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Clustering Faculty Members fortheBetterment ofResearch Outcomes: A Fuzzy Multi-criteria Decision-Making Approach inTeam Formation
From a talent-pool of people, choosing an efficient team is tough. Faculty members of a higher education institution constitute the talent-pool. Teams have to be formed from them so that research output of each team is maximum. Amongst numerous research skills, thirteen are identified as most desirable skills. The level of these thirteen skills, viz., concept articulation, formatting according to templates/style sheets, identifying the relevant literature, initiative, logical reasoning, patience, problem formulation/problem finding, proof reading skills/identifying mistakes in written communication, searching/browsing skills/quick search techniques, sense of positive criticism, statistical knowledge, the ability to stay calm, and written communication skills, varies from person to person. Historical ranking of these skills and self-evaluation of the level of acquisition of these skills is used along with the years of experience, educational qualification, gender, marital status, etc., to rank individual faculty members. The fuzzy ranking of the faculty members thus obtained is used to cluster them into teams that are efficient in complementary skills. Each team thus formed is involved in collaborative research leading to research publication. The model is successfully implemented in a university department with 40 faculty members. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Clustering of low-mass stars around Herbig Be star IL Cep - Evidence of 'Rocket Effect' using Gaia EDR3 ?
We study the formation and the kinematic evolution of the early-type Herbig Be star IL Cep and its environment. The young star is a member of the Cep OB3 association, at a distance of 798 9 pc, and has a 'cavity' associated with it. We found that the B0V star HD 216658, which is astrometrically associated with IL Cep, is at the centre of the cavity. From the evaluation of various pressure components created by HD 216658, it is established that the star is capable of creating the cavity. We identified 79 co-moving stars of IL Cep at 2-pc radius from the analysis of Gaia EDR3 astrometry. The transverse velocity analysis of the co-moving stars shows that they belong to two different populations associated with IL Cep and HD 216658, respectively. Further analysis confirms that all the stars in the IL Cep population are mostly coeval (?0.1 Myr). Infrared photometry revealed that there are 26 Class II objects among the co-moving stars. The stars without circumstellar disc (Class III) are 65 per cent of all the co-moving stars. There are nine intense H ? emission candidates identified among the co-moving stars using IPHAS H ? narrow-band photometry. The dendrogram analysis on the Hydrogen column density map identified 11 molecular clump structures on the expanding cavity around IL Cep, making it an active star-forming region. The formation of the IL Cep stellar group due to the 'rocket effect' by HD 216658 is discussed. 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Clustering-based Optimal Resource Allocation Strategy in Title Insurance Underwriting
Production of insurance policies in all types of Insurance requires a thorough examination of the entity against which the Insurance is to be issued. In health insurance, it is the past medical data of the individuals. Vehicle insurance needs the examination of the vehicle and the owner's data. Likewise, in Title Insurance, it is the historical data of the property which needs scrutiny before the policy issuance. Underwriters perform the job of examining the property records. The scrutiny of the property records requires a high degree of the domain and legal expertise, and title insurance underwriters are often associated with legal professions. They do the final round of validation of the examination process. There are examination teams that take care of the initial set of regular examination tasks associated with each title insurance order. Some human experts assign the orders to the team associates. Not all the orders are of the same complexity in terms of examination. The allocation of the tasks happens based on the gut feeling of the supervisor, considering their experience with the team members. Our research creates clusters of the orders based on specific parameters associated with the orders. It builds a cost model of the past associates working on orders belonging to different clusters. Based on this cost matrix, we have built an optimal task allocation model that assigns the orders to the associates with the promise of optimal cost using a Linear programming solution used frequently in operations research. 2022 IEEE. -
Clustering-Based Recommendation System for Preliminary Disease Detection
The catastrophic outbreak COVID-19 has brought threat to the society and also placed severe stress on the healthcare systems worldwide. Different segments of society are contributing to their best effort to curb the spread of COVID-19. As a part of this contribution, in this research, a clustering-based recommender system is proposed for early detection of COVID-19 based on the symptoms of an individual. For this, the suspected patients symptoms are compared with the patient who has already contracted COVID-19 by computing similarity between symptoms. Based on this, the suspected person is classified into either of the three risk categories: high, medium, and low. This is not a confirmed test but only a mechanism to alert the suspected patient. The accuracy of the algorithm is more than 85%. 2022 IGI Global. All rights reserved. -
CMSFE: Cross-Model SSL Feature Extraction for Enhanced Remote Sensing Data Representation
