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Edge incident 2-edge coloring of graphs
The edge incident 2-edge coloring of a graph G is an edge coloring of the graph G such that not more than two colors are assigned to the edges incident to an edge e = uv in G. In other words, for every edge e in G, the edge e and all the edges that are incident to the edge e is in at most two different color classes. The edge incident 2-edge coloring number ?'ein2 (G) is the maximum number of colors in any edge incident 2-edge coloring of G. The main objective of this paper is to study the edge incident 2-edge coloring concept and apply the same to some graph classes. Besides finding the exact values of these parameters, we also obtain some bounds. 2025 World Scientific Publishing Company. -
On the Maximization of Some Graph Coloring Problems
A graph coloring problem involves labeling the vertices or edges in a graph with newlinecolors or numbers subject to some constraints. The most frequently known graph newlinecoloring problems are the ones that usually minimize the number of colors used in newlinecoloring the vertices or edges. The chromatic number of a graph G, denoted by and#967;(G), is the least number of colors used in a proper coloring of G. The chromatic sum of a graph G, denoted as P(G), was introduced in [1], which is to and the smallest possible coloring sum in a proper coloring of the graph G using natural numbers. Lately, a few studies have endured in a distinct area of the literature where the number of colors used in a graph coloring problem is maximized under certain conditions. Some of these works have applications in network sciences. newlineThe concerned study focuses on the maximization of three dierent edge coloring newlineconcepts, viz., the vertex induced kand#8722;edge coloring, vertex incident kand#8722;edge coloring, newlineand edge incident 2and#8722;edge coloring of a simple connected graph G, where k and#8805; 2. The newlinenumber of colors assigned to the edges of the graph G has been maximized under certain conditions. The vertex induced kand#8722;edge coloring and the vertex incident newlinekand#8722;edge coloring concepts are the generalized version of the edge coloring approach newlineintroduced and studied in [2]. Furthermore, the concept of the achromatic sum of a graph G has also been introduced here. This concept is to and the greatest possible coloring sum of the graph G in an improper edge coloring using natural numbers. An extensive study newlineon three achromatic sums, namely the vertex induced 2and#8722;edge coloring sum, the vertex incident 2and#8722;edge coloring sum, and the edge incident 2and#8722;edge coloring sum are carried out. A few bounds for these parameters on a simple connected graph G and the exact values for some elementary graph classes have been investigated. A few comparative results between some of these parameters have also been obtained. -
Breast Cancer Prediction using a Stacked Ensemble of XGBoost and LightGBM with Logistic Regression Meta-Learning
Breast cancer remains one of the major reasons for cancer deaths in women, which is why it is key to develop and improve diagnostic systems for accurate predictions. Currently, the advent of Machine learning has helped in providing powerful algorithms to achieve advancements in cancer detection. However, the main motivation of this research is to focus on building more complex ensemble architectures, as they are known for significantly improving predictive accuracy, robustness, and generalisation, especially in performing complex tasks such as medical diagnosis. In this research, a Hybrid stacking ensemble was built using two gradient boosting techniques, XGBoost and LightGBM, with a Logistic Regression meta-learner to predict breast cancer and compare their performance with standard classifiers. The Breast Cancer Wisconsin (Diagnostic) dataset, which consists of 569 patient records, was utilised for model training and analysis. The data was preprocessed using Z-score normalisation and stratified 5-fold cross-validation. The machine learning algorithms, such as Decision Tree, Logistic Regression, and Random Forest, were compared with the hybrid model, and the metrics used for comparison were accuracy, precision, recall, F1-score, and ROC-AUC. The proposed hybrid model performed well, achieving a high accuracy rate of 97.37% and a recall rate of 93.00% for malignant cases. McNemar's test (p > 0.05) confirms that this accuracy rate is statistically equivalent to the Random Forest classifier. These findings proved that the proposed model can perform optimally in predicting complex data with the same degree of precision as the standard models. Therefore, the hybrid model can be considered a robust and reliable new alternative for breast cancer prediction. 2026 IEEE. -
Technopedagogy in teacher education: exploring challenges and possibilities
