Browse Items (14428 total)
Sort by:
-
Identification of Driver Drowsiness Detection using a Regularized Extreme Learning Machine
In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved. 2023 IEEE. -
Sales Promotion Practices In Apparel Retail Sector And Challenges Ahead
International Journal of Research in Commerce & Management, Vol-5 (1), pp. 25-28. ISSN-0976-2183 -
Study on factors influencing purchase of branded formal apparel in Indian apparel industry /
International Journal of Business, Management & Social Sciences, Vol-3 (5(2), pp. 51-54. ISSN-2249-7463. -
Enhanced Edge Computing Model by using Data Combs for Big Data in Metaverse
The Metaverse is a huge project undertaken by Facebook in order to bring the world closer together and help people live out their dreams. Even handicapped can travel across the world. People can visit any place and would be safe in the comfort of their homes. Meta (Previously Facebook) plans to execute this by using a combination of AR and VR (Augmented Reality and Virtual Reality). Facebook aims to bring this technology to the people soon. However, a big factor in this idea that needs to be accounted for is the amount of data generation that will take place. Many Computer Science professors and scientists believe that the amount of data Meta is going to generate in one day would almost be equal to the amount of data Instagram/Facebook would have generated in their entire lifetime. This will push the entire data generation by at least 30%, if not more. Using traditional methods such as cloud computing might seem to become a shortcoming in the near future. This is because the servers might not be able to handle such large amounts of data. The solution to this problem should be a system that is designed specifically for handling data that is extremely large. A system that is not only secure, resilient and robust but also must be able to handle multiple requests and connections at once and yet not slow down when the number of requests increases gradually over time. In this model, a solution called the DHA (Data Hive Architecture) is provided. These DHAs are made up of multiple subunits called Data Combs and those are further broken down into data cells. These are small units of memory which can process big data extremely fast. When information is requested from a client (Example: A Data Warehouse) that is stored in multiple edges across the world, then these Data Combs rearrange the data cells within them on the basis of the requested criteria. This article aims to explain this concept of data combs and its usage in the Metaverse. 2023 IEEE. -
A Study of In-store Factors Affecting Impulse Buying of Apparels amongst College Students and Young Professionals in Bangalore
Asian Journal of Research in Business Economics and Management, Vol. 7, Issue 3, pp. 1-15, ISSN No. 2249-7307. -
A Study on the Factors Influencing Customer Satisfaction in Multi-brand Apparel Retail
International Academic Research Journal of Business and Management Vol.1, Issue No. 7 ISSN No. 2227-1287 -
Optimized Fuzzy SVM with Chaotic Henry Gas Solubility Algorithm for Fault Identification in Rotating Machinery
Reliable and accurate fault diagnosis in rotating machinery is vital for minimizing unplanned downtime, reducing maintenance costs, and ensuring operational safety in industrial environments. Traditional diagnostic approaches depend heavily on manual feature extraction from vibration signals, which can be time-consuming, expertise-dependent, and prone to missing subtle fault patterns. This study presents a novel hybrid frameworkIDL-OFSVMthat combines Intelligent Deep Learning (IDL) with an Optimized Fuzzy Support Vector Machine (OFSVM) for automated fault classification. Vibration signals are first transformed using the Continuous Wavelet Transform (CWT), and deep features are extracted via the lightweight MobileNet architecture. The Chaotic Henry Gas Solubility Optimization (CHGSO) algorithm significantly enhances the classification model's performance, which effectively tunes the FSVM parameters. Experimental evaluations on benchmark datasets show that the proposed method achieves 99.8% training and 99.7% testing accuracy, outperforming several state-of-the-art approaches. Beyond technical accuracy, the framework offers practical advantages, including reduced dependency on domain expertise, suitability for real-time monitoring, and potential integration into predictive maintenance systems. These benefits make the IDL-OFSVM model a promising solution for industrial fault diagnosis applications, where reliability, speed, and scalability are crucial. 2025 by the Dr. Mohan S B, Dr. Prajith Prabhakar, Dr. Yokesh V, M Bharathi, Dr. Gayathry S Warrier, and Dr Mahalakshmi J. -
Polyaniline/zinc oxide nanocomposites for Dye-sensitized solar cell device fabrication and analysis