Automatic Labeling of Remote Sensing Data fastens analysis in various applications such as environmental monitoring, urban planning, and disaster management. Supervised machine learning approaches rely on labeled datasets created through time-consuming processes. Creation of labeled datasets requires higher resources and such datasets are harder to obtain in most of the domains, and especially in Remote Sensing. This study proposes Cross-Model Self-Supervised Feature Extraction (CMSFE), a novel approach that enhances representation learning in unlabeled remote sensing datasets by integrating features from multiple pre-trained models and refining them through self-supervised learning (SSL). The extracted features are integrated to form a comprehensive and robust feature set that aids in separating different cluster of imagery. Experimental results with EuroSAT dataset demonstrate the quality of feature extraction in separating various classes without any manual intervention or labeling. Dimensionality Reduction and Manifold Learning is applied for visual interpretation of extracted feature space. These features can be further reused for analysis or modeling, highlighting the potential of SSL-based feature extraction methods in remote sensing to enhance representation learning and reduce dependency on labeled data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
CMT-CNN: colposcopic multimodal temporal hybrid deep learning model to detect cervical intraepithelial neoplasia
Cervical cancer poses a significant threat to women's health in developing countries, necessitating effective early detection methods. In this study, we introduce the Colposcopic Multimodal Temporal Convolution Neural Network (CMT-CNN), a novel model designed for classifying cervical intraepithelial neoplasia by leveraging sequential colposcope images and integrating extracted features with clinical data. Our approach incorporates Mask R-CNN for precise cervix region segmentation and deploys the EfficientNet B7 architecture to extract features from saline, iodine, and acetic acid images. The fusion of clinical data at the decision level, coupled with Atrous Spatial Pyramid Pooling-based classification, yields remarkable results: an accuracy of 92.31%, precision of 90.19%, recall of 89.63%, and an F-1 score of 90.72. This achievement not only establishes the superiority of the CMT-CNN model over baselines but also paves the way for future research endeavours aiming to harness heterogeneous data types in the development of deep learning models for cervical cancer screening. The implications of this work are profound, offering a potent tool for early cervical cancer detection that combines multimodal data and clinical insights, potentially saving countless lives. 2024, Universitas Ahmad Dahlan. All rights reserved. -
CNN based Model for Severity Analysis of Diabetic Retinopathy to aid Medical Treatment with Ayurvedic Perspective
One among the major modern life-style diseases is Diabetes. Diabetic Retinopathy is a major cause for blindness even at an early age. Clinical assessments for eye disease are done using visual examinations and probing. Retinal vessel segmentation is an important technique which helps in detection of changes that happens in blood vessel as well as gives information regarding the location of vessels. The work presented in this paper tries to detect and analyze the changes occurred in the blood vessels of human retina caused by diabetic retinopathy. Using digital imaging techniques, the severity screening technique facilitates the diagnosis of diabetic retinopathy. The model works in such a way that it helps the Ayurvedic treatment methodology for Diabetic Retinopathy. Results are obtained to categorize the data elements according to the severity of the disease and different classifications. 2022 IEEE. -
CNN-based approach for brain tumor detection and severity prediction
Artificial intelligence is widely used in healthcare, especially in medical imaging. It leads to advanced diagnosis using innovative approaches to analyze complex data more accurately and provide personalized treatments. This helps the clinicians efficiently analyze the imaging data, leading to early detection of diseases like brain tumors, cancer, cardiovascular diseases, etc. The research work focuses on the detection and severity prediction of brain tumors. Magnetic resonance imaging (MRI) scan images are preprocessed in the proposed model using different methods. The convolutional neural network model (CNN) is used to detect and predict brain tumors and can be used in personalized treatments. The proposed method has an accuracy of about 98% in classification and severity prediction. 2025, IGI Global Scientific Publishing. All rights reserved. -
CNN-based Indian medicinal leaf type identification and medical use recommendation
Medicinal leaves are playing a vital role in our everyday life. There are an enormous amount of species present in the world. Identification of each type would be a tedious task. Using image processing technology, we can overcome this problem by providing computer vision with the help of a convolution neural network (CNN). The objective of this research is to find out the best CNN model that helps in classifying the plant leaf species and identifying its category. In this research work, the proposed basic CNN model consisting of four convolution layers uses ten different medicinal leaf species each belonging to two categories providing an accuracy of 96.88%. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
CNN-Bidirectional LSTM based Approach for Financial Fraud Detection and Prevention System
