was employed. SETTING Questionnaires were administered at a tertiary care hospital in Mumbai, India. PARTICIPANTS The study included a convenience sample of 423 children with a neurodevelopmental disability (aged 0-60 months). The self-report survey was administered to the parents of the children. RESULTS Analyses showed that 92.7% (n = 392) of all respondents have smartphones. 61% (n = 258) of the respondents stated that their children used mobile devices before 2 years of age. 58% (n = 246) of the parents gave children devices while feeding. A statistically significant difference was found in the mobile media usage between groups of children with different diagnoses (p < 0.001). Children diagnosed with ASD appeared to spend the largest amount of time on mobile media (m = 180.44 mins), as compared to children included with other diagnoses. Of the diagnosed children, only 13.4% (n = 57) of parents were informed about the possible negative effects of media use by their paediatricians. CONCLUSION The results suggest premature mobile media habits, frequent use and lack of awareness about the effects of mobile media usage among children diagnosed with a neurodevelopmental disability. We suggest there is a need to update recommendations for caregivers on the use of mobile media by young children with disability.Implications for rehabilitationThe usage and consequences of mobile media use differ based on the type of neurodevelopmental disorder diagnosis. Parents of children with neurodevelopmental disorders often use mobile media as a distraction while engaging in various activities themselves, this information helps identify times at which mobile media might be purposefully used by parents as distractorsThere is an urgent need for clinical guidelines regarding mobile media usage among young children with neurodevelopmental disorders.

An Approach to Reduce Interference Using FFR in Heterogeneous Network

Abstract: As the world is progressing towards 5G technology, its primary requirement is high throughput, low latency, high spectrum and energy efficiency and guaranteed QoS. High throughput is achievable when the user (UE) and the base station/Macro eNodeB (MeNB) are close to each other. This is achieved by using femtocell or Home eNodeB (HeNB). It is a kind of small cell. The femtocell needs to exist along with macrocell. Hence, they will share the resource along with the macrocell. There arises serious inter-channel and intra-channel interference (ICIC). To mitigate the interference, various solution methodologies, e.g. power control, co-operative communication and absolute blank sub-frame, can be used. In this research work, fractional frequency reuse method is discussed where the cell is segregated into centred and edge regions. All the centred, i.e. inner, regions can be assigned with the same frequency sub-band. Edge regions, i.e. outer region, can be assigned with different frequency sub-band with reuse factor of three. In this technique, ICIC can be reduced significantly. Hence, QoS can be improved and throughput of the system can also be enhanced. The outage probability can be reduced since cell-edge users are assigned with different sub-bands with high power.

“Woven Blessings” of a Woolen Blanket “Ghongadi” of India for “Holistic Social Design”

Abstract: The current scenario of garbage piles rising continuously around us, scarce resources like water, over consumption of energy, use of chemicals, carbon footprints, pollution, and so on; has created a huge demand for ecofriendly and sustainable options in textiles and clothing. India, having a rich culture of Crafts resources, its optimization, waste management and sustainable material usage has been greatly interconnected with the entities such as economy, community, social and their exceptional hand crafted manner; all together; for its Sustainable Design.

Way Finding Behavior of University Library Users in Mumbai an Explorative Study

Abstract: Libraries often intimidate new or potential users through their size, complexity as well as unfamiliar tools and technology. Observing library users coping with the environment and perceiving their behavior, assists in planning and designing an ideal guidance system. The present article is based on an observation of university library users in Mumbai. The study provides narratives of variety of tasks completed by participants with average task completion time (ATCT) and further explores users’ facial expressions and behavior and conducted cause analysis. Findings revealed that majority of participants in university libraries felt that wayfinding is complex and not self-oriented even after attending library orientation, due to the complexity of library buildings and lack of appropriate signage. The analysis of ATCT highlighted that maximum users of the university libraries in Mumbai require inordinate time to find their way and locate the required information source. Many participants experienced confusion, disorientation, indecisiveness and anxiousness while navigating and searching for information sources in libraries. It further revealed that major reasons behind prolonged time required in task completion were user-specific such as user unawareness about library physical settings, classification schemes, floor-wise splits in stacking arrangement, availability of facilities, etc. The observational findings and recommendations lead to serve as a starting point in defining patron orientation needs

