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On ?(k)-coloring of graph products
An edge which is incident on two vertices that are assigned the same color is called a bad edge. A near proper coloring is a coloring that minimises the number of bad edges in a graph G, by permitting few color classes to have adjacency between the elements in it. A near proper coloring, that uses k colors where 1 ? k ? ?(G) ? 1, which allows at most one color class to be a non independent set to minimise the number of bad edges resulting from the same is called a ?(k)-coloring. In this paper, we determine the minimum number of bad edges, bk(G), resulting from a ?(k)- coloring of some graph products viz. direct product of two graphs G H and corona product of two graphs G?H, for all different possible values of k by investigating an optimal ?(k)-coloring that results in minimum number of bad edges. (2023), (Institute of Combinatorics and its Applications). All Rights Reserved. -
A note on ?(k)-colouring of the Cartesian product of some graphs
The chromatic number, x(G) of a graph G is the minimum number of colours used in a proper colouring of G. In an improper colouring, an edge uv is bad if the colours assigned to the end vertices of the edge is the same. Now, if the available colours are less than that of the chromatic number of graph G, then colouring the graph with the available colours lead to bad edges in G. The number of bad edges resulting from a ? (k)-colouring of G is denoted by bk(G). In this paper, we use the concept of (k)-colouring and determine the number of bad edges in Cartesian product of some graphs. 2022 by the authors. -
On ?(k)-coloring of powers of helm and closed helm graphs
If the availability of colors to color a graph G is less than that of the chromatic number ?(G) of the graph, then coloring the graph with available colors, say k colors, where 1 ? k ? ?(G)-1, will cause the end vertices of at least one edge to be colored with same color. Such an edge whose end vertices receive a same color is called as a bad edge. A coloring that restricts few color classes to have adjacency between the elements in it so as to minimize the number of bad edges obtained from it in a graph G is called as a near proper coloring and a near proper coloring that uses k colors where 1 ? k ? ?(G)-1 to color a graph G by permitting only one color class to have adjacency among the elements in it and thereby minimize the number of bad edges resulting from the permitted color class is called as a ?(k)-coloring of the graph G. In this paper, we determine the number of bad edges of powers of helm graphs H1,nr and powers of closed helm graphs CH1,nr. 2022 World Scientific Publishing Company. -
On (k) -coloring of generalized Petersen graphs
The chromatic number, ?(G) of a graph G is the minimum number of colors used in a proper coloring of G. In an improper coloring, an edge uv is bad if the colors assigned to the end vertices of the edge is the same. Now, if the available colors are less than that of the chromatic number of graph G, then coloring the graph with the available colors leads to bad edges in G. In this paper, we use the concept of (k)-coloring and determine the number of bad edges in generalized Petersen graph (P(n,t)). The number of bad edges which result from a (k)-coloring of G is denoted by bk(G). 2022 World Scientific Publishing Company. -
On ?(k)-colouring of Some Wheel Related Graphs
The question on how to colour a graph G when the number of available colours to colour G is less than that of the chromatic number ?(G), such that the resulting colouring gives a minimum number of edges whose end vertices have the same colour, has been a study of great interest. Such an edge whose end vertices receives the same colour is called a bad edge. In this paper, we use the concept of ?(k)-colouring, where 1 ? k ? ?(G) ? 1, which is a near proper colouring that permits a single colour class to have adjacency between the vertices in it and restricts every other colour class to be an independent set, to find the minimum number of bad edges obtained from the same for some wheel related graphs. The minimum number of bad edges obtained from ?(k)-colouring of any graph G is denoted by bk(G). 2024 the Author(s), licensee Combinatorial Press. -
Some New Results on ?(k) -Coloring of Graphs
Let ? be the minimum number of distinct resources or equipment such as channels, transmitters, antennas and surveillance equipment required for a system's stability. These resources are placed on a system. The system is stable only if the resources of the same type are placed far away from each other or, in other words, they are not adjacent to each other. Let these distinct resources represent different colors assigned on the vertices of a graph G. Suppose the available resources, denoted by k, are less than ?. In that case, placing k resources on the vertices of G will make at least one equipment of the same type adjacent to each other, which thereby make the system unstable. In ?(k)-coloring, the adjacency between the resources of a single resource type is tolerated. The remaining resources are placed on the vertices so that no two resources of the same type are adjacent to each other. In this paper, we discuss some general results on the ?(k)-coloring and the number of bad edges obtained from the same for a graph G. Also, we determine the minimum number of bad edges obtained from ?(k)-coloring of few derived graph of graphs. The number of bad edges which result from a ?(k)-coloring of G is denoted by bk(G). 2023 World Scientific Publishing Company. -
