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Heart Disease Prediction Using Ensemble Voting Methods in Machine Learning
Heart disease is the leading cause of mortality globally according to the World Health Organization. Every year, it results in millions of mortalities and thus billions of dollars in economic damage throughout the world. Many lives can be saved if the disease is detected early and accurately. The typical methods to predict or diagnosis heart diseases require medical expertise. Such facilities and experts are relatively expensive and not very commonly available in under developed and developing countries. Recent times, much research is done on leveraging technology for the prediction as well as diagnosis of heart diseases. Machine Learning techniques have been extensively deployed as quick, inexpensive, and noninvasive ways for heart disease identification. In this work, we present a machine learning approach in detecting heart disease using a dataset that contains vital body parameters. We used seven different models and combined them with Soft-Voting and Hard-Voting ensemble approaches to improve accuracy in 7-model and various 5-model combinations. The ensemble combinations of 5 models achieved the highest test accuracy score of 94.2%. 2022 IEEE. -
Individual and Relational Outcomes of Inter-religious Marriage: A Scoping Review
Inter-religious marriages, where partners come from different religious affiliations, pose unique challenges and opportunities. This scoping review aims to examine the literature on individual and relational outcomes of inter-religious couples and their families, synthesising existing evidence on their social, psychological, and cultural aspects. While numerous studies exist on this topic, their findings have not yet been systematically synthesised. The question for this scoping review was how existing studies explore the individual and relational outcomes of interfaith marriages. A comparative search, following Arksey and OMalleys five-step framework and PRISMA-ScR guidelines, was conducted across Scopus, ScienceDirect, APA PsycNet, JSTOR, PubMed, Google Scholar, and ProQuest databases from 2004 to 2024. After screening 1,276 references based on inclusion criteria, 19 peer-reviewed articles were selected for the scoping review. Four key themes emerged: (1) Marital adjustment and tensions, (2) Psychological impacts, (3) Marital instability and dissolution, and (4) Strengths and opportunities. This scoping review emphasises the intricate challenges encountered by inter-religious couples, encompassing familial opposition, identity dilemmas, cultural and religious disputes, marital instability, and psychological distress. The review highlights the need for increased societal and institutional support and calls for further research into adaptive coping strategies across diverse cultural contexts. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Attitude toward inter-religious marriage: interplay of generational shifts with religious affiliations and educational attainments
This quantitative research examined the interaction of generational shifts, religious affiliations, and educational attainments in shaping attitudes toward inter-religious marriage. Data were collected from 1231 Indian respondents from iGen/Gen Z, Xennials & Millennials, and Baby Boomers through a demographic response sheet and the Attitude Scale developed by Parker et al. where lower ratings signified positive attitudes and higher ratings indicated negative attitudes. The result revealed that generational shifts were significantly associated with religion (?2 = 96.6, p=<.001) and education (?2 = 279, p=<.001). Significant interaction effects were found between generational shifts and religious affiliations (F = 5.36, p <.001, ?2 p =.017) and generational shifts and educational attainments (F = 6.79, p <.001, ?2 p =.027) concerning attitudes toward inter-religious marriage. This study uncovered the interaction of the demographic variables in shaping the attitude toward inter-religious marriage. 2026 Taylor & Francis Group, LLC. -
What fuels the employees in startups?: Data on hybrid/colocated/virtual working environment towards efficiency
Purpose: This article examines the concepts of workplace satisfaction and productivity using data. The data will be used to investigate the variables contributing to employee satisfaction to achieve optimum efficiency through various startup working environments. Design/ Methodology/ Approach: Descriptive causal investigation. A structured instrument scale questionnaire via the internet to 256 employees working for highly organized organizations in Bangalore, India, using Qualtrics. The researcher adopted a simple random sampling method. Findings: The respondents in the data believed that the pre-covid workplace was advantageous. The hybrid model's prevalence of autonomy and flexibility increases work productivity. When employees are given more responsibility, their job satisfaction and productivity increase. Research Limitations/ Implications: Collecting data in a startup was extremely difficult due to the difficulty of obtaining permission, and through the analysis, it was determined that businesses have a responsibility to provide supplemental benefits to remote employees, which may increase the level of job satisfaction and enjoyment experienced by these individuals. 2023 The Author(s) -
