b) 2024 - 140 Documents

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    A COMPREHENSIVE REVIEW OF LEARNING RULES AND ARCHITECTURE OF PERCEPTRON IN ARTIFICIAL NEURAL NETWORKS (ANNS) (Book Chapter)
    (CRC Press, 2024-1) Shanthini S; Devi, M. Sindhana; Grace, R. Suriya
    The complicated neural networks of the human mind have acted as a significant model for creating artificial neural networks (ANNs) of computational intelligence. ANNs can recognize patterns in data, make decisions, and perform other functions. The study provides a comprehensive review that explores ANNs by analysing the crucial elements of learning rules and perceptron architectures. This chapter clarifies the foundational learning rules underlying ANNs’ ability to adapt and generalize from data. The investigation comprehensively inspects the vital elements of learning rules and perceptron architectures in Artificial Neural Networks (ANNs) inspired by the detailed neuronal networks of the human brain. This chapter subsequently explores the dynamic realm of perceptron architectures within ANNs. Single-layer perceptrons are examined for their inability to handle intricate relationships. In contrast, multilayer perceptrons (MLPs) emerge as formidable solutions. The complex composition of MLPs, characterized by input, hidden, and output layers, is deconstructed, highlighting their potential to capture intricate non-linear patterns through the strategic deployment of activation functions. This analysis showcases a merging of academic notions and actionable effects. The combined effect between learning rules and perceptron architectures forms the foundation of ANNs’ expertise in pattern recognition, prediction, and decision-making tasks. By comprehensively understanding these underpinnings, researchers and practitioners can connect the potential of ANNs across diverse domains.
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    HARNESSING INTERNET OF THINGS (IOT) FOR OPERATIONAL EFFICIENCY IN RETAIL STORES: INNOVATIONS, BENEFITS, AND CHALLENGES (Book Chapter)
    (Springer Science and Business Media Deutschland GmbH, 2024) Karthikeyan K; Brindha T.C; Sujaritha D.; Dhanasekar E; Mathuthra O; Gowrishankar R
    This study explores how the Internet of Things (IoT) positively impacts retail store operations, integrating retail marketing, working capital, analytics, and cloud systems to achieve greater control over data and enhance technological sophistication. IoT supports decision-making in product category management, pricing strategies, promotional mix, communication channels, supply chain management, customer care services, cash management, payment systems, inventory management, data privacy and security, feature customization, and employee management through the automation of big data analysis. By addressing challenges in retail store operations, the study emphasizes the need for specific and comprehensive conceptualizations and measures of analysis within organized retail outlets. The article defines and operationalizes control activities to capture retail store effectiveness using IoT, based on data collected from 230 functional heads in organized retail stores whose well adopted with IoT. Focusing on quantitative measure validation, the study confirms that retail heads can make strategic decisions for retail expansion by leveraging IoT in their businesses. The findings are validated through simple percentage analysis, ANOVA, correlation, regression, and the SEM model, presenting both theoretical and practical implications. The Internet of Things has impact on the retail store operation and performance. Further the key variables in the segment should given focus analysis for rapid growth and development of retail sectors.
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    INTRODUCTION TO SMART AND INTELLIGENT NANOMATERIALS FOR BIOSENSORS (Book Chapter)
    (Elsevier, 2024-1) Natarajan, Arunadevi; Singh, Kshitij RB; Rai, Harshita; Singh, Jay; Pandey, Shyam S; Singh, Ravindra Pratap; Malhotra, Bansi D
    The upgraded version of nanotechnology is the development of novel material with multiple functionalities by integrating the fields of physics, chemistry, material science, engineering, and biotechnology. The small-sized nanoparticles have outstanding characteristics like improved absorption, large surface area, quantum effects, and high reactivity. Its high targeting nature, unpredictable side effects, and drug delivery to the healthy tissues when exposed largely are the major drawbacks in the biomedical field compared with other areas. Smart and intelligent materials are developed to overcome all these challenges. It offers important prospects like flexibility, sensitivity, selectivity, easily portable, and user friendly, which provides fair and accurate detection of diseases. The integration of nanotechnology-based diagnostics devices with IoT will deliver positive impacts in the food, environment, medical, and agriculture sectors. This chapter provides an overview of classification, tuneable properties of the smart material by varying external stimuli, and recent applications in the biomedical area.
