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Convolutional Neural Network based Di-Strategy Cheetah Optimization Algorithm for Automatic Diabetes Prediction
Diabetes is a chronic metabolic disease characterized by elevated blood sugar levels. Diabetes prediction leverages patient data to assess the risk of developing the condition, facilitating early diagnosis and intervention. However, existing models struggle to capture the complex interactions between risk factors due to limited feature representation, leading to inaccurate predictions. This research proposes a Convolutional Neural Network-based Di-Strategy Cheetah Optimization Algorithm (CNN-DS-COA) for automatic diabetes prediction using patient data. The COA is enhanced with tent chaotic mapping and an adaptive search agent, which improves population diversity distribution and convergence speed. Initially, the Pima Indians Diabetes Database (PIMA) and Germany datasets are employed to evaluate the performance of CNN-DS-COA. Min-max normalization is applied to scale the data within a uniform range while preserving relationships among values. The CNN is then used for automatic diabetes prediction, with DS-COA fine-tuning the CNNs parameter values effectively using two strategies. The proposed CNN-DS-COA achieves superior accuracy, with 99.90% and 99.72% on the PIMA and Frankfurt Hospital, Germany datasets, respectively, outperforming existing methods such as stacked ensemble approaches and statistical predictive models. 2025, Research Institute of Intelligent Computer Systems. All rights reserved. -
Convolutional neural network for stock trading using technical indicators
Stock market prediction is a very hot topic in financial world. Successful prediction of stock market movement may promise high profits. However, an accurate prediction of stock movement is a highly complicated and very difficult task because there are many factors that may affect the stock price such as global economy, politics, investor expectation and others. Several non-linear models such as Artificial Neural Network, fuzzy systems and hybrid models are being used for forecasting stock market. These models have limitations like slow convergence and overfitting problem. To solve the aforementioned issues, this paper intends to develop a robust stock trading model using deep learning network. In this paper, a stock trading model by integrating Technical Indicators and Convolutional Neural Network (TI-CNN) is developed and implemented. The stock data investigated in this work were collected from publicly available sources. Ten technical indicators are extracted from the historical data and taken as feature vectors. Subsequently, feature vectors are converted into an image using Gramian Angular Field and fed as an input to the CNN. Closing price of stock data are manually labelled as sell, buy, and hold points by determining the top and bottom points in a sliding window. The duration considered over a period from January 2009 to December 2018. Prediction ability of the developed TI-CNN model is tested on NASDAQ and NYSE data. Performance indicators such as accuracy and F1 score are calculated and compared to prove effectiveness of the proposed stock trading model. Experimental results demonstrate that the proposed TI-CNN achieves high prediction accuracy than that of the earlier models considered for comparison. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images
There has been a continued transmission of malaria throughout the world due to protozoan parasites from the Plasmodium species. As for treatment and control, it is very important to make correct and more efficient diagnostic. In order to observe the efficiency of the proposed approach, This Research built a Convolutional Neural Network (CNN) model for Automated detection and classification on thin blood smear images of Plasmodium species. This model was built on a corpus of 27558 images, included five Plasmodium species. Our CNN model got an overall accuracy of 96% for the cheating detection with an F 1score of 0.94. In the detection of the presence of malaria parasites the test accuracy conducted was as follows: 8%. Species-specific classification accuracies were: P. falciparum (95.7%), P. vivax (94.9%), P. ovale (93.2%), P. malaria (92.8%) and P. Knowles (91, 5%). As for the model SL was found to have sensitivity of 97.3% And the specificity in this case is 9 6. 1 %. The proposed CNN-based approach provides a sound and fully automated solution for malarial parasite detection and species determination, which could lead to better diagnostic performances in day-to-day practices. 2024 IEEE. -
Cooperation affects NGO staff performance patterns
In order to optimise employee productivity and overall profitability, non-profits must invest heavily in their human resources. Contrarily, the focus of this study will be on the value of cooperation and the strategies the non-governmental organisation (NGO) should use to improve the performance of the bank as a whole. Once the data have been collected using quantitative and qualitative techniques, SPSS descriptive statistics will be utilised to maintain the findings and support the research hypothesis. According to the study, qualities like trust, camaraderie, job happiness, and benefits directly impact employees productivity at the bank. The degree of teamwork among co-workers directly affects how productive an employee is. Using the statistical program SPSS, managers and staff of NGOs were surveyed; the results revealed a favourable correlation between employee performance and NGO cooperation. When employees cooperate at work, their productivity increases, and the efficacy of the organisations they work for rises. Good news for charitable organisations. Because of this, the collaborative NGO outperforms the non-collaborative NGO in terms of productivity. It was found that better communication results in greater cooperation amongst NGOs. Copyright 2023 Inderscience Enterprises Ltd. -
