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Cat Swarm Optimization Algorithm Tuned Multilayer Perceptron for Stock Price Prediction
Due to the nonlinear and dynamic nature of stock data, prediction is one of the most challenging tasks in the financial market. Nowadays, soft and bio-inspired computing algorithms are used to forecast the stock price. This article assesses the efficiency of the hybrid stock prediction model using the multilayer perceptron (MLP) and cat swarm optimization (CSO) algorithm. The CSO algorithm is a bio-inspired algorithm inspired by the behavior traits of cats. CSO is employed to find the appropriate value of MLP parameters. Technical indicators calculated from historical data are used as input variables for the proposed model. The model's performance is validated using historical data not used for training. The model's prediction efficiency is evaluated in terms of MSE, MAPE, RMSE and MAE. The model's results are compared with other models optimized by various bio-inspired algorithms presented in the literature to prove its efficiency. The empirical findings confirm that the proposed CSO-MLP prediction model provides the best performance compared to other models taken for analysis. 2022 Polish Academy of Sciences. All rights reserved. -
Cataloging of happy facial affect using a radial basis function neural network
The paper entitled "Cataloging of Happy facial Affect using a Radial Basis Function Neural Network" has developed an affect recognition system for identifying happy affect from faces using a RBF neural network. The methodology adapted by this research is a four step process: image preprocessing, marking of region of interest, feature extraction and a classification network. The emotion recognition system has been a momentous field in human-computer interaction. Though it is considerably a challenging field to make a system intelligent that is able to identify and understand human emotions for various vital purposes, e.g. security, society, entertainment but many research work has been done and going on, in order to produce an accurate and effective emotion recognition system. Emotion recognition system can be classified into facial emotion recognition and speech emotion recognition. This work is on facial emotion recognition that identifies one of the seven basic emotions i.e. happy affect. This is carried out by extracting unique facial expression feature; calculating euclidean distance, and building the feature vector. For classification radial basis function neural network is used. The deployment was done in Matlab. The happy affect recognition system gave satisfactory results. 2013 Springer. -
Catalysts of Change: The Transformative Journey from HR 1.0 to HR 5.0 Innovations, Challenges, and Strategies in Human Resource Management with Technology and Data-Driven Integration
Human Resource Management (HRM) has evolved significantly, transitioning from HR 1.0 to HR 5.0 due to technological advancements, shifting demographics, and the demands of the global business environment. This chapter highlights the evolution of HRM discussions, focusing on the key changes, concerns, and approaches that have characterized this transformation. In the HR 1.0 phase, the emphasis was on paperwork and routine clerical tasks. This laid the basis for the kind of advancement that was HR 2.0, which brought into account computerization and rudimentary data interconnection. In HR 3.0, SHRM became dominant, meaning that organizational HR practices were oriented only to achieving strategic objectives. There was a shift in the application of digital technology in performing human resource activities under the emergence of HR 4.0. The present phase is called Human Resources 5.0 (HR 5.0), which marked the strain referred to as ''People First." This approach is a mixture of Technologies Focus and Humanity Focus with an ample concern on the experience and emotional state of the employees fused with the capacity of the organizations to deal with competitive issues common to most firms. It also explores the influences that have led to these changes such as; technological developments, geographic expansion, and prospective staff demands. It also looks at possible risks linked to each shift among them, resistance to change in organization cultures, threats to data privacy amongst others plus the need to acquire higher skills. Moreover, the triple-layered paper maps actionable approaches toward the transformation of HR that are supported by cases that focus on learning culture, diversification, inclusion, and the utilization of big data. Reading through this paper to understand how HR has evolved from 1.0 to 5.0, will help HR managers, and organizations get ideas on how to meanwhile shape their human resource management strategies and advancement in the ever-growing global economy. 2025, The Research Publication. All rights reserved. -
Catalytic Activity and Reusability of Mesoporous Iron Aluminophosphate Catalyst in Pharmacologically Important Organic Transformations
Journal Atoms and Molecules an International Online Journal, Vol-4 (1), pp. 675-681. ISSN-2277-1247 -