Analysis of Challenges Experienced by Students with Online Classes During the COVID-19 Pandemic
In the current context of the COVID-19 pandemic, due to restrictions in mobility and the closure of schools, people had to shift to work from home. India has the worlds second-largest pool of internet users, yet half its population lacks internet access or knowledge to use digital services. The shift to online mediums for education has exposed the stark digital divide in the education system. The digitization of education proved to be a significant challenge for students who lacked the devices, internet facility, and infrastructure to support the online mode of education or lacked the training to use these devices. These challenges raise concerns about the effectiveness of the future of education, as teachers and students find it challenging to communicate, connect, and assess meaningful learning. This study was conducted at one of the universities in India using a purposive sampling method to understand the challenges faced by the students during the online study and their satisfaction level. This paper aims to draw insight from the survey into the concerns raised by students from different backgrounds while learning from their homes and the decline in the effectiveness of education. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization enabled deep learning model for diabetic retinopathy detection and severity level classification
Diabetic Retinopathy (DR) is one the most important problems of diabetics and it directs to the main cause of blindness. When proper treatment is afforded for DR patients, almost 90% of patients are protected from visual damage. DR does not produce any symptoms at the initial phase of the disease, thus various physical assessments, namely pupil dilation, visual acuity test, and so on are needed for DR disease detection. It is more complex to detect the DR during manual testing, because of the variations and complications of DR. The early detection and appropriate treatment assist to prevent vision loss for DR patients. Thus, it is very indispensable to categorize the levels and severity of DR for recommendation of essential treatment. In this paper, Autoregressive-Henry Gas Sailfish Optimization (Ar-HGSO)-based deep learning technique is proposed for DR detection and severity level classification of DR and Macular Edema (ME) based on color fundus images. The segmentation process is more essential for proper detection and classification process, which segments the image into various subgroups. The Deep Learning approach is utilized for effective identification of DR and severity classification of DR and ME. Moreover, the deep learning technique is trained by the designed Ar-HGSO scheme for obtaining better performance. The performance of the devised technique is evaluated using the IDRID dataset and DDR dataset. The introduced Ar-HGSO-based deep learning approach obtained better performance than other existing DR detection and classification techniques with regards to testing accuracy, sensitivity, and specificity of 0.9142, 0.9254, and 0.9142 using the IDRID dataset. 2022 Elsevier Ltd -
FACVO-DNFN: Deep learning-based feature fusion and Distributed Denial of Service attack detection in cloud computing
Cloud computing offers a broad range of resource pools for conserving a huge quantity of information. Due to the intrusion of attackers, the information that exists in the cloud is threatened. Distributed Denial of Service (DDoS) attack is the main reason for attacks in the cloud. In this study, a Fractional Anti Corona Virus Optimization-based Deep Neuro-Fuzzy Network (FACVO-based DNFN) is devised for detecting DDoS in the cloud. The production of log files, feature fusion, data augmentation, and DDoS attack detection is the processing stages involved in this phase of the DDoS attack detection process. The feature fusion is carried out by RV coefficient and Deep Quantum Neural Network (Deep QNN), and the data augmentation is performed. Then, the Anti Corona Virus Optimization (ACVO) method and Fractional Calculus (FC) are both incorporated to create the FACVO algorithm. The DNFN is trained by the created FACVO algorithm, which identifies the DDoS attack. The proposed approach achieved testing accuracy, TPR, TNR, and precision values of 0.9304, 0.9088, 0.9293, and 0.8745 for using the NSL-KDD dataset without attack, and 0.9200, 0.8991, 0.9015, and 0.8648 for using the BoT-IoT dataset without attack. 2022 Elsevier B.V. -
Movie Success Prediction from Movie Trailer Engagement and Sentiment Analysis
The diverse movie industry faces many challenges in the promotion of the product across different demographics. Movie trailer engagements provide valuable information about how the audience perceives the movie. This information can be used to predict the success of the upcoming movie before it gets released. The previous research works were mainly concentrating on Hindi language movies to predict success. The current research paper includes the success prediction of movies other than Hindi. This paper aims to analyze various Machine Learning models performance and select the best performing model to predict movie success. The developed model can efficiently classify successful and unsuccessful movies. For the current research, the data is collected from various sources through web scrapping and API calls in Sacnilk, The Movie Database (TMDB), YouTube, and Twitter. Different machine learning classification models such as Random Forest, Logistic Regression, KNN, and Gaussian Nae Bayes are tested to develop the best-performing prediction model. This research can help moviemakers to understand the popularity of the movie among the viewers and decide on an efficient promotional strategy to make the movie more successful. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Tailoring the properties of tin dioxide thin films by spray pyrolysis technique
