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Combined Enzymatic Action of Pseudomonas psedoalcaligenes Protease and Stutzerimonas xanthomarina Amylase in Textile Blood Stain Removal
Blood is a body fluid. When it contacts oxygen, it can clot, forming a dark solid substance. In accidents involving wounds, blood is released and can stain clothing when absorbed by fabrics. Removing blood stains from fabrics is challenging with various commercial detergents. This study aimed to determine the efficacy of commercially available detergent powder supplemented with amylase and protease enzymes in removing bloodstains from textiles. Stutzerimonas xanthomarina and Pseudomonas pseudoalcaligenes were isolated from soil and examined for their enzymatic activity. The results demonstrated that these enzymes can effectively remove bloodstains from cotton fabric, with maximum activity observed at 50% saturation with ammonium sulfate and specific activity of protease at 93.45 2.17 U/mg and amylase at 36.28 1.36 U/mg. This study suggests that the addition of enzymes to chemical detergents can enhance their surface impact and improve their effectiveness. These findings indicate a potential for the development of more efficient detergents. The Author(s) 2025. -
Exploring Star Fruit Extract as an Alternative Substrate for Polyhydroxyalkanoates Production by Bacillus licheniformis NJ04
Polyhydroxyalkanoates (PHA) production from diverse group of microorganisms has been a topic for extensive research for several decades. Despite this extensive research explorations, commercialization of PHA is still facing major hurdles, mainly due to the high cost involved in PHA production and recovery. This study was designed to determine a sustainable approach to produce PHA using an underutilized fruit extract. The major novelty of this research work is the use of starfruit (Averrhoa carambola L.), a tropical fruit, as a substrate for PHA production employing Bacillus licheniformis NJ04. Commercialization of PHA production can help to tackle global issues like raising microplastic pollution and biomagnification. The maximum PHA production reported in this work was 3.8 g/L under optimized conditions like temperature of 37 C, pH 7 under shaking conditions (120 rpm) with 2% glycerol and starfruit extract as a carbon source after 72 h of incubation. The extracted PHA was further characterized through (FTIR) Fourier-transform infrared radiation, differential scanning calorimetry (DSC), Thermogravimetric analysis (TGA), X-Ray diffraction (XRD), and Proton Nuclear magnetic resonance (1H NMR). Thus, the present work highlights a novel strategy for using starfruit waste as a cost-effective substrate for PHA production. The Author(s) 2025. -
Antibacterial Efficacy of Oreochromis niloticus Mucus and its Characterization
Investigating new antimicrobial agents from various biological sources is necessary due to the rise of bacteria that are resistant to drugs. A neglected source of bioactive substances with possible antibacterial qualities is fish, especially Oreochromis niloticus (Nile tilapia). This study identifies the bioactive components of extracts made from O. niloticus and examines their antibacterial activity. PBS and sterile water were used to remove fish mucus using a solvent. Using the agar well diffusion method, extracts were tested for antibacterial activity against a panel of Gram-positive bacteria (Staphylococcus aureus, B. cereus), Gram-negative bacteria (Klebsiella pneumoniae, P. aeruginosa), and fungus (Candida albicans). The presence of hydroxyl and amide functional groups, which are suggestive of proteins and polyphenolic substances, was further validated by Fourier-transform infrared spectroscopy (FTIR). The study demonstrates O. niloticuss antibacterial potential, with its mucus being an exceptionally abundant source of bioactive substances. These results highlight the potential of fish-derived antimicrobial compounds as substitutes for traditional antibiotics. The isolation, structural clarification, and possible therapeutic uses of these chemicals should be the main areas of future study. Furthermore, to guarantee the efficacy and safety of these natural compounds, knowledge of their toxicity profiles and mechanisms of action is crucial. In the fight against antibiotic resistance, this study helps to establish sustainable bioresources and expands our knowledge of O. niloticuss antibacterial potential. The Author(s) 2025. -
Extended-spectrum ?-Lactamase (ESBL) Producing Bacterial Pathogens Associated with Respiratory Tract Infections
Respiratory tract infections (RTIs) have been critically associated with health care problems globally. Subsequently, increased antibiotic resistance rates have limited treatment options that are further exaggerated due to lack of newer novel drugs and therapies. Current study highlights, antibiotic resistance profiling along with extended-spectrum beta-lactamase (ESBL) producers of RTI pathogens from Bengaluru. During June 2020-May 2021, 1016 clinical samples collected, prevalence rate of 22.4% was exhibited, with highest in male (74.5%). Following age group, 30-35 years displayed highest (24.1%) though, lowest was in 45-50 years (1.3%). The standard microbiological characterization revealed Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii as predominant bacterial pathogens associated with RTIs. While, Antibiotic susceptibility test (AST) exhibited highest resistance rates for different antibiotics in the following pathogens, as K. pneumoniae for ampicillin (74.8%), P. aeruginosa for doripenem (66.6%), A baumannii to piperacillin/tazobactam (76.9%), E. coli for penicillin and ?