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    DFT STUDY ON THE ELECTRONIC AND STRUCTURAL PROPERTIES OF M-SALEN AND M-SALPHEN ELECTROCATALYSTS TOWARDS EFFECTIVE HER
    (Elsevier B.V, 2024-11) Arumugam, Saravanapriya; Angamuthu, Abiram; Gopalan, Praveena
    We report on the electrochemical properties of Salen (N, N’- bis salicylaldehyde ehtylenediamine) and Salphen (N, N’- bis salicylaldehyde phenylenediamine) ligands using density functional approach. The structural and electronic properties, and the reduction potentials of metalated M-Salen and M-Salphen ligands (where M = Sb & Mo) that involve in hydrogen evolution reaction were explored. Optimized geometries of the chosen metalated complexes were obtained at B3LYP/6-31+G(d, p) & LANL2DZ level of theory. The effects solvation on the electrochemical properties of M-Salen and M-Salphen systems were considered in the presence of solvent acetonitrile using conductor-like polarisable continuum model (CPCM) at the same level of theory. Upon reduction process, the charge distribution around the metal centers Mo and Sb, and C, N and O atoms that lie in the coordination sphere is found to change considerably. As the first unoccupied orbital LUMO is directly connected to the electron affinity, the greater negative values of LUMO observed in Mo substituted Salen and Salphen ligands indicate their ability to exhibit better reduction process. Calculated reduction potential values of M-Salen systems were found to vary from −2.23V to −0.62V and hence the catalytic activity of M-Salen ligands follows the order of Mo-Salen > Sb-Salen > Salen and the same trend has been observed in M-Salphen systems with enhanced reduction potential of −0.54V recorded for Mo-Salphen system.
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    PROBING HEAVY METALS IN TROPOSPHERIC PARTICULATE MATTER – AN APPROACH TOWARDS SUSTAINABILITY
    (Elsevier B.V, 2024-10-15) Inobeme, Abel; Natarajan, Arunadevi; Pradhan, Safalmani; Adetunji, Charles Oluwaseun; Devolli, Ariola; Chauhan, Chetan; Mathew, John Tsado; Inobeme, Jonathan; Pal, Kaushik; Singh, Kshitij RB; Pandey, S Shyam
    Various organic and inorganic contaminants are increasingly found in the atmosphere due to several anthropogenic sources of alarming concern. Amongst the various classes of pollutants, heavy metals (HMs) have drawn global attention due to their bioaccumulation and biomagnification potential in cellular and sub-cellular structures of living organisms. In comparison to other media of dispersion of HMs, the mechanism involved in particulate matter (PM) in the atmosphere makes it easier for the transport of HMs over long distances at a global and regional scale due to the non-confinement of air masses. The transport of HMs through atmospheric PM requires close assessment and monitoring. At present, there is limited literature focusing on heavy metals in aerosols and dust. Most of the available studies have emphasized heavy metals in soil and water. The current review comprehensively highlights the various aspects of HMs including the sources, recent trends, and investigation models explored for the assessment and monitoring of HMs in PM and atmospheric aerosols, which will further help in achieving sustainable environmental monitoring aspects more precisely.
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    BINARY SOFT SIMPLY* ALPHA OPEN SETS AND CONTINUOUS FUNCTION
    (Forum-Editrice Universitaria Udinese SRL, 2024) Parvathy, C R; Sofia, A
    A topological rough approximation space is defined over two different universes using binary soft relations. A new class of binary soft set and its corresponding soft topology is obtained. Continuity functions for the newly defined set are introduced. The characteristics of continuity functions between two binary soft topological spaces and that between binary soft topological space and topological rough approximation space are examined. The proposed definitions and properties are demonstrated with suitable examples.
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    A NON-CATALYTIC OXIDATIVE DESULFURIZATION OF THIOPHENE AND BENZOTHIOPHENE BY PEROXYACETIC ACID OXIDANT
    (Springer Science and Business Media Deutschland GmbH, 2024-12) Yuvarani, S; Abiram, A; Praveena, G
    Oxidative desulfurization (ODS) is one of the most efficient methods in the removal of aromatic sulfur compounds from the combustion fuels. The present study explores the potential energy surface of thiophene (TH) and benzothiophene (BTH) oxidation reaction in the presence of peroxyacetic acid (PAA) as oxidizing agent using density functional theory (DFT). The structural and thermodynamical parameters of all the stationary points are obtained and characterized at B3LYP/6–311 +  + G(d,p) and ωB97X-D/6–311 + G(d,p) levels of theory. In order to study the influence of solvents (water and acetonitrile) in the considered ODS process; solvent effects are included via conductor like polarizable continuum model (CPCM). The natural bond orbital (NBO) analysis was carried out to understand the charge transfer mechanism during the oxidation process. Fukui function calculations are performed to determine the reactivity of the kth atom site, for electrophilic ({\text{f}}_{\text{k}}^{-}) and nucleophilic ({\text{f}}_{\text{k}}^{+}) attacks. Rate constant calculations considering the quantum tunneling effects are also made. The results obtained suggest that the oxidation of benzothiophene into benzothiophene sulfoxide to be more favorable than thiophene oxidation. The presence of solvent is found to have a significant effect in reducing the barrier energy of the considered ODS process. Further this study elucidates the selective reactivity of peroxyacetic acid towards the compounds of high nucleophilicity, which makes it to be an effective oxidant in the uncatalyzed ODS in both gas and solvent phases when compared to hydrogen peroxide (H2O2) oxidant in the presence of catalyst that is available in the literature.