Detecting fraudulent activity has become a pressing issue in the ever-expanding realm of financial services, which is vital to ensuring a positive ecosystem for everyone involved. Traditional approaches to fraud detection typically rely on rule-based algorithms or manually pick a subset of attributes to perform prediction. Yet, users have complex interactions and always display a wealth of information when using financial services. These data provide a sizable Multiview network that is underutilized by standard approaches. The proposed method solves this problem by first cleaning and normalizing the data, then using Kernel principal component analysis to extract features, and finally using these features to train a model with CNN-BiLS TM, a neural network architecture that combines the best parts of the Bidirectional Long Short-Term Memory (BiLS TM) network and the Convolution Neural Network (CNN). BiLSTM makes better use of how text fits into time by looking at both the historical context and the context of what came after. 2023 IEEE. -
CNN-RNN based Hybrid Machine Learning Model to Predict the Currency Exchange Rate: USD to INR
Foreign currency exchange plays an imperative part in the global business and in monetary market. It is also an opportunity for many traders as an investment option and the advance knowledge of fluctuation helps the investors making right decision on time. However, due to its volatile nature, prediction of foreign currency exchange is a challenging task. This paper implements two models based on machine learning, namely Recurrent Neural Networks (RNN) and a Hybrid model of Convolutional Neural Networks (CNN) with RNN known as CNN-RNN to assess the accuracy in predicting the conversion rate of US Dollar (USD) to Indian Rupees (INR). The data set used to verify and validate the models is the daily currency exchange rate (USD to INR) available in public domain. The experimental results show that the simple RNN model performs slightly better than the hybrid model in this particular case. Though the accuracy of the hybrid model is very high in terms of error calculation still the single RNN model is the better performer. This does not straight away reject the hybrid model rather needs more experimental analysis with changing architecture and data set. 2022 IEEE. -
Co Adaptation Vs Signal Altering Regularization Layer in Deep Learning: A Trade Off Analysis via Node Redundancy and Transfer Learning
This study investigates the trade-off between incorporating regularization layers-such as Dropout, R-Drop, and Gaussian Dropout-and the phenomenon of co-adaptation in deep learning models. While regularization is designed to enhance generalization by disrupting hidden layer activations and reducing overfitting, it may also introduce node redundancy, potentially diminishing the model's capacity to learn efficiently. Conversely, co-adaptation, though often considered undesirable, may help preserve beneficial internal representations that contribute to learning generalizable data patterns-particularly in transfer learning scenarios-where regularization may inadvertently hinder such learning. Using the CIFAR-10 dataset, this study conducts an empirical analysis of how various regularization strategies influence neuron redundancy and downstream transfer performance. The results indicate that, although regularization effectively controls overfitting, excessive distortion in hidden representations can impair the model's ability to generalize across tasks. These findings provide insights into the need for balanced regularization strategies that maintain useful structure while minimizing detrimental redundancy. 2025 IEEE. -
Co- Integration and Causality between Macroeconomics Variables and Bitcoin
The fintech sector has been booming for the past decade, especially with the unprecedented expansion in cryptocurrency innovation. Many countries and their central banks are working to accommodate cryptocurrency in a regulated format into their financial system anywise. This research paper investigates the long-run and short-run relationship between Bitcoin (INR) and the macroeconomic variables of the Indian economy, such as two major stock indices (NSE and BSE), money supply M1, foreign exchange rate (INR/US dollar), and indicators of inflation rate (CPI and WPI). For this purpose, monthly data of the variables from October 2014 to December 2020 are considered. The Johansen co-integration approach depicts the long-run association between Bitcoin and the economic variables, whilst VECM and the Wald coefficient reveal no short-run causality between the variables. The Granger Causality test shows a one-way causal relationship of NSE, BSE and WPI to Bitcoin. Hence, it concluded that stock indices and inflation have a cogent effect and exert on bitcoin prices. The findings will be helpful for policy-makers and investors alike, for an outlook to strategize and explore this everchanging digital instrument. 2024 CRC Press. -
Co-curricular activities in Catholic schools: perceived benefits
The study attempts to understand how the Catholic schools in India organise co-curricular activities aligning to their mission as perceived by senior teachers and students. Researchers further examined the types of co-curricular activities offered at Catholic schools, their impact on students personal growth and the challenges faced in achieving the set-objectives of the activities. Data were collected through semi-structured interviews of 25 senior teachers teaching in Catholic schools and focus group discussions (FGD) with students. The key findings of the thematic analysis revealed three main themes: holistic development, leadership qualities, and implementation challenges. Findings indicated that co-curricular activities such as sports, literary fests, and cultural events contribute to developing resilience, confidence, and leadership skills and are in alignment with the Catholic education values. These activities provide a platform for students to explore their potentials, nurture their talents, and build relationships. Further study may explore understanding the values and implementation of co-curricular activities in other schools. 2026 Informa UK Limited, trading as Taylor & Francis Group. -