-lactamase inhibitors ranging between 56-92%, E. cloacae to ticarcillin/clavulanic acid besides cefuroxime (100%). However, prevalence of Gram-positive strains were lowest and exhibited highest resistance to penicillin, and fluoroquinolone (83.3%). ESBL producers were predominantly K. pneumoniae, followed by E. coli, and E. cloacae with 21.9%, 6.5% and 1.3%, respectively. Notably, all the Gram-negative strains showed 100% sensitivity towards colistin with remarkable sensitivity was observed in oxazolidinone, glycopeptides by S. aureus and Coagulase-neagtive Staphylococcus aureus (CoNS). The study emphasizes increased antimicrobial resistance antimicrobial and ESBL resistance, suggesting AST as a systematic approach for apprising treatment guidelines in current scenario. The present study denotes polypeptide colistin as choice of drugs for treating RTI pathogens, however its not recommended in all cases. The Author(s) 2025. -
Indias credit growth and asset prices movements; Does the global financial cycle have a moderating role to play?; [Evoluci del crecimiento del crito y de los precios de los activos en la India; Desempe el ciclo financiero mundial un papel moderador?]
This study examines the effect of the global financial cycle on different financial indicators of the Indian economy through experimental analysis. It detects evidence of a connection between contemporaneous changes in capital flows, asset prices, and credit growth, which are related to the Global Financial Cycle (GFCy). The evolution of the cycle is largely driven by the monetary policy decisions of the Federal Reserve, and existing studies have examined the influence of these decisions in different contexts. The current study experimentally examines the effect of the global financial cycle on credit growth and asset prices in India during the period 2010-2023. For the purpose of achieving its goals, the study utilizes advanced time-series econometric techniques, such as the Granger Causality Test, Vector Autoregression (VAR) methodology, and the Impulse Response Function (IRF) test. The outcomes show that the global financial cycle has significant effects on the stock market, as confirmed by the Granger causality and IRF findings. 2019 Universidad Nacional Automa de Mico, Facultad de Contadur y Administraci. This is an open access article under the CC BY-NC-SA (https://creativecommons.org/licenses/by-nc-sa/4.0/) -
Investor's behaviour to COVID-19 vaccination campaign; An event study and panel data analysis in the southeast asian region
This study examines how the COVID-19 immunization campaign has influenced the stock market responses in the WHO Southeast Asian Region. The effects of the immunization campaign on the WHO Southeast Asian countries were different, and the study used event study techniques and panel-data regression models to investigate the impact of the WHO South-East Asian capital market. Some countries like India, Sri Lanka, and South Korea had positive markets that responded to the news, while others did not. The findings of this study suggest that investors make fair assessments and respond to events and announcements, but they tend to have a more visible reaction to negative incidents than to positive news/events. However, after 51 days, the WHO South East region as a whole had internalized the encouraging news. The study has a few limitations, such as a small dataset and period, only a few variables and models, and so on. Future studies could include a few additional countries and periods to produce more significant results. Originality/value- This study contributes to the existing knowledge about the impact of drugs and vaccinations on stock markets. It is the first study to investigate how the WHO Southeast Asian Region's COVID-19 immunization program affects the stock market reaction. The study used keywords such as Immunization campaign, abnormal returns, Cumulative average abnormal returns, Event Study, and WHO Southeast Asian Region. 2025 Universidad Nacional Autonoma de Mexico. All rights reserved. -
Urban Heat Dynamics in Pune: The Influence of Land Cover and Local Climate
Urban areas with high population density and extensive infrastructure development have been experiencing an increasing strain on the local heat budget, leading to a surge in heat-related illnesses and discomfort. This study examined the impact of climate and land use as heat islands in Pune, India, from 2012 to 2023 at six different locations representing varying degree of urbanization. Satellite land cover observations revealed that 55.17% of the total area was urbanized in the city itself, which was limited to 44.8% in 2012. This urbanization has significantly impacted the increasing tendency of maximum temperature (Tmax; 0.13? year?1 to 1.63? year?1) at almost each study site and minimum temperature (Tmin; 0.06? year?1 to 0.23? year?1) at a specific location during night. The mutual effect of land cover changes and meteorological conditions have evidenced the heat islands with varying intensities (2? to 8?) at four of the six sites, with significantly intensifying rates from 0.05? year?1 to 0.39? year?1. The estimation of dominating land cover type for the formation of heat islands demonstrated a significant simple determination (r2 = 0.001 to 0.013) and probability (P < 7.910?13 to 2.330?5) with heat island temperature identifying urban land cover as the primary factor at two sites, while the other two were affected by mixed land covers influenced by local meteorological characteristics. The outcomes of this study offer valuable insights into the development of heat islands in Pune and could guide strategies for alleviating urban heat, ultimately improving climate resilience and thermal comfort citywide. 2025, Binghamton University Libraries. All rights reserved. -
