Browse Items (5511 total)
Sort by:
-
An efficient optimization based lung cancer pre-diagnosis system with aid of feed forward back propagation neural network ( FFBNN)
Vol. 56. No.2, October. ISSN: 1817-3195 -
Feature selection based on the classifier models: Performance issues in the pre-diagnosis of lung cancer /
Journal of Theoretical and Applied Information Technology, Vol-59(3), pp.549-555. ISSN-1992-8645. -
Feature selection based on the classifier models: Performance issues in the prediagnosis of lung cancer
Dimensionality reduction is generally carried out to reduce the complexity of the computations in the large data set environment by removing redundant or de-pendent attributes. For the Lung cancer disease prediction, in the pre-diagnosis stage, symptoms and risk factors are the main information carriers. Large number of symptoms and risk attributes poses major challenge in the computation. Here in this study an attempt is made to compare the performance of the attribute selection models prior and after applying the classifier models. A total of 16 classifier models are preferred based on relevancy of the models with respect to the data types chosen, which are based on statistical, rule based, logic based and artificial neural network approaches. Feature set selection and ranking of attributes are done based on individual models. Based on the confusion matrix parameters the models prediction outcomes are found out in the supervisory training mode. The Confusion matrix of the models before and after dimensionality reduction is computed. Models are compared based on weighted Reader Operator Characteristics. Normalized weights are assigned based for the result of individual models and predictive model is developed. Predictive models performance is studied with target under supervised classifier model and it is observed that it is tallying with the expected outcome. 2005 - 2014 JATIT & LLS. All rights reserved. -
An efficient optimization based lung cancer pre-diagnosis system with aid of feed forward back propagation neural network (FFBNN)
World Health Organization (WHO) reports that worldwide 7.6 million deaths are caused by cancer each year. Uncontrollable cell development in the tissues of the lung is called as lung cancer. These uncontrollable cells restrict the growth of healthy lung tissues. If not treated, this growth can spread beyond the lung in the nearby tissue called metastasis and, form tumors. In order to preserve the life of the people who are suffered by the lung cancer disease, it should be pre-diagonized. So there is a need of pre diagnosis system for lung cancer disease which should provide better results. The proposed lung cancer prediagnosis technique is the combination of FFBNN and ABC. By using the Artificial Bee Colony (ABC) algorithm, the dimensionality of the dataset is reduced in order to reduce the computation complexity. Then the risk factors and the symptoms from the dimensional reduced dataset are given to the FFBNN to accomplish the training process. In order to get higher accuracy in the prediagnosis process, the FFBNN parameters are optimized using ABC algorithm. In the testing process, more data are given to well trained FFBNN-ABC to validate whether the given testing data predict the lung disease perfectly or not. 2005-2013 JATIT & LLS.All rights reserved. -
Recent developments in melamine detection: Applications of gold and silver nanostructures in colorimetric and fluorometric assays
The purity of milk, traditionally regarded as a symbol of health and nourishment, has been undermined by the alarming issue of melamine (MLM) adulteration. This nitrogen-rich compound is illicitly introduced to falsely enhance protein content, posing significant health risks. Traditional detection methods are often labor-intensive, time-consuming, or require expensive equipment. In response, researchers have developed colorimetric detection techniques to efficiently screen milk for MLM contamination. These methods are particularly promising due to their ease of preparation, rapid detection, high sensitivity, and capability for naked-eye detection. Furthermore, the unique optical properties of advanced nanomaterials have facilitated fluorometric detection, wherein the presence of contaminants induces detectable changes in fluorescence intensity or wavelength. This study offers an in-depth review of recent advancements in colorimetric and fluorometric probes based on silver (Ag) and gold (Au) nanostructures, exploring their application in food analysis. It delves into the underlying sensing mechanisms of these probes, showcasing their efficacy in detecting food contaminants. Despite the numerous advantages of Ag and Au nanostructure-based probes, challenges remain, particularly in addressing the complexity of food matrices, achieving simultaneous detection of multiple analytes, and mitigating interference from testing conditions. Additionally, this review highlights the emergence of immunoassay-based sensors, noting that many commercially available MLM testing kits utilize ELISA and LFIA platforms. For the first time, a comprehensive list of MLM testing devices and assay kits is presented, accompanied by key findings from recent studies and recommendations for future research directions. 2024 The Author(s) -
The Evolving Prospects of Bharatanatyam: An Enquiry on Changing Religious Landscape