Digital technologies allowed teachers to overcome spatial and temporal limitations in education, particularly during the COVID-19 pandemic-imposed restrictions. While access to technological resources proved beneficial, teachers faced initial challenges. It is crucial to address the significance of digital education training in teacher education institutions, particularly in implementing the Integrated Teacher Education Programme based on the National Education Policy 2020 in India. This study explores approaches to techno-pedagogical skills in teacher education in Kerala, India, and the potential solutions to bridge the digital gap between training and teaching in the classroom. The researchers have used qualitative methods to gather and analyse data, including archival research and interviews with teacher educators and student teachers in the Bachelor of Education Programme. The findings indicate an urgent need for infrastructural upgrades and continuous professional development practices. Copyright 2025 Inderscience Enterprises Ltd. -
Antimagic labeling of n-uniform cactus chain graphs
A graph G = (V,E) is considered antimagic if it admits antimagic labeling. The antimagic labeling of a finite, simple graph with |V | = n and |E| = m is a bijective function from the set of edges to the set of integers {1, 2,,m} such that the vertex sum of n vertices is pairwise distinct. The vertex sum of a vertex is obtained by summing the labels of all edges incident to it. Hartsfield and Ringel conjectured that every connected graph different from K2 is antimagic. Supporting this conjecture, it was shown that the dense graphs are antimagic. A cactus graph is a connected graph where no edge lies within more than one cycle. A cactus graph in which each block is a cycle of the same size n is called an n-uniform cactus graph. We proved that Hartsfield and Ringels conjecture is true for n-uniform cactus chain graphs with and without pendant vertices, which are specific cases of sparse graphs. 2026 World Scientific Publishing Company. -
Prediction of Next-Day Stock Price Using Stacked Ensemble Learning TechniquesAn Exploration of Model Compatibility
Trading professionals can make well-informed decisions about what to purchase or sell in order to maximize short-term gains by forecasting stock prices for the next day. This research study focuses on exploring the compatibility of ensemble learning techniques through stacking to predict next-day stock prices. The models involvedRandom Forest, Extra Trees, AdaBoost, and Gradient Boosting, were paired two at a time, and their predictions were used as inputs to a Multi-Layer Perceptron (MLP) Regressor, which served as the meta-learner. The results revealed that the combination of Extra Trees Regressor and Gradient Boosting outperformed the individual base models, due to their complementary strengths and ability to capture non-linear relationships effectively. However, other model combinations showed only average performance. This outcome was attributed to overlapping model strengths, leading to increase in error and overfitting. The findings highlight the importance of thoughtful model selection in ensemble methods and suggest that not all combinations are equally beneficial. Understanding the compatibility of different models is crucial to improving performance in ensemble learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Novel Preprocessing Model for Multi Modal Brain MRI image Classification for Stroke Prognosis
Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data Fluid Attenuated Inversion Recovery (FLAIR), Apparent Diffusion Coefficient (ADC) and Diffusion-Weighted Imaging (DWI). Multimodal data gives a comprehensive understanding of the brain and the stroke lesions. Complex algorithms and processing steps are required to ensure that the data is prepared for further processing. The objective of this experimental research is to create a novel multimodal preprocessing model that can be used for the preprocessing of the multimodal data from various MRI modalities (FLAIR, DWI and ADC). The proposed model supports the automatic removal of artefacts from the multimodal data, by identifying and applying the best preprocessing techniques for Image Registration (Affine or non-rigid transformations), Normalization (Z Score or min-max normalizations), Denoising Techniques (Gaussian, Median, Non-Local Means, or Anisotropic Diffusion filters) and Bias Field correction. The best technique is identified using the evaluation techniques of Dice Coefficient, Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Root Mean Squared Error (RMSE). Preprocessing is critical process to improve the outcome of the subsequent analysis including segmentation. Here, we propose an Enhanced Image Registration and Artefact Correction (EIRAC) model with Best Image Registration Technique (BIRT) and Multiple Orientation Normalization Denoising and Bias field correction Parallelly (MONDBP) algorithms for the preprocessing of multimodal MRI images to provides better results for the segmentation of stroke lesions through Machine Learning models. 2025, Binghamton University Libraries. All rights reserved. -