Catalytic Conversion of 2- Methyl Phenol to Salicylaldehyde Using Manganese-Oxide Doped Cellulose-Derived Carbon Spheres
In this study, we report on the hydrothermal synthesis of MnOx-anchored carbon spheres as an effective catalyst. A new method is employed to prepare carbon spheres from cellulose. Using a solvent-free process, the prepared catalyst is used to convert o-cresol to salicylaldehyde. Methods including XRD, Raman spectroscopy, FE-SEM, EDS mapping, and HR-TEM are used to examine the structural and morphological characteristics of the catalyst. It is confirmed from the BET analysis that doping with MnOx increases the surface area of the carbon spheres. At standard atmospheric pressure, the conversion of o-cresol to salicylaldehyde is highly selective due to the enhanced surface area and active sites of MnOx-doped carbon spheres. Under atmospheric pressure, the MnOx/CS catalysts show excellent efficiency, yielding 96% salicylaldehyde in under 1 h. This study underscores the feasibility of using MnOx-doped carbon spheres as a robust catalyst for the controlled oxidation of o-cresol. The results show that the catalyst has a great deal of activity and effectiveness, as well as being cheap and reusable, which greatly increases its potential for real-world catalytic applications. 2025 Wiley-VCH GmbH. -
Catalytic performance and SERS substrate efficiency of PdNPs@GOQDs
Graphene Oxide Quantum Dots (GOQDs) have received significant attention for diverse applications owing to their unique physical and chemical properties. Herein, yellow luminescent graphene oxide quantum dots are achieved via hydrothermal pathway and are characterized using different analytical methods. GOQDs, noted for their reducing character, are utilized for the synthesis of palladium nanoparticles (PdNPs). PdNPs@GOQDs obtained are explored for their catalytic efficiency and SERS substrate performance. The combined effect of electromagnetic enhancement and chemical enhancement factors make the composite a good SERS substrate, as exemplied using Rhodamine B molecule. PdNPs@GOQDs exhibited catalytic activity that effectively promoted the reduction of several functionalized aromatic nitroarenes. Notably, sensitive groups like nitrile and ester were well-tolerant under the reaction conditions, warranting the potential of the system in synthesising amines of industrial and environmental significance with high selectivity. This journal is The Royal Society of Chemistry, 2026 -
Catalytic potential of fluorescein under visible light irradiation: Enabling single-pot open flask synthesis of novel pyrazolyl methanesulfonamides
This groundbreaking study introduces a novel and efficient method for synthesizing a range of substituted pyrazolyl methanesulfonamides through a five-component cyclocondensation reaction. This reaction incorporates five different components, such as ethyl acetoacetate, hydrazine, dimedone, benzaldehydes, substituted phenyl acetonitriles, and methyl sulfonyl chloride was made to react under visible light irradiation, with fluorescein serving as an effective catalyst and ethanol as solvent for 30 mintues. This method offers significant advantages, including simplified handling, higher yields of target products with shorter reaction times, and easier purification processes. We successfully synthesized around 15 novel pyrazolyl methanesulfonamide derivatives with high efficiency. Comprehensive spectral characterization confirmed the structural integrity and purity of these derivatives, demonstrating the robustness and versatility of this approach. Facilitated by visible light and utilizing fluorescein as a bio-friendly catalyst, this methodology is both green and sustainable. This innovative approach not only streamlines the synthesis of pyrazolyl methanesulfonamides but also holds considerable promise for advancing research and applications in fields such as medicinal chemistry and materials science. 2024 The Author(s) -
Catalytic potential of fluorescein under visible light irradiation: Enabling single-pot open flask synthesis of novel pyrazolyl methanesulfonamides
This groundbreaking study introduces a novel and efficient method for synthesizing a range of substituted pyrazolyl methanesulfonamides through a five-component cyclocondensation reaction. This reaction incorporates five different components, such as ethyl acetoacetate, hydrazine, dimedone, benzaldehydes, substituted phenyl acetonitriles, and methyl sulfonyl chloride was made to react under visible light irradiation, with fluorescein serving as an effective catalyst and ethanol as solvent for 30 mintues. This method offers significant advantages, including simplified handling, higher yields of target products with shorter reaction times, and easier purification processes. We successfully synthesized around 15 novel pyrazolyl methanesulfonamide derivatives with high efficiency. Comprehensive spectral characterization confirmed the structural integrity and purity of these derivatives, demonstrating the robustness and versatility of this approach. Facilitated by visible light and utilizing fluorescein as a bio-friendly catalyst, this methodology is both green and sustainable. This innovative approach not only streamlines the synthesis of pyrazolyl methanesulfonamides but also holds considerable promise for advancing research and applications in fields such as medicinal chemistry and materials science. 2024 The Author(s) -