Nanostructured transparent conducting SnO2 thin films have been grown on glass substrates via an environmentally benign chemical route viz spray pyrolysis. All samples were grown for various concentrations of precursor solution with the substrate kept at 350 C maintaining a spray rate of 10 mL/min. The characterizations revealed orthorhombic crystal structure with preferential growth in (112) plane for all samples. Ellipsometric analysis confirmed the good quality of the films. The sample prepared at 0.2 M concentration of precursor solution showed average transmission of 60% in the visible region with maximum conductivity of 24.86 S/cm. As synthesized samples exhibited overall Photoluminescence (PL) emission colours of green, greenish white and bluish white depending on the intensities of excitonic and oxygen vacancy defect level emissions. 2021 Elsevier B.V. -
P type copper doped tin oxide thin films and p-n homojunction diodes based on them
P-type copper doped tin oxide (SnO2:Cu) thin films were prepared by chemical spray pyrolysis method on glass substrates for different doping concentrations. Their structural, optical, surface morphological, elemental and electrical studies were investigated. We fabricated two transparent homojunction diodes using optimized sample of SnO2:Cu which are p- SnO2:Cu/n-SnO2 and p-SnO2:Cu/n- SnO2:F.These diodes are reported for the first time by this method. 2021 Elsevier B.V. -
Multimodal data analytics for climate and water resources management
The incorporation of multimodal data analytics into climate and water resource management has become a groundbreaking strategy for tackling intricate environmental issues. This chapter examines the importance of integrating various data sourcesincluding satellite imagery, weather sensors, textual reports, and social media feedsto develop a comprehensive perspective on climate and water systems. It addresses key challenges such as data heterogeneity, computational demands, and potential biases while showcasing the significant benefits of multimodal data in enhancing predictive modeling and decision-making. The discussion extends to advanced methodologies for data acquisition, integration, and feature extraction, with a focus on machine learning and deep learning techniques. Additionally, real-world applications in climate prediction, drought and flood forecasting, and water quality assessment are explored. The chapter also considers ethical concerns and future advancements in multimodal analytics, emphasizing the importance of responsible data utilization and innovative research to strengthen climate adaptation and water resource management efforts. 2026 Elsevier Inc. All rights reserved. -
Multimodal Learning Using Heterogeneous Data
Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities. The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data. Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Augmented intelligent water drops optimisation model for virtual machine placement in cloud environment
Virtual machine placement in cloud computing is to allocate the virtual machines (VMs) (user request) to suitable physical machines (PMs) so that the wastage of resources is reduced. Allocation of appropriate VMs to suitable and effective PMs will lead the service provider to be a better competitor with more available resources for affording a greater number of VMs simultaneously which in turn reflects with the growth in the economy. In this research work, an augmented intelligent water drop (IWD) algorithm is used for effectively placing VMs. The preliminary goal of this proposed work is to reduce the overall resource utilisation by packing the VMs to appropriate PMs effectively. The proposed IWD model is tested under the standard simulation process as it is given in the literature. Performance of IWD is compared with the existing techniques first fit decreasing, least loaded and ant colony optimisation algorithm. Performance analysis shows the significance of the proposed method over existing techniques. The Institution of Engineering and Technology 2020. -
An enhanced network intrusion detection system for malicious crawler detection and security event correlations in ubiquitous banking infrastructure