Surface bound copper- grafted TiO2 nanocatalyst for carbon-sulfur cross coupling reaction
This study reports the synthesis of TiO2-based nanocatalyst for efficient diarylsulfide synthesis via Ullmann-type reaction strategy, addressing challenges in conventional methods that are reliant on toxic reagents and harsh reaction conditions. The nanocatalyst comprises an amine-functionalized TiO2 core followed by copper doping. This nanocatalyst demonstrates exceptional performance in cross coupling reactions under mild conditions, achieving yields up to 5098 % with broad-substrate scope. The pure products were characterized using 1H NMR, 13C NMR, FT-IR, and mass spectrometry. The catalyst's heterogeneous nature enables easy recovery and reuse for upto 5 cycles without any significant activity loss. The synthesized nanocatalyst was characterized using various characterization techniques such as FT-IR, TGA, XRD, EDX, SEM, and STEM. This approach aligns with the green chemistry principles, minimizing waste and energy consumption and replacing highly expensive transition metal catalysts. The work highlights the potential of functionalized TiO2 nanomaterials in sustainable organic synthesis, contributing to SDGs 3 (Health through safer pharmaceuticals), 9 (industry innovation), and 12 (responsible production). 2025 Elsevier B.V. -
Copper immobilized on a layered magnetite-based nanocatalyst for sustainable Ullmann cross-coupling reaction
This study demonstrates the efficient synthesis of diarylthioethers via CS cross-coupling between diverse aryl halides and arylthiols utilizing a magnetically retractable Fe3O4@SiO2PrNH2SACu(ii) nanocatalyst using K2CO3 as a base in DMF. The heterogeneous nanocatalyst was fabricated through a multistep process. The designed catalyst was characterized using various techniques, such as XRD, HRTEM, FESEM, STEM, EDAX, elemental mapping, TGA, VSM, XPS, ICP-OES and FT-IR. The catalyst design provides a dual role of the Schiff base-anchoring copper ions, to accelerate the oxidative addition and reductive elimination steps. This method makes use of ligand-free synthesis of diarylsulfides, enabling magnetic recovery and reuse of the catalyst for up to 6 cycles. The nanocatalyst exhibited high catalytic activity and a broad substrate scope. The magnetic nature of the nanocatalyst enabled easy separation from the reaction mixture using an external magnet, thus simplifying the workup. The synthesized nanocatalyst was then utilized for the synthesis of diarylthioethers and heterodiarylthioethers. The pure compounds were characterized using 1H and 13C NMR. This catalytic system offers a cost-effective, efficient, and simple protocol for the formation of the CS bond. This journal is The Royal Society of Chemistry, 2026. -
A Review on Development and Properties of Ultra-High-Performance Concrete
This study presents a review of literature on ultra-high-performance concrete that brings in information regarding the preparation of UHPC, mix design of UHPC on the basis of various particle packing density models, microstructural analysis, durability studies, and strength characteristics. A data base is collected to study the performance, mechanical strength and durability of UHPC from various research works. UHPC can found to be a long-term solution for present day challenges that is faced in construction industry when conventional concrete is used and makes this concrete a novel concept in concrete technology. The advantages of UHPC are: less porosity, high abrasion resistance, greater mechanical properties, high packing density, and improvement in fatigue behavior though the cost of UHPC is high. The non-availability of a standard code for mix design of UHPC makes it difficult to arrive at consistent and comparable mix. The cost of UHPC can be controlled with the use of naturally available materials and utilizing agricultural and industrial waste materials in UHPC. From the data collected, it is observed that the binder content can be optimized and cement which is residual in UHPC can be replaced by industrial residue like fly ash, GGBS, glass powder, etc. and thereby brings down the cost without compromising on the strength and performance. The information shared in this paper will help the contractors, consultants, engineers, industry stakeholders and researchers to alleviate the confusions regarding the use of UHPC in construction industry and to encourage research to make this concrete construction-industry friendly. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Comparative study of various metals in the sewage samples of three major drains of the city-Patna, Bihar, India /