Zinc oxide nanoparticles synthesized by hydrothermal technique are used as reinforcements to synthesize polyaniline nanocomposite via in-situ chemical oxidation method. The XRD analysis confirms the formation of polyaniline/zinc oxide nanocomposites. SEM images shows that nano reinforcement is covered with polymer matrix. Nanoparticles are found to be immersed in polyaniline matrix in the TEM images of nanocomposites. Significance of filler morphology in polyaniline nanocomposites is analysed by reinforcing zinc oxide nanoflowers, rods and spheres into polyaniline. Addition of metal oxide into polyaniline improves thermal stability. An increase of 227 % in UV absorption is observed for nanoflower composite compared to pure polyaniline in the absorption spectrum. Photoluminescence spectral analysis shows minimum peak intensity for polyaniline/zinc oxide nanoflower composite. Based on the optical property analysis, Dye-sensitized solar cells are fabricated by coating polyaniline and its nanocomposite with zinc oxide nanoflower. Power conversion efficiency of 5.23 % is obtained for the device based on pristine polyaniline and 7.03 % for nanoflower composite. 2026 Indian Chemical Society. -
Empowering Indigenous Women Through Community-Based Tourism
Community-based tourism (CBT) is an emerging tool for community development, fostering social and economic growth through the active participation of Indigenous communities. This study employs a conceptual approach to examine the current application of CBT and sustainable tourism development (STD), focusing on their potential to address challenges faced by Indigenous women. Drawing on previous research, it highlights womens empowerment and community development advancements while exploring future trends. Mental health in Indigenous communities remains a significant concern, with societal changes, environmental threats, and resource exploitation exacerbating issues such as collective suicide, alcoholism, and violence. Political and socioeconomic factors further contribute to high incidences of mental distress. This chapter explores how CBT and STD can tackle these challenges, empowering Indigenous women and fostering community development and environmental conservation. A comprehensive blueprint for sustainable communities is proposed, addressing environmental, economic, and social factors through collaboration among various stakeholders. This model, grounded in existing literature, aims to guide future research and practical applications. The study is relevant for government agencies, local businesses, residents, tourists, academics, and scholars, offering insights into the empowering potential of CBT and STD for Indigenous women and their communities. 2025 Andrea Edurne Jimenez Ruiz, Volha Rudkouskaya, and Shivam Bhartiya. -
Empowering Indigenous and Rural Women through Community-Based Sustainable Tourism Development: Building Resilient Communities
Gender inequality and climate change are pressing global issues, with income disparity further exacerbating the challenges faced by women, especially in rural and tribal communities. This chapter investigates the empowerment journey of rural and tribal women through rural entrepreneurship, specifically focusing on the efforts of the Uravu Organization in Wayanad, Kerala, India. Uravu, a non-profit non-governmental organization, aims to empower rural communities through sustainable practices, particularly emphasizing the bamboo industry and womens skill enhancement in handicrafts. Using Scheyvens theoretical framework, the qualitative research this chapter is based on in-depth interviews with female employees of Uravu to evaluate their empowerment levels. The findings reveal that while these women experience significant psychological and economic empowerment, they continue to face challenges in achieving political and social empowerment. This study underscores the potential of zero waste and environmentally friendly organizations in fostering job creation for rural and tribal women, thereby contributing to resilient community building and community-based sustainable development. CAB International 2026. -
Forecasting Global Microplastic Exposure from Processed Foods: Data-Driven Forecasts and Detection
Microplastics are one of the major contaminants of processed foods at a global scale and they contain high risks for human health. Even though the public understanding of the issue has become wider, the knowledge of individual levels of exposure is still very much limited together with the practical tools which can estimate microplastic ingestion. This study proposes a complete data pipeline and a machine learning framework for predicting microplastic contamination and estimating personalised exposure to microplastics depending on country, specific consumption patterns and contamination trends of a long, term nature. The dataset consisted of approximately 18 food groups across 109 countries. So far the data has been through a very thorough preprocessing stage, exploratory analysis, and feature engineering was undertaken, which among other things, included microplastic load aggregation, the addition of lagged variables, and mixing serving sizes information. Random Forest and XGBoost regressors models were trained to predict the levels of contamination from 2019 to 2030. Polynomial Regression delivered the highest accuracy on the training data of R2= 0.9897. While XGBoost gave the best generalization result of R2 = 0.9469 and was therefore chosen as a final forecasting model. The consumption of microplastics through the global food chains is predicted to keep increasing. The originality of this study is in the combination of the long, term contamination data with the selective food, category modelling that allows to generate a reliable framework for the forecasting of the individual intake and to provide to the policy