Catalyzing Green Mobility: Consumer Preferences for Green Energy Vehicles
Due to growing urbanization and the increase of vehicles, most Indian cities endure traffic congestion and significant air pollution. As a result, alternate technology in autos, such as electric vehicles, may become necessary (EV). This study aims to identify consumer preferences toward electric vehicles in the Indian market. This research conducted a survey and analyzed the opinions of people regarding their preferences for electric vehicles, demographics, and some of the demotivation which might be stopping them to switch to electric vehicles altogether. This research will help in determining different factors influencing the perception of consumers toward electric vehicles and what they expect when they think about purchasing a new electric vehicle. It is important to understand that electric vehicles are really getting popular now because of the rising fuel prices and environmental concerns. People are thinking about electric vehicles and replacing them with their regular petrol or diesel vehicles. In this research there might be some challenges or roadblocks in switching to electric vehicles. This research found out that despite a favorable attitude toward electric vehicles, individuals are hesitant to transition to electric vehicles due to different hurdles connected with them. This research found out that mostly the preferences of the consumers are good charging infrastructure, a good range of the electric vehicle, pocket-friendly vehicles are the most common preferences of consumers buying an electric vehicle. 2023 EDP Sciences. All rights reserved. -
Catalyzing Security and Efficiency: Blockchains Integration with IoT and Cloud Computing
Blockchain technology is a system that combines a number of computer technologies, encryption, shared storage, namely intelligent contracts, consensus processes, and peer-to-peer (P2P) networks. This research project begins with a description of the architecture of blockchains, followed by a comparison of the various consensus techniques used across various blockchain implementations. This studys scope includes a thorough analysis of the entire blockchain ecosystem. Our investigation also explores the complexity of the consensus models built into different blockchain platforms. This research painstakingly dissects these elements to pinpoint crucial elements that are essential for propelling the adoption and development of blockchain technology. In conclusion, our research corrects misconceptions about blockchains expansive potential and helps to direct the development of the technology across a wide range of industries. These results are significant for determining the future direction of blockchains enduring influence. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Catalyzing the Affordability of Perovskite Solar Cells with Aluminum-Modified Cubic Titania
As a key component of perovskite solar cells (PSCs), the electron transport layer (ETL) extracts charges efficiently. While TiO2 is widely recognized as a superior electron transport material (ETM) for its numerous advantages, the morphological limitations of spherical TiO2 nanoparticles (NPs) lead to significant electron losses. Therefore, as an alternative to nanospheres, TiO2 nanocubes are synthesized through a solvothermal route and employed as ETM in the low-cost carbon electrode-based perovskite solar cells (CPSCs). The structural, morphological, and optical properties of the TiO2 nanocubes (NCs) are studied and compared with TiO2 nanospheres (NSs) in detail. The device possessing cubic TiO2 achieved a power conversion efficiency (PCE) of 10.6% with a current density (Jsc) of 21.79 mA/cm2. Recognizing that the oxygen vacancies in cubic TiO2 are lower than in spherical TiO2, it is inferred that further reduction of oxygen vacancies in cubic TiO2 could enhance the current collection. Hence, to get rid of the oxygen vacancy (which acts as an electron trap) in the cubical TiO2, aluminum (Al3+) is incorporated into its matrix. A comprehensive analysis of its impact on structural and optical behavior follows. In addition to its cost-effectiveness and conductive nature, it has been observed that the stable form of Al3+ replaces the unstable Ti3+ (which acts as a trap state), thereby reducing the recombination rate. With the highest current collection of 22.85 mA/cm2, a PCE of 11.3% has been recorded for the solar cell that possessed 1% Al-doped TNC. Furthermore, the ambient stability of the respective device shows ?85% of its initial PCE. The effect of the TiO2 nanostructure and Al3+ doping in TiO2 nanocubes is discussed elaborately in this work. 2024 American Chemical Society -
