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Detection of Alzheimers Disease Stages Based on Deep Learning Architectures from MRI Images
Acquiring, utilizing and storing information of any sort is known as memory. The power of memory makes the life of mankind to be more alive and reasonable. Thus, the loss of one such great capability is a rather painful phase of human life which can be destructed by multiple reasons such as diseases and disorders. One such disease is Alzheimers disease (AD). Alzheimers disease progressively damages brain cells and degrades mental activity that leads to mental illness. The accurate diagnosis of AD at earlier stages will help to prevent the disease before the brain gets damaged completely. In analyzing neurodegenerative disorders, neuroimaging plays an important role in diagnosing subjects with AD, mild cognitive impairment (MCI), and cognitively normal (CN). In this study, advanced deep learning (DL) architectures with brain imaging techniques were employed to maximize the diagnostic accuracy of the model developed. The proposed method works with convolutional neural networks (CNNs) to analyze the MRI input-output modalities. The method is evaluated using Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. Binary classification is done on AD and MCI subjects from CN. This method is efficient to analyze multiple classes with a less amount of training data. 2023 selection and editorial matter, Jyotismita Chaki; individual chapters, the contributors. -
Classification on Alzheimers Disease MRI Images with VGG-16 and VGG-19
Balancing thoughts and memories of our life is indeed the most critical part of the human brain.Thus, its stability and sustenance are also important for smooth functioning.The changes in the structure can lead to disorders such as dementia and one such type of condition is known as Alzheimers disease.Multi modal neuroimaging like magnetic resonance imaging (MRI) and positron emission tomography (PET) is used for the early diagnosis of Alzheimers disease (AD) by providing complementary information.Different modalities like PET and MRI data were acquired from the same subject, there exists markable materiality between MRI and PET data.Mild cognitive impairment (MCI) is the initial stage with few symptoms of AD.To recognise the subjects which are capable of converting from MCI to AD is to be analysed for further treatments.In this research, specific convolutional neural networks (CNN) which are designed for classifications like VGG-16 and VGG-19 deep learning architectures were used to check the accuracy of cognitively normal (CN) versus MCI, CN versus AD and MCI to AD conversion using MRI data.The proposed research is analysed and tested using MRI data from Alzheimers disease neuroimaging initiative (ADNI). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Paradigm shift in Family therapy in India : Exploration from Socioeconomic, Cultural and Spiritual Perspectives
International Journal of Physical and Social Sciences Vol. 3, Issue 3,pp. 153-166 , ISSN No. 2249-5894 -
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Exploring the effectiveness of mindfulness-based intervention among college students in India
This study investigated the effectiveness of an eight-week mindfulness-based intervention program on the trait mindfulness, psychological well-being and emotion regulation of college-going students. The experimental group participants were college-going students (N = 40) who enrolled for the intervention, and the participants in the control group (N = 40) were interested in the intervention and considered as a wait-list control group. The experimental group underwent mindfulness-based interventions, which included 1112 sessions, including brief exercises and meditations related to their trait mindfulness, emotion regulation, and psychological well-being. They received 23h of training per week for eight weeks. Repeated Measures of ANOVA together with an independent sample t-test were used to evaluate the effectiveness of this intervention programme. Further, Cohens d was used to calculate the effect size to explain the variance caused by the intervention program in trait mindfulness, emotion regulation, and psychological well-being. The results indicated that students significantly improved in their trait mindfulness, emotion regulation, and psychological well-being after receiving mindfulness training. In conclusion, the application of this eight-week mindfulness-based intervention sheds light on the common psychological issues confronted by college students in India, presenting itself as an advantageous tool for the professionals working in this field and offering positive effects on the overall well-being of college students. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Artificial Intelligence Involvement in Graphic Game Development