As cultural boundaries expand, symbols of cultural identity, like dance forms, evolve in terms of content and practice. Bharatanatyam, originally a temple dance, originated in the Hindu culture and had long been considered a religious art. However, the art form has gradually expanded its scope beyond its religious context. Contemporary evidence suggests that artists increasingly engage in performances addressing themes that are secular and even compositions based on other religious beliefs, but not without challenges. This article brings to light the evolving religious aspects of Bharatanatyam and investigates novel elements being introduced by cross-religious practices, such as thematic innovations, choreographic patterns and symbolic representations. By analysing data from in-depth interviews with twenty artists from diverse religious backgrounds, the authors argue that religious conservatism in society hinders the evolution of art forms such as Bharatanatyam that have the potential to adapt across and beyond religions. Edinburgh University Press. -
Bharatanatyam and Art activism in the Networked Digital Space
All over the world, traditional models of art activism through dance involved performances that reached a limited audience, while the advent of networked digital spaces has vastly expanded the scope of art activism to a global level. Offering a qualitative netnographic exploration of how Bharatanatyam has been employed for such art activism in the digital space, this article examines the implications for this prominent traditional South Indian dance form in terms of stylistic changes as well as viewer reactions. Through content analysis of the viewer responses to ten popular renditions uploaded on YouTube over five years (20162020), we trace how the art form is evolving and how activist goals are reciprocated by the audience. Our findings confirm that Bharatanatyam has great potential to evolve by adapting novel social themes. However, while such contemporary renditions may elicit viewer responses that critically appraise specific social issues and pave the way for social change, the resulting innovations continue to co-exist with old conflicts and tensions about traditional art and its uses. 2023 The Author(s). -
Organic food products: A study on perceptions of indian consumers
Organic food products are popular across Europe and United States of America. Asia is not far behind with India being a prominent player. The concept of organic food products is not new to Indian farmers. However, there is not much of a consumption taking place domestically despite the fact that India is one of the top 10 players in the world when it comes to the number of farmers engaged in organic cultivation. This study was conducted to understand the factors of consumer perception towards organic food products. The study covered both primary investigation and secondary literature review. Data was collected with the help of a structured questionnaire and was analyzed using percentage analysis and factor analysis to identify the factors of consumer perception. -
MLLR based speaker adaptation for indian accents
Speech Recognition has become an inherent and important feature of today's mobile based apps. Speech input is a very popular option for people with limitations of using the keyboard / mouse in a computer system. Nowadays, more voice messages are used than written text as they also convey the emotions of the speakers. As solutions are developed with native speakers of a language, many of the English input systems have higher accuracy for native speakers than for people with English as their second language (L2), especially for Asian population. The complexity increases since the accent and intonation of Indian speakers are varied from region to region and state to state. This paper analyses an effective speaker adaptation mechanism implemented with Indian speaker profiles and with a very small amount of adaptation data. This research is to facilitate a speaker adaptive system for the speech disabled users with limited disabilities like stuttering and/or unintelligible speech due to illness like cerebral palsy. Experimental results show improvements in the recognition accuracy for speakers speaking small sentences. 2017 University of Bahrain. All rights reserved. -
Exploring factors of consumer perception and attitude towards organic food consumption in India
Organic food market is witnessing an exponential growth in India. However, contradictory to expectations consumption of organic foods as compared to conventional food is still at nascent stage and many empirical studies have indicated this trend. Many of the food retailers have started organic food business across the nation but consumption level has remained significantly low. Impetus for this study came from this contrarian trend and it is crucial to garner insights from awareness and attitude of consumers towards organic food products in terms of why there is gap between awareness and attitude and actual consumption. While there are many empirical studies, not many studies have been conducted in India context. This study is based on descriptive research design constituting a sample of 250 respondents and the data was collected by administering a questionnaire on Likert scale. Study revealed that there was significant gap between perception and attitude of consumers. Factors namely health benefits and concern for environment have higher influence Price sensitivity. Thus, this study helps to bring about an understanding regarding the awareness and attitude of consumers towards organic food products in terms of opportunities ahead and overcoming unaddressed issues. 2021 Ecological Society of India. All rights reserved. -
Performance Evaluation Frameworks in the Context of Indian Microfinance Institutions