Analysis of Systematic Trade-offs between Military and Healthcare Expenditure alongside GDP Growth of Select Asian and Western Exporting Economies in the 21st Century
This study explores the complexity in the trade-offs between military expenditure, healthcare expenditure, and GDP growth across select Asian nations and major weapon-exporting countries, examining how nations allocate finite resources between national security and human well-being over the past two decades. Using a systems science approach, the research integrates Granger causality testing to analyze temporal and directional relationships among GDP growth, military expenditure, and healthcare expenditure, uncovering their dynamic interdependencies. The methodology includes trend and slope analysis, Granger causality testing, outlier detection, and clustering to identify heterogeneity in resource allocation strategies. Developed, weapon-exporting nations exhibit complementary trends, with strong causality between GDP growth and healthcare expenditure, reflecting economic stability and balanced allocation patterns. In contrast, developing Asian nations display fragmented and volatile relationships due to resource constraints and inefficiencies. Outlier analysis reveals country-specific dynamics, such as conflict-driven spending in Afghanistan and Myanmar and growth-focused strategies in China. Temporal trends show that economic crises, like the COVID-19 pandemic, significantly disrupt GDP growth but have limited long-term effects on healthcare or military expenditures. Clustering analysis identifies distinct groups of nations, shaped by economic capacity and geopolitical pressures. The findings emphasize the need for tailored policy frameworks to balance national security and human well-being, particularly in developing nations facing structural challenges. For sustainable development, policies must align resource allocation with economic priorities, geopolitical contexts, and societal needs. 2025, Binghamton University Libraries. All rights reserved. -
Greening the Workplace: Can Sustainable Practices Reduce Anxiety and Enhance Meaningful Work Engagement?
This academic research examines the relationship between job engagement, green work climate, job-related anxiety, meaningfulness at work within the organization. It draws attention to identify the significant relations among all these factors and highlights the role of a green work climate in promoting meaningful work and alleviating job-related anxiety. The research emphasizes a diverse sample of employees from various organisations using structural modelling to find the mediating roles of job engagement and work meaningfulness in the correlation between organizational practices, environmental sustainability, and employee satisfaction. The study finds that a green work climate significantly enhances meaningful work experiences and reduces job-related anxiety. It emphasises the mediating role of job engagement and work meaningfulness in linking organizational practices focused on environmental sustainability with employee satisfaction. The results of the study give practical understanding for organizations aiming to create resilient and committed workforces. Aligning green initiatives with strategies to boost individual fulfilment and reducing anxiety which can strengthen employee engagement and improve overall organizational outcomes. This study underscores the importance for an integrated approach to workplace management that blends environmental sustainability with employee well-being. It offers valuable contributions to understand how organizational practices can promote sustainability and promote a motivated and satisfied workforce. 2025, Binghamton University Libraries. All rights reserved. -
Impact of Node Failures on Productivity in Multilayer Supply Chain Networks: An Influence Network Analysis in the Indian Electronics Sector
Supply chain networks are essential for the delivery of goods and information, but disruptions such as natural disasters or trade embargoes can severely impact them. Resilience of entire networks under different types of disruptions when nodes or edges fail has been extensively studied. However, the extent to which the failure of a particular company affects another company of interest within a network has not been widely explored. To address this, we created a multilayer physical supply chain network of companies in the Indian electronics industry. Through systematic node removal simulations, we examined how the productivity of one company is impacted by the removal of another. Extending these simulations to include all possible combinations of companies yielded an influence network that represents interdependence among nodes in terms of productivity. We observed that removing a critical node could lead to not only a decrease but, quite counter-intuitively, an increase as well in the productivity of affected nodes. This study identifies the factors that influence these productivity changes and offers insights to supply chain managers to maintain network resilience in the face of node failures. 2025, Binghamton University Libraries. All rights reserved. -