COOPERATIVE FEDERALISM IN A MULTINATIONAL COUNTRY: Examining the Case of Pakistan
Pakistan, as a multilingual and multiethnic country, has had to deal with issues of ethnic conflict and separatism. Cooperative federalism is used as a device by countries across the world to accommodate and manage the immense diversities they possess. This chapter examines the need for cooperative federalism in a multinational country like Pakistan to strengthen its federal model, ensuring that ethnic groups in the country do not feel insecure and alienated from the union, demanding secession. Beyond national security concerns, cooperative federalism in Pakistan will ensure economic security, human rights, social security, effective policymaking and much more, which form the basis of a welfare state. 2024 selection and editorial matter, M.J. Vinod, Stefy V Joseph, Joseph Chacko Chennatuserry and Dimitris N. Chryssochoou; individual chapters, the contributors. -
Cooperative Federalism in South Asia and Europe: Contemporary Issues and Trends
This book explores the challenges, opportunities, and trends impacting the working of federations in South Asia and Europe. It deliberates on the changing socio-economic realities, challenges facing the existing structures of governance, degrees of consociationalism, and the growing aspirations of people in South Asia and Europe.Through case studies from Greece, Germany, Austria, Switzerland, Spain, France, Sri Lanka, Pakistan, Nepal, Maldives, Bhutan, and India, the volume focuses on critical issues relating to cooperative federalism its complexities, institutional dilemmas, and trends in South Asia and Europe. It discusses a variety of themes relevant to Cooperative Federalism including federal-state relations; cooperative governance; constitution; multiculturalism, fiscal relations, democratization, devolution of powers, consociationalism, and global citizenship in South Asia and Europe. The book further emphasizes the need to strike a balance between the federal government and the constituent units in these two regions. Topical and lucid, this book will be of interest to teachers, scholars, and researchers of political science, comparative government and politics, federalism, South Asian politics, European politics, governance studies, and political studies. 2024 selection and editorial matter, M.J. Vinod, Stefy V Joseph, Joseph Chacko Chennatuserry and Dimitris N. Chryssochoou; individual chapters, the contributors. -
Cooperative Social Entrepreneurship Among Rural Artisans: A YouTube-Based NLP Analysis of Environmental Practices and Livelihood Outcome
This paper presents a novel data-driven study of rural artisans in Karnataka by leveraging YouTube video transcripts and natural language processing (NLP) to examine how cooperative social entrepreneurship (CSE) relates to environmental practices and livelihood outcomes. CSE initiatives in India typically rely on primary surveys to understand how artisanal groups adopt eco-friendly practices and how this affects their livelihoods, but such data are costly to collect and difficult to scale. We investigate whether publicly available video narratives can serve as a scalable secondary data source for studying CSE among rural artisans. We compile a corpus of YouTube videos on banana-fibre craft, Anegundi/Hampi artisan collectives, and Karnataka handicrafts. Audio is transcribed using an automatic speech recognition pipeline, and the resulting bilingual/multilingual text (English-Hindi-Kannada) is processed with a rule-based NLP tagger to identify three constructs central to our CSE perspective: (i) artisan and community references (CSE signals), (ii) environmental practices (e.g., "banana waste to fibre,""eco-friendly,""sustainable"), and (iii) livelihood/product mentions (e.g., baskets, mats, runners) as observable proxies for livelihood outcomes. On top of this, we apply text mining techniques topic modeling, sentiment analysis, and supervised classification with fine-tuned transformer models (BERT) to classify transcript segments (e.g., environmental focus vs livelihood focus) and extract key thematic topics. Experimental results show that our BERT-based classifier achieves over 90% accuracy, substantially outperforming traditional baselines such as TF-IDF+SVM and LSTM. The videos frequently encode both CSE signals and explicit environmental practices, and a non-trivial subset articulates marketable products, suggesting that platform narratives can partially capture the CSE-environment-outcome chain without questionnaires. However, explicit statements about market, seasonality, or constraint variables remain sparse, revealing limitations of video-based secondary data. The study contributes methodologically by integrating digital media analytics into rural development research, offers complexity and performance analysis of the employed algorithms, and stresses reproducibility through transparent documentation of data sources, model architectures, training configurations, and evaluation metrics. 2025 IEEE. -
