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Carbon-Based Nanomaterials for Cancer Treatment and Diagnosis: A Review
Early detection and treatment is a successful method to fight against cancer. The use of conventional methods often limits the early-stage detection of the same. Nanotechnology, especially nanomedicine, has become an evolving field in research today. Nanoparticles, with their diverse application in medicine, have grown tremendously. Despite their physicochemical properties, the toxicity of nanoparticles in living organisms has helped the scientific community develop these nanoparticles for cancer treatment. In this review, we highlight the two different types of synthesis of nanoparticles: Top-down and Bottom-up approaches, many instances of various techniques and other inorganic nanoparticles that are good platforms for bioimaging and biosensing applications, drug delivery nanocarriers for specific tumor targeting, thereby reducing the toxicity to healthy tissues and Photodynamic therapy (PDT) and Photo Thermal Therapy (PTT), their mechanism and nanoparticles and examples of their surface functionalization used in cancer treatment. 2022 Wiley-VCH GmbH. -
ThermAI: Exploring Temperature Analysis Through Diverse Machine Learning Models
Meteorological forecasting is crucial in multiple industries, including agriculture, aviation, and daily routines. The objective of this inquiry is to improve temperature predictions by examining and comparing several machine learning methods, such as linear regression, decision trees, and random forests. This work aims to fill the gap in assessing machine learning models for temperature forecasting on a broader scale by utilising the comprehensive Indian meteorological dataset, which covers a wide range of geographical regions. The research utilises a thorough technique that includes gathering data, selecting relevant features, choosing appropriate models, and evaluating the results using R-squared and Mean Square Error metrics. The findings demonstrate that the Random Forest model surpasses both multiple linear regression and decision trees in terms of performance, displaying superior accuracy and reduced prediction errors. This study enhances proactive weather management and decision-making processes by offering valuable insights and tools to stakeholders in various industries. The work is organised into distinct sections that encompass a literature review, methodology, results, and conclusions, providing a comprehensive viewpoint on developments in temperature forecasting. 2024 IEEE. -
Kuriakose elias chavara in the formation of human capital and its impact on indian society
Developing knowledge societies encompasses personal, social, ethical and political dimensions, integrating all its members and promoting new forms of solidarity involving both present and future generations. Kuriakose Elias Chavara had realized the power of human capital formation for the integral development of the society and initiated pioneering efforts to develop human capital, and established two indigenous religious congregations, Carmelites of Mary Immaculate (CMI) for men and Congregation of the Mother of Carmel (CMC) for women. The objective of this paper is to synthesize the vision of Kuriakose Elias Chavara regarding the concept of human capital formation and examine its result based on vision actualization of CMC congregation and its members and institutions. The paper is divided into three sections; the first section discusses the meaning of human capital formation in the Kerala and Indian context. The second section deals with the human capital formation through the vision and mission of Chavara and the last section explains the human capital formation and women empowerment, and its impact on knowledge societies through CMC congregation. It explains the transformation of individuals inspired by the vision of founder and support of the Congregation, which brings tremendous changes in the society. 2019 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore). -
Development and validation procedure of the higher educational facilities scale (HEFS)
Purpose: The purpose of this study was to develop and validate a scale to assess the influence of Higher Educational Facilities for the growth of education in higher education institutions. Design/methodology/approach: The first step in the process of scale development is to generate an item pool containing as many items as possible which captures the construct of interest. A total of 111 items were constructed for the initial try-out of the scale measuring the construct of higher educational facilities. This rating scale was based on the Likert-type was designed, where each item had to be rated on a five-point scale. The scale consisted of a few items involving the dimensions of infrastructure, quality assessment and quality assurance regard to the vision actualization. Findings: Higher Educational Facilities Scale (HEFS) was developed by the investigator and designed in the format of a 5-point rating scale of the Likert type. There are different phases identified for the scale construction. In