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Comparison of Machine Learning Algorithms for Predicting Chronic Kidney Disease
Early detection and characterization of chronic renal disease are crucial to ensure that patients receive the best possible treatment. This study uses data mining techniques to uncover hidden information about patients. The outcomes of using the Random Forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree, XGBoost, LGBM Classifier, GaussianNB, KNeighbors Classifier, and XGBRF classifier have been compared. In our study, we demonstrate that Random Forest and XGBoost algorithms are more effective in classifying and predicting the severity level of chronic kidney disease 2022 IEEE. -
The Effect of Logotherapy on Meaning and Quality of Life of the Elderly in Old Age Homes.
Today the elderly in India is faced with a changing life situation where the traditional joint family system, according to which the elders were recognized and respected have given way to nuclear families where the elderly try to fit in. The number of old age homes in India is on increase especially in the state of Kerala. The existing literature shows that the elderly in old age homes in India undergo the problems like, relational issues, poverty, health issues, rejection from children, death of the partner, death anxiety etc, which lead to meaninglessness and poor quality of life. The current research is aimed at studying the effectiveness of a logotherapy intervention called Meaning in Life Programme on the meaning and quality of life of the elderly in old age homes. For the purpose of the study three old age homes with similar administration were selected from the districts of Kottayam, Kannur and Ernakulam district of Kerala where the highest number of old age homes of India are situated. 60 of them who met the inclusion criteria were randomely put into two groups, namely control group and experimental group. At par with the existing literature the study proves that the elderly in old age homes have a low meaning and quality of life. The average mean score of meaning in the pre-test was 14.23 in MiLS and the average mean score of quality of life in the pre-test is 51.83 in WHOQOL. The experimental group was given an intensive Logotherapy- meaning in life programme for five days and the control group had normal life. After a period of one month both groups were tested with MiLS and WHOQOL and there was significant difference between the control group and the experimental group. The Experimental group scored 22.06 in MiLS at the significant level of p < .001 and 59.73 in WHOQOL at the significant level of p < .001. The study revealed that logotherapy is effective in enhancing meaning and quality of life of the elderly in old age homes. Key words: meaning, quality, elderly, logotherapy -
Structural, luminescence and NMR studies on Nd3+-doped sodiumcalcium-borate glasses for lasing applications
In this work, Neodymium (Nd3+) -doped borate glasses were synthesised by melt-quenching method and their structural as well as optical properties were analysed through XRD, Raman, NMR, DSC, UVVisible, luminescence and decay studies for the possible application as laser gain medium. DSC and XRD results revealed that the glasses have high transition temperature and are in amorphous nature, respectively. The vibrational characteristics of the host matrices as well as the effect of Nd3+ incorporation were analysed by using Raman spectra, which exhibit majorly borate groups as supported by NMR results. The band gap energy of the glasses decreases with an increase in Nd3+ concentration. Using Judd-Oflet theory the characteristic intensity parameters (??, ? = 2, 4 and 6) were calculated and further used for calculating the various radiative parameters from the emission spectra. The emission cross-section (?em) was estimated as high as 1.15 10?20 cm2 from the FhtbauerLandenburg (FL) equation for the dominant 4F3/2?4I11/2 (1056 nm) transition. The effect of Nd3+ concentration on the lifetime of the 4F3/2 luminescent level was analysed from the decay curve analyses. From which, the corresponding quantum efficiency (?) was estimated and found as high as 54%. The investigated result suggests the prepared glasses can be utilized as gain medium to generate laser at around 1.05 ?m. 2020 Elsevier Ltd and Techna Group S.r.l. -
Drinking straw from coconut leaf: A study of its epicuticular wax content and phenol extrusion properties
Background and Objectives: Plastics are a ubiquitous part of our daily life but now posing a major threat to marine life, animal and human health. More than 50% of the manufactured plastic including straws are being disposed of after single-use. There is an increasing need to mitigate this trend so that the damage could be brought under control. The aim of this research was to develop a compostable, eco-friendly alternative to plastic straws using the leaves of Cocos nucifera L. Materials and Methods: The biochemical properties of 6 varieties of Cocos nucifera L. leaflets were studied in order to screen the most suitable material for making sustainable straws. Epicuticular wax content was analyzed to choose the best variety for preparation of hydrophobic straws. Total antioxidant activity, total tannin content, phenolic and flavonoid content were assayed to evaluate the potential functionality of the leaflets. The phenol extrusion properties of the material were also checked in acidic and normal beverages. Results: Estimation of epicuticular wax and phytochemical analysis in all 6 varieties revealed that all varieties of Cocos nucifera L. leaves provide a potent biomaterial for straw preparation. Silicon 732 was found to be a good adhesive agent for straw preparation. Phenol extrusion assays revealed that there is a negligible difference in the release of phytochemicals before and after dipping of straws in the beverages. Conclusion: The outcome of this research opens up vistas to carry out further research in a hitherto unexplored area of utilizing the leaf of Cocos nucifera in a novel way with far reaching economic and employment implications. 2019 Jyoti Jeena James et al. -