Cop-edge critical generalized Petersen and Paley graphs
Cop Robber game is a two player game played on an undirected graph. In this game, the cops try to capture a robber moving on the vertices of the graph. The cop number of a graph is the least number of cops needed to guarantee that the robber will be caught. We study cop-edge critical graphs, i.e. graphs G such that for any edge e in E(G) either c(G?e) < c(G) or c(G?e) > c(G). In this article, we study the edge criticality of generalized Petersen graphs and Paley graphs. 2023 Azarbaijan Shahid Madani University. -
Coping with Burnout Across Cultures
The well-being of employees is impacted by numerous factors within their work realm. These factors consist of internal elements, such as the work environment, relationships with coworkers, and satisfaction with their jobs, as well as external factors like job security, working conditions, pay, and growth opportunities. Unfortunately, the COVID-19 pandemic has introduced significant changes that have greatly disrupted the factors that were crucial for employees to maintain a healthy and productive career. These changes include the global economic downturn, shifts in workplace culture, and a decline in worklife balance, all contributing to increased job insecurity among employees. The weight of unemployment and job insecurity often materialises as burnout and enduring fatigue among employees, consequently lessening their peak efficiency level. However, while exploring coping techniques for burnout, cultural practices are persistently overlooked. However, each culture possesses distinctive norms that shape an individuals way of handling workplace stress and pressure. This paper will predominantly look at secondary data published in online databases to explore previously existing literature on differences in culture while coping with burnout. Through the literature review, the authors compare the coping mechanisms employees adhere to between individualistic cultures and collectivist cultures. The paper highlights employees routines at a broader level, emphasising the need for organisations to be aware of diverse coping styles, especially on sites that act as a melting pot of cultures. It aims to promote safer work environments by articulating the differences in coping mechanisms of employees in different cultures. The paper explores sustainable practices for employees and workers to enhance their job satisfaction and well-being. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Coping with Public and Private Face-to-Face and Cyber Victimization among Adolescents in Six Countries: Roles of Severity and Country
This study investigated the role of medium (face-to-face, cyber) and publicity (public, private) in adolescents perceptions of severity and coping strategies (i.e., avoidant, ignoring, helplessness, social support seeking, retaliation) for victimization, while accounting for gender and cultural values. There were 3432 adolescents (ages 1115, 49% girls) in this study; they were from China, Cyprus, the Czech Republic, India, Japan, and the United States. Adolescents completed questionnaires on individualism and collectivism, and ratings of coping strategies and severity for public face-to-face victimization, private face-to-face victimization, public cyber victimization, and private cyber victimization. Findings revealed similarities in adolescents coping strategies based on perceptions of severity, publicity, and medium for some coping strategies (i.e., social support seeking, retaliation) but differential associations for other coping strategies (i.e., avoidance, helplessness, ignoring). The results of this study are important for prevention and intervention efforts because they underscore the importance of teaching effective coping strategies to adolescents, and to consider how perceptions of severity, publicity, and medium might influence the implementation of these coping strategies. 2022 by the authors. -
Copper immobilized on a layered magnetite-based nanocatalyst for sustainable Ullmann cross-coupling reaction
This study demonstrates the efficient synthesis of diarylthioethers via CS cross-coupling between diverse aryl halides and arylthiols utilizing a magnetically retractable Fe3O4@SiO2PrNH2SACu(ii) nanocatalyst using K2CO3 as a base in DMF. The heterogeneous nanocatalyst was fabricated through a multistep process. The designed catalyst was characterized using various techniques, such as XRD, HRTEM, FESEM, STEM, EDAX, elemental mapping, TGA, VSM, XPS, ICP-OES and FT-IR. The catalyst design provides a dual role of the Schiff base-anchoring copper ions, to accelerate the oxidative addition and reductive elimination steps. This method makes use of ligand-free synthesis of diarylsulfides, enabling magnetic recovery and reuse of the catalyst for up to 6 cycles. The nanocatalyst exhibited high catalytic activity and a broad substrate scope. The magnetic nature of the nanocatalyst enabled easy separation from the reaction mixture using an external magnet, thus simplifying the workup. The synthesized nanocatalyst was then utilized for the synthesis of diarylthioethers and heterodiarylthioethers. The pure compounds were characterized using 1H and 13C NMR. This catalytic system offers a cost-effective, efficient, and simple protocol for the formation of the CS bond. This journal is The Royal Society of Chemistry, 2026. -