the first phase, items are created and the contents validity is determined. The scale is constructed in the second phase. Pre-testing the questions, administering the survey, reducing the number of items and determining how many factors the scale captures are all steps in the scale construction process. The number of dimensions, reliability and validity are all verified in the third phase, scale evaluation. In developing the scale, the content and face validity was ascertained. The reliability of the scale and its three subscales were established. This scale has potential value for policymakers to assess the perception held by the religious faculty members working in higher education institutions. Originality/value: The research is part of the doctoral thesis by Dr Deepa Thomas under the supervision of Dr Fr. Joseph C. C. and the co-supervision of Dr Kennedy Andrew Thomas. The purpose of the scale is to assess the higher educational facilities of in institutions of higher Education. Quality, excellence and service are the vision and purpose of higher education institutions to provide ample opportunities and good facilities for their beneficiaries, thus creating tremendous changes in the Indian education scenario. 2024, Emerald Publishing Limited. -
Brain tumor segmentation and detection using MRI images
Brain tumor is caused due to the increased abnormal in brain. It is not something that we might say is limited to aged people alone, but is known to affect newborn babies as well. It affects many people worldwide. With the applications of Machine Learning (ML) and Image Processing (IP), the early detection of brain tumor is possible. In this research work, the different stages in image processing which help to detect brain tumor, is addressed vividly. This work provides information about the various sets of filtering and segmentation methods which can be used to detect whether it is brain tumor or not. All of the filtering methods are defined in image preprocessing techniques. The next procedure is to apply segmentation methods namely watershed segmentation and gray level threshold segmentation. After this, certain features are considered for feature extraction such as area, major axis, minor axis and eccentricity. According to the outcomes from the feature extraction technique, the classification of the tumor is done. In this paper, we achieve an accuracy of 92.35 by using K-Nearest Neighbor (KNN) algorithm. IAEME Publication. -
The Effect of Bloom's Taxonomy on Random Forest Classifier for cognitive level identification of E-content
With the advancement in internet, the efficiency of e-learning increased and currently e-learning is one of the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning web sites increased exponentially over the years because of the advancement in internet and e-learning technologies. The learner can find many website with lots of information on the relevant domain. However learners often found it difficult to Figure out the right leaning content from the humongous availability of e-content. In the proposed work an intelligent framework is developed to address this issue. The framework recommend the right learning content to a user from the e-learning web sites with the knowledge level of the user. The e-contents available in web sites were divided in to three cognitive levels such as beginner, intermediate and advanced level. The current work uses Blooms Taxonomy verbs and its synonyms to improve the accuracy of the classifier used in the framework. 2020 IEEE. -
Random forest application on cognitive level classification of E-learning content
The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through E-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with random forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level. The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Preparation for parenthood programme: Experiences from southern India
Parenting skills are critically important to ensure that children are brought up in a safe environment. Recent evidence shows that studies of parenting skills are still at a preliminary stage in low-and middle-income countries. These need to involve family practitioners and religious groups who often play a major role in preparing young people in India. There are organized programmes available in the country for Christian adults to prepare themselves for marriage and family life through various church initiatives and activities. In order to develop a programme which can be used to prepare young parents for responsibilities of parenthood, a needs assessment was carried out among 70 young adults who attended a marriage preparation course in Bangalore, India. All the participants belonged to the Christian faith. Participants consisted of 53% men and 47% women whose average age for deciding to get married was 26.8 years. All of them expressed a need for such a preparatory programme for parenthood. They considered they needed to know about normal child development, behavioural management of children, to develop adequate skills in handling children at different ages, and deal with their own past issues with their own parents when they were being parented. The results suggest that the development of a preparatory programme for young adults to support them in the role of parenthood must take their views and needs into account. 2014 Institute of Psychiatry. -
