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Factors Influencing Consumer Purchasing Behaviour Towards Purchase of Palm Leaf Craft
The handicraft sector plays a significant role in providing employment opportunities in rural and semi-urban areas of the country. It helps in generating substantial income for the artisans. The uses for palm leaves are abundant. Attractive items made of Palm Leaves are very popular in South India, mostly in Southern Andhra Pradesh and Tamil Nadu. However, to generate sustainable revenue through their products, the artisans need to understand the buyers requirements. This study aims to understand the consumers purchasing behavior when buying palm leaf products. The sample consists of 233 consumers with diverse backgrounds from the state of Tamil Nadu, India. A questionnaire was developed to measure awareness and attitude influence while purchasing palm leaf products. The results can help the artisans to get an insight into end-user expectations and requirements toward palm leaf products and suggest strategies to increase the income of artisans. 2022 Taylor & Francis. -
An Enhanced Data-Driven Weather Forecasting using Deep Learning Model
Predicting present climate and the evolution of the ecosystem is more crucial than ever because of the huge climatic shift that has occurred in nature. Weather forecasts normally are made through compiling numerical data on from the atmospheric state at the moment and also applying scientific knowledge in the atmospheric processes to forecast on how the weather atmosphere would evolve. The most popular study subject nowadays is rainfall forecasting because of complexity in handling the data processing in addition to applications in weather monitoring. Four different state temperature data were collected and applied deep learning methods to predict the temperature level in the forthcoming months. The results brought out with the accuracy from 92.5% to 97.2% for different state temperature data. 2023 IEEE. -
Predicting the Thyroid Disease Using Machine Learning Techniques
An endocrine gland that is allocated in the front of the neck is called the thyroid, which produces thyroid hormones as its main job. Thyroid hormone may be produced insufficiently or excessively as a result of its potential malfunction. There are various thyroid types including Hyperthyroidism, Hypothyroidism, Thyroid Cancer Thyroiditis, swelling of the thyroid. A goiter is an enlarged thyroid gland. When your thyroid gland produces more thyroid hormones than your body requires, you have hyperthyroidism. When the thyroid gland in our body doesnt provide enough thyroid hormones, then our body has hypothyroidism; when you have euthyroid sick, your thyroid function tests during critical illness taken in an inpatient or intensive care setting show alterations. Hypothyroid, hyperthyroid, and euthyroid conditions are expected from these thyroid conditions. The Three similarly used machine learning algorithms are: Support Vector Machine (SVM), Logistic Regression, and Random Forest methods, were evaluated from among the various machine learning techniques to forecast and evaluate their performance in terms of accuracy. Random forest can perform both regression and classification tasks. Logistic Regression is used to calculate or predict the probability of a binary (yes/no) event occurring. SVM classifiers offers great accuracy and work well with high dimensional space. A thyroid data set from Kaggle is used for this. This study has demonstrated the use of SVM, logistic regression, and random forest as classification tools, as well as the understanding of how to forecast thyroid disease. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Identification of Cyberbullying and Finding Target User's Intention on Public Forums
Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. 2023 IEEE. -
Epilepsy Detection Using Supervised Learning Algorithms
In the current scenario, people are suffering and isolated by themselves by seizure detection and prediction in epilepsy. Also, it is highly essential that it needs to be identified through wearable devices. Researchers discussed this issue and outlined future developments in this field, suggesting that Machine Learning (ML) techniques could radically change how we diagnose and manage patients with epilepsy. However, as data availability has increased, Deep Learning (DL) techniques have become the most cutting-edge approach to adopt and use with wearable devices. On the other hand, large amounts of data are needed to train DL models, making overfitting problematic. DL models are created with open-source toolboxes and Python, allowing researchers to create automated systems and broaden computational accessibility. This work thoroughly overviews deep learning (DL) methods and neuroimaging modalities for automated epileptic seizure identification. It covers several MRI and EEG techniques for epileptic seizure diagnosis and treatment programmes designed to treat these seizures. The study also covers the difficulties in precise detection, the benefits and drawbacks of DL-based strategies, potential DL models and upcoming research in this area. 2024 IEEE. -
