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Dharma as a binary identity
The idea of Dharma has different connotation in History from that of religion as is popularly understood. While it is accepted as righteousness, it transcends the notion that Dharma represents piety, spirituality, belief and nobility. On the contrary, History is replete with instances of how religion, an institutionalized aspect of Dharma, was constantly articulated as representing Authority, Power, Status and Hierarchy. Due to these interpretations Dharma often was projected as a tool for realization of the above by various institutions, be they, political, social, cultural or economic, and Dharma provided legitimacy and justified their identities. The present paper juxtaposes this articulation in the context of Ancient and Medieval India, spanning a period approximately from 3rd century BCE to 10th century CE. It argues that the different trajectories that flowed between Dharma and various other secular institutions constantly witnessed divergence as well as assimilation at various points of time. 2015 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bengaluru), ISSN: 0253-7222. -
An enhanced framework to design intelligent course advisory systems using learning analytics
Education for a person plays an anchor role in shaping an individuals career. In order to achieve success in the academic path, care should be taken in choosing an appropriate course for the learners. This research work is based on the framework to design a course advisory system in an efficient way. The design approach is based on overlapping of learning analytics, academic analytics, and personalized systems. This approach provides an efficient way to build course advisory system. Also, mapping of course advisory systems into the reference model of learning analytics is discussed in this paper. Course advisory system is considered as enhanced personalized system. The challenges involved in the implementation of course advisory system is also elaborated in this paper. Springer Science+Business Media Singapore 2017. -
Phonon limited diffusion thermopower in phosphorene
A theoretical investigation of diffusion thermopower, Sd, of phosphorene employing Boltzmann transport formalism is presented. We assume carriers in phosphorene to be scattered by in-plane single and flexural two-phonon processes via deformation potential coupling. Our calculations of Sd in phosphorene show that, at low temperatures (T?< 20 K) Sd increases linearly with temperature and for the range of temperatures considered single phonon contribution to Sd dominates. As function of carrier concentration, ns, considered (1016?1018 m-2), at T = 300K, Sd decreases from 189?V/K to 9.9 ?V/K. 2017 Author(s). -
SAARC Regional Disaster Law: Need for Progressive Development
[No abstract available] -
Physical fitness recommender framework for thyroid patients using restricted boltzmann machines
These days, people can easily acquire the information from online sources. Individuals are generally using recommendation services before buying products considering the availability of online. Recommendation systems propose the relevant services or products to users. But sometimes people face issues while retrieving health related information from the recommender systems. A focus on keeping people healthy is one way to address the serious societal concern of healthcare domain. A health-based physical recommender system suggests workout plans for users using their activity level and health condition. A personalized approach is the most effective solution for the fitnessbased recommender framework based on user's desired characteristics. This article presents a personalized fitness recommender system for thyroid patients. The proposed fitness recommender model integrates the user's data like personal and health profile, preferences, calorie intake, and activity level. The proposed hybrid model is built using Restricted Boltzmann Machines (RBM) integrating content based and matrix factorization techniques. The results of experiments prove that the proposed hybrid model outperforms than content based, pure RBM and matrix factorization recommendation techniques. The current proposal achieves the personalization approach by incorporating user's thyroid health condition and exercise preferences in recommendation process. The recommended result of hybrid RBM method is revised based on user's new preferences. 2020, Intelligent Network and Systems Society. -
Design of personalized diet and physical activities recommendation framework for hypothyroid patients
These days, hypothyroid disease is quickly growing among individuals. In India, one out of eight women experiences hypothyroid disorder because of iodine deficiency. It is necessary to maintain the thyroid hormone levels because it may lead to thyroid cancer. There is a need to consume an adequate amount of iodine intake and other nutrients required to balance thyroid hormones levels. So, patients should follow a customized daily diet and exercise plan to meet their nutritional needs. These recommendations help hypothyroid patients to enhance their metabolism and to adjust thyroid hormones levels. Most of the existing online systems usually provide diet recommendations in general forms. Such recommendations are insufficient for any patient suffering from a specific disease. This paper provides a personalized recommendation framework to provide appropriate diet plans and physical activities to patients. These recommendations are based on their clinical data and personal choices. Validation of recommendations can be made by combining both domains like human expertise and computer technologies. BEIESP. -