Mapana Journal Of Science, Vol.16, Issue 4, pp.23-35, ISSN: 0975-3303. -
Enhancing the efficiency of parallel genetic algorithms for medical image processing with Hadoop /
International Journal of Computer Applications, Vol.108, Issue 17, pp.92-97, ISSN No: 0975-8887. -
Financial management analysis of dividend policy pursued by selected Indian manufacturing companies /
Journal of Financial Management and Analysis, Vol.27, Issue 1, pp.223-229 -
Pothole Detection and Powertrain Control for Vehicular Safety
A new era of automotive technology has begun with the rapid advancement of electric vehicles (EVs), which promise efficiency and sustainability. With electric vehicles (EVs) becoming an integrated part of the traction systems, there is a growing need for novel safety and performance-enhancing features. The development of an Adaptive Cruise Control (ACC) system for autonomous powertrain control and pothole detection in electric vehicles is examined in this paper. The paper focuses on integrating an intelligent system that can detect potholes and autonomously regulate the powertrain to improve both the driving experience and safety of electric vehicles. The system makes use of Jetson Nano as the processing unit for regulation of the EV powertrain. This board enables quick and accurate reactions to changing road conditions by facilitating real-time data analysis and decision-making. The powertrain regulation will be performed by controlling the acceleration and braking signal provided to the powertrain. 2024 IEEE. -
Emotional needs of women post-rescue from sex trafficking in India
Sex trafficking has persisted a social crime that maintains its status despite being unlawful. Since it prevails, there is a need to investigate it to understand the effects and consequences of the same on the survivors. The current study aims to understand the emotional needs of survivors post-rescue from sex trafficking living in aftercare homes in India and to look into survivors suggestions post-rescue to NGOs, society, family, government and police. It included ten survivors from sex trafficking, ages between 18 to 24years old. They are emerging adults who have experienced sex trafficking for at least one year, regardless of whether trafficking happened in childhood, adolescence or early adulthood, rescued one to five years ago. The researcher used a phenomenological approach. Thematic analysis was employed to identify themes within the data collected from the participants. Findings revealed that survivors had got a better life after the rescue, and they need acceptance, respect, understanding, and they need to develop trust on people around them. They still have many challenges post-rescue such as lack of education and job opportunities. They need guidance to start a new life. Mostly, sex trafficking survivors need safety and protection. 2019, 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Integrating Big Data Management with Machine Learning in Cloud Environments
In past years, the consideration of cloud environments for big data management and machine learning techniques has increased exponentially. However, the massive amount of data that is made by companies and Internet of Things (IoT) devices has presented the industry with a challenge in storage. In addition, conventional data management methods are unable to manage the complexity, diversity, and volume of this big data. Consequently, cloud environments integrate big data management and machine learning techniques. Combining big data management through cloud environments equipped with machine learning has multiple benefits. First, it is an efficient way to process and analyze large datasets in a distributed and scalable manner. Second, it helps businesses to provide insights and make time to make datadriven decisions. Thirdly, it enables the system to learn from data on an ongoing basis, which helps improve the quality and accuracy of the data. Now, this integration also enhances the overall performance of cloud systems as data management tasks are automated, minimizing manual efforts. Utilizing Big Data Management and Machine Learning Techniques in Cloud Environment to Address the Drawbacks of Conventional Data Management Approaches and Exploit the Value of Big Data These two technologies, when integrated, will allow the enterprise to manage significant amounts of data and derive important insights to arrive at better decisions. Cloud environments provide the automation and scalability required to push this integration further and change considerable data use in businesses. 2025 IEEE. -
Barbell-shaped giant radio galaxy with ? 100 kpc kink in the jet