Games have always been a popular form of entertainment and with the advancements in technology, the integration of Artificial Intelligence (AI) in gaming has revolutionized the gaming industry. This research article aims to explore the various applications of AI in gaming and its impact on the industry and player experience. Unlike the typical straightforward nature of AI, this research paper takes a more human approach to discussing the topic. It delves into the evolution of AI in games and the various types of AI used in game development. These include rule-based AI, learning- based AI, and evolutionary AI, which have all contributed to the development of increasingly immersive gaming experiences. The benefits and challenges of using AI in games are also explored, considering the impact on player experience. While AI-powered opponents can provide a greater challenge, balancing the difficulty level is critical to ensuring the game remains enjoyable. The potential ethical concerns of using AI in games are also discussed, such as data privacy, bias, and fairness. Furthermore, this research paper looks into the future of AI in games and how it may shape the gaming industry and player experience in the years to come. With the continued development of AI techniques such as reinforcement learning and GANs, the possibilities for more immersive and engaging gaming experiences are endless. 2023 IEEE. -
Impact of Meltdown and Spectre Threats in Parallel Processing
Threat characterization is critical for associations, as it is an imperative move towards execution of data security. Vast majority of the current threat characterizations recorded threats in static courses without connecting risks to information system zones. The aim of this paper is to represent each threat in different areas of the information system the methodology to solve the problem. Data security is habitually represented to different kinds of threats which may cause distinctive types of harms that can prompt to critical monetary losses. Data security problems can go from small losses to entire data framework destruction. The effect of various threats vary extensively: some manipulate the integrity or confidentiality of information while others manipulate the accessibility of a framework. At present, associations are trying to comprehend what are the threats to their data resources are and what are the ways to get the significant intends to combat them which keep on representing a challenge. Springer Nature Switzerland AG 2020. -
Impact of Meltdown and Spectre Threats in Parallel Processing
[No abstract available] -
Optimal locations for PMUs maintaining observability in power systems
Population of Phasor Measurement Units (PMUs) in power systems are increasing day by day as PMUs measure the electrical quantities more accurately with time-stamping. The measurements done by PMU can be used for monitoring, controlling and for state estimation of the power system. Since the installation of PMUs demand high capital cost, their number and location to be chosen optimally is by minimizing investment without losing observability of the system. In this paper Integer Programming techniques used to solve Optimal Placement of PMU (OPP) problem. The OPP problem is solved for normal power system as well as for a few contingency conditions like one PMU outage, considering zero injection bus, outage of single line on various standard IEEE Bus Systems. The work is also trying to place PMUs under planned islanding in certain standard networks. 2016 IEEE. -
Further studies on circulant completion of graphs
A circulant graph C(n, S) is a graph having its adjacency matrix as a circulant matrix. It can also be interpreted as a graph with vertices v0, v1,,vn?1 that are in one-to-one correspondence with the members of Zn and with edge set {vivj: i ? j ? S}, where S known as the connection set or symbol, is a subset of non-identity members of Zn that is closed under inverses. This work extends the study of circulant completion and general formulae for calculating circulant completion numbers in two different perspectives, one in terms of circulant span and the other in terms of the adjacency matrix. (2024), (SciELO-Scientific Electronic Library Online). All Rights Reserved. -
On Equitable Chromatic Completion of Some Graph Classes
An edge of a properly vertex-colored graph is said to be a good edge if it has end vertices of different color. The chromatic completion graph of a graph G is a graph obtained by adding all possible good edges to G. The chromatic completion number of G is the maximum number of new good edges added to G. An equitable coloring of a graph G is a proper vertex coloring of G such that the difference of cardinalities of any two color classes is at most 1. In this paper, we discuss the chromatic completion graphs and chromatic completion number of certain graph classes, with respect to their equitable coloring. 2022 American Institute of Physics Inc.. All rights reserved. -
On Circulant Completion of Graphs
A graph G with vertex set as {v0, v1, v2,.., vn-1} corresponding to the elements of Zn, the group of integers under addition modulo n, is said to be a circulant graph if the edge set of G consists of all edges of the form {vi, vj} where (i-j)(modn)?S?{1,2,,n-1}, that is, closed under inverses. The set S is known as the connection set. In this paper, we present some techniques and characterisations which enable us to obtain a circulant completion graph of a given graph and thereby evaluate the circulant completion number. The obtained results provide the basic eligibilities for a graph to have a particular circulant completion graph. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Predicting of Credit Risk Using Machine Learning Algorithms