An Integrated Detection and Treatment Recommendation Framework for Breast Cancer Using Convolutional Neural Networks and TOPSIS

Abstract: Breast cancer is the most prevalent type of cancer, accounting for 14 percent of all cancers among women in India, according to National Health Portal Various machine learning including deep learning methods find extensive applications in the field of medicine ie Computer Aided Diagnosis (CAD) and one of its major examples is detection of cancerous cells While many such systems explore the idea of classifying the region of abnormality as benign or malign, traditional treatment recommendation solely depends on the knowledge of the physician examining This paper explores regression using Convolutional Neural Networks (CNN) to pin-point the probable abnormal region in mammograms and calculate several morphological, texture and histogram features associated with it These features are then leveraged to recommend the next probable treatments The recommendation is based on the extracted features and therefore constitutes a Multi-Criteria Decision Making (MCDM) problem To implement this type of decision making rightfully, the paper makes use of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) where the identified features are used as criteria and experts’ opinion are used as alternatives to obtain ranked recommendations The methodology proposed in the paper is capable of recommending the correct treatment with an accuracy of 815% The proposed methodology would make treatment recommendation reachable even at the most remote places where advanced facilities including consultation with specialists are not easily available

Minimizing Delay Using New Dynamic Blocking Expanding Ring Search Technique for Ad hoc Networks

Abstract: Energy and latency are the significant Quality of Service parameters of ad hoc networks. Lower latency and Limited energy expenditure of nodes in the ad hoc network contributes to a prolonged lifetime of the network. Reactive protocols determine the route to the destination using a route discovery process which results in increased delay and increased energy expenditure. This paper proposes a new technique of route discovery,Dynamic Blocking Expanded Ring Search (DBERS) which minimizes time delay and energy required for route discovery process. DBERS reduces energy expenditure and time delay occurring in the existing route discovery techniques of reactive protocols. The performance of DBERS is simulated with various network topologies by considering a different number of hop lengths. The analytical results of DBERS are validated through conduction of extensive experiments by simulations that consider topologies with varying hop lengths.The analytical and simulated results of DBERS are evaluated and compared with widely used route discovery techniques such as BERS, BERS+. The comparison of results demonstrates that DBERS provides substantial improvement in time efficiency(53.88% and 26.66% comapred with BERS and BERS+ respectively) and also minimizes energy consumption.

De-Speckling of Synthetic Aperture Radar Using Discrete Fourier Transform

Abstract: The role of Synthetic Aperture Radar (SAR) images is inevitable in remote sensing applications. One of the major concern in SAR imagery is that basic textures are generally affected by multiplicative speckle noise. Speckle noise is a consequence of image formation under coherent radiation, but it often carries useful information about the scene being imaged. However, speckle noise is considered undesirable as it damages the resolution and affects the tasks of human interpretation. The proposed framework uses Boxcar filter for reduction of speckle-noise whilst retaining the point targets, edges and texture with the inclusion of Discrete Fourier Transform (DFT) in the speckle reduction framework. A novel technique is propounded, which aims at having a fine control on the high-frequency components by tuning the radius of the frequency rectangle.

Split-Window Based Flood Mapping with L-Band ALOS-2 SAR Images: A Case of Kerala Flood Event in 2018

Abstract: The Kerala state of India experienced a devastating flood during Aug 2018, which incurred huge socio-economic losses and human fatalities. ALOS-2 L-band SAR image acquired during the peak flood was used in this study. A split-window approach combined with thresholding algorithm was used for analyzing the 2018 flood event of Kerala, India. The SAR image splitting and tile selection was carried out based on two parameters, namely the Coefficient of Variation (CV) and ratio to the scene. Kittler and Illingworth's thresholding algorithm was implemented on the selected split images. Euclidean distance was used to shortlist the split images with large variation in the data representing both thematic classes (flood/non-flood). An independent split based analysis (ISBA) was implemented in which respective threshold values obtained from the split images are averaged to get an optimum threshold value. From the results, we observe that an underestimation of flood area in urban land use due to double bounce, volume scattering and shadow effects. Validation is carried out on a small subset area for which the field data was available, and an accuracy of 73 % was obtained.