Recommendation of food items for thyroid patients using content-based knn method
Food recommendation system has become a recent topic of research due to increase use of web services. A balanced food intake is significant to maintain individuals physical health. Due to unhealthy eating patterns, it results in various diseases like diabetes, thyroid disorder, and even cancer. The choice of food items with proper nutritional values depends on individuals health conditions and food preferences. Therefore, personalized food recommendations are provided based on personal requirements. People can easily access a huge amount of food details from online sources like healthcare forums, dietitian blogs, and social media websites. Personal food preferences, health conditions, and reviews or ratings of food items are required to recommend diet for thyroid patients. We propose a unified food recommendation framework to identify food items by incorporating various content-based features. The framework uses the domain knowledge to build the private model to analyze unique food characteristics. The proposed recommender model generates diet recommendation list for thyroid patients using food items rating patterns and similarity scores. The experimental setup validated the proposed food recommender system with various evaluation criteria, and the proposed framework provides better results than conventional food recommender systems. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Recommendation of diet using hybrid collaborative filtering learning methods
These days, various recommender systems exist for online advertisement services which recommend the products considering users interests. Similarly, health recommendation systems are becoming most important component in individuals life. Due to the modernization and busy schedule, people give less concern to their eating patterns. This leads to various health issues like obesity, thyroid disorder, diabetes and others. Every individual has different health issues and food habits. Therefore, diet recommendations should be suggested by considering their personal health profile and food preferences. So, it becomes essential to analyze individuals health concerns before recommending the diet with required nutrient values. Thus, it helps people to minimize the further risks associated with the current health conditions. The proposed diet and exercise recommender framework suggests a balanced diet for thyroid patients. It takes care of the food intake with necessary nutrients requirement based on thyroid disorders. This paper applies K-nearest neighbor collaborative filtering models using various similarity measures. The paper assessed two-hybrid learning methods, KNN with alternating least squares: KNN-ALS and KNN with stochastic gradient decent: KNN-SGD. The experimental setup analyzed and evaluated the performances of all algorithms using mean absolute error (MAE) and root mean squared error (RMSE) values. Springer Nature Singapore Pte Ltd 2020. -
Recommendation Framework for Diet and Exercise Based on Clinical Data: A Systematic Review
Nowadays, diet and exercise recommender frameworks have gaining expanding consideration because of their importance for living healthy lifestyle. Due of the expanded utilization of the web, people obtain the applicable wellbeing data with respect to their medicinal problem and available medications. Since diseases have a strong relationship with food and exercise, it is especially essential for the patients to focus on adopting good food habits and normal exercise routine. Most existing systems on the diet concentrate on proposals that recommend legitimate food items by considering their food choices or medical issues. These frameworks provide functionalities to monitor nutritional requirement and additionally suggest the clients to change their eating conduct in an interactive way. We present a review of diet and physical activity recommendation frameworks for people suffering from specific diseases in this paper. We demonstrate the advancement made towards recommendation frameworks helping clients to find customized, complex medical facilities or make them available some preventive services measures. We recognize few challenges for diet and exercise recommendation frameworks which are required to be addressed in sensitive areas like health care. 2019, Springer Nature Singapore Pte Ltd. -
Classification of Hypothyroid Disorder using Optimized SVM Method