The paper conducts a detailed examination of the existing evaluative frameworks for microfinance institutions to gauge the differences and similarities. Efficiency evaluates how MFIs are meeting the performance standards considering time and budget constraints. Outreach evaluates the effectiveness of MFIs in reaching the beneficiaries. Relative efficiency scores were calculated using data envelopment analysis and outreach was measured in five different dimensions (pentagon model). Further, cluster analysis assisted in categorizing the MFIs into five value clusters. The study compares both outreach performance and relative efficiency scores employing ANOVA and correlation analysis. The study was conducted among the Indian context when the sector was hit by crisis during 2010. Paper brought out important insights about the sample. Indian MFIs were found to be more socially efficient, since the social dimension taken into consideration was number of female clients and majority of Indian MFIs has exclusive female focus. The correlation tests found that relative efficiency scores are positively related to depth (poor focus) and length (sustainability) outreach. The results showed that cluster analysis model basing outreach scores was more comprehensive and captured more information compared to the data envelopment model relative efficiency scores. The study is original in its approach in using cluster analysis for outreach performance and in the objective of comparing the two different models. 2019 Aruna Balammal et al., published by Sciendo 2019. -
A study on prediction of health care data using machine learning
Every clinical-decision relies on the doctors experience and knowledge. Perhaps this conventional practice may look appropriate, but it may lead to unpredictable errors, biases, and maximized costs that may affect QoS (Quality-of-Service) given to patients. To help the doctor to save time, the conventional practice to analyze the data for clinical-decision support has to be updated. Machine Learning (ML) and Data Mining (DM) algorithms have applied to have greater and higher predictions. This paper studies a set of ML algorithms by which clinical-predictions are going to be more appropriate and cost-effective. IJSTR 2020. -
Customer preferences to select a restaurant through smart phone applications: An exploratory study
The increasing number of Smart Phone Applications (SPA) user and fast growing restaurant industry proves the great potential of using SPA as business marketing opportunity in Malaysia. The constant growth in mobile technology has created a prospect for the restaurant industry to use SPA as a restaurant promotion tool. The growing attention of use of SPA among the Malaysian customer, marketing research remains understudied in the field of SPA based restaurant promotion activities. The aim of this study is to explore the increase in customer acceptance to use SPA based restaurant promotion and to identify the customer preference to use SPA to select the restaurant. Thus, this paper mainly focuses on restaurant information on product and promotion as antecedents of customer acceptance of smart phone apps by underpinning the Unified theory of acceptance and use of technology (UTAUT) model. A conceptual model and hypotheses are tested with a sample of 116 students from a private university at Selangor district, Malaysia. The findings indicate that there is a positive relationship to increase customer acceptance level through SPA based restaurant product information and also strong relationship with the restaurant promotion information. It also indicates that customer acceptance of SPA through experience and satisfaction has a positive significant effect on customer preference to select a restaurant. Based on the results, this paper rounds off with conclusion, recommendations for future marketing research and provides a new marketing strategy to formulate among the restaurant business sector. 2015 American Scientific Publishers. All rights reserved. -
RF-ShCNN: A combination of two deep models for tumor detection in brain using MRI
The tumor in the brain is the reason for jagged cell enlargement in the brain. Magnetic resonance imaging (MRI) is a common scheme to identify tumor existence in the brain. With these MRIs, the medical practitioner can examine and detect the abnormal growth of tissues and corroborate if the brain is influenced by a tumor or not. Due to the appearance of artificial intelligence models, the discovery of brain tumor is performed by adapting different models which thereby help in making decisions and selecting the most suitable diagnosis for patients. The main motivation of this work is to reduce the death rate. If they are not adequately treated, the survival rate of the patient decreases. The correct diagnoses help patients receive accurate treatments and survive for a long time. This paper develops a hybrid model, namely the Residual fused Shepherd convolution neural network (RF-ShCNN) for discovering tumor in the brain considering MRI. Thus, the Adaptive wiener filtering is adapted to filter image-commencing noise. Thereafter, Conditional Random Fields-Recurrent Neural Networks (CRF-RNN) are adapted for segmentation followed by the mining of essential features. Lastly, the features employed in RF-ShCNN for making effective brain tumor detection by means of MRI. Thus, the RF-ShCNN is built by unifying the deep residual network and Shepherd convolution neural network. The hybridization is done by adding a regression layer wherein the regression is fused with Fractional calculus (FC) to make effective detection. The RF-ShCNN provided better accuracy of 94%, sensitivity of 95% and specificity of 94.9%. 2023 -
Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection
In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of the digital content. The computer vision features based on the frame change are extracted using a proposed deep learning model called the Cascaded Deep Sparse Auto Encoder (CDSAE) trained by temporal CNN. The detection process is performed using a Deep Neural Network (DNN) to classify the deep fake image/video from the real image/video. The proposed model is implemented using Face2Face, FaceSwap, and DFDC datasets which have secured an improved detection rate when compared to the traditional deep fake detection approaches. 2022. Balasubramanian et al. -