Copper Nanoparticles: A Review on Synthesis, Characterization and Applications
An emerging field of science Nanotechnology which is involved in manipulation of atoms and molecules has shown great potential in all fields of sciences. Nanotechnology deals with nanoparticles ranging from size 1 to 100 nm in diameter, due to small size and high surface area eventually increases the state of activity. This review focuses on metal and metal oxide nanoparticles and mainly on green synthesis, characterization and application of copper nanoparticles. Green synthesis of copper and copper oxide (Cu and CuO) is economically beneficial and ecofriendly. Copper nanoparticles are used in diverse fields such as biomedicine, pharmaceuticals, bioremediation, molecular biology, bioengineering, genetic engineering, dye degradation, catalysis, cosmetics and textiles. Structural properties and biological effects of copper nanoparticles have promising effectivity in field of life sciences 2020. All rights reserved. -
Copper oxide modified biphasic titania for enhanced hydrogen production through photocatalytic water splitting
Recently, TiO2(B) has been extensively used in catalytic and energy fields owing to its exceptional crystal structure. But being a metastable state, TiO2(B) is transformed easily into other stable crystalline forms like anatase or rutile phase, and the low crystallinity limits the application of the material in catalysis. A combination of TiO2(B) with anatase, which is benefitted by a homojunction, is proven to be blessed with high activity. Herein, hydrogen production via photocatalytic water-splitting is presented using Cu modified biphasic titania nanotubes achieved by a facile hydrothermal procedure. The systems are well characterized using SEM, TEM, XRD analysis, N2 adsorption study, FTIR, DR-UV, Raman, Photoluminescence, and X-ray photoelectron spectral analysis. The homo-junction developed in titania due to anatase TiO2 (B), as well as the heterojunction created by the co-catalyst, tune the photocatalytic activity of TiO2 nanotubes positively, as evident from the enhanced hydrogen production over the system. 2023 -
Copper-boosted thiol-functionalized carbon nanospheres from biomass: a novel non-noble metal based recoverable catalyst for efficient nitro-to-amine reduction
In this work, the synthesis and catalytic activity of thiol-functionalized copper-deposited porous carbon derived from dry oil palm leaves (Cu/TF-CNS) was investigated for the reduction of aromatic nitro compounds. The procedure to synthesize porous carbon nanospheres involves the pyrolysis of oil palm leaves in a nitrogen atmosphere at 1000 C. The resulting porous carbon material was further functionalized with thiol groups to facilitate the uniform deposition of copper nanoparticles and serve as an efficient support. Excellent catalytic performance was shown by the Cu/TF-CNS catalyst in reducing aromatic nitro compounds to their corresponding aromatic amines with a low copper loading of only 4 mol% which is an inexpensive non-noble metal in the presence of NaBH4 as a reducing agent and EtOH/H2O as green solvents. The products were identified using 1H NMR spectroscopy. The catalyst was isolated from the reaction mixture and reused upto 10 cycles without any significant loss in the activity. The ICPAES analysis confirmed the successful incorporation of approximately 8.9% Cu during the deposition process and the reusability of the catalyst underscores its efficacy as a sustainable and effective heterogeneous catalyst for nitroarene reduction. 2025 The Royal Society of Chemistry. -
Copper-Embedded Aminothiazole-Engineered Nanocatalyst for Electrochemical Reduction of CO2to Alcohols
The electrochemical reduction of CO2(CO2ER) to value-added products such as methanol and ethanol is gaining significant attention as a sustainable solution to excess carbon footprints and increased energy demand. To this end, we present the electrochemical preparation of a copper-coordinated aminothiazole metallopolymer (CAM), which fosters efficient charge transfer through multiple redox couples. The prepared CAM electrode displayed excellent efficiency toward the selective production of methanol and ethanol at a low potential of ?0.73 V vs RHE, marking a significant achievement. Notably, the incorporation of Cu species along with the nitrogen- and sulfur-containing heterocyclic group of polyaminothiazole (AMp) allowed easy stabilization of the intermediates over the electrode surface, with a marked shift from C1to C2product formation. The study explores the dynamic aspects of the electrocatalyst leading to such pronounced selectivity. These findings are pivotal in encouraging more research toward the profitable production of electrofuels, particularly for decarbonizing the transportation and industrial sectors. 2025 American Chemical Society -
Coronal Elemental Abundances During A-Class Solar Flares Observed by Chandrayaan-2 XSM
