Browse Items (11810 total)
Sort by:
-
Content Based Deep Factorization Framework for Scientific Article Recommender System
With the advancement in technology and the tremendous number of citations available in the digital libraries, it has become difficult for the research scholars to find a relevant set of reference papers. The accelerating rate of scientific publications results in the problem of information overload because of which the scholars spend their 70% of the time finding relevant papers. A citation recommendation system resolves the issue of spending a good amount of time and other resources for collecting a set of papers by providing the user with personalised recommendations of the articles. Existing state of art models do not take high-low order feature interactions into consideration, due to which the recommendations are not up to the desired level of performance. In this paper, we propose a content-based model which combines Deep Neural Network (DNN) and Factorization Machines (FM) where no pre-trainings are required for providing the citation recommendations. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Content Based Scientific Article Recommendation System Using Deep Learning Technique
The emergence of the era of big data has increased the ease with which scientific users can access academic articles with better efficiency and accuracy from a pool of papers available. With the exponential increase in the number of research papers that are getting published every year, it has made scholars face the problem of information overload where they find it difficult to conduct comprehensive literature surveys. An article recommendation system helps in overcoming this issue by providing users with personalized recommendations based on their interests and choices. The common approaches used for recommendation are Content-Based Filtering (CBF) and Collaborative Filtering (CF). Even though there is much advancement in the field of article recommendation systems, a content-based approach using a deep learning technology is still in its inception. In this work, a C-SAR model using Gated Recurrent Unit (GRU) and association rule mining Apriori algorithm to provide a recommendation of articles based on the similarity in the content were proposed. The combination of a deep learning technique along with a classical algorithm in data mining is expected to provide better results than the state-of-art model in suggesting similar papers. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Content suggestion in e-learning
Self ??directed e-learning focuses on the independent learner one who engages in education at his own space free from curricular obligation. In this research, user's access interests were introduced into the design of a recommendation framework by using web browsing behaviour of the user. According to the user's access interest the recommendation framework can provide users with personalised information. The research thesis is to determine if users access interest can be extracted and to associate users access interest with other attributes to design a recommendation framework that attempts to recommend the information items, here the research papers to a researcher searching for interesting topics of research in her field. The proposed system is very useful to the new researchers as initially they are not aware of the research areas where they can work on. This system assists the researcher in searching the papers they are interested to search. The documents of interest to the user are used to build the user profile and this profile is used to re-rank the web search results. The paper presents the overall architecture of the proposed system and its implementation via a prototype design. In this dissertation an attempt is made to design a system prototype which will recommend the information items to the user according to the user access interest, which is captured from the web browsing behaviour of the user. The content similarity of the information items is also taken into consideration. The information items are suggested to the user based on the relevance. The organization of the thesis is done into various chapters. Introduction contains the brief introduction about personalisation services in e-learning scenario, recommender systems in e-learning. Literature review discusses about the web-based self-directed e-learning and the works done in the field of recommender systems in e-learning. Chapter 3 discusses the methodology adopted to design the system. In chapter 4 results are being analysed and discussed. Chapter 5 gives the future enhancement that can be done to the system design. -
Content-Based Music Recommendation Using Non-Stationary Bayesian Reinforcement Learning
This paper presents a music recommendation system for the offline libraries of songs that employs the concepts of reinforcement learning to obtain satisfactory recommendations based on the various content-based parameters. In order to obtain insights about the effectiveness of the generated recommendations, parallel instances of single-play multi-arm bandit algorithms are maintained. In conjunction to this, the concepts of Bayesian learning are considered to model the user preferences by assuming the environment's reward generating process to be non-stationary and stochastic. The system is designed to be simple, easy to implement, and on-par with user satisfaction within the bounds of the input data capabilities. Copyright 2021, IGI Global. -