Purpose: In the recent era, banking infrastructure constructs various remotely handled platforms for users. However, the security risk toward the banking sector has also elevated, as it is visible from the rising number of reported attacks against these security systems. Intelligence shows that cyberattacks of the crawlers are increasing. Malicious crawlers can crawl the Web pages, crack the passwords and reap the private data of the users. Besides, intrusion detection systems in a dynamic environment provide more false positives. The purpose of this research paper is to propose an efficient methodology to sense the attacks for creating low levels of false positives. Design/methodology/approach: In this research, the authors have developed an efficient approach for malicious crawler detection and correlated the security alerts. The behavioral features of the crawlers are examined for the recognition of the malicious crawlers, and a novel methodology is proposed to improvise the bank user portal security. The authors have compared various machine learning strategies including Bayesian network, support sector machine (SVM) and decision tree. Findings: This proposed work stretches in various aspects. Initially, the outcomes are stated for the mixture of different kinds of log files. Then, distinct sites of various log files are selected for the construction of the acceptable data sets. Session identification, attribute extraction, session labeling and classification were held. Moreover, this approach clustered the meta-alerts into higher level meta-alerts for fusing multistages of attacks and the various types of attacks. Originality/value: This methodology used incremental clustering techniques and analyzed the probability of existing topologies in SVM classifiers for more deterministic classification. It also enhanced the taxonomy for various domains. 2021, Emerald Publishing Limited. -
Quantum-enhanced neuro-fusion framework for intelligent decision-making in smart home IoT surveillance
Smart-home surveillance systems increasingly rely on heterogeneous IoT data streams, requiring efficient fusion, scalability, and robustness under noisy sensing conditions. This paper proposes a Quantum-Inspired Deep Neuro-Fusion Architecture (QDNFA) for anomaly detection in edgecloud IoT environments. The framework integrates modular encoders, temporal alignment, and a quantum-inspired optimisation mechanism to support multi-modal data processing while maintaining real-time performance. Experimental evaluation is conducted on the CASAS Smart Home dataset to validate sensor-centric anomaly detection, scalability across multiple devices, and edgecloud inference efficiency. While the architecture is designed to support audio and video modalities, the present study focuses on low-dimensional sensor data, and large-scale benchmarking on audiovisual surveillance datasets is identified as future work. Results demonstrate improved detection accuracy and reduced latency compared to baseline methods in sensor-driven smart-home scenarios. 2026 The Author(s). -
Comparative Analysis of Machine Learning Models and Interpolation Techniques for Seasonal Rainfall Prediction in Tamil Nadu
This paper explains the rainfall patterns in the state of Tamil Nadu in October 2024, which is the monsoon season, with respect to the differences between the actual rainfall and what is experienced normally over districts. This study uses machine learning techniques from regression models of Random Forest and Gradient Boosting to anticipate future trends about rainfall based on the precedent data. Evaluation using Performance Metrics. The Proposed models are very well tested in terms of performance metrics like RMSE and R-squared, which gives insight about how accurate the forecasts of their results are. This research shows the applicability of QGIS to achieve geospatial analysis for visualizations of the rain distribution as well as anomalies across districts. The current work depicts the integration of data science methodology with geospatial analysis into the knowledge about climate dynamics in the state of Tamil Nadu. Research will help in deepening the understanding of regional climate impacts by bridging predictive analytics with spatial visualization, lending support to informed decision-making in the environment management context. 2025 IEEE. -
Serendipitous detection of an intense X-ray flare in the weak-line T Tauri star KM Ori with SRG/eROSITA
Weak-line T Tauri stars (WTTS) exhibit X-ray flares, likely resulting from magnetic reconnection that heats the stellar plasma to very high temperatures. These flares are difficult to identify through targeted observations. Here, we report the serendipitous detection of the brightest X-ray flaring state of the WTTS KM Ori in the eROSITA DR1 survey. Observations from SRG/eROSITA, Chandra X-ray Observatory, and XMM-Newton are analysed to assess the X-ray properties of KM Ori, thereby establishing its flaring state at the eROSITA epoch. The long-term (1999-2020) X-ray light curve generated for the Chandra observations confirmed that eROSITA captured the source at its highest X-ray flaring state recorded to date. Multi-instrument observations support the X-ray flaring state of the source, with time-averaged X-ray luminosity reaching at the eROSITA epoch, marking it the brightest and possibly the longest flare observed so far. Such intense X-ray flares have been detected only in a few WTTS. The X-ray spectral analysis unveils the presence of multiple thermal plasma components at all epochs. The notably high luminosity , energy (erg), and the elevated emission measures of the thermal components in the eROSITA epoch indicate a superflare/megaflare state of KM Ori. Additionally, the H line equivalent width of from our optical spectral analysis, combined with the lack of infrared excess in the spectral energy distribution, were used to re-confirm the WTTS (thin disc/disc-less) classification of the source. The long-duration flare of KM Ori observed by eROSITA indicates the possibility of a slow-rise top-flat flare. The detection demonstrates the potential of eROSITA to uncover such rare, transient events, thereby providing new insights into the X-ray activity of WTTS. The Author(s), 2025. Published by Cambridge University Press on behalf of Astronomical Society of Australia.