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Color image segmentation based on improved sine cosine optimization algorithm
Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signalnoise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
The use of self-protective measures to prevent COVID-19 spread: an application of the health belief model
This study uses a health belief model to examine the preventive behavioral orientation or self-protective measures adopted by people in the face of the current COVID-19 pandemic. A total of 603 participants were selected from the city of Bangalore, India. The data was collected through an online survey with participants age varying between 17 and 54 and mean as 23 years (SD = 4.32). The findings revealed that perceived barrier has significant negative impact, while perceived threat, perceived consequences, perceived benefits, community and individual self-efficacy, and general health cues have a positive influence on an individuals intention to follow self-protective measures against COVID-19. Based on the constructs of the health belief model, this study proposes multiple health-related interventions to reduce the spread of COVID-19. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Investigating the Impact of Emotional Contagion on Customer Attitude, Trust and Brand Engagement: A Social Commerce Perspective
Social Commerce networks are a powerful platform for spreading positive and negative emotional contagion, which is affecting users from different perspectives, i.e., psychology, attitude, buying decision. Emotional contagion is the phenomenon of having a person's emotions and behaviours directly trigger similar emotions or behaviour in other people. This research proposes a model to analyze the factors influencing emotional contagion that, in turn, impact consumer's attitudes, trust, and brand engagement. This study used a survey approach using a structured questionnaire. Primary data was collected from 174 social media users who shop online. The proposed model was tested using multiple regression analysis. The results demonstrated that effective content, visual or text, triggers customers' emotional contagion, influencing customer attitude and trust leading to brand engagement. The research study's findings can be used for deciding on content strategies of advertisements pertaining to social commerce. 2022 Academy of Taiwan Information Systems Research. All rights reserved. -
Comparing Influence of Depression and Negative Affect on Decision Making
The current study aimed to explore differential value-based decision-making patterns across three groupsindividuals diagnosed with mild-to-moderate depression, a healthy matched control group, and a negative mood induction group. In the current study, drug- and therapy-nae individuals diagnosed with first episode of mild-to-moderate depression (n = 40), healthy individuals matched on age, gender, and education (n = 40), and healthy individuals with no current, past, or family history of any psychiatric conditions in a negative mood-induced state (n = 40) were administered the IOWA Gambling Task (IGT) and the Balloon Analog Risk Task (BART). Results indicated that individuals with depression showed heightened punishment sensitivity on both the IGT and the BART (p < 0.05 on the BART and p < 0.05 on the IGT), andperformed poorly on the IGT indicating poor and slow learning (p < 0.01). A similar, less severe, pattern was observed in the negative mood induction group. Individuals with mild-to-moderate depression performed poorly on tasks of value-based decision making. The significance of process factors in decision making, such as reward and punishment sensitivity, valuation of outcomes and learning, was highlighted in this study. The study also demonstrated how a negative affective state, without the other clusters of depressive symptomatology, can also lead to a less severe, but impaired decision making. 2023, The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India. -
Lung cancer prediction with advanced graph neural networks
This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
WELL-BEING AND PROSPERITY: Multidirectional Disciplinary Interactions with Religion
Despite significant advancements in science and technology, religion continues to influence human lives. The twentieth-century perspectives from social sciences, influenced by the secular hypothesis, mainly highlight the negative influence of religion on human progress and practically ignore its influential and positive impact on various fields of knowledge/disciplines. In this paper, we have examined literature from politics, economics, and psychology to understand religions impact on these disciplines and vice versa. We find that religions contribution to human society in the 20th and 21st centuries has been mostly positive, especially in education, healthcare, social justice, economic growth, ethics, and initiatives for eradicating inequality and injustice. For instance, religion provides effective coping measures and strategies when humans face uncertainties and catastrophes and facilitate comfort, confidence, and emotional wellness. Further, we realised that (i) the contemporary research literature in social sciences generally highlights the interaction between religion and various fields of knowledge in a unidirectional way i.e., religion influencing disciplines and not how disciplines influence religion, and (ii) that it fails to reveal a more complex multidirectional and circular relationship between religion and social sciences. This paper proposes ways to bring together social scientists and religious scholars to facilitate the much-needed discussion on the multidirectional relationship between religion and social sciences, thereby paving the way toward the well-being of individuals and social transformation. 2022 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore),. -