We present for the first time a study of peculiar giant radio galaxy (GRG) J223301+131502 using deep multi-frequency radio observations from GMRT (323, 612, and 1300 MHz) and LOFAR (144 MHz) along with optical spectroscopic observations with the WHT 4.2m optical telescope. Our observations have firmly established its redshift of 0.09956 and unveiled its exceptional jet structure extending more than ? 200 kpc leading to a peculiar kink structure of ? 100 kpc. We measure the overall size of this GRG to be ? 1.83 Mpc; it exhibits lobes without any prominent hotspots and closely resembles a barbell. Our deep low-frequency radio maps clearly reveal the steep-spectrum diffuse emission from the lobes of the GRG. The magnetic field strength of ? 5 ?G and spectral ages between about 110 to 200 mega years for the radio lobes were estimated using radio data from LOFAR 144 MHz observations and GMRT 323 and 612 MHz observations. We discuss the possible causes leading to the formation of the observed kink feature for the GRG, which include precession of the jet axis, development of instabilities and magnetic reconnection. Despite its enormous size, the Barbell GRG is found to be residing in a low-mass (M200 ? 1014 M) galaxy cluster. This GRG with two-sided large-scale jets with a kink and diffuse outer lobes residing in a cluster environment, provides an opportunity to explore the structure and growth of GRGs in different environments. 2022 EDP Sciences. All rights reserved. -
Anti-epileptic medication induced disturbed calcium-vitamin D metabolism: A behavioral analysis using association rule mining technique
BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy, despite the growing recognition of the importance of bone health in individuals with epilepsy. Associations one statistical method for finding correlations between variables in big datasets is called association rule mining (ARM). This technique finds patterns of common items or events in the data set, including associations. Through the analysis of patient data, including demographics, genetic information, and reactions with previous treatments, ARM can identify harmful drug reactions, possible novel combinations of medicines, and trends which connect particular individual features to treatment outcomes. AIM To investigate the evidence on the effects of anti-epileptic drugs (AEDs) on calcium metabolism and supple-menting with vitamin D to help lower the likelihood of bone-related issues using ARM technique. METHODS ARM technique was used to analyze patients behavior on calcium metabolism, vitamin D and anti-epileptic medicines. Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study. There were three patient groups: Group 1 received one AED, group 2 received two AEDs, and group 3 received more than two AEDs. The researchers analyzed the alkaline phosphatase, ionized calcium, total calcium, phosphorus, vitamin D levels, or parathyroid hormone values. RESULTS A total of 150 patients, aged 12 years to 60 years, were studied, with 50 in each group (1, 2, and 3). 60% were men, this gender imbalance may affect the studys findings, as women have different bone metabolism dynamics influenced by hormonal variations, including menopause. The results may not fully capture the distinct effects of AEDs on female patients. A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs. 86 patients had generalized epilepsy, 64 partial. 42% of patients had AEDs for > 5 years. Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy. Polytherapy elevated alkaline phosphatase and phosphorus levels. CONCLUSION ARM revealed the possible effects of variables like age, gender, and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication. The Author(s) 2025. -
Machine Learning for Mental Health: A Sentiment Analysis Approach for Detecting Depressive Tendencies on LinkedIn During Layoffs Using RoBERTa
In the present corporate set-up, layoffs are an unfortunate yet common occurrence. Such occurrences lead to loss of job security and can have direconsequences on an individual's mental health, leading to depression. Depression was a global health concern well before the current downsizing came into the picture. These trying times have acted as a catalyst for this illness that affects not just mental health but all aspects of an individuals life. The study investigates the use of sentiment analysis on LinkedIn data to identify and examine depressive tendencies among victims of layoffs. Web-scraped information was taken from LinkedIn profiles of individuals affected directly or indirectly by layoffs. RoBERTa, a transformers model, is used to classify people as depressed or not by evaluating sentiment and emotional cues. A comparison between four machine learning algorithms- Decision Tree, Logistic Regression, SVM, and Nae Bayes is drawn to check their ability to detect depression. The SVM classifier performed best with an accuracy of 95.59% and 83.52% with the CountVectorizer and TF-IDF feature selection methods, respectively. Sentiment analysis aids in this research by examining the melancholic undertones in the words and phrases used in texts authored by people affected by layoffs directly or indirectly. The knowledge gained from this research can significantly affect corporate initiatives, mental health services, and human resource practices during such challenging times. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Scoping review of Deep Reinforcement Learning methods in Visual Navigation