Content-Restricted Boltzmann Machines for Diet Recommendation
Nowadays, society is leading towards an unhealthy and inactive and lifestyle. Recent studies show the rapid growth of people suffering from diseases caused due to unhealthy lifestyles and diet. Considering this, recognizing the right type and amount of food to eat with a suitable exercise set is essential to obtain good health. The proposed work develops a framework to recommend the proper diet plans for thyroid patients, and medical experts validate results. The experiments results illustrate that the proposed Content-Restricted Boltzmann Machines (Content-RBM) produces more relevant recommendations with content-based information. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Context Driven Software Development
The Context-Driven Software Development (CDSD) is a novel software development approach with an ability to thrive upon challenges of 21st century digital and disruptive technologies by using its innovative practices and implementation prowess. CDSD is a coherent set of multidisciplinary innovative and best practices like context-aware and self-adaptive system modelling, human-computer interaction, quality engineering, software development-testing-and continuous deployment frameworks, open-source tools-technology-and end-to-end automation, software governance, engaging stakeholders, adaptive solutioning, design thinking, and group creativity. Implementation prowess of CDSD approach stems from its three unique characteristics, namely, its principles, Contextualize-Build-Validate-Evolve (CBVE) product development element, and iterative and lean CDSD life cycle with Profiling, Contextualizing, Modelling, Transforming, and Deploying phases with in-process and phase-end Governance and Compliances. CDSD approach helps to address issues like complexity, software ageing, risks related to internal and external ecosystem, user diversity, and process-related issues including cost, documentation, and delay. 2021, Springer Nature Switzerland AG. -
Context matters: A case study of community development approaches in tribal areas
Tribal development initiatives aim to address aspects of marginality covering educational, occupational, social and political dimensions. A review of literature on the challenges in effective tribal development discusses narrow vision, implementation strategy, the attitude of people involved in the implementation and lack of community participation as hindrances in realising the desired goals. This chapter explores the need to have a context-specific approach to realise the community development goals in tribal areas considering the cultural and economic diversity of Scheduled Tribes in India. A single embedded case study has been used to understand tribal development initiatives in the three project sites, namely Manikgarh, Maharashtra; Bastar, Chhatisgarh and Niravilpuzha, Kerala. All these projects aim at the overall development of the community either through a child-centred approach or through a self-help approach. A constructive outcome-based evaluation model has been used in analysing the developmental approach, and in-depth interviews and FGDs were conducted with the project staff. An attempt has been made to study the impact on direct beneficiaries at micro, meso and macro levels. It explores the ways in which universities could become change agents through socially responsible engagements in tribal areas. This chapter would contribute towards developing a social responsibility model for other universities to emulate. 2024 Nova Science Publishers, Inc. -
Contextual Recommendation System: A Revolutionary Approach Using Hadoop, Spark, NLP and LLMs
This study presents a novel framework for contextual recommendations on platforms like Wikipedia, integrating Hadoop, Spark, NLP, and LLMs. Leveraging these technologies, the framework aims to enhance user experiences by delivering personalized article suggestions aligned with their current interests. Through scalable data processing, advanced NLP techniques, and LLM-powered semantic understanding, the framework offers a transformative approach to recommendation systems, promising to revolutionize knowledge exploration on digital platforms. 2024 IEEE. -
Continuance Intention of ChatGPT Use by Students
ChatGPT, an AI language model, has gained significant attention for its potential to enhance educational experiences and foster interactive learning environments. The potential of student interaction via ChatGPT has engendered significant debate around educational technology. It is apparent that the current literature has yet to fully explore the role of ChatGPT in management education. Amidst the increasing integration of ChatGPT into educational contexts, the concept of continuance intention takes center stage. This research paper delves into the nuanced landscape of students continuance intention regarding the use of ChatGPT in educational settings. We ground our study in Technology Continuance Theory and Theory of Planned Behavior to examine students continuance intention to use ChatGPT. By investigating the determinants that shape this intention, we aim to provide insights that inform educators and educational technology designers in optimizing the integration of AI-driven tools like ChatGPT. This study contributes to the growing body of research at the intersection of AI and education, offering valuable implications for both theory and practice. 2024, IFIP International Federation for Information Processing. -