The abundances of low first ionization potential (FIP) elements are three to four times higher in the closed loop active corona than in the photosphere, known as the FIP effect. Observations suggest that the abundances vary in different coronal structures. Here, we use the soft X-ray spectroscopic measurements from the Solar X-ray Monitor (XSM) onboard the Chandrayaan-2 orbiter to study the FIP effect in multiple A-class flares observed during the minimum of Solar Cycle 24. Using time-integrated spectral analysis, we derive the average temperature, emission measure, and the abundances of four elements Mg, Al, Si, and S. We find that the temperature and emission measure scales with the sub-class of flares while the measured abundances show an intermediate FIP bias for the lower A-flares (e.g. A1), while for the higher A-flares, the FIP bias is near unity. To investigate it further, we perform a time-resolved spectral analysis for a sample of the A-class flares and examine the evolution of temperature, emission measure, and abundances. We find that the abundances drop from the coronal values towards their photospheric values in the impulsive phase of the flares and, after the impulsive phase, they quickly return to the usual coronal values. The transition of the abundances from the coronal to photospheric values in the impulsive phase of the flares indicates the injection of fresh unfractionated material from the lower solar atmosphere to the corona due to chromospheric evaporation. However, explaining the quick recovery of the abundances from the photospheric to coronal values in the decay phase of the flare is challenging. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Corporate Credit Rating Assessment for Financial Risk and Regulatory Compliance
Accurate corporate credit rating is crucial to financial risk management and regulation but the current models tend to use narrow data modalities, fail to consider time and relational relationships and have weak probabilistic calibration. These constraints make them less effective in detecting the risk of default and under pinning decision-making that is in line with the regulator. The objective of this study was to formulate and test a multimodal model with a time-dependent credit rating system to incorporate financial, textual, market and relational information. The publicly available corporate financial statements, market time series data, text disclosures and inter-firm relational information were used to conduct an experimental study. Baseline logistic regression, a hybrid XGBoost with FinBERT embeddings model, and a proposed Temporal Heterogeneous Graph Transformer with cross-modal fusion were implemented and compared using discrimination, calibration, and computational efficiency metrics. The model proposed had the best predictive performance up to a ROC-AUC of 0.903 and PR-AUC of 0.482 which is better than the baseline (0.761) and hybrid (0.842) models. Calibration analysis revealed more correspondence with observed default frequencies, and confusion matrices revealed that the number of true default detection improved as 64 (baseline) to 158. Ablation and Pareto analysis was used to verify that multimodal fusion and temporal graph modelling were the major sources of performance improvements. These findings indicate that the combination of multimodal, temporal, and relational data has a significant positive effect on the accuracy and reliability of credit ratings and provides an institutional and supervisory-appropriate credit risk evaluation framework to the regulator. 2026 IEEE. -
Corporate Default Prediction Model: Evidence from the Indian Industrial Sector
The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions. 2021 MDI. -
Corporate diversification and firms financial performance: an empirical evidence from Indian IT sector
The aim of this research paper is to provide empirical evidence on the effect of geographic and segment diversification on the financial performance of the Indian IT sector. The study was done on 12 listed IT firms representing 93% market share on BSE/NSE. Standard econometric regression analysis on panel data was carried out to find the stated relationship. The results of the regression analysis revealed that international/geographic diversification impacted strongly on IT firms profitability whereas product/segment diversification had no significant impact on the firms profitability. This study also proves the existence of demand for Indian IT sector in other countries. These results could be useful in decision making for top managers of IT companies as they advocate the need for diversification (specialisation) and growth in size and also provide encouragement to small-scale Indian IT companies to undertake international diversification activities with confidence. Copyright 2023 Inderscience Enterprises Ltd. -
Corporate governance for sustainable development
Governance relates to structures and processes within an organisation to ensure greater accountability, a higher sense of responsiveness, transparency, and rule of law. Corporate governance balances the interests of a company's many stakeholders, such as shareholders, customers, vendors, financiers, the government, the community, and, very importantly, its own employees. While traditionally corporates had one clear agenda-i.e., make more profits and increase the shareholders' wealth-the 21st century saw the corporates turning a new leaf and looking at their growth from a societal perspective, specifically those relating to sustainable development like environmental protection. In the classical case of poor corporate governance, in the year 1984, the city Bhopal in India witnessed the most nightmarish experience, with the death of 16,000 people due to the leakage of a poisonous gas. 2024, IGI Global. All rights reserved.