A review on anti-cancer plants of India
India has a high level of endemism and a diverse range of floral species. Cancer is one of the most significant challenges facing global health today. The indigenous peoples and residents who live in India have, for a very long time, made use of specific medicinal plants to fight cancer. This practice is still prevalent today. Several different drugs may be utilized in the treatment of cancer. Because of the potential drawbacks associated with such treatments and the development of drug resistance, the quest for new therapies that are both safer and much more effective is still the most challenging field of study. Several cancer medicines used today come from natural sources. We're returning to our old ways because medicinal plants are a good, natural way to make medicines that prevent cancer without causing major side effects. Within the scope of this study, a few herbs traditionally used to treat cancer are looked at to see what they might be good for. The cytotoxicity of these plants, the processes that lead to them, and the different compounds they make were looked into. This study has tried to focus on how these plants fight cancer. The Author(s). -
Investigations on Affective Computing to Improve Classroom Engagement Analysis in Higher Education by Deep Learning
The learning and teaching experience can be improved by using approaches that are not obtrusive to perform a comprehensive student engagement analysis throughout the classroom. In these modern times, when courses are conducted online, it is vital to accurately measure the levels of participation that each individual student has. It is crucial and essential to provide assistance to educators so that they may annotate and comprehend the signifcant learning rate of the students. A system that can perceive data and transpire it into information automates the learning and teaching experience in a classroom. In this study, videos are collected from online and ofand#64258;ine classes that have one single student per frame or many students per frame and are analysed for emotions and behavioural engagement through a multimodal system. newlineLarge amounts of video data processing call for an increase in the hardware resources newlineas well as the time required for processing images. This is particularly true in a newlineclassroom setting, where there are a large number of frames to analyse each and every minute in order to handle classroom involvement detection. Hierarchical Video newlineSummarization is used as a preliminary step on the videos to detect important frames newlinethat have the sum of all the information in the local neighborhood. These key frames newlineserve as important information units that provide details of facial emotions and behavioural aspects. The local maxima estimation based on the frst derivative provides summative information about the local neighborhood. The key frames serve newlineas an input for face detection and emotional analysis. In this research, the method newlinecan perform video summarization on a varied category of videos and with different newlineresolutions. Face detection in a temporal environment have not been trivial. Though there are methods that can identify multiple faces with varied sizes in a frame, it is still a current research topic to address false localization of faces in a frame. -
A Deterministic Key-Frame Indexing and Selection for Surveillance Video Summarization
Video data is voluminous and impacts the data storage devices as there are CCTV surveillance videos being created every minute and stored continuously. Due to this increase in data there is a need to create semantic information out of the frames that are being stored. Video Summarization is a process that continuously monitors changes and helps in reducing the number of frames being stored. This work enables summarization to be carried out based on selecting threshold-based system that can select key-frames ideally suit for storage and further analysis. Initially a Global threshold based on Otsus method is carried out for all frames of a surveillance video and based on the set threshold a retrospective comparison is done on each frame based on statistical methods to converge on determining the keyframes. A similarity index is generated based on the iterative comparison of frames based on global and local threshold comparison. The local threshold is indexed based on Analysing Method Patterns to Locate Errors(AMPLE), An-derbergs D(AbD), Cohens Kappa(CK), Tanimoto Similarity(TS), Tversky feature contrast model(TFCM), Pearson coefficient of mean square contingency(Pmsc). The Global threshold is updated each time a keyframe is selected based on the comparison of local and global threshold. The results are compared with five surveillance videos and six methods to identify keyframes Selection Rate is the metric used for calculating the performance. 2019 IEEE. -
A classified study on semantic analysis of video summarization