Hypothyroidism is an endocrine disorder where the thyroid organ doesn't provide the enough amount of thyroid hormones. It is one of the common diseases found in women. Detection of hypothyroidism needs suitable diagnostic tests to encourage prompt analysis and medication. Accurate and early detection of a disease is more important and compulsory in healthcare domain to facilitate correct and prompt diagnosis and timely treatment. The information generated in healthcare domain is on large scale, crucial and complex with multiple parameters. To interpret and understand such a huge data and retrieve the accurate and relevant information from it is a tedious as well as challenging task. However, there is a need and importance to facilitate the patients with better medical solutions. This will help to reduce the cost, time and give more relief to users by applying advanced and upgraded knowledge. It will also assist to prevent the further complications. The proposed study gains the knowledge from the hypothyroid dataset to predict the level of disease. To identify the level of hypothyroid disorder, we used four classification machine learning techniques, namely KNN (K-Nearest Neighbour), SVM (Support Vector Machines), LR (Logistic Regression) and NN (Artificial Neural Network). The Experimental results compared the classification accuracy of four methods. Logistic Regression method achieved 96.08% accuracy among other three classifiers. But, SVM is found the best classifier after standardizing the data and parameter tuning with accuracy of 99.08%. 2019 IEEE. -
Goodness of fit test for Rayleigh distribution with censored observations
We develop new goodness of fit tests for Rayleigh distribution based on fixed point characterization. We use U-Statistic theory to derive the test statistics. First we develop a test for complete data and then discuss, how the right censored observations can be incorporated in the testing procedure. The asymptotic properties of the test statistic in both uncensored and censored cases are studied in detail. Extensive Monte Carlo simulation studies are carried out to validate the performance of the proposed tests. We illustrate the procedures using real data sets. We also provide, a goodness of fit test for the standard Rayleigh distribution based on jackknife empirical likelihood. 2023, Korean Statistical Society. -
Quad-band SIW antenna with micro-pocket enabled frequency-agile design for 5G/6G IoT applications
A single polarized substrate integrated waveguide (SIW) cavity supported self-quadruplexing antenna, designed for 5G/6G IoT applications is proposed and prototyped. The model is backed by a rectangular substrate integrated waveguide (RSIW) cavity and features four resonating patches excited separately through four different 50? feed lines. The antenna center frequencies are obtained at 3.29GHz, 4.47GHz, 5.85GHz, and 7.07GHz. Additionally, the cavity is engineered with four sets of micro pockets beneath the patches which can be filled with different materials to offer frequency-agile response. The operating frequencies can be tuned over a wide range between 3.29GHz and 8.4GHz as per the required targets. The layout of the model is chosen meticulously to ensure all ports are co-polarized and isolation between any two is better than 32 dB. The proposed antenna design exhibits competitive performances with a compact size of 0.09 ?g, Front-To-Back-Ratio (FTBR) above 17.83 dB and peak gain of 7.6 dBi. Importantly, all ports are single polarized for the first time in their class. The performance is validated by an equivalent circuit model and prototype characterization. The proposed antenna specifications and configurations well suit for future high-end applications like IoT/5G/6G/satellite communications. The Author(s) 2026. -
Does Packaging Elements Affects Consumers Preference During The Purchase Of Chocolate?
Chocolate is one of the highest consumed products and the packaging of such a product is important. The primary goal of the study is to understand if the packaging of chocolate has an impact on the consumer's preference during the purchase of chocolate. The researcher concentrates on the elements of packaging which are the color of the packaging, shape, and size of the packaging, labeling information on the packaging, and the material of packaging. The study helps the producer to understand what factors on the packaging impacts the customer during the purchase of chocolate. The researcher concentrates on how these elements of packaging play a role in affecting the consumer at the point of purchase of chocolate. Through this study one will be able to deliver the product i.e., chocolate more efficiently and effectively way to the consumers or the buyers. The Electrochemical Society -
Parental Involvement in School Counseling Services: Challenges and Experience of Counselor
School counselors include parents as informants in school counseling for various reasons like for consultation and clients intervention which could lead to a different experience. The objective of the present study was to explore the challenges faced by school counselors when involving parents in the school counseling process and the ways they handle the challenges. The participants consisted of seven school counselors, with whom in-depth interviews were conducted. The subjective experiences of the school counselors were objectively interpreted by the researcher using interpretative phenomenological analysis. The research results were categorized into master and subordinate themes using double hermeneutics. The results revealed that although school counselors have their own individual perceptions of challenges and methods of handling parental inclusion in the school counseling context, there still exist similarities in their methods of handling. The study highlights school counselors beliefs about parents perceptions and expectations of counseling as well as parents reaction toward their childs problem as a challenge. The findings throw light on the need for stronger evidence-based practices and policies for school counseling programs. Training and competence-building programs for school counselors would improve the service delivery. 2018, National Academy of Psychology (NAOP) India. -