A Comprehensive Study on Electric Vehicle Charging Infrastructure
Issues of global warming and hike in the fuel price have taken electric vehicles (EVs) to be popular among the ordinary people. But the main drawbacks are related to the vehicle price and the scarcity of charging infrastructure. In this paper, a review of various charging infrastructures of electric vehicles that are existing and emerging are discussed. The paper also gives an overview of the charging standards for EVs. The Electrochemical Society -
Enhancing Stroke Prediction: Leveraging Ensemble Learning for Improved Healthcare
Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employed to enhance the accuracy of stroke prediction. The method combines four different machine learning algorithms, Adaboost, CatBoost, XGBoost, and LightGBM, to produce a strong predictive model. The data was composed of a rich set of demographic, medical, and lifestyle information. The data was preprocessed and features were engineered to maximize predictive performance. Results showed that the stacked ensemble model, which is composed of Adaboost, CatBoost, XGB, LightGBM, and Logistic Regression, meta-model, outperformed other models. The model has the potential to be used as a decision support tool in an early stroke risk assessment system, enhancing clinician decision-making and improving healthcare outcomes. 2024 IEEE. -
Presence of red giant population in the foreground stellar substructure of the Small Magellanic Cloud
The eastern region of the Small Magellanic Cloud (SMC) is found to have a foreground stellar substructure, which is identified as a distance bimodality (?12 kpc apart) in the previous studies using red clump (RC) stars. Interestingly, studies of red giant branch (RGB) stars in the eastern SMC indicate a bimodal radial velocity (RV) distribution. In this study, we investigate the connection between these two bimodal distributions to better understand the nature and origin of the foreground stellar substructure in the eastern SMC. We use the Gaia Early Data Release 3 astrometric data and archival RV data of RGB stars for this study. We find a bimodal RV distribution of RGB stars (separated by ?35-45 km s-1) in the eastern and south-western (SW) outer regions. The observed proper motion values of the lower and higher RV RGB components in the eastern regions are similar to those of the foreground and main-body RC stars, respectively. This suggests that the two RGB populations in the eastern region are separated by a similar distance to those of the RC stars, and the RGB stars in the lower RV component are part of the foreground substructure. Based on the differences in the distance and RV of the two components, we estimate an approximate time of formation of this substructure as 307 65 Myr ago. This is comparable with the values predicted by simulations for the recent epoch of tidal interaction between the Magellanic Clouds. Comparison of the observed properties of RGB stars, in the outer SW region, with N-body simulations shows that the higher RV component in the SW region is at a farther distance than the main body, indicating the presence of a stellar counter-bridge in the SW region of the SMC. 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Young adults experience of housing and real estate chatbots in India: effort expectancy moderated model
Purpose: This study aims to recognize the role of information system (IS) model on young adults experience of housing and real estate chatbots. This model of IS takes into account the quality of information, the quality of system and the quality of service. Design/methodology/approach: This study uses a sample frame for analysis which comprises young adult population in India, i.e. between the ages of 18 and 35. A questionnaire consisting of five components was used to collect information in a structured manner. The 386 responses thus collected were analysed using the structural equation model. Findings: It was found that there is a significant influence of the quality of information, quality of system and quality of service on young adults experience of housing and real estate chatbots. The findings also showed that there is moderation role of effort expectancy between the quality parameters and young adults user experience of housing and real estate chatbots. Research limitations/implications: This study focusses exclusively on the young adults from various parts of India. Future research can consider larger population categories across age groups and across sectors employing chatbots. Practical implications: This study will enable in-depth understanding of IS model quality dimensions relation with the user experience. In particular, housing and real estate organisations will profit from the expanded usage of artificial intelligence through chatbots for user correspondence and communication. Originality/value: To the best of the authors knowledge, this study is first of its kind, as it investigates how IS model quality dimensions affect the young adults experience of housing and real estate chatbots in India. This study also ventures into identifying the moderation role of effort expectancy between the quality dimensions as per IS model and young adults experience of housing and real estate chatbots. This study will be useful for the stakeholders of housing and real estate industry. 2023, Emerald Publishing Limited. -