Credit risk management is one of the key processes for banks and is crucial to ensuring the banks stability and success. However, due to the need for more rigid forecasting models with strong mapping abilities, credit risk prediction has become challenging for the banking industry. Therefore, this paper attempts to predict commercial banks credit risk (CR) by using various machine learning algorithms. Machine learning algorithms, namely linear regression, KNN, SVR, DT, RF, XGB, and MLP, are compared with and without feature selection and feature extraction techniques to examine their prediction capabilities. Various determinants of credit risk (features) have been extracted to predict credit risk, and these features have been used to train machine learning models. Findings revealed that the decision tree algorithm had the highest performance, with the lowest mean absolute error (MSE) value of 0.1637 and the lowest root mean squared error (RMSE) value of 0.2158. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
DETERMINANTS OF CREDIT RISK: EMPIRICAL EVIDENCE FROM INDIAN COMMERCIAL BANKS
Credit risk is a significant factor affecting the financial stability of banks. Keeping the credit risk under control is essential to maintain a banks cash flow. This paper examines the various profitability, microeconomic and macroeconomic indicators that affect a banks credit risk. The study uses the dataset of 31 banks from 2012 to 2021 and employs a panel data modelling approach to account for any variations in risk-taking behavior. The results revealed a statistically significant negative relationship between return on equity and credit risk when nonperforming loans proxy credit risk. This finding was consistent across fixed effect, random effect, and pooled OLS methods, at 1 percent significance (P value < 0.00), indicating that the extent of credit risk decreases as profitability increases. It was further found that bank age and ownership type positively affect a banks credit risk, while factors such as bank size and operational efficiency negatively affect credit risk when nonperforming loans proxy credit risk. Further, macroeconomic variables showed that gross domestic product is positively associated with credit risk, while inflation negatively affects credit risk. Overall, the findings of this paper demonstrated that credit risk is affected by both micro and macroeconomic factors. The paper also addresses significant policy implications as it helps various stakeholders to examine the determinants of credit risk, make credit decisions, and ultimately lower their credit risk. Tisa Maria Antony, Suresh G., 2023. -
New trends in malayalam cinema: A case study on Amal Neerad films /
Amal Neerad is an Indian Film Maker and Cinematographer hailing from Kerala. He is also a producer and has recently produced several movies. Amal Neerad is known for his style in cinematography which is unique. The Director uses his typical style of film making in all his movies. Certain cinematic techniques like continuity cuts and extreme angles are extensively used in the movies of Amal Neerad. -
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Towards An 'Alternate' Mythical Reality : A Postmodern Reading of the Graphic Narratives of Appupen and Amruta Patil
The graphic novel landscape in India has witnessed a significant change with newlineinnovative and revolutionary ideas. The new age writers of the gra -
Transitioning from anthropocene to anthropause? A study of the consumer behavioural shifts in the hospitality sector
The COVID-19 pandemic has pushed the human race to transition from anthropocene to anthropause philosophy. Against this backdrop, the term "anthropause" describes the abrupt and intentional cessation of regular human activities caused by the disruption caused by the COVID-19 pandemic. The human response to this global health crisis has massively affected and altered human behaviour. The pandemic had an enormous impact with a sudden halt in the global human mobility phenomenon, and businesses were pushed to cater to altered consumption patterns. Hospitality is one such sector that has been impacted immensely. The study examines the key factors influencing consumer behaviour in the hospitality industry during the post-pandemic times. The study can be classified as conceptual research and would assess the theoretical underpinnings of the studies focusing on altered consumer behaviour. The chapter would also lead to a critical discussion on the transformation of consumption behaviour in the aftermath of this outbreak. Thematic content analysis of the data collected through the review of critical works in the domain of hospitality management has been triangulated with the authors' reflections to reach a consensual position. This work would also focus on understanding the theoretical standpoint of the researchers about the various stages of evolution in consumer behaviour in the field of hospitality businesses. Lastly, this conceptual study aims to analyse the current trends and future directions in consumer behaviour with particular reference to hospitality businesses. As post-pandemic consumer behaviour is still evolving, the present chapter would help trigger a broader discussion within academia and practitioners of hospitality businesses. Springer Nature Switzerland AG 2023. All rights reserved.