Multigrain Beetroot Crisps: A Healthy Snacking Option

Abstract: Aim: With the rise of metabolic disorders like obesity in developed countries, maintenance of healthy lifestyle has become very challenging. Food plays a very important role in this process. Consumption of healthy foods with proper portion control can help in controlling excess weight gain and can prevent metabolic disorders like obesity. Obesity is characterized by excess accumulation of fat. This further leads to complications like dyslipidemia, hypertension, diabetes mellitus and cardiovascular diseases. Increased consumption of calorie dense fatty snacks or high sugar processed foods between meals is one of the causes of obesity. Consumption of low fat high fiber snacks has shown to help in weight reduction. Therefore development of a healthy snacking option was considered. The aim of the study was to develop and standardize a healthy snack for obese as well as healthy population. Place and Duration of Study: Department of Clinical Nutrition and Dietetics, Dr. BMN College of Home Science, between November 2019 and February 2020. Methodology: A healthy snacking option in the form of Multigrain Beetroot Crisps was prepared by using a mixture of cereals and millets flours. The snack was baked instead of frying to reduce the Short Research Article Singh and Sengupta; AJMAH, 18(3): 19-25, 2020; Article no.AJMAH.56263 20 calorific and fat content. Standardization was done by testing for sensory evaluation using 5 point hedonic scale for four weeks. Shelf life study was carried out to find stability of developed product. Results: Sensory evaluation studies showed the product was well accepted by the target population. On comparison with the commercially available product, Multigrain Beetroot Crisps was high in fiber, calcium and iron. Shelf life study showed, the product was stable for four weeks at room temperature when kept in an airtight container. Conclusion: A healthy snacking option in the form of Multigrain Beetroot Crisps was developed for obese as well as healthy population which is nutritionally better as compared to commercially available beet chips owing to its fiber, calcium and iron content which are beneficial in obesity related complications.

Improving network lifetime and speed for 6LoWPAN networks using machine learning

Abstract: Wireless communication networks have an inherent optimisation problem of effectively routing data between nodes. This problem is multi-objective in nature, and covers optimisation of routing speed, the network lifetime, packet delivery ratio and overall network throughput. In this paper, a machine learning (ML)-based algorithm is proposed with an objective to minimise the network delay and increase network lifetime for 6LoWPAN networks based on RPL routing. The ML-based approach is compared with normal RPL routing in order to check the performance of the system when compared to recent routing protocols. It is observed that the proposed machine learning-based approach reduces the network delay by more than 20% and improves the network lifetime by more than 25% when compared to RPL-based 6LoWPAN networks. The machine learning approach also takes into account the link quality between the nodes, thereby improving the overall QoS of the communication system by selecting paths with minimal delay, minimal energy consumption and maximum link quality.

Assessment of quality of life of severe acute malnourished (sam) children in mumbai, maharashtraaged 2 – 4 years

Abstract: Background: Malnutrition is a serious health problem affecting children globally. Nutritional status of an individual is a factor determining his / her quality of life. Quality of Life (QOL) means a sense of well-being, satisfaction and happiness experienced by an individual. The aim of the study was to assess the Quality Of Life (QOL) of SAM children aged 2 – 4 years. Methodology: It was a cross sectional study for which children were selected from the Nutrition Rehabilitation, Research and Training Centre (NRRTC) located at Sion, Mumbai based on the inclusion and exclusion criteria. A structured interview was conducted to administer the case report form for data collection and consisted of anthropometry and the Pediatric Quality of Life Inventory tool. The ethical approval for the study was received from Inter System Biomedica Ethics Committee (ISBEC), Mumbai, India. Statistical Analysis: The data was analyzed using SPSS software version 25 for Windows. Results: The mean age of children was 38.56 ± 7.99 months. The mean total scale score for children with SAM aged 2 – 4 years was found to be 74.47 ± 22.57 and was significantly correlated with height, weight and MUAC. Amongst the subscales, social functioning had the highest scores and emotional functioning had the lowest scores. Conclusion: SAM children have low scores on the quality of life scale. The quality of life total scale scores have a statistically significant correlation with anthropometry.