Sociocultural aspects of the medicalisation of infertility: a comparative reading of two illness narratives
This paper is a comparative reading of variations in the medicalisation of infertility caused by sociocultural aspects, in two illness narratives by patients: Elizabeth Katkins Conceivability (2018), a story of navigating a fertility industry with polycystic ovarian syndrome and antiphospholipid syndrome in America and Rohini Rajagopals Whats a Lemon Squeezer Doing in My Vagina (2021), a discussion from India of a growing awareness of medicalisation in treatment of unexplained infertility. For this purpose, it first charts scholarship on illness narratives and medicalisation, noting a historical association. Following this, it shows how infertility, a physiological symptom of reproductive incapacity or failure to show clinical pregnancy, is generally medicalised. This paper reads the texts as showing hitherto unaddressed sociocultural aspects of infertilitys medicalisation. At the same time, drawing from existing sociological and anthropological scholarship, it shows how a reading of sociocultural aspects in medicalised infertility nuances understanding of its medicalisation. This comparative reading attends to sociocultural values and norms within the texts, including pronatalism, fetal personhood, kinship organisation, purity/pollution, individual reliance, sacred duty and so forth. It draws from scholarship on embodiment, rhetorical strategies and the language of medicine. It also shows how a patients non-medicalised, affective history ofdeep sickness caused by the biographical disruption of infertility is not that of apoor historian. In laying out the particularisation of such sociocultural values and norms across America and India, medicalisations migration from its origins to the margins reveals subjectivised, stratified reproduction in infertility illness narratives. This paper is part of a turn in scholarship away from understanding the medicalisation of infertility as naturalised and decontextualised. Author(s) (or their employer(s)) 2024. -
Unpacking the Psychology of Investment Intention: The Role of Emotional Intelligence, Personality Traits, and Risk Behaviour
In the dynamic realm of wealth accumulation, investments demand a meticulous evaluation of both financial and non-financial aspects inherent in securities. Prudent decision-making surpasses a fixation on anticipated returns, requiring a nuanced assessment of an investment's potential to actualize desired earnings. This study challenges the presumption of investor rationality in traditional financial theories, emphasizing the profound impact of non-financial determinants on decision-making, including personality traits, emotional intelligence, and risk behavior. With a robust sample size of 396 respondents, the research establishes a statistically significant correlation between emotional intelligence, personality traits, risk behavior, and the intricate domain of investment decisions. For middle-class investors, a pivotal recommendation emerges: fostering a discerning comprehension of one's psychological attributes. Active collaboration with seasoned financial advisers is imperative, serving as a compass through the complexities of the modern financial milieu. This holistic approach, harmonizing financial acumen with nuanced psychological insight, proves indispensable for navigating intricacies and facilitating judicious investment decisions aligned with individual aspirations and risk thresholds. The nuanced integration of financial prudence and psychological acuity fortifies investment portfolios and establishes a resilient foundation for adeptly navigating the dynamic terrain of wealth management. 2024, Iquz Galaxy Publisher. All rights reserved. -
Families Experience with Family Therapy: A Qualitative Inquiry
Various studies have found out that the experiences of the families with family therapy in several countries have generally been positive and the number of the people who benefit from family therapy is also high. Studies reveal that though there is a heightened need for family therapy in India, there is a kind of reluctance among the people towards it. When there is good number of literature in India that reveals the general attitude of Indian population towards family therapy, there is a lack of studies that explore the lived experience of the family members who have undergone family therapy. This study was designed to examine therapy from the point of view of the families. The participants of this study were eight families from Kerala and Karnataka who had completed entire family therapy under the trained professionals in the duration of last five months. The data was collected through in-depth interviews. The study reveals the common factors that led the families to family therapy, their experience with the whole therapeutic process, the barriers that prevented them and the perceived benefits of family therapy. Ten global themes and twenty seven organizing themes have been identified on the whole in relation with the research query. The findings are described along with the practical implication, suggestions and future research agendas. Keywords: Therapeutic process, Rapport building, Working phase, Termination, Interventions, Mental well-being -