Treating troubled families: Therapeutic scenario in India
India, a country of diverse cultures, languages, life styles, and ethnicities, is becoming a land of economic change, political stability, technological advancement, and changing traditional structures of relationships as well as health consciousness. Being known for its ancient traditions, rituals, religious orientation, spiritual outlook and folk beliefs, Indian families attempt to continue certain healthy and traditional elements such as warmth, strong bond, hierarchy, extended support, cultural orientation, shared values and time, tolerance, respect for the aged and inculcation of religious teachings and traditions in families. These factors, or practices, in fact have strong therapeutic value in supplementing the growth and development of individuals in the family system in spite of its transitional position. This paper deals with the review of family-based mental health services and focuses on the changing trends of those practices in India and the advancement of Indian families in their engaging ability with mentally ill members as well as with the treating team. 2012 Institute of Psychiatry. -
The vertex distance complement spectrum of subdivision vertex join and subdivision edge join of two regular graphs
The vertex distance complement (VDC) matrix C, of a connected graph G with vertex set consisting of n vertices, is a real symmetric matrix [cij ] that takes the value n ? dij where dij is the distance between the vertices vi and vj of G for i ? j and 0 otherwise. The vertex distance complement spectrum of the subdivision vertex join, G1 ??G 2 and the subdivision edge join G1 ?G2 of regular graphs G1 and G2 in terms of the adjacency spectrum are determined in this paper. 2021, Krasovskii Institute of Mathematics and Mechanics. All rights reserved. -
Spectrum of corona products based on splitting graphs
Let G be a simple undirected graph. Three new corona products of graphs based on splitting graph of G are defined. The adjacency spectra of the three new graphs based on splitting graph of G are determined. The number of spanning trees and the Kirchoff index of the new graphs are determined using their nonzero Laplacian eigenvalues. 2023 World Scientific Publishing Company. -
DISTANCE SPECTRUM OF TWO FAMILIES OF GRAPHS
Let H1 and H2 be two copies of the complete graph Kn, n ? 3 with vertex sets V(H1) = {v1,v2...,vn} and V(H2) = {u1,u2,...,un}. Graph ?(n,p), 1 ? p ? n-1, is obtained from the union of graphs H1 and H2 by adding edges {uivi)|i ? {1, 2...,p}}. Graph ?(n) is obtained from the union of graphs H1 and H2 by joining each vertex vi of H1 to every vertex in {u1, u2, ..., un} \ {ui}, i = 1, 2, ..., n. The adjacency spectrum of ?(n, p) and ?(n) were determined in [9]. An open problem posed in [7] was to find families of graphs of diameter greater than two, for which the adjacency and distance spectrum are both integral. To answer the open problem, the distance spectrum of the above family of graphs is calculated, and new distance equienergetic graphs are constructed in this paper. 2024 Jangjeon Research Institute for Mathematical Sciences and Physics. All rights reserved. -
Travails of New Mothers Returning to Work in Corporate India: A Phenomenological Study
A womans life is a myriad of experiences and none, perhaps, leaves a more lasting impression on her than motherhood. The child-birth event along with all its highs and lows not only has a deep psychological impact on her as a person but also impacts her career in many ways. Using interpretive phenomenological analysis, we have studied the lived experience of women who returned to work in corporate settings after maternity leave. Our study found that not only do they go through an emotional upheaval during this phase, but they also see a marked shift in the way they approach their careers. A womans natural instinct to mother her child comes in conflict with another natural (and equally important) desire to succeed in the workplace. Most women in our study experienced a stalling/break in their careers after childbirth and wished they had a mentor to assist them in transitioning back to office life. Besides trying to evaluate if childbirth was perceived as a threat or potential impediment to a high-flying career, we also explored how women were treated in their work environments, and whether their coworkers helped the women to cope during this phase. While the women in our study wanted to achieve success and satisfaction both within their families and careers, they found it most challenging to do so. 2022 Journal of International Womens Studies. -
Comparative study of recommender systems