Impact of DFT Based Speckle Reduction Filter on Classification Accuracy of Synthetic Aperture Radar Images

Abstract: Speckle in SAR images makes it difficult to interpret the image thus reducing the effectiveness of image processing. In remote sensing, image scene classification is an elementary problem which aims to label an image automatically with a specific semantic category. The classification performance of SAR data with speckle is inadequate for many applications. Thus, speckle removal becomes an important pre-processing step for SAR data classification. This study investigates the impact and importance of speckle filtering for classification using ALOS-PALSAR-2 data on San Fran-cisco area. Wishart classifier is chosen for classification of filtered and unfiltered SAR data. The influence of DFT based speckle reduction framework is investigated in terms of classification accuracy.

Cross-cultural mentoring: A pathway to building professional relationships and professional learning beyond boundaries

Abstract: This paper offers insight from an informal cross-cultural mentoring experience of course development in higher education framed by the UNESCO Chair on Open Technologies for Open Educational Resources and Open Learning project. The Open Education for a Better World is a tuition-free international online mentoring program established to unlock the potential of open education in achieving the United Nation Sustainable Development Goals. Drawing from mentor/protege conversations and reflections, and examining the experiences of mentoring in the development of an online course for Indian teacher education faculty development, the authors illuminate a pathway toward building professional relationships and professional learning beyond borders and boundaries. This paper describes how mentorship can develop digital competencies foundational for transferring tacit knowledge about planning, designing, recording, implementing, and evaluating teaching and learning in education. Explicit knowledge-building for professional learning within a supportive mentoring relationship is explored.

Generating Graph from 2D Flowchart using Region-Based Segmentation

Abstract: Visual content-based information retrieval intended message retrieval from infographics and their applications are gaining attention in many scientific domains. This seeks an understanding of the structure and correct semantics of infographics. Parsing flowchart and analyzing each element of infographics in the correct sequence is challenging. Using computer vision techniques, reconstruction of the structure in digital format is possible and can help to understand the features of the structure. This paper describes technical details to extract various elements of a 2D flowchart in a meaningful sequence, relate elements as connected in a flowchart and represent in conventional data structures. Region-based segmentation with adaptive thresholding and morphological operations are applied for feature extraction. The graph is generated by maintaining the semantics of the flowchart. The experimental results are evaluated for semantic correctness of flowchart and found that 90% of generated graphs are semantically correct as a flowchart.

Urban area classification with quad-pol L-band ALOS-2 SAR data: A case of Chennai city, India

Abstract: Globally, 55% of the population lives in urban areas in 2018, and this number is expected to hit 68% by 2050. Earth Observation (EO) images based mapping of the urban regions is a critical parameter in the sustainable urban planning process. In recent years, rapid urban growth is experienced in the coastal metropolitan city of India-Chennai. The two land regions, having heterogeneous land uses, as high-rise high-density and medium-rise low-density of the Chennai city are taken as study area. The fully-polarimetric L-band ALOS-2 Synthetic Aperture Radar (SAR) data is used for rapid identification of the urban regions. With respect to this, a comparative assessment of the two supervised classification algorithms such as Wishart and Support Vector Machine (SVM) is presented. The same training data set is used for both algorithms, and a confusion matrix is created algorithm wise. The results of classification with the two classes as urban and non urban indicate that the SVM outperformed the Wishart supervised classification algorithm.

Non-Parametric Method for Enhancement of Darker Portion in an Image

Abstract: Images caught in darker area builds complexities in handling and removing essential data. Improvement of such pictures encourages us to recover significant information. ANN based error back propagation (BP) algorithm is used for enhancing shadow region of an image. Dataset used in this paper is a shadow image with its enhanced output (log transformed), so that model will be able to learn to enhance the shadow region of any given image. Darker locale in an image are successfully reduced in the results obtained. Still there is a scope of improvement through adjustments and variations into various parameters of proposed non-parametric approach.