Continuity and changes in food consumption pattern among Tibetan refugee community in India
The Food consumption pattern of refugee communities is being carried out by many scholars and few acknowledged the food continuity, its implications on the health of refugees in the host country. The present study highlights food continuity among Tibetan refugees in the Bylakuppe settlement, India. 200 household data were administered to understand food consumption patterns by employing a structured household questionnaire. Simultaneously, 23 individual data were collected consisting of first migrants (15) and second-generation (8) for the qualitative study. Households derive energy mainly from carbohydrates and animal fats, and there is a prevalence of food insecurity among the Tibetan community. It is a proven fact that food insecurity will have serious health consequences in terms of emotional and mental well-being and suggest the need for further study of food insecurity among Tibetan refugees across the world. 2021 -
Continuous emotion estimation for human machine interaction
Humans are able to interact and bond very efficiently with other species because every living organism has some form of emotion in them. Due to the advances in science and technology human life has become more dependent on machines for better living. The recent advances in technology enabled machines to become smarter but not efficient in terms of interaction with humans. Hence to address this issue and to bridge the gap between human machine interactions we propose a system to estimate human emotions from facial expressions. We believe that facial expressions are a form of nonverbal communication and primary means of conveying information. The system uses linear regression model to calculate emotional state of a facial expression which is mapped onto continuous 2-D coordinates with arousal and valence as axis from a captured digital image. Thus the proposed method estimates emotion continuously and predictively like humans rather than classifying the emotions because emotions are continuous and they have many dimensions. By estimating emotions continuously machines can better interact with humans. Experimental results showed that our system provides superior predictive performance. 2015 American Scientific Publishers. All rights reserved. -
Contradictions in conservation: Indias forest (Conservation) Amendment Bill, 2023
[No abstract available] -
CONTRARIAN AND MOMENTUM STRATEGIES IN THE INDIAN STOCK FUTURES MARKET: A STUDY ON BANKING SECTOR
This thesis tries to investigate the contrarian and momentum strategy can help the investors to lay down the major guidelines for undertaking any derivative transaction. Contrarian strategies are based on the reversal pattern in stock returns and imply buying past losers and selling past winners. On the contrary, Momentum strategies are based on the continuation pattern in stock returns and imply buying past winners and selling past losers. For the purpose of analysis, the stock returns for the Indian stock futures market segment for Indian banking sector for the period from July 1, 2005 to June 30 2011 by using the Fama and French multifactor model. The Fama-French model involves the use of three factors for explaining common stock returns: the market factor proposed by the CAPM, and factors relating to size and value. The company used in this research consists of 16 Banks which were ranked in an ascending order based on their average returns. The ranked securities are then used to form five equal portfolios. While portfolio P1 contains the bottom 20 per cent securities and is called "losers' portfolio," portfolio P5 contains the top 20 per cent securities and is termed as "winners' portfolio." The findings suggests that the stock-return behavior in banking sector for short-term momentum profits and long term contrarian profits exist in this case. Further, the contrarian trading strategy based on long term returns provides moderately positive payoffs and short-term returns show a continuation pattern and the investment strategy based on momentum effect provides significantly high returns. Finally, the study generally supportive of the Fama-French model applied to Indian futures stock market related to banking sector. Keywords: Contrarian, Momentum, Stock Returns, CAPM, Fama-French Model JEL Classification: C12, C22, E43, G11 -
Contribution of media towards body image dissatisfaction in men /
The thesis tries to explore and find out how media has been a contributing factor in the phenomenon of body image dissatisfaction prevalent amongst the male population. With the advent of technology, advertising and promotion of male grooming products has become aggressive and relentless. The phenomenon of body image dissatisfaction has existed for a long time amongst the female population but with passing time it has caught onto the men also. -