Reinforcement Learning (RL) is a subset of Machine Learning that trains an agent to make a series of decisions and take action by interacting directly with the environment. In this approach, the agent learns to attain the goal by the response from its action as rewards or punishment. Recent advances in reinforcement learning combined with deep learning methods have led to breakthrough research in solving many complex problems in the field of Artificial Intelligence. This paper presents recent literature on autonomous visual navigation of robots using Deep Reinforcement Learning (DRL) algorithms and methods. It also describes the algorithms evaluated, the environment used for implementation, and the policy applied to maximize the rewards earned by the agent. The paper concludes with a discussion of the new models created by various authors, their merits over the existing methods, and a briefing on further research. 2023 IEEE. -
Stochastic frontier analysis to measure technical efficiency: Evidence from skilled and unskilled agricultural labour in india
This paper comprises the stochastic frontier model which has been applied to measure the technical efficiency of skilled and unskilled labour. By considering the certain input variables listed in the cost of cultivation suggested by the Commission of Agricultural Costs and Prices (CACP) for Indian states during the main cropping season. Result of the study shows that the evaluated average technical efficiency estimates have been found between 71 to 84 % for both type of labour. Factors i.e. use of seeds (77 % efficient), fertilizers (29 % inefficient), manure (3 % efficient), land, human (9 % efficient), attached (10 % efficient) and casual (103 % efficient) labor, animal labor (is between 1 to 4 % efficient), hired machine (33 % inefficient), owned machine (7 % efficient), insecticides (20 % efficient), irrigational cost (31 % efficient), fixed cost (36 % inefficient) and operational cost (197 % inefficient) have a significant at 1, 5 and 10 % level of significance1. 2020 DAV College. All rights reserved. -
Comparative Study of Graph Theory for Network System
The historical background of how graph theory emerged into world and gradually gained importance in different fields of study is very well stated in many books and articles. Some of the most important applications of graph theory can be seen in the field network theory. Its significance can be seen in some of the complex network systems in the field of biological system, ecological system, social systems as well as technological systems. In this paper, the basic concepts of graph theory in terms of network theory have been provided. The various network models like star network model, ring network model, and mesh network model have been presented along with their graphical representation. We have tried to establish the link between the models with the existing concepts in graph theory. Also, many application-based examples that links graph theory with network theory have been looked upon. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Alzheimer's Disease Detection using Machine Learning: A Review
Alzheimer's is a progressive brain disorder which is an untreatable, and inoperable and mostly affect the elderly people. There is a new case of Alzheimer's disease being discovered globally in every four seconds. The outcome is fatal, as it results in death. Timely identification of Alzheimer's disease can be beneficial for us to get necessary care and possibly even avert brain tissue damage by the time. Effective automated techniques are required for detecting Alzheimer's disease at very early stage. Researchers use a variety of novel approaches to classify Alzheimer's disease. machine learning, an AI branch use probabilistic technique that allow system to acquire knowledge from huge amount of data. In this paper we represent a analysis report of the work which is done by researcher in this field. Research has achieved quite promising prediction accuracies however they were evaluated the the non-existent datasets from various imaging modalities which makes it difficult to make the fair comparison with the other methods comparison among them. In this paper, we conducted a study on the effectiveness of using human brain MRI scans to detect Alzheimer's disease and ended with a future discussion of Alzheimer's research trends. 2021 IEEE.