In today's world data represented in the form of a video are prolific and has increased the requisite of storage devices unconditionally. These video sets takes up a huge space for amassing data and takes a long time to ascertain the content that requires a higher cognitive process for content search and retrieval. The efficient method for storing video data is to remove high-degree redundancies and for creating an index of important events, objects and a preview video based on vital key-frames. These requirements imbibes the need to build algorithms that can concise the necessity of space and time for video and adequate approaches are to be developed to solve the needs of summarization. The three effective attributes for a semantic summarized video system are Un-supervision, efficient and dynamically scalable system that can help in reducing time and space complexities. Dimensionality reduction based on sub space analysis helps in plummeting the multidimensional data into a low-dimensional data to enable faster feature extraction and summarization. In this paper we have made a study and description related to several summarization methodologies for video's that are available. 2017 IEEE. -
Effect of calcination temperature on surface morphology and photocatalytic activity in TiO2 thin films prepared by Spin Coating technique
TiO2 thin films were deposited on glass substrate using Sol-Gel derived precursor by Spin Coating technique at different calcination temperatures. Structural identity of the prepared films was con-firmed by powder X-ray diffraction measurements. Morphology of the films was monitored using Atomic force microscopy and it was observed that calcination temperature of 400 C favored TiO2 nano-fibers. Photocatalytic activity of the films was checked by observing the degradation of herbicide Atrazine in UV region and the percentage of degradation was analyzed by HPLC method. 2014 BCREC UNDIP. All rights reserved. -
Cu/Pd bimetallic supported on mesoporous TiO2 for suzuki coupling reaction /
Bulletin of Chemical Reaction Engineering & Catalysis, Vol.13, Issue 2, pp. 1-11, ISSN No. 1978-2993. -
Data Modeling and Analysis for the Internet of Medical Things
Smart biomedical technology greatly assists in rapid disease screening and diagnosis within hospitals. One innovative device, a smart inhaler, incorporates sensors to track medication doses, usage patterns, and effectiveness. These inhalers provide valuable support to asthma sufferers, allowing for improved condition management and better patient outcomes. Asthma, a chronic respiratory disease affecting millions worldwide, causes airway constriction and swelling, resulting in breathing difficulties. Typically, medication such as inhaled corticosteroids and bronchodilators is used for management. However, medication adherence is often inadequate, leading to worsened outcomes and exacerbations. Smart inhalers aim to address this challenge by enabling users to monitor medication usage and compliance. Equipped with sensors, the inhalers track when, how much, and how frequently the prescribed medication is taken. The collected data is then transmitted to a mobile app or web portal, accessible to patients and healthcare providers. This integration facilitates medication tracking and provides personalized coaching for improved asthma control. The gathered data serves multiple purposes, including helping patients monitor their medication use and adherence. Patients can receive feedback on their treatment plan adherence and utilize the app to set medication reminders, promoting adherence and enhancing outcomes. 2024 CRC Press. -
Rainbow degree-jump coloring of graphs
In this paper, we introduce a new notion called the rainbow degree-jump coloring of a graph. For a vertex v ? V(G), let the degree-jump closed neighbourhood of this vertex be defined as Ndeg [v] = {u: d(v, u) ? d(v)}. A proper coloring of a graph G is said to be a rainbow degree-jump coloring of G if for all v in V(G), c(Ndeg [v]) contains at least one of each color class. We determine a necessary and sufficient condition for a graph G to permit a rainbow degree-jump coloring. We also determine the rainbow degree-jump chromatic number, denoted by ?rdj (G), for certain classes of cycle related graphs. Mphako-Banda E.G., Kok J., Naduvath S., 2021. -
Biodegradation studies of polyhydroxyalkanoates extracted from bacillus subtilis NCDC 0671 /
Research Journal of Chemistry And Environment, Vol.23, Issue 6, pp.107-114 -
Valorization of pineapple peels through single cell protein production using saccharomyces cerevisiae NCDC364 /
Applied Food Biotechnology, Vol.6, Issue 4, pp.255-263, ISSN No: 2345-5357. -
Surface tuning of nanostructured graphitic carbon nitrides for enhanced electrocatalytic applications: a review
The precursors for developing sustainable and environment-friendly energy conversion and storage devices requires the utilization of advanced, highly efficient, and economical nano-structured electrocatalysts instead of conventional and expensive noble metals. Therefore, graphitic carbon nitride (g-C3N4) as a material has gained wide attention due to its relative ease of synthesis, high nitrogen content, conductivity, and tuneable band gap energy. In recent years, their tunable electronic properties along with physicochemical stability have given rise to numerous research delving into their diverse range of applications. With the advancement in the tuning of their electrochemical performance, the electrical conductivity of g-C3N4 can be enhanced by structurally modifying the g-C3N4 framework accordingly. This review focuses on various structural modifications of g-C3N4 by functionalization, elemental doping, and hybridizing techniques for ameliorating the number of active sites resulting in enhanced electrocatalytic performance. Herein, the prospective researchers are given a concise perspective regarding the surface tuning of g-C3N4 for improving their electrocatalytic applications. 2023 Elsevier Ltd