Control of chaos and intermittent periodic motions in Rayleigh-Bard convection using a feedback controller
Control of regular convective motion, chaos and periodic motion in the Rayleigh-Bard system is studied by considering a feedback control mechanism that considers the dependence of the heating (cooling) of the two boundary plates on one another. This set up ensures that the different flow regimes (convective, chaotic and periodic) in the system have no mechanical interference and the control remains an external mechanism. The rheostatic influence of feedback control on these flows is demonstrated by investigating in detail the critical Rayleigh number in the case of regular convective motion and the Hopf-Rayleigh number in the case of chaotic motion. For mild coupling between lower and upper boundary temperatures, periodic motions are intermittently observed in an otherwise chaotic regime at times when the system arrives at a situation (fuelling zone) wherein it needs to conserve energy in order to sustain chaos at subsequent times. For strong coupling between the boundary temperatures, an interesting situation arises wherein chaos makes a delayed and brief appearance and gives way to a prolonged spell of periodic motion. Features of the classical Rayleigh-Bard system are retained but each regime makes a delayed appearance. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Control of chaos in Darcy-Bard axisymmetric convection in a cylindrical enclosure using a uniform vertical cross-flow
The linear and weakly nonlinear stability analyses of Darcy-Bard convection of a Newtonian fluid experiencing a uniform vertical cross-flow is investigated in the paper for various aspect ratios. Making use of the Maclaurin series representation, an expression for axial eigenfunctions is obtained with the radial eigenfunction being a Bessel function of first kind. These eigenfunctions are influenced by the Peclet number, Pe, the non-dimensional number that signifies the rate of vertical cross-flow. The modified-Vadasz-Lorenz model obtained in this paper has newly defined non-dimensional parameters that capture the influence of vertical cross-flow. From the linear stability analysis, it is found that the effect of introducing vertical cross-flow is to stabilize the system. Using a weakly nonlinear stability analysis, the closed-form expression of the Hopf-Rayleigh number as a function of Pe is obtained. Furthermore, the behavior of the modified-Vadasz-Lorenz model is analyzed using the largest Lyapunov exponent and the bifurcation diagram. This gives information about the intensity of chaos and occurrence of the periodic motion. We observe that the influence of vertical cross-flow is to increase the value of the Hopf-Rayleigh number and thereby to delay the onset of chaos. Furthermore, the appearance of the first periodic point is preponed due to the vertical cross-flow. As the rate of vertical cross-flow increases, the intensity of chaos decreases, thereby indicating that the effect of introducing vertical cross-flow is to suppress chaos. 2024 Author(s). -
Control of NOx from a DI diesel engine with hot EGR and ethanol fumigation: An experimental investigation
Oxides of nitrogen (NOx) are one of the major hazardous pollutants from diesel engine emission. Various control technologies exist for its control but each technique has advantages and disadvantages. At present, there is no single optimal technique that can control NOx without other side effects. Technologies available for NOx reductions either cause fuel penalty or increase other polluting emissions. Exhaust Gas Recirculation is an effective technique in controlling oxides of nitrogen in diesel engines but do not become attractive at higher loads and higher percentage of recirculated gas as combustion tends to deteriorate at higher loads leading to reduced engine thermal efficiency and increased hydrocarbon and smoke emission. Ethanol is an established alternate fuel used in diesel engine either as a blend or fumigated using a separate injector. Experiments were conducted on a single cylinder diesel engine to examine the effect of EGR temperature on NOx and other emission constituents. Hot EGR gave better results up to 30% EGR rate but EGR cooling was found better in terms of NOx reductions and efficiency. It was found that NOx reductions up to 88% was possible but at the cost of about 18% loss in thermal efficiency. This inconvenience of fuel penalty caused by Exhaust Gas Recirculation can be overcome by applying ethanol fumigation. The findings of experimental results for this combined technique are presented in this paper. With this combined technique, apart from reducing the oxides of nitrogen, engine power and efficiency approaches to that of only diesel combustion condition with improvements in smoke, hydrocarbon and carbon monoxide emissions. 2013 The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg. -
Controlled reaction time of TiO2 and cocktail co-sensitization for improved DSSC performance