Co-Electrodeposited Pi-MnO2-rGO as an Efficient Electrode for the Selective Oxidation of Piperonyl Alcohol
Pi-MnO2-rGO-CFP electrode was developed through a concurrent deposition of Pi-MnO2 and reduced graphene oxide (rGO) on carbon fiber paper (CFP). Cyclic voltammetry (CV) and electrochemical impedance studies (EIS) were applied for the electrochemical characterization of the electrode. The electro catalytic activity of the modified electrode was improved by the increased synergistic characteristics of the CFP and electrochemically deposited rGO-Pi-MnO2 composite. The performance of the modified electrode was remarkable due to its lowest charge transfer resistance (R ct), and highest surface area offering more active sites and quicker electron transport kinetics. X-ray diffraction spectroscopy (XRD), Raman spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and optical profilometry (OP) were employed to study the physicochemical properties. Furthermore, the modified electrode was availed to oxidize piperonyl alcohol mediated by 4-acetamido-2,2,6,6-tetramethylpiperidine-1-oxyl (4-acetamido TEMPO or 4-ACT). The product obtained was purified and characterized by 1HNMR. The turnover frequency of 4-ACT was studied at different concentrations of the reactant, and the reaction parameters were also optimized using statistical tool design of experiment. This methodology is demonstrated to be economical, environmentally benign, and highly efficient in obtaining piperonal as it is carried out under milder reaction conditions. 2023 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. -
Co-Existence of Union and Management is Possible
ITIHAS The Journal of Indian Management, Vol-2 (4), pp. 100-101. ISSN-2249-7803 -
Co-MoS2 nanoflower coated carbon fabric as a flexible electrode for supercapacitor
Cobalt doped MoS2 (Co-MoS2) nanoflowers have been successfully synthesized via a simple one-step hydrothermal method for supercapacitor applications. To identify the crystalline nature and morphology, the as-prepared material is characterized by XRD, SEM, and TEM measurements. The material exhibits a specific capacitance value of 86 F g-1 at a current density of 1 Ag-1 in symmetric two-electrode configuration with excellent cyclic stability of 98.5% even after 10,000 chargedischarge cycles. The results suggest the suitability of Co-MoS2 as an efficient electrode material for supercapacitors. 2021 Elsevier Ltd. All rights reserved. -
Co-sputtered V2O5TiN composite on Ag-network current collector for high-performance flexible transparent thin-film supercapacitors
Next-generation wearables require extremely capable electrochemical energy-storage devices that exhibit improved performance with high flexibility and transparency. Herein, we present a highly flexible and transparent electrochemical thin-film supercapacitor electrode fabricated by co-sputtering V2O5 and TiN on an Ag-network-based current collector. The electrodes' physical properties, optical properties, and structural morphologies are studied using X-ray diffraction, UVvisible spectroscopy, and scanning electron microscopy, respectively. A symmetric device is fabricated using V2O5 and TiN on an Ag network, and the TiN sputter power is varied to optimize the performance. The device performance of the co-sputtered electrodes at various composition ratios is studied. The optimized V2O5TiN (200?40)/Ag electrode device with pseudocapacitive behavior delivers an excellent areal specific capacitance of 98.66 mF cm2 at a current density of 4 mA cm2 with a charge retention of 90.12 % after 6000 cycles. The V2O5TiN (200?40)/Ag electrode device outperforms other reported electrodes, with an energy density and power density of 30.83 ?Wh cm2 and 2999.67 ?W cm2, respectively, and excellent mechanical stability. 2023 -
Coal-Based Fluorescent Zero-Dimensional Carbon Nanomaterials: A Short Review
Carbon nanomaterials (CNs) have fascinated material scientist owing to their outstanding properties, making them potential materials for numerous critical applications. Over the years, scientists have reported several techniques for the production of CNs from distinctive precursors using various top-down and bottom-up approaches. Coal is being proven as an inexpensive, abundant, and effective carbon source for harvesting CNs with unique characteristics, properties, and high production yield. CNs derived from coal exhibit excellent fluorescence property, which can be tuned by varying the degree of oxidation, particle size, surface functionalization, etc. The fluorescent coal-derived CNs are effective materials for label-free bioimaging and selective sensing applications. In this review, we encompass the synthesis of different kinds of CNs from coal, their fluorescence properties, proposed fluorescence mechanisms, factors affecting fluorescence property, and fluorescence sensing and bioimaging applications. 2020 American Chemical Society. All rights reserved. -
Coalition partitions of n-inordinate invariant intersection graphs
A class of algebraic intersection graphs, called the n-inordinate invariant intersection graphs, has been introduced and various properties of these graphs are investigated in the literature. Domination is an important structural property in graphs and the notion of coalition in graphs has been recently introduced based on different types of domination in graphs. In this article, we analyse the structure of the n-inordinate invariant intersection graphs by investigating certain variants of domination based coalition partitions of them. 2025 World Scientific Publishing Company.