Efficient Load Balancing and Resource Allocation in Networked Sensing SystemsAn Algorithmic Study
In the current environment, data generation and data transmission are increasing exponentially in day-to-day life. These exponentially growing data might create heavy traffic when transmitted between systems. Also, this affects many functionalities like configuration of networked systems, system and routing configuration parameters, load managing factors of network devices, etc. A dynamic traffic control mechanism needs to be adopted with the help of load-balancing algorithms and efficient resource allocation mechanisms to deal with heavy data traffic. Load balancing algorithms in networked sensing systems aim to distribute the workload evenly among sensor nodes to optimize network performance and energy efficiency and prolong the network lifetime. Resource allocation mechanisms in a networked sensing system involve allocating and distributing network resources efficiently, such as energy, bandwidth, processing power, etc., to optimize performance and increase the networks lifetime. To achieve efficient resource allocation with a balanced load, notable works have been done in optimization and machine learning. The work gives a scientific analysis of traditional and Artificial Intelligence algorithms from a centralized and distributed perspective. Researchers can take this analysis forward when deciding on algorithms based on their application and infrastructural needs. 2025 Scrivener Publishing LLC. -
Countering educational disruptions through an inclusive approach: Bridging the digital divide in distance education
The COVID-19 pandemic has created havoc across the globe, irrespective of governments, industries, and societies. The education sector is one of the most extensively affected by the global health crisis, manifesting expansive negative consequences to learners from various age groups and socioeconomic statuses. Despite the predicaments posed by the pandemic, academic institutions continue to provide education through a distance learning approach. However, the educational disruptions have underscored the lack of digital resources and competencies, excluding poor and unconnected students. Likewise, transitioning to remote education exposed the digital divide and inequalities that have been neglected for a long time. If the ultimate objective is to provide distance education, it is vital to devise solutions to problems faced by underprivileged students. This chapter investigates these challenges that impede the successful adoption of distance education and offers strategies to counter the disruptions as it seems apparent that online education is here to stay. 2022, IGI Global. All rights reserved. -
Crisis and Man: Literary Responses Across Cultures
Journal of Business Management & Social Sciences Research, Vol-1 (3), pp. 29-31. ISSN-2319-5614 -
Unopened windows: European existentialism and Indian classrooms /
International Journal Of English Language Literature and humanities, Vol.3, Issue 9, pp.434-440, ISSN No: 2321-7065. -
U.R Ananthamurthy - A man more sinned against than sinning? /
Indian Literature, Vol.59, Issue 6, pp.138-147, ISSN No: 0019-5804. -
From knowledge tradition to knowledge economy : Positive interludes in India higher education /
International Journal Of Educational Planning & Administration, Vol.5, Issue 1, pp.19-23, ISSN No: 2249-3093. -
Representation of moral crisis and social order across cultures An analysis of three texts goldings lord of the flies camus s the plague and U R Ananthamurthy s samskara
The growth of human civilization has always been accompanied by unexpected turns and twists caused by individuals and communities exhibiting inexplicable behavior. For every effort of greatness, there has been an equal amount of meanness and debauchery making the humankind more and more inscrutable. Human beings have defied all definitions about themselves and still go about with a perplexing image of both noble and brutal. They create a social order to ensure a newlinecohesive existence but end up breaking it, unable to face the moral crisis caused by the extreme turn of events. This study attempts to see the staging of human recovery from such situations through the fictional narratives of three writers belonging to three cultures and the crises faced by those societies, through a study of The Plague (1947 ) by the French writer, Albert Camus (1913 -1960 ), Lord of the Flies (1954) by the English writer, William Golding (1911 1993) newlineand Samskara (1965) by the Indian writer, U. R. Ananthamurthy (1932 - newline2014). newlineNo society or culture has escaped the throes of crisis be it moral, social, political or otherwise. The crisis that may be surmountable in one culture may shake the foundation of another. Of all the crises prevalent in society, one of the major causes for concern in the eyes of the participants newlineof the New Dialogue is moral crisis . Studies undertaken across the globe have thrown up the alarming fact that this is a crisis which could jolt the social order with its amoral way of thinking. America s moral integrity has been eroded by an anything goes culture abetted by the moral permissiveness of contemporary liberalism. The concern that the waning of tradition is giving way to moral confusion and anarchy is shared the world over including China and India despite their strong traditions. The reasons could be aplenty-ranging from the outbreak of wars, outbreak of epidemics and even crumbling of societies under the burden of orthodoxy of religion and caste. -