User Perception of Mobile Banking: Application of Sentiment Analysis and Topic Modelling Approaches to Online Reviews
The digital revolution has led to significant changes in the global as well as Indian banking sector. The introduction of mobile banking apps has provided increased convenience to customers, who can now avail various banking services remotely. Thus, it is imperative to study the customers' sentiments regarding these applications and find scope for improvement, so that customers can seamlessly operate their bank accounts without having to visit bank branches. Thus, the primary purpose of this research is to study the perceptions of customers towards mobile applications of six major banks in India. A sample of 3000 reviews left by users of these apps was scraped from Google Play Store and sentiment analysis was conducted using RoBERTa-base model from the Transformers library. This was followed by topic modeling using Latent Dirichlet Allocation to find the aspects that are most important to the users. Results revealed that user experience is majorly driven by customer support service, features and functionality of apps, and app performance. Our findings shall help banks identify key areas of improvement so that they can work on enhancing overall customer experience. Despite the growing popularity of mobile banking, this study is the first of its kind in Indian context. 2024 IEEE. -
An Empirical and Statistical Analysis of Regression Algorithms Used for Mental Fitness Prediction
In today's focus on mental well-being, technology's capability to predict and comprehend mental fitness holds substantial significance. This study delves into the relationship between mental health indicators and mental fitness levels through diverse machine learning algorithms. Drawing from a vast dataset spanning countries and years, the research unveils concealed patterns shaping mental well-being. Precise analysis of key mental health conditions reveals their prevalence and interactions across demographics. Enriched by insights into Disability-Adjusted Life Years (DALYs), the dataset offers a comprehensive view of mental health's broader impact. Through rigorous comparative analysis, algorithms like Linear Regression, Random Forest, Support Vector Regression, Gradient Boosting, K-nearest neighbors and Theil Sen Regression are assessed for predictive accuracy. Mean squared error (MSE), root mean squared error (RMSE), and Rsquared (R2) scores are used to assess the predictive accuracy of each algorithm. Results show that Mean Squared Error (MSE) ranged from 0.030 to 1.277, Root Mean Squared Error (RMSE) from 0.236 to 1.130, and R-squared (R2) scores ranged between 0.734 and 0.993, with Random Forest Regressor achieving the highest accuracy. This study offers precise prognostications regarding mental fitness and establishes the underpinnings for the creation of effective tracking tools. Amidst society's endeavor to tackle intricate issues surrounding mental health, our research facilitates well-informed interventions and individualized strategies. This underscores the noteworthy contribution of technology in shaping a more Invigorating trajectory for the future. 2023 IEEE. -
Structural, Morphological and Optical Properties of MoS2-Based Materials for Photocatalytic Degradation of Organic Dye
Molybdenum disulfide (MoS2) is a transition metal dichalcogenide (TMDCs) having versatile properties and plays a great role in the photodegradation of organic dyes. MoS2 also finds applications in diverse fields such as catalysis, electronics, and nanomedicine transportation. MoS2 can be prepared by using chemical and physical methods such as hydrothermal, solvothermal, and chemical vapour deposition methods. The preparation method employed can produce subtle but significant changes in the morphology. To increase the efficiency of MoS2, it can be combined with different materials to produce composites that improve the photodegradation efficiency of MoS2. The various methods of preparation, the morphology of MoS2, and photodegradation activity of the MoS2-based nanocomposites are briefly discussed in this review. 2022 by the authors. -
Statistical and experimental studies of MoS2/g-C3N4/TiO2: a ternary Z-scheme hybrid composite
Abstract: A ternary photocatalyst, MoS2/g-C3N4/TiO2, was prepared using layered and exfoliated MoS2, g-C3N4, and TiO2 viahydrothermal and wet chemical method. It was characterized using various methods to evaluate the structural, morphological and optical properties. Successful incorporation of g-C3N4 and TiO2into MoS2 was confirmed by X-ray photoelectron spectroscopy, and the formation of heterojunctions among MoS2, g-C3N4 and TiO2 particles was established by transmission electron microscopy. These hybrid composites exhibited excellent efficiency in the degradation of malachite green dye. The composite can be recycled four times without loss of photoactivity. The remarkable improvement in photocatalytic efficiency was because of the synergism among the three nanoparticlesthrough the Z-scheme pathway which allows separation of electronhole pairs and makes MoS2/g-C3N4/TiO2 an outstanding material in the fields of photocatalysis and water treatment. The optimized experimental conditions for the degradation of the dye were assessed by the BoxBehnken design of the response surface methodology. Graphical abstract: [Figure not available: see fulltext.]. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature. -