Catchment-specific approaches in human resource management: Enhancing recruitment practices
In today's dynamic business outdoors, identifying the most skilled employees has become a challenging and captivating challenge. This chapter explores the catchment-specific approach in human resource management in the information technology (IT) industry. This conceptual chapter analyzed peer-reviewed academic literature, the business press, and other media outlets. This conceptual chapter outlines the key issues for catchment-specific approaches in human resource management in the area of recruitment with the changing trends of the recruitment process. Certain emergent practices include analyzing the catchment area, tailoring recruitment strategies, and evaluating and refining catchment-specific in recruitment. This chapter helps raise awareness and understanding of this new and emerging aspect of catchment-specific approach in human resource management. 2024, IGI Global. All rights reserved. -
Categorization of artwork images based on painters using CNN
Artworks and paintings has been an integral part of human civilization since the dawn of the Stone Age. Paintings gives more insight about any subject compared to the scriptures and documents. Archiving of digital form of paintings helps to preserve the artworks of different painters. The anticipated work is aimed for the classification of painters' artworks. The artworks of Foreign & Indian painters are considered for the proposed work. The foreign painters' artworks are obtained from [14]. At present, the Indian painters' artwork dataset is not readily available. The images were downloaded from the specific genuine website [13]. Conventional Neural Network is used for Feature learning and classification. Around 20k images of artworks is used for the experiment and got an average accuracy of 85.05%. Published under licence by IOP Publishing Ltd. -
Categorizing Disaster Tweets Using Learning Based Models for Emergency Crisis Management
Social media communication is essential to the crisis response aftermath of a massive tragedy. Facebook, Twitter, and other social media network platforms are effective instruments for connecting and fostering collaboration among catastrophe victims and other groups. As a result, numerous research publications on tweet analysis have been released. Tweet analysis during a crisis helps in understanding the nuances of the incident. Existing works primarily focused on tweet sentiment analysis and binary categorization of tweets into catastrophe relevant or not. Our work mainly categorizes catastrophe tweets into seven categories: Blizzard, earthquake, flood, hurricane, tornado, wildfire, and not-relevant tweets. Deep learning and machine learning methods were employed to categorize the tweets. The annotated data is subjected to classification using Support Vector Machine (SVM) utilizing Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer and Word2Vec Vectorizer and compares the accuracy of different kernel functions. Bidirectional Long Short Term Memory (Bi-LSTM) is used on the labeled data as a deep learning technique. SVM exhibited 88% accuracy compared to 87% for Bi-LSTM. Empirical evidence shows that our methodology is more productive and efficient than previous approaches. From this knowledge of the incident, emergency aid organizations may draw conclusions and act immediately. 2023 IEEE. -
Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques
Mental stress, a critical concern worldwide, necessitates precise and nuanced characterization. This study introduces a novel approach to effectively characterize mental stress through a multi-label, multi-class classification framework through natural language processing techniques. Building on existing literature, discussions with psychologists and other mental health practitioners, we developed a taxonomy of 27 distinctive markers spread across 4 label categories; aiming to create a preliminary screening tool leveraging textual data. The core objective is to identify the most suitable model for this complex task, encompassing comprehensive evaluation of various machine learning and deep learning algorithms. we experimented with support vector machines (SVM), random forest (RF) and long short-term memory (LSTM) algorithms incorporating various feature combinations involving Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA). The best performer of this comparative study was further evaluated against an LLM. The potential of large language models (LLMs), including their language understanding and prediction capabilities, is another key focus. We explore how these models could augment and advance mental health research, offering new perspectives and insights into the characterization of mental stress. Our findings show that the top model, an LSTM with TF-IDF and LDA (class weights assigned) outperformed the PaLM model with a coefficient of variation as low as 0.87% across all labels. Despite the PaLM model's superior average performance, it exhibited higher variability among different labels. 2025 The Author(s) -
Cation-controlled wetting properties of vermiculite membranes and its promise for fouling resistant oilwater separation
Manipulating the surface energy, and thereby the wetting properties of solids, has promise for various physical, chemical, biological and industrial processes. Typically, this is achieved by either chemical modification or by controlling the hierarchical structures of surfaces. Here we report a phenomenon whereby the wetting properties of vermiculite laminates are controlled by the hydrated cations on the surface and in the interlamellar space. We find that vermiculite laminates can be tuned from superhydrophilic to hydrophobic simply by exchanging the cations; hydrophilicity decreases with increasing cation hydration free energy, except for lithium. The lithium-exchanged vermiculite laminate is found to provide a superhydrophilic surface due to its anomalous hydrated structure at the vermiculite surface. Building on these findings, we demonstrate the potential application of superhydrophilic lithium exchanged vermiculite as a thin coating layer on microfiltration membranes to resist fouling, and thus, we address a major challenge for oilwater separation technology. 2020, The Author(s). -
Cattaneo-Christov heat flux on UCM nanofluid flow across a melting surface with double stratification and exponential space dependent internal heat source
Melting and exponential space dependent internal heat source effects on magnetohydrodynamic of upper convected Maxwell liquid towards a horizontal flat surface are addressed. The combined effect of Brownian motion and thermophoresis in nanofluid modeling are retained. The Cattaneo-Christov heat flux model is imposed. Impacts of thermal and solutal stratifications are also accounted. A set of similarity variables are utilized to form ordinary differential system from the prevailing partial differential equations. The problem of ordinary differential system is analyzed numerically through Runge-Kutta-Fehlberg based shooting method. Graphical results of pertinent parameters on the velocity, temperature and nanoparticle concentration are studied. Skin friction coefficient, local Nusselt number and Sherwood number are also addressed. 2017 -
Cattaneo-Christov Theory to model heat flux effect on nanoliquid slip flow over a spinning disk with nanoparticle aggregation and Hall current
The heat transport of a nanoliquid on a spinning disk with velocity slip and thermal jump boundary conditions is modeled. The effects of external magnetism and the aggregation of nanoparticles are analyzed. The Cattaneo-Christov heat flux model and the Joule heating phenomenon are incorporated in the thermal analysis. The central composite design (CCD) of the response surface methodology is implemented to optimize heat transfer in the nanoliquid. The sensitivity of the heat transport is analyzed. The partial differential governing model is converted into a system of ordinary differential equations using a novel von Karmans transformation, the consequent system is solved numerically. The significance of physical operating parameters is analyzed through a detailed parametric study. Optimal levels of Hall parameter, Hartmann number, and Eckert number, that optimize the heat transport are determined. The Lorentz force expands the structure of the thermal layer and subsequently reduces the heat transport of the system. The Hall current improves the thickness of the velocity layer in the radial direction, while the thickness of the thermal layer is reduced. Viscous dissipation improves the thickness of the thermal boundary layer. The isothermal boundary condition causes less heat transport in the system than the temperature jump condition. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Causal relationship among various development indices: A panel study
The concept of development has been regarded as a broader phenomenon encompassing various interrelated factors leading to improvement in the overall human wellbeing. So, it is important to understand the interlinkages between various dimensions of development. The present study was an attempt to analyze the causal relationship between the four aspects of development measured by the indices, namely the Economic Development Index (EDI), Social Development Index (SDI), Environment Development Index (ENDI), and Institutional Development Index (IDI) for a panel of 102 counties from 1996 to 2015. The long?run relationship between these indices through the panel ARDL model were also examined. The results indicated that there existed a bidirectional causal relationship between EDI and SDI, IDI and SDI, ENDI and SDI, and between IDI and ENDI. The one-way causality runs from IDI to EDI and ENDI to EDI. Further, given the nature of the variables considered here, panel autoregressive distributed lag models were used to examine the long?run relationship between the indices of development. The results showed that the impact of development indices with one another was statistically significant in the long run. 2021 The Society of Economics and Development, except certain content provided by third parties.