A Novel Preprocessing Model for Multi Modal Brain MRI image Classification for Stroke Prognosis
Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data Fluid Attenuated Inversion Recovery (FLAIR), Apparent Diffusion Coefficient (ADC) and Diffusion-Weighted Imaging (DWI). Multimodal data gives a comprehensive understanding of the brain and the stroke lesions. Complex algorithms and processing steps are required to ensure that the data is prepared for further processing. The objective of this experimental research is to create a novel multimodal preprocessing model that can be used for the preprocessing of the multimodal data from various MRI modalities (FLAIR, DWI and ADC). The proposed model supports the automatic removal of artefacts from the multimodal data, by identifying and applying the best preprocessing techniques for Image Registration (Affine or non-rigid transformations), Normalization (Z Score or min-max normalizations), Denoising Techniques (Gaussian, Median, Non-Local Means, or Anisotropic Diffusion filters) and Bias Field correction. The best technique is identified using the evaluation techniques of Dice Coefficient, Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Root Mean Squared Error (RMSE). Preprocessing is critical process to improve the outcome of the subsequent analysis including segmentation. Here, we propose an Enhanced Image Registration and Artefact Correction (EIRAC) model with Best Image Registration Technique (BIRT) and Multiple Orientation Normalization Denoising and Bias field correction Parallelly (MONDBP) algorithms for the preprocessing of multimodal MRI images to provides better results for the segmentation of stroke lesions through Machine Learning models. 2025, Binghamton University Libraries. All rights reserved. -
ORDER SUM SIGNED GRAPH OF A GROUP
The order sum graph associated with the group G, denoted by ?os, is a graph with vertex set consisting of elements of G and two vertices say a,b ? ?os are adjacent if o(a) + o(b) > o(G), where o(?) denotes the order of a group or an element of a group. In this paper, we introduce a signed graph called order sum signed graph where the underlying graph is a complete graph of order n and the edges receive positive and negative signs based on the order sum graph. We characterise the balanced negated order sum signed graphs. We also characterise the positive and negative homogeneous order sum signed graphs. Further, we study the properties such as clusterability, sign-compatibility, consistency and switching of signed graphs. Further, we obtain the adjacency spectra, Laplacian spectra and signless Laplacian spectra of the order sum signed graphs associated with cyclic groups. 2025, University of Guilan. All rights reserved. -
ON THE INDICES OF CERTAIN GRAPH PRODUCTS
Molecular descriptors are numerical graph invariants that are used to study the chemical structure of molecules. In this paper, we determine the upper bound of the Sombor index based on four operations involving the subdivision graph, semi-total point graph, semi-total line graph, and total graph related to the lexicographic and tensor product. The exact expressions of the first reformulated Zagreb index and the second hyper-Zagreb index of the tensor product are formulated on the basis of the four significant graphs. Further, the descriptors for certain standard graphs are obtained and the graphical comparison for the first reformulated Zagreb index has also been illustrated to understand the result better. 2025 University of Isfahan -
An Efficient Approach for Gene Selection through Parallel Bio-Inspired Algorithms and Shapley Value Analysis
The fast development of microarray technology has significantly assisted in the use of gene expression analysis to forecast cancer subtypes. Analyzing high-dimensional microarray data is still challenging, as existing hybrid methods cannot find highly discriminative genes. This study aims to use Shapley value analysis and hybrid bio-inspired algorithms to develop a scalable, parallel gene selection technique to increase computing efficiency and classification accuracy in high-dimensional microarray data. This study used hybrid feature selection approaches inspired by bio-organisms to create a scalable parallel gene selection system. The dataset size is initially enlarged by Adaptive Synthetic Sampling (ADASYN). They use the Recursive Feature Elimination (RFE) approach to extract features and determine their Shapley values. In addition, the Whale Optimization Algorithm (WOA) works to determine which genes are most important. After that, Machine Learning (ML) techniques assist in classifying the chosen characteristics. According to the experiment results, the suggested strategy surpasses standard gene selection techniques with the same datasets, employing improved classification accuracy and reducing computing time. K-NN achieved an accuracy of 85.44%, while LR showed improved results with an accuracy of 91.72%. RF further increased accuracy to 94.69%. SVM demonstrated exceptional performance, reaching an accuracy of 97.63%. Ultimately, XGBoost excelled among all models with the highest accuracy of 98.49%, highlighting its robust ability to classify SRBCT samples effectively based on gene expression data. 2025, Ayandegan Institute of Higher Education. All rights reserved. -