An Integrated Scalable Healthcare Management System Using IOT
Healthcare management is the challenging task of maintaining the patients medical-related data and images. Pervasive computing, which consists of a wireless network, is an innovative medium for medical data transmission. Here, we propose SHMS (Scalable Healthcare Management System) and interoperability, an available and user-friendly platform. It utilizes a huge amount of data and medical images that must be managed and stored for processing and further investigation. In our work, data like heartbeat, temperature, blood pressure, and ECG readings are collected using different sensors and in one gateway protocol. This design is used for transferring, managing, and accessing documents containing health-related information, which is scattered across different system and organization domains. It is scalable because cloud platforms provide communication APIs, the web service interfaces ensure interoperability, the availability makes patients, doctors, or administrators able to access medical-related data anywhere, and Android OS makes it user-friendly. The security of the data collected can be achieved by authenticating storage using a cryptographic ECC algorithm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
HR analytics in employee engagement and turnover
HR Analytics has expanded increasingly in the last two decades. Nowadays, several Companies use the supremacy of analytics, in gaining a competitive edge over others by recognizing all essentials of the employees. Organizations aim to optimize employee-performance, and hence are making use of HR analytics to drive strategic HR decisions. This study examines the advent of HR analytics by in measuring and improving employee engagement and turnover. Employee engagement analytics stands for exercising the use of data in decision-making process by integrating employee engagement with other HR and non-HR data. Employee engagement analytics is a subsection of workforce-analytics. It has become the standard contemporary system in advanced employee administration and retention. Employee engagement analytics benefits all stakeholders within the organization. 2023, IGI Global. All rights reserved. -
Enhancing the digital consumer experience: The role of artificial intelligence
In the era of rapid technological advancement, the integration of artificial intelligence (AI) and digital consumerism has created new trends in business. Consumers are able to communicate with brands in new ways owing to developments in digital media. AI-powered recommendation systems, chatbots and virtual assistants are the main drivers of this change. It allows businesses to offer product recommendations, customer support, and personalized content to increase user engagement and satisfaction. This chapter provides real-world examples of AI applications across industries, highlighting success stories of companies using AI to create better value and customer satisfaction. Finally, the integration of artificial intelligence and digital customer experience has the potential to transform the future of e-commerce, marketing, and customer service, opening new horizons for both businesses and consumers. 2024, IGI Global. All rights reserved. -
Demand Forecasting Methods: Using Machine Learning to Predict Future Sales
To thrive in the market today, businesses must increase the effectiveness, dependability, and accessibility of their services. Sales estimation and operative demand scheduling definitely impact the end result of the organizations, influencing their procurement process, production, delivery, supply chain, marketing communications, etc. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Evaluating the impact of microlearning and micro-lessons and implications for general education
The educator training approach known as microteaching, which is used now all over the world, offers teachers the chance to sharpen their teaching abilities by enhancing the many straightforward activities referred to as teaching skills. Microteaching supports the growth of in-person teaching experiences thanks to its success with both beginners and older students. The fundamental abilities of microteaching, such as exposition and reinforcing abilities, aid new instructors in mastering the craft of instruction with ease and to the fullest. This method's effects have been widely observed in a variety of educational settings, including the biological sciences, health sciences, and other fields. The chapter reflects upon the basic concepts of microteaching, microlearning, and micro lessons. The study discusses the fundamental teaching techniques, implementation issues, and the effects of microlearning on education. The study also throws light on the impact and advances of technology on microlearning in the context of the digital age. 2024, IGI Global.