Normalized group activations based feature extraction technique using heterogeneous data for Alzheimers disease classification
Several deep learning networks are developed to identify the complex atrophic patterns of Alzheimers disease (AD). Among various activation functions used in deep neural networks, the rectifier linear unit is the most used one. Even though these functions are analyzed individually, group activations and their interpretations are still not explored for neuroimaging analysis. In this study, a unique feature extraction technique based on normalized group activations that can be applied to both structural MRI and resting-state-fMRI (rs-fMRI) is proposed. This method is split into two phases: multi-trait condensed feature extraction networks and regional association networks. The initial phase involves extracting features from various brain regions using different multi-layered convolutional networks. Then, multiple regional association networks with normalized group activations for all the regional pairs are trained and the output of these networks is given as input to a classifier. To provide an unbiased estimate, an automated diagnosis system equipped with the proposed feature extraction is designed and analyzed on multi-cohort Alzheimers Disease Neuroimaging Initiative (ADNI) data to predict multi-stages of AD. This system is also trained/tested on heterogeneous features such as non-transformed features, curvelets, wavelets, shearlets, textures, and scattering operators. Baseline scans of 185 rs-fMRIs and 1442 MRIs from ADNI-1, ADNI-2, and ADNI-GO datasets are used for validation. For MCI (mild cognitive impairment) classifications, there is an increase of 14% in performance. The outcome demonstrates the good discriminatory behaviour of the proposed features and its efficiency on rs-fMRI time-series and MRI data to classify multiple stages of AD. 2024 Vaithianathan et al. -
Pearson correlation-based clustering with collaborative task allocation in 5G Industrial Internet of Things divergent health networks
Simultaneous task allocation is crucial for enhancing service quality in Industrial Internet of Things (IIoT) environments. The distribution and management of tasks remain among the biggest challenges in the IIoT era. Efficient allocation strategies are needed to enable transparent network configurations and maximize task throughput. Although recent methods address the dynamic management of objects, they often overlook the correlations between tasks and their associated functionalities. This paper introduces a novel Connected Harmonical Adaptive Task Allocation (CHATA) model for IIoT health networks to ensure fair task distribution. CHATA leverages similarity measures of object functionalities to identify the most suitable object to perform each task. Simulations conducted in NS-3 demonstrate that CHATA achieves up to 90% allocation efficiency in 5G Radio Access Technologies IIoT health environments and significantly outperforms recent approaches in task assignment performance. The Author(s) 2025. -
An improved web caching system with locally normalized user intervals
Caching is one of the most promising areas in the field of future internet architecture like Information-centric Networking, Software Defined Networking, and IoT. In Web caching, most of the web content is readily available across the network, even if the webserver is not reachable. Several existing traditional caching methods and cache replacement strategies are evaluated based on the metrics like hit ratio and byte hit Ratio. However, these metrics have not been improved over the period because of the traditional caching policies. So, in this paper, we have used an intelligent function like locally normalized intervals of page visit, website duration, users' interest between user groups is proposed. These intervals are combined with multiple distance metrics like Manhattan, squared Euclidean, and 3-,4-,5-norm Minkowski. In order to obtain significant common user navigation patterns, the clustering relation between the users using different intervals and distances is thoroughly analyzed. These patterns are successfully coupled with greedy web cache replacement strategies to improve the efficiency of the proposed web cache system. Particularly for improving the caching metrics more, we used an AI-based intelligent approach like Random Forest classifier to boost the prefetch buffer performance and achieves the maximum hit rate of 0.89, 0.90, and byte hit rate of 0.87, 0.89 for Greedy Dual Size Frequency and Weighted Greedy Dual Size Frequency algorithms, respectively. Our experiments show good hit/byte hit rates than the frequently used algorithms like least recently used and least frequently used. 2013 IEEE. -
Revolutionizing legal services with blockchain and artificial intelligence
[No abstract available] -
Organization is an Incubator to Develop Intrapreneurship
Global Journal of Arts and Management, Vol. 2, No.3, pp 216-218, ISSN No. 2249-2658 -
An Understanding of Knowledge Management Perception and Implementation in Higher Education
Global Journal of Arts and Management, Vol. 2, No.3, pp 204-206, ISSN No. 2249-2658