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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. -
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. -
Metabolomics Pathway Prediction Using Enhanced-Graph Convolutional Networks with Graph Attention Networks
Metabolomics, the comprehensive study of small molecules in biological systems, has a central role to play in the diagnosis of diseases, biomarker detection, and the design of new drugs. Although there have been major breakthroughs in analytical toolsets such as mass spectrometry (MS) coupled with chromatography, it is hard to predict metabolomics pathways because biochemical interactions are inherently complex. To meet this end, the current research suggests a deep learning-based approach using graph neural networks (GNN), which have shown high efficiency for graph-structured biological data. We specifically propose an enhanced graph convolutional network integrated with graph attention networks (EGCNGAT) to enhance pathway prediction performance. The hybrid framework employs graph convolutional networks (GCN) to represent molecular structural data and graph attention networks (GAT) to provide context-sensitive feature importance, thus improving the models capacity for learning complex pathway patterns. Comparative experiments against current deep learning approaches show that the introduced EGCN-GAT model obtains an accuracy of 98.90 percent, which is a 0.26 percent increase compared to the baseline MLGL-MP model. In addition, it demonstrates a 0.94 percent gain in precision as well as a slight gain in recall. The findings validate the performance of the proposed method and highlight its utility for developing pathway-level predictions in metabolomics studies. 2025 by the authors of this article. Published under CC-BY. -
Enhancing Metabolomics Pathway Prediction with Sequential Graph Convolutional Network
Metabolomics is a powerful tool for the understanding of biological systems by analysis of metabolites and their related pathways. Prediction of metabolic pathways is still one of the most challenging tasks because of the complexity of molecular structures and graph-structured metabolomics data. This article presents a robust framework using Graph Convolutional Networks (GCNs) to address the challenges. The methodology proposed includes first preprocessing through metabolite identification by mass spectrometry, and then it utilizes feature extraction through the RDKit library. The objective of the research is aim to metabolic pathway prediction using machine learning algorithm. Complex patterns and relationships are captured from the SMILES representation through the molecular graphs constructed and passed on for the GCN model to learn structured data. ReLU activation functions have been employed within a three-layer sequential GCN architecture that enables it to deliver highly accurate results while ensuring that they are understandable as well. The proposed sequential GCN Model was evaluated on the KEGG dataset with an accuracy of 98.00%, precision of 92.10%, and recall of 93.02%. The performance of these metrics is well beyond traditional approaches such as KNN, ensemble logistic regression, and other GCN based approaches. Thus, this work brings GCN based approaches closer to revolutionizing metabolic pathway prediction and the advancement of the metabolomics field. 2025 IEEE. -
Improved Indian currency recognition: neighbourhood-centred image processing and CNNs with region of pixel selection techniques
The paper proposes an improved approach for Indian currency recognition using neighbourhood-centred image processing and convolutional neural networks (CNNs) with region of pixel selection techniques. The method includes image pre-processing steps such as noise reduction, contrast enhancement, and resizing. A neighbourhood-centred image processing technique is applied to capture contextual information from local neighbourhoods around each pixel. A CNN-based model is then trained on the pre-processed images to learn discriminative features for currency recognition. To enhance accuracy and efficiency, a region of pixel selection technique is introduced to select only relevant regions of interest for CNN training and inference, reducing computational overhead. Experimental results demonstrate the effectiveness of the proposed approach, achieving high accuracy in currency recognition and improved efficiency in terms of computational time and memory requirements. The proposed method has potential applications in automated cash-handling machines, vending machines, and mobile payment systems where reliable currency recognition is essential. Copyright 2025 Inderscience Enterprises Ltd. -
A Travellers Melancholy
On possibly the most humid day in the month of June, I lay on a bed in a backpacking hostel, journalling my melancholy. 2025, The Assosiation FormAkademisk. 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),. -
An exposition on complete androgen insensitivity syndrome and a case report; [??????? ?????? ?????????????????? ? ??????????: ??????????? ??????]
Complete androgen insensitivity syndrome (CAIS) is a rare X-linked sexual development condition typified by 46,XY karyotype, presence of external female genitalia along with intra-abdominal testes in labia majora or inguinal ring region. This syndrome results from alterations in the androgen receptor (AR) gene leading to primary amenorrhea and uterine agenesis (Mlerian agenesis) in adolescent teens or two-sided labial/inguinal hernia with testes in children around prepubertal age. Our paper reports a case of CAIS in a 16-year-old woman with no menarche and 46,XY karyotyping. Gonadectomy results showed hyperplasia of Leydig cells. The current research encompasses the case report and the available knowledge to date on the understanding, diagnosis, treatment, and management of CAIS. 2025 IRBIS LLC. All rights reserved. -
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). -
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. -
Evaluating the effectiveness of virtual reality-based rehabilitation programs for post-injury recovery in adolescent athletes: a mixed-methods study; [Evaluaci de la eficacia de los programas de rehabilitaci basados en realidad virtual para la recuperaci de deportistas adolescentes tras lesiones: un estudio de modos mixtos]
Introduction: the importance of post-injury rehabilitation for teenage athletes demands innovative methods because traditional practices fail to sustain student athlete participation. VR-based rehabilitation creates interactive recovery programs which might advance physical healing together with mental drive. Objective: the research investigates how well VR-based rehabilitation works against traditional approaches for both physical healing and psychological involvement in adolescent athletes. Methodology: sixty adolescent athletes (aged 1318) received their rehabilitation through random assignment into two groups: one involved traditional approaches while the other received VR-based rehabilitation. The research measured recovery outcomes at three time points: baseline, 4 weeks and 8 weeks. The measured outcomes included range of motion (ROM), muscle strength, return to sport (RTS) time and pain perception. The VR group members shared their experiences through semi-structured interview methods. Results: the subjects in the VR group achieved greater improvements in ROM (p = 0.02) and muscle strength (p = 0.03) and RTS time (p = 0.01). People who used VR reported stronger motivation and engagement although these benefits brought increased worry about re-injuring their knee. Subject participants achieved better results in their rehabilitation by using immersive VR interventions. Conclusions: virtual reality-based rehabilitation enables adolescent athletes to restore physical well-being as well as emotional well-being. The interactive features of this approach improve patient commitment which accelerates their recovery time. Future investigations need to analyze extended advantages and expanded medical applications within sports medicine. 2025 Federacion Espanola de Docentes de Educacion Fisica. All rights reserved. -
Biodegradation studies of polyhydroxyalkanoates extracted from bacillus subtilis NCDC 0671 /
Research Journal of Chemistry And Environment, Vol.23, Issue 6, pp.107-114