Indigenous womens carework and environmental pedagogy in select contemporary native American fiction
This article explores how Indigenous womens fiction functions as a site of environmental education, reimagining pedagogy through relational ethics, affective care, and land-based knowledge. Drawing on The Night Watchman by Louise Erdrich (Chippewa), Crooked Hallelujah by Kelli Jo Ford (Cherokee), and The Removed by Brandon Hobson (Cherokee), the paper examines how storytelling, domestic labour, and ecological care become pedagogical practices that sustain cultural continuity. Grounded in Indigenous feminist and environmental humanities frameworks, the study integrates Leanne Betasamosake Simpsons (Michi Saagiig Nishnaabeg) concept of land as pedagogy, Mishuana Goemans (Tonawanda Band of Seneca) idea of spatial sovereignty, Kelli Keelers (Cherokee Nation) theorization of land as agent, and Beth Piatotes (Nez Perce) notion of the feminine everyday. Together, these theories reveal how the everyday practices of Indigenous women enact sustainable learning rooted in reciprocity and care. The analysis demonstrates that these literary works do not simply represent ecological consciousnessthey perform ittransforming narrative into a relational curriculum. By situating Indigenous storytelling within environmental education, the article argues that carework, kinship, and ecological reciprocity form a decolonial pedagogy aligned with Sustainable Development Goals (Quality Education), (Climate Action), and (Life on Land). 2026 Informa UK Limited, trading as Taylor & Francis Group. -
The unseen dilemma of AI in mental healthcare
[No abstract available] -
A Deep Learning Approach to Clinical Decision Support in Heart Disease Diagnosis
Heart disease is the dominant cause of extinction worldwide, emphasizing the importance of early diagnosis and treatment planning. In this article, the authors developed a Clinical Decision Support System (CDSS) for heart disease prediction using deep learning techniques. This system will suggest a neural network architecture with Leaky ReLU as the activation function in the hidden layers and Sigmoid as the activation function in the output layer for binary classification. The configuration neural network is enhanced across three to nine hidden layers. The proposed approach is evaluated using accuracy as the measurable value on five multivariate datasets. By integrating advanced deep learning with clinical expertise, this study aims to enhance predictive accuracy, contributing to reduced heart disease mortality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
EDGE INCIDENT 2-EDGE COLORING SUM OF GRAPHS
The edge incident 2-edge coloring number, ?ein2(G), of a graph G is the highest coloring number used in an edge coloring of a graph G such that the edges incident to an edge e = uv in G is colored with at most two distinct colors. The edge incident 2-edge coloring sum of a graph G, denoted as (Formula presented.), is the greatest sum among all the edge incident 2-edge coloring of graph G which receives maximum ?ein2(G) colors. The main objective of this paper is to study the edge incident 2-edge coloring sum of graphs and find the exact values of this parameter for some known graphs. I??k University, Department of Mathematics, 2025; all rights reserved. -
A STUDY ON THE INFLUENCE OF FAMILY ENVIRONMENT IN THE DEVELOPMENT OF AGGRESSIVE BEHAVIOR IN CHILDREN
This study designates the influence of family environment in the development of aggressive behavior in children. The purpose of the study was to find out how the family environment influences the aggressive behavior in children. The study was conducted in 5 government aided schools run by the diocese of Mananthavady in Wayanad district of Kerala State. The study included fifty, 10 to 12 year old school going children and their mothers. Family environment scale was used to find the family environment of the children and Aggressive Questionnaire was used to find the level of aggression in children. The raw data were subjected to various statistical analyses. The study brings in (1) the structure and systems in the families (2) the types and levels of aggression in children. The study found that 54 % of the children are with high aggression and 36 % of the children are with very high aggression. Over all the result found that the family environment has a significant role in the development of aggressive behavior in children. The more poor structure and systems in family environment creates the more aggressive children. Key words: family environment, physical aggression, verbal aggression, anger, hostility. -
Linear and non-linear analyses of electrothermo convection in a micropolar fluid