Multilingual Voice-Assisted for Traffic Sign Detection and Classification in Adverse Weather Conditions
In a world where millions of people are wounded in auto accidents each year due to negligence, a lack of understanding of traffic laws, and bad weather, there is an urgent need for greater road safety. This is particularly the case in India, where a disproportionately high number of traffic accidents lead to numerous fatalities. Ignoring traffic signs raises these risks and endangers not only vehicles but also passengers and pedestrians. This project addresses the significant issue of traffic sign recognition in bad weather and offers voice-based instruction in many languages to increase road safety. Using a mix of state-of-the-art technologies, including YOLOv8 for real-time sign detection and the Google Translate API, which supports NLP tasks, this research offers a full solution. The model's remarkable precision and efficacy underscore its capacity to revolutionize traffic safety and furnish a more secure and expedient driving encounter. With the world moving towards more autonomous mobility, this study is laying the groundwork for safer and more effective driving in the future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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Analysis of Routing Protocols in MANET Networks
The scientific article is a review and comparative analysis of routing protocols for MANETs. The study examines the main protocols connected to mobile ad hoc networks such as B.A.T.M.A.N, BMX7, OLSRv1, Babel and provides a detailed analysis of their characteristics, advantages and disadvantages. To empirically evaluate performance, tests were carried out in a network simulator. The results of the study allow us to draw conclusions about the effectiveness and reliability of each of the monitoring protocols under various operating conditions of MANET. This article is a valuable contribution to the field of MANET research and can be used in the development of new technologies and solutions for mobile wireless networks. The work is relevant and practically significant because it helps researchers and engineers make informed decisions when choosing the optimal routing protocol in MANET networks. The results obtained can be useful in the design of mobile applications, emergency communication systems, transport management and other areas where the efficient operation of wireless networks is important. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Cognitive marketing and purchase decision with reference to pop up and banner advertisements
The aim of this research paper is to employ a mixed research approach and to check how the past data differs from the present and hence it uses an argument mapping to find the reality using focus group. Since genders have different opinion on pop-up and banner advertisements, two focus groups, one group consisting the female gender and the other focus group consisting the male respondents have been taken for the data collection. Small sample has been used for the argument mapping (N=45/Male) and (N=47/Female). A series of steps has been conducted in the argument mapping and relevant maps have been developed for drawing inference. It is found that, male have no patience to deal with the pop-up and banner advertisements but women are keener and patient enough to make the best use of these advertisements. On the other hand a questionnaire was framed from the variables found from the literature review and the same was distributed to both the genders and it was found collectively that though pop-up advertisements and banner advertisements are useful in some way, it is always considered to be a negative aspect. Misleading advertisements, data security scam are a few negative aspects of such advertisements and hence, there are a lot of ugly truth behind pop up and banner advertisements. The mixed research approach (triangulation) between the quantitative and qualitative is a new initiative taken by the researchers in this research and holds originality of the study. 2018 Academic Research Publishing Group. -
Study of internal heat source generated natural convection with sinusoidal and non-sinusoidal time-periodic vertical oscillations