Enhancing Experimental Efficiency in Uncertain Data: A Comparative Analysis of Neutrosophic and Classical Latin Square Designs
This research investigates the relative efficiency between Neutrosophic Latin Square Design (NLSD) and Classical Latin Square Design (CLSD), with a particular focus on their use in situations where data is uncertain and ambiguous. Although CLSD is a classic experiment designed for systematic error control, its utility is limited in fields like agriculture and behavioral sciences due to its performance bottleneck regarding data imprecision. The NLSD can relatively easily be extended to incorporate neutrosophic logic to address these challenges, making it a more powerful tool for modeling uncertainty. In this paper, a systematic efficiency evaluation of NLSD against CLSD is performed for inconsistent data. It is found that the NLSD enables significant improvements in experimental efficiency while providing clearer inferences regarding treatment effects and supporting more reliable conclusions. Despite these limitations, these benefits establish NLSD as a promising candidate for overcoming environmental uncertainties, and these observations hold significant potential to further the advancement of experimental designs. The results demonstrate that NLSD conveys a 55 % chance to enhance efficiency relative to LSD, which is especially important in processes that must attain maximum resource utilization and high experimental efficiency. 2025, Ayandegan Institute of Higher Education. All rights reserved. -
Integrating Hesitant Fuzzy Sets with Machine Learning for Enhanced Healthcare Predictive Analytics
This study examines how Hesitant Fuzzy Sets (HFS) and Machine Learning (ML) might improve healthcare predictive analytics. HFS, which accommodates uncertainty and hesitation in decision-making, is used to improve healthcare projections. Predictive analytics methods struggle with data ambiguity and imprecision, resulting in poor decision-making. Traditional ML algorithms may not be able to collect hesitant information, resulting in less accurate patient outcomes and treatment recommendations. The Integrating Hesitant Fuzzy Sets with ML (IHFS-ML) framework overcomes these issues by integrating HFS flexibility with advanced ML approaches. This connection allows the representation of ambiguous patient data for better healthcare analytics. Data pre-processing in the IHFS-ML framework improves healthcare analytics prediction. These methods transform uncertain fuzzy data into an ML-friendly format. Disease prediction, patient risk assessment, and therapeutic effectiveness analysis are recommended. The approach aims to improve healthcare decision-making and deliver new insights by merging hesitant and ambiguous information. IHFS-ML uses HFS to characterize imprecise and confusing patient data. These HFS are combined with powerful ML classifiers like Random Forest (RF) and Logistic Regression. The IHFS-ML system outperforms current prediction accuracy and reliability methods, suggesting it might transform healthcare analytics. HFS improves ML model interpretability, improving patient outcomes and healthcare decisions. Compared to other methods, the IHFS-ML model improves prediction analysis reliability by 99.7%, scalability by 97.6%, data pre-processing efficiency by 97.1%, interpretability by 98.9%, and accuracy by 97.8%. 2025, Research Expansion Alliance (REA). All rights reserved. -
Adaptive Fuzzy Heuristic Algorithm for Dynamic Data Mining in IoT Integrated Big Data Environments
The explosion of Internet of Things (IoT) devices has created enormous amounts of real-time data, requiring sophisticated Data Mining Methods (DMT) that can rapidly extract valuable insights. Managing the computational complexity of processing high data volumes, integrating various IoT data formats, and ensuring that the system can scale are among the most significant issues. Fuzzy Dynamic Adaptive Classifier Optimization Analysis (FDACOA) is a method that has been suggested as an approach to the difficulties caused by changes in data patterns, processing in real-time, and data heterogeneity. By incorporating Adaptive Fuzzy Logic (AFL) and heuristic optimization, FDACOA enhances data classification accuracy and efficiency while simultaneously assuring that the algorithm can adapt to changes in data streams. This adaptability is crucial in IoT applications, where data fluctuation might affect analysis quality. FDACOA uses dynamic adaptation to alter classifier parameters based on real-time feedback to improve prediction accuracy and reduce computing costs. An optimization layer fine-tunes fuzzy rules and membership functions to optimize performance across data situations. Simulation analyses proved the algorithm's capacity to classify with high accuracy and low computational cost. Smart healthcare, predictive maintenance in industrial IoT, and intelligent transportation systems use FDACOA for real-time decision-making and data-driven insights. FDACOA is a viable approach for dynamic data mining in IoT-enabled big data contexts because of its faster, more accurate, and more adaptable simulation results. 2025, Research Expansion Alliance (REA). All rights reserved. -
Influence of Cooking and Fermentation on Nutrient and Anti-nutrient Profiles of Millet and Rice; [????? ??? ? ????? ?? ?????????? ???? ???? ? ?????? ?? ?????? ? ??? ?]