The linear and weakly non-linear stability analyses of electrothermo convention in a micropolar fluid layer heated from below are studied. The linear and non-linear analyses are, respectively, based on normal mode technique and truncated representation of Fourier series. The influence of various parameters on the onset of convection has been analyzed in the linear case. The resulting autonomous Lorenz model obtained in non-linear analysis is solved numerically to quantify the heat transfer through Nusselt number. It is observed that the increase in concentration of suspended particles stabilizes the system and decreases the heat transfer and increase in electric Rayleigh number destabilizes the system and increases the heat transfer. 2017 Pushpa Publishing House, Allahabad, India. -
Detection of Various Security Threats in IoT and Cloud Computing using Machine Learning
Due to the growth of internet technology, there is a sharp rise in the growth of IoT enabled devices. IoT (Internet of Things) refers to the connection of various embedded devices with limited processing and memory. With the heavy adoption of IoT applications, cloud computing is gaining traction with the ever-increasing demand to process and compute a massive amount of data coming from various devices. Hence, cloud computing and IoT are often related to each other. However, there are two challenges in deploying the IoT and cloud computing frameworks: security and Privacy. This article discusses various types of security threats affecting IoT and cloud computing, and threats are classified using machine learning (ML). ML has gained much momentum in recent years and is applied in various domains. One of the main subdomains of machine learning is used in IoT and cloud security. A machine learning model can be trained with data based on which the model can predict the impending security threats. Popular security techniques to protect IoT devices from hackers are IoT authentication, access control, malware detection, and secure overloading. Supervised learning algorithms can be used to detect malware in the runtime behavior of applications. The malware is detected from network traffic and is labeled based on its suspicious behavior. Post identification of malware, the application data is stored in a database trained via an ML classifier algorithm (KNN or Random Forest). With increased training, the model can identify malware applications with higher accuracy. 2022 IEEE. -
Analysis of Multinomial Classification for Legal Document Categorization
A major area of research today is the application of Machine Learning Techniques for Document or Text Classification. Document Classification is an important aspect of Electronic Discovery in the Legal domain. The need for the process to be automated has been realized over the past few years. Multinomial Classification is a well-known Supervised Machine Learning Technique that helps us classify if there are more than two classes used for the purpose of Classification. Evaluation metrics such as Precision, Recall, and F1 Score have been used to measure the efficiency of Classification. Logistic Regression and Gradient Boosting Algorithms have outperformed other Multiclass Classification techniques. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Supreme court dialogue classification using machine learning models
Legal classification models help lawyers identify the relevant documents required for a study. In this study, the focus is on sentence level classification. To be more precise, the work undertaken focuses on a conversation in the supreme court between the justice and other correspondents. In the study, both the nae Bayes classifier and logistic regression are used to classify conversations at the sentence level. The performance is measured with the help of the area under the curve score. The study found that the model that was trained on a specific case yielded better results than a model that was trained on a larger number of conversations. Case specificity is found to be more crucial in gaining better results from the classifier. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
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India as a climate leader in the indo-pacific: challenges and opportunities
The non-traditional security threats in the form of incessant floods, cyclones, and all-time rising sea levels in the Indo-Pacific region call for an integrated and constructive response led by a climate leader. Climate change is seen way beyond the lens of a mere environmental catastrophe having the potential to destabilize a nations economy and polity. The global state and non-state actors have acknowledged climate change to be an alarming global security threat. However, the failure of collective management of the climate crisis has mandated a responsible climate leader to monitor the mitigation efforts. In the context of initiatives like the National Solar Mission that envisages India to be a global leader in solar energy, the paper intends to weigh the possibilities for Indias role as a cogent climate leader in the Indo-Pacific region. It seeks to evaluate Indias climate leadership based on its green policies and assistance to Indo-Pacific countries. 2024 Indian Ocean Research Group.