Recommendation System is a quickly progressing study area. Many new approaches are offered so far. In this particular paper we have researched on various applications of recommender system and various techniques used in recommender system like collaborative filtering, content-based filtering and hybrid filtering. Collaborative filtering is amongst the common methods utilized in recommending process. So comparative study on various collaborative filtering is done and the results are plotted graphically. 2016 IEEE. -
Global Analysis of Quantum Technology Discourse
he study provides a thorough exploration of the global quantum technology landscape, offering valuable insights for researchers, policymakers, and industry stakeholders. It employs advanced analytical methods such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) for topic modeling. The research focuses on understanding discussion intensity, geographical distribution, co-mentioning patterns among countries, prevalent topics, and keyword-based trends. Utilizing diverse datasets, the study employs heatmaps, network analysis, and thematic analysis to categorize textual data. Evaluation metrics like Topic Coherence and Network Centrality Measures contribute to a robust methodology.Key findings include dominant discussions on quantum computing and investment strategies, with focused attention on governmental roles in R&D and specific quantum computer research. Notably, there is a niche focus on quantum algorithmic risks in Australia. Document characteristics vary, with some blending multiple themes and others centered around a single topic. LDA topic modeling and network analysis identify key countries, showcasing global hotspots and potential collaborations in quantum technology discussions. 2024 IEEE. -
The Evolution of Forecasting Techniques: Traditional Versus Machine Learning Methods
Forecasting is used effectively and efficiently to support decision-making for the future. Over time, several methods have been created to conduct forecasting. Finding a forecasting technique with the ability to provide the best estimate of the system being modeled has always been a challenge. The selection and comparison criteria for forecasting methodologies can be organized in a variety of ways. Accurate forecasting has a great demand for various fields like weather prediction, economic condition, business forecasting, demand and supply forecasts and many more. When deciding whether to utilize a certain model to predict future events, accuracy is very important. In every field, machine learning (ML) algorithms are being used to forecast future events. These algorithms can handle more complex data and make predictions that are more accurate. Based on the least values of forecasting errors, forecasters create a model to determine the best strategy for prediction. For centuries, forecasting has been used to assist individuals in making future-related decisions. In the past, forecasts were based on intuition and experience, but as technology has advanced, so have forecasting methods. Currently, advanced ML models and methods for data analysis are used to provide forecasts. To forecast the future, these models incorporate a range of inputs, including historical data, present trends, and economic indicators. Forecasting is a vital tool for businesses to employ when making future plans. It is used in a wide range of industries, from finance to weather prediction. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Analysis and Forecasting of Crude Oil Price Based on Univariate and Multivariate Time Series Approaches
This paper discusses the notion of multivariate and univariate analysis for the prediction of crude oil price in India. The study also looks at the long-term relationship between the crude oil prices and its petroleum products price such as diesel, gasoline, and natural gas in India. Both univariate and multivariate time series analyses are used to predict the relationship between crude oil price and other petroleum products. The Johansen cointegration test, EngleGranger test, vector error correction (VEC) model, and vector auto regressive (VAR) model are used in this study to assess the long- and short-run dynamics between crude oil prices and other petroleum products. Prediction of crude oil price has also been modeled with respect to the univariate time series models such as autoregressive integrated moving average (ARIMA) model, Holt exponential smoothing, and generalized autoregressive conditional heteroskedasticity (GARCH). The cointegration test indicated that diesel prices and crude oil prices have a long-run link. The Granger causality test revealed a bidirectional relationship between the price of diesel and the price of gasoline, as well as a unidirectional association between the price of diesel and the price of crude oil. Based on in-sample forecasts, accuracy metrics such as root mean square logarithmic error (RMSLE), mean absolute percentage error (MAPE), and mean absolute square error (MASE) were derived, and it was discovered that VECM and ARIMA models can efficiently predict crude oil prices. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effect of Gravity Modulation on the Onset of Ferroconvection in a Densely Packed Porous Layer /
IOSR Journal of Applied Physics, Vol.3, Issue 3, pp.30-40, ISSN No: 2278-4861.