Urban Flood Mapping with C-band RISAT-1 SAR Images: 2016 Flood Event of Bangalore City, India

Abstract: Flood mapping in urban areas is a rigorous and crucial task in disaster management. Bangalore, one of the Indian megacities, has experienced severe flooding in July 2016. To analyze this flood event, RISAT-1 satellite images were acquired before and after the flood. Various change detection methods were applied to the processed SAR images to identify the flood area. Horizontal like polarized data (HH) is highly sensitive to identify permanent water bodies and also flood affected areas. Permanent water bodies and high elevated areas extracted from DEM were masked out form the results for accurate urban flood mapping. The results show that the spatial distribution of flood was better identified by Normalized Change Index (NCI) method. The results reveal that difference and ratio change detection methods ensued in over and underestimation of flood area, which may be due to the use of moderate resolution RISAT-1 SAR images. In urban areas, the use of images acquired with RISAT FRS mode may give better results due to its high spatial resolution.

The Effect of Dash Diet on the Blood Pressure of the Male Hypertensive Office Employees between 33-55 Years of Age

Abstract: Aims: To determine the effect of the DASH Diet on the Blood Pressure of the male hypertensive office employees between 33-55 years of age. Place and Duration of Study: The study was conducted at Abhitex International Company, Panipat, Haryana, India between November 2018 to January 2019. Methodology: The Clinical blood pressure of the office employees of Abhitex International company, Panipat, between the age group of 35 to 55 years was recorded, following the international guidelines given by American Heart Association(AHA) and American College of Cardiology. Purposive sampling was done and the employees who had clinical blood pressure above 120/80 mm Hg were considered as the samples for the study. The dietary pattern of the samples was evaluated by checking the adherence to the DASH diet which was done through a questionnaire and 3 days 24-hour diet recall. Dietary adherence was assessed using a scoring scheme adopted by Folsom and colleagues. Samples were made aware of the DASH Diet and post one month the adherence to DASH Diet was again checked and the change in the clinical blood pressure was observed. Results: There were 50 study participants. The mean age of the participants was 41.5 years. The average Systolic blood pressure of the 50 participants in the pre-test was 149.3 mm of Hg and average diastolic blood pressure was 89.58 mm of Hg. Pre Nutrition Education Program, the total mean DASH adherence score was 4.3 out of 10 but post NEP and after following DASH Diet, the adherence score for the DASH Diet improved and resulted in 6.7 which indicated that the samples adhered more to the DASH Diet post NEP. There were reductions in systolic (149.30±18.98 mmHg to 146.12±14.85 mmHg) and diastolic (89.58±8.76 mmHg to 86.28±4.76 mmHg) blood pressures when the subjects adhered towards the DASH Diet. A significant difference at p=0.05(p=0.039*) in the pre and post-systolic blood pressure was observed in the study. A similar trend was also noticed in the pre and post-diastolic blood pressure which showed a highly significant difference at p=0.05(p=0.002**). Conclusion: It was concluded from the study that the improvement in the DASH Diet adherence score was associated with a reduction in the Clinical blood pressure. There was a reduction in systolic (149.30±18.98 mmHg to 146.12±14.85 mmHg) and diastolic (89.58±8.76 mmHg to 86.28±4.76 mmHg) blood pressures with an improvement of DASH Adherence Score (4.3±1.27 to 6.7±1.19).

Relationship Analysis between Socio-Economic Variables and Job Performance of Accredited Social Health Activists in Maharashtra

Abstract: National Rural Health Mission (NRHM) was launched in India in 2005. It brought a new concept of Accredited Social Health Activist (ASHA) in to the arena of health sector of India. Since it is a new concept it is imperative to study the effect of socio economic variables on the job performance of ASHAs. Coefficient of correlation was applied to carry out the relational analysis between socio-economic variables and job performance of ASHAs. It was seen that age was positively related with job performance whereas all other independent variables were negatively and significantly related with job performance.