Microlearning and Learning Performance in Higher Education: A Post-Test Control Group Study
This study aimed at evaluating the effectiveness of microlearning in higher education. The sample consisted of first-year MBA students, and a post-test control group design was used to assess the effectiveness of a microlearning module. The results indicated that the use of microlearning was significantly related to learning performance and participants' reactions to the module. Moreover, the microlearning group scored significantly higher than the control group. The findings suggest that microlearning has the potential to improve learning outcomes and enhance participant engagement. However, the study has certain limitations, and future research is needed to gain a comprehensive understanding of the optimal design and delivery of microlearning modules. The study supports the use of microlearning in higher education as an effective instructional strategy. 2024, Commonwealth of Learning. All rights reserved. -
Jugaad in organizational settings: exploring the Jugaad leadership competencies
The Hindi term 'jugaad' is closely linked to frugal innovation. In resource-scarce environments, organizations can thrive by developing jugaad-related leadership abilities. Previous research on jugaad has focused primarily on individual problem-solving and overlooked the leadership skills necessary to implement it in organizational settings. This study employs a theoretical lens of leadership competency models, interpretive phenomenology, purposive sampling, and an inductive data-driven coding approach to explore the jugaad leadership competencies of 28 Indian business leaders and managers. The study presents the Jugaad Leadership Competency (JLC) model, identifying ten competency clusters exhibited by jugaad leaders. This is the first study to develop a model for jugaad leadership in organizational settings. In environments characterized by scarcity and intense competition, the JLC model can aid individuals and organizations in acquiring the necessary competencies for frugal innovation. The study evaluates the theoretical and practical implications of the findings, their transferability, and limitations and offers suggestions for future research. 2023, Springer Nature Limited. -
Development and validation of gaming disorder and hazardous gaming scale (GDHGS) based on the WHO framework (ICD-11 criteria) of disordered gaming
This study aimed to develop and validate a brief psychometric scale for gaming disorder and hazardous gaming based on the WHO framework as defined in the ICD-11. The study was carried out among college students using face to face interview. A panel of mental health experts examined the face validity of the new Gaming Disorder and Hazardous Gaming Scale (GDHGS). An Exploratory Factor Analysis (EFA) using the principle component analysis (PCA) method with direct oblimin rotation on the five items of GDHGS was used for assessment of construct validity. The results of Kaiser Meyer Olkin (KMO) measure used for sampling adequacy and Bartlett's test (BT) of sphericity used to show the appropriateness of using factor analysis, confirmed the appropriateness of EFA for the present study sample. The factor analysis extracted single component with an eigenvalue of greater than one, which was further supported by the examination of scree plot. To examine the criterion related validity of the GDHGS, correlation between GDHGS and IGDS-SF scores was assessed. Spearman correlational analysis showed strong positive correlation of GDGHS score with IGDS-SF score (rs = 0.878, p < 0.01). Further, the sum of first four item score of GDHGS among participants diagnosed with GD (median: 15.00; IQR: 15.0015.75) was significantly greater than those without GD (median: 4.00; IQR: 3.006.50) according to the diagnostic interview based on the ICD-11 criteria (U = 0.000, p < 0.001). The internal consistency of GDHGS as measured by the Cronbach's alpha was 0.914. Further, the GDHGS did not have its reliability increased by removal of any of the five items included in the scale. Also, the threshold for significant floor and ceiling effect was not reached. In conclusion, GDHGS is a valid measurement scale for disorders involving gaming behaviour based on the ICD- 11 construct. 2020 Elsevier B.V. -
Fake news and social media: Indian perspective
The unlimited freedom made social media platforms are susceptible to misuse, misinformation, and thus, fake news. In the last few years, social media has turned out to be a massive player in shaping public discourse in a democratic space (Marda & Milan, 2018). Though there have been pressures from policymakers on service/platform providers, nothing concrete has built up towards accountability of the user or platform proprietors. In India, there has been a consistent increase of social media users and instances of the misuse of this medium. This paper seeks to examine how the propagation of fake news has disrupted the public sphere and possible policies that can be implemented to curb the plague of fake news. The relationship between various events of violence reported in India media and the role of fake news in instigating chaos are discussed in this paper. It also tries to review policies initiatives taken by various countries, especially in Europe and possible measures which India could take to restrict the flow of fake news. Media Watch. -
Teaching and learning practices initiated in department of management studies, Christ University, Bangalore to meet global standards /
European Journal of Business and Management, Vol.6, Issue 31, pp.329-334, ISSN No: 2222-1906 (Paper), 2222-2839 (Online)