This study explores the effect of gravity modulation on natural convection induced by a uniform internal heat source within a fluid-saturated porous medium, a topic of growing relevance in advanced thermal management applications. Four distinct gravity waveforms, square, sinusoidal, triangular, and sawtooth, are examined under three boundary condition combinations: Rigid-Adiabatic-Rigid-Isothermal (RARI), Rigid-Adiabatic-Free-Isothermal (RAFI), and Free-Adiabatic-Free-Isothermal (FAFI). A novel analytical framework is developed by integrating a Maclaurin series expansion with a minimal FourierGalerkin approach to derive a generalized Lorenz model. Linear stability analysis, via a modified Venezian method, to determine the critical internal Rayleigh number and its correction due to modulation. A weakly nonlinear analysis based on the GinzburgLandau equation also provides closed-form expressions for the mean Nusselt number, capturing heat transfer characteristics. The findings demonstrate that square wave modulation most effectively enhances heat transport, followed by sinusoidal, triangular, and sawtooth forms. The influence of key physical parameters reveals that increasing porous parameter (?2) and Brinkman number (?) suppress heat transfer, as do higher Prandtl numbers (Pr) and modulation frequencies (?). FAFI yields the highest heat transfer among the boundary types, while RARI performs the least. The novelty of this work lies in the combined analytical treatment of diverse waveform modulations while considering a uniform internal heat source and boundary condition for natural convection. 2025 The Author(s) -
STUDY OF THE LINEAR AND NONLINEAR REGIMES OF NATURAL CONVECTION WITH WEAK OR DOMINATING INTERNAL HEAT GENERATION FOR RIGID-FREE BOUNDARIES
The paper presents the linear and non-linear regimes of natural convection in the presence of uniform internal heat generation for rigid-free boundaries. A linear stability analysis followed by nonlinear stability analysis is carried out for using a novel procedure. The eigenvalue of the two problems are different. The first one has Rayleigh number based on internal heat generation as the eigenvalue while the second, which is of the classical Bard type, has a buoyancyRayleigh number. The critical Rayleigh number in both problems is initially determined using the single-term Galerkin method, followed by a refinement of the value by the Maclaurin series method. The findings indicate that the system becomes stable with increasing values of the porous parameter and the Brinkman number. The percentage relative error in the eigenvalue obtained by the single-term Galerkin method relative to that obtained by the Maclaurin series method is presented. In the second natural convection problem, we have two Rayleigh numbers, viz., the weak internal Rayleigh number, RI, and the external Rayleigh number, Ra. The effect of RI on Rac is to reduce it in the case of a heat source and increase it in the case a of heat sink. Additionally, conditions facilitating the transition from Brinkman Bard convection to DarcyBard convection are presented. The GinzburgLandau equation is obtained for both the problems and the scaled Lorenz model is derived in the case of second problem. The solution from the GinzburgLandau equation is used to plot for the amplitude and results are illustrated. 2025 by Begell House,. -
Enhancements of women's entrepreneurship: A theme-based study
Woman entrepreneurs are defined as a group of women who initiate, organize, and run a business concern, from a situation where a woman was not even allowed to get out of their home, to today, running most of the successful brands of the world, contributing a major part to the economic growth, and breaking the stereotypes by providing a reality check to the male dominance. There has been a wide range of public policies enrolled out to facilitate and encourage the growth of women's entrepreneurship. A few such policies from India have proved to be successful, which will be outlined in this book chapter. From the past times of not gaining adequate recognition for their support, women have emerged successful in overcoming hardships such as lack of visibility, lack of training and educative support about public policies provided by governments to women entrepreneurs, fewer opportunities, and walking out of the social stigma. 2023, IGI Global. All rights reserved. -
Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting
In the field of meteorology, temperature forecasting is a significant task as it has been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy in temperature forecasting is needed for decision-making in advance. Since temperature varies over time and has been studied to have non-trivial long-range correlation, non-linear behavior, and seasonal variability, it is important to implement an appropriate methodology to forecast accurately. In this paper, we have reviewed the performance of statistical approaches such as AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally. 2024 Bentham Science Publishers.