Solar energy stands out as a promising alternative to traditional energy sources, with dye-sensitized solar cells (DSSC) proving to be a highly effective means of harnessing this renewable energy. However, recent studies highlight the efficacy of employing a photoanode with mixed phases of Titanium Dioxide (TiO2) nanoparticles in DSSCs. The conventional approach to preparing mixed-phase TiO2 involves a time-consuming process with high-temperature annealing. In the present work, a novel microwave-assisted solvothermal synthesis of mixed-phase TiO2, which significantly reduces the preparation time has been reported. Moreover, we have enhanced device performance by co-sensitizing carbon dots (CDs) with various natural dyes and synthetic dye. The device prepared using CDs co-sensitized with Brassica oleraceavar.capitataf.rubra exhibited comparable efficiency (3.66%) with that obtained for N719-sensitized DSSC (3.85%). Further improvement in efficiency (4.81%) was obtained on sensitizing CDs with N719 dye. The comprehensive analysis of device performance using these innovative methods represents a noteworthy advancement in the realm of solar energy harvesting, with unexplored possibilities that could shape the future of sustainable energy solutions. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Controlling Node Failure Localization in Data Networks Using Probing Mechanisms
In this paper, we prospect the potency of node failure localization in network communication from dual states (normal/fizzle) of the source to destination paths. To localize the failure nodes individually in the scheduled nodes, dissimilar path states must connect with various events of failure nodes. But, this situation is inapplicable or not easier to investigate or apply on enormous networks due to the obligation of any viable failure nodes. This objective is to deploy the set of adequate conditions for recognizing a set of failures in a set of arbitrary nodes which can be verified in a stipulated time. To avoid the above situation, probing mechanisms are assimilated additionally as a combination for network topology and locations of scrutinizes. Three probing mechanisms are considering which vary depending on measurement paths. Both the procedures can be transformed into single-node possessions by which they can be calculated effectively based on the given conditions. The exceeding measures are proposed for measuring the potency of failure localization which can be utilized for assessing the effect of different factors, which comprises topology, total monitors, and probing mechanisms. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Controlling RayleighBard Magnetoconvection in Newtonian Nanoliquids by Rotational, Gravitational and Temperature Modulations: A Comparative Study
The effect of three different types of time periodic modulations on the RayleighBard magnetic system involving Newtonian nanoliquids is studied. Multiple-scale analysis (homogenization method) is used to arrive at the GinzburgLandau equation. The curiosity in the work is to know the individual effects of (1) rotation, (2) gravity and (3) temperature modulations on RayleighBard magnetoconvection in weakly electrically conducting Newtonian nanoliquids. A significant effort in this research is devoted toward linear and nonlinear stability analyses as well as the homogenization method which leads to the GinzburgLandau evolution equation. Although several studies have concluded similar results for nanoliquids compared with those of pure base fluids, many fundamental issues like the choice of phenomenological models for the thermo-physical properties and the best type of nanoparticles are not well understood. This research focuses on several important issues involving mathematical and computational problems arising in heat transfer analysis in the presence of nanoliquids. Effects of various nanoliquid parameters, frequency and amplitude of modulation on heat transport are analyzed. This investigation focuses on five nanoliquids, with water as a carrier liquid and five nanoparticles, viz. copper, copper oxide, silver, alumina and titania. Enhanced heat transport was observed for rotation, gravity and temperature modulations. In the case of rotation modulation, it is found that increase in the amplitude of modulation results in a decrease in the critical Rayleigh number and thereby to an increase in the mean Nusselt number. The increase in the amplitude of the gravity modulation is shown to enhance the heat transport, whereas increase in frequency is to inhibit the heat transport. Two types of temperature modulations are considered, viz. in-phase (synchronous) and out-of-phase (asynchronous) temperature modulations with the assumption that the boundary temperatures vary sinusoidally with time. The amplitudes of modulation are considered to be very small. In the case of in-phase modulation, there is no significant difference between the heat transports in the presence and in the absence of temperature modulation. On this reason, out-of-phase temperature modulation is used to either enhance or diminish heat transport by suitably adjusting the frequency and phase difference of the modulated temperature. The effect of magnetic field, in all three cases of modulations, is to inhibit the onset of convection and thereby diminish the heat transport. 2022, King Fahd University of Petroleum & Minerals.