The stability of a horizontal porous layer of a ferromagnetic fluid heated from below is studied when
the fluid layer is subject to a time-periodic body force.Modified Darcy law is used to describe the fluid motion.
The effect of gravity modulation is treated by a perturbation expansion in powers of the amplitude of
modulation. The stability of the system,characterized by a correction Rayleigh number,is determined as a
function of the frequency of modulation, magnetic parameters, and Vadasz number. -
Quantum approaches to sustainable resource management in supply chains
Quantum computing leverages the principles of quantum mechanics to process information in ways that classical computers cannot. This capability is particularly advantageous for solving complex optimization problems that are common in supply chain management. Quantum algorithms, such as the quantum approximate optimization algorithm (QAOA) and quantum annealing, have shown promise in efficiently solving these problems by exploring numerous potential solutions simultaneously and identifying optimal strategies. The purpose of this chapter is to investigate the rapidly developing topic of quantum computing and its potential applications in managing sustainable resources within supply chains. Traditional resource allocation methods often struggle to maximize efficiency while minimizing environmental impact. However, new developments in quantum computing have opened up potentially fruitful pathways for addressing these issues. This study aims to explore how quantum computing can revolutionize through an examination of quantum algorithms, optimization approaches, and case studies. 2024 by IGI Global. All rights reserved. -
Security and privacy aspects in intelligence systems through blockchain and explainable AI
Explainable AI (XAI) is a method of creating artificial intelligence (AI) systems that are transparent and understandable to humans. By allowing people to understand how the system arrived at its conclusions or suggestions, XAI systems strive to make AI more accountable, trustworthy, and ethical. Responsibility, trust, ethics, regulation, and innovation are some of the societal ramifications of XAI. By making AI systems more transparent, XAI fosters accountability. This means that consumers will be able to understand how the system made its decisions and hold it accountable if something goes wrong. By making the decision-making process more transparent, XAI fosters trust between people and AI systems. This boosts user trust in the system and encourages wider adoption of AI technologies. It also contributes to the ethical design of AI systems by making the decision-making process public in order to uncover and mitigate biases and other ethical issues that may occur in AI systems. It aids regulators and policymakers in understanding and regulating AI systems. XAI gives insight into how AI systems operate, which can assist regulators in developing laws that promote ethical and responsible AI use. Because XAI can help developers better and innovate new systems by making it easier for them to design new AI systems and by providing insights into how AI systems work. The proposed chapter will focus on important aspects of algorithmic bias and changing notions of privacy in XAI, which will necessitate the need for AI systems that can adapt accountability, trust, ethics, and compliance with regulations, as well as produce better innovation that can benefit humanity. More openness, greater control over personal data, new types of data privacy, and newer privacy networks are all required. To address algorithmic bias in XAI, it is critical to build the system so that it is aware of the possibility of bias and actively mitigates it. This can involve employing diverse and representative data, inspecting the system for unwanted features, offering detailed explanations, and incorporating a wide range of stakeholders in the system's development and deployment. The envisaged report provides a framework that combines XAI and blockchain to provide a secure and transparent way to store and track the provenance of data used by XAI systems, validate the performance of AI models stored on the blockchain on decentralized systems so that the models are stored and executed on a distributed network of nodes rather than a centralized server, and create a token-based economy that encourages data sharing and AI development. Tokens can be used to compensate individuals and organizations who contribute data or algorithms to the blockchain or who employ AI models stored on the blockchain. Overall, the combination of XAI and blockchain can lead to more trustworthy, transparent, and decentralized AI systems. This approach can have a significant impact on various industries such as finance, healthcare, and supply chain management by increasing efficiency, reducing costs, and improving data privacy and security. 2024 Elsevier Inc. All rights reserved.