This study investigates how the traditional processing methods, such as cooking and fermentation, affect the nutritional, anti-nutritional, and mineral composition of the six edible grains, including the three rice varieties (Oryza sativa: Matta, Boiled and Brown rice) and the three millets (foxtail, jowar and pearl millet). The grains were analyzed in their raw, cooked, and fermented forms. The carbohydrates and the protein content were determined along with the anti-nutritional compounds such as the flavonoids, oxalates, phenolics, phytates and the tannins. The mineral concentrations of calcium, potassium, iron, magnesium, manganese, and zinc were determined using Atomic Absorption Spectroscopy. The results showed that cooking significantly reduced carbohydrate content by 85-90% across all grains, while fermentation caused an even greater reduction of up to 95%. Protein levels were grain-specific, and fermentation generally enhanced the protein concentration by 20 50%. Flavonoid content was reduced by 70-90% while phytates, and oxalates were reduced substantially by 60 90% through both treatments due to leaching and thermal degradation, while the phenolic content increased by 25-40%, particularly in the foxtail millet. The tannin levels decreased with cooking by 40-60%, but they increased after the fermentation, likely due to the enzymatic release of the bound compounds. Mineral concentrations were consistently declined in the cooking and fermented forms, yet fermentation improved bioavailability by reducing the anti-nutrients. Overall, the cooking was more effective in lowering the anti nutritional factors, whereas the fermentation enhanced the protein and improved the accessibility of the essential minerals such as iron and zinc. These findings emphasizes the importance of the traditional household processing methods in enhancing the nutritional quality of rice and millet-based diets, particularly in the regions dependent on cereal staples. 2025, Ferdowsi University of Mashhad. All rights reserved. -
Attentional Deep Learning with Inverse Transform Sampling for Robust Respiratory Sound Classification
The necessity for efficient breathing sound classification systems originates from respiratory diseases, which impair oxygen-carbon dioxide exchange and impact lung function. Feature extraction and pattern categorization are general components of such systems. Because of their effectiveness with big datasets, deep neural networks have acquired popularity recently in the category of breathing sounds. Enhancing medical care requires cooperation amongst researchers, medical professionals, and patients. An attentional deep learning model with inverse transform sampling is presented in this study to classify respiratory diseases from audio data. Robust models were developed to classify and detect respiratory elements using the Respiratory Sound dataset. The primary objectives include effectively determining lung sounds and determining respiratory illnesses. The architectures of CNN, VGG16, and ResNet50 were developed to extract features and categorize data. Also, the pre-trained models ResNet50 and VGG16 identify critical characteristics in spectrum pictures more accurately. Inverse transfer sampling is used to rectify class imbalance in respiratory datasets. The models achieved 98% accuracy with the CNN model, 83% accuracy with VGG16, and 95% accuracy with ResNet50. Moreover, LSTM and CRNN models offer more information on how respiratory illnesses are classified. 2026, Hemanth K S, Harisha Naik T, N Kartik, N Nanda kumar, S Senthilkumar and Ramya R. -
Examining the Components of Organizational Attractiveness: An Employee Perspective
This study examines the key components influencing organizational attractiveness in the Information Technology (IT) sector in Bangalore, India. A multistage sampling technique was employed to gather data from employees across twenty IT software companies in Bangalore. A structured questionnaire was administered, and a total of 740 responses were collected. Data analysis was conducted using the Statistical Programme for Social Sciences (SPSS 25.0), incorporating descriptive and inferential statistical methods. Exploratory factor analysis with Promax rotation was applied to extract the primary factors contributing to organizational attractiveness. The analysis revealed nine critical factors influencing organizational attractiveness: career growth opportunities (CGO), corporate social responsibility (CSR), flexible work practices (FWP), perceived organizational prestige (POP), perceived organizational support (POS), happiness at work (HAW), professional stability (PS), work options (WO), and compressed workweeks (CW). CGO and FWP emerged as the most impactful factors, reflecting employees' preferences for career advancement and work-life balance. CSR and POP highlight the importance of organizational values and reputation. The Author(s).