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    GRAPH THEORETICAL DESCRIPTORS DIFFERENTIATE D-MANNOSE ISOMERS IN THE PRINCIPAL COMPONENT PROPOSED FEATURE SPACE: A COMPUTATIONAL APPROACH
    (Elsevier Ltd, 2024-07) Dhanalakshmi, M; Sruthi, D; Das, Kajari; Iqbal, Muhammed; Mohanan, V P; Dave, Sushma; Muthulakshmi Andal, N
    The intricate nature of carbohydrates, particularly monosaccharides, stems from the existence of several chiral centers within their tertiary structures. Predicting and characterizing the molecular geometries and electrostatic landscapes of these substances is difficult due to their complex electrical properties. Moreover, these structures can display a substantial degree of conformational flexibility due to the presence of many rotatable bonds. Moreover, identifying and distinguishing between D and L enantiomers of monosaccharides presents a significant analytical obstacle since there is a need for empirically measurable properties that can distinguish them. This work uses Principal Component Analysis (PCA) to explore the chemical information included in 3D descriptors in order to comprehend the conformational space of d-Mannose stereoisomers. The isomers may be discriminated by utilizing 3D matrix-based indices, geometrical descriptors, and RDF descriptors. The isomers can be distinguished by descriptors, such as the Harary-like index from the reciprocal squared geometrical matrix (H_RG), Harary-like index from Coulomb matrix (H_Coulomb), Wiener-like index from Coulomb matrix (Wi_Coulomb), Wiener-like index from geometrical matrix (Wi_G), Graph energy from Coulomb matrix (SpAbs_Coulomb), Spectral absolute deviation from Coulomb matrix (SpAD_Coulomb), and Spectral positive sum from Coulomb matrix (SpPos_Coulomb). Among these descriptors, the first two, H_RG and H_Coulomb, perform the best in differentiation among the 3D-Matrix-Based Descriptors (3D-MBD) class. The results obtained from this study provide a significant chemical insight into the structural characteristics of the compounds inside the graph theoretical framework. These findings are likely to serve as the basis for developing new methods for analytical experiments.
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    FLEXING THE EMOTIONAL MUSCLE: INVESTIGATING HOW TEAM REFLEXIBILITY SHAPES PERFORMANCE VIA EMOTIONAL INTELLIGENCE
    (Arts and Science Press Pte. Ltd, 2024) Sinnappan, Lara Priyadharshini; Prabhakaran, Saranya Boopathi; Karunakaran, Ranchana; Chinnasamy, Arunadevi Kalaivani; Gopal, Ravikumar; Rajan, Sathya; Purath, Rajesh Adangam
    In team settings, the development of reflexibility and emotional intelligence significantly influences performance outcomes. Identifying these relationships is crucial for optimizing team dynamics and achieving superior results. Present research investigates the relationship between team reflexivity and team performance through the mediating role of Emotional Intelligence. Team reflexibility affects team performance positively and developing emotional intelligence skills improves team performance. SPSS and AMOS software was used to analyze the data and the hypothesis was tested using structural equation modeling (SEM) and data obtained from 317 IT company employees in Coimbatore, India. The outcomes of the study provide both practical and theoretical insights for IT company employees to increase team performance through flexible and emotional intelligence skills. Employees with a high level of Intelligence Quotient (IQ) are insufficient for today's business. To be successful, workers must have a certain level of Emotional Intelligence (EI). Employees with a high EI tend to have better relationships and connections, higher performance due to collaboration, and a more positive and peaceful workplace. This research gives managers and leaders an in-depth understanding of highly reflective teams, which will allow them to be more inventive while also altering their emotional abilities to perform better and retain the organization in the long run.
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    A NEW HYBRID ADAPTIVE OPTIMIZATION ALGORITHM BASED WAVELET NEURAL NETWORK FOR SEVERITY LEVEL PREDICTION FOR LUNG CANCER DATASET
    (Intelligent Network and Systems Society, 2024) Divya, T; Gripsy J, Viji
    This study proposes three contributions focused on lung cancer detection and severity level identification. The absence of non-invasive technologies for predicting lung cancer necessitates faster, more efficient, and more accurate classification procedures due to the absence of non-invasive technologies for predicting lung cancer. Creating an automated and intelligent prediction system is crucial for identifying phases and predicting the possibility of a recurrence. The objective is to create an automated detection system for identifying lung cancer using an optimizationfocused deep learning model. We develop an adaptive multi-swarm PSO and combine it with the firefly algorithm to determine the ideal weight values for the Wavelet Neural Network (WNN) model. We use the HAPSO-FFA-WNN method to explore problems with multiple optimal solutions. This study evaluated two lung cancer datasets, and the proposed HAPSO-FFA-WNN model achieved 97.58% accuracy for dataset 1 and 98.54% accuracy for dataset 2. Furthermore, the proposed model achieved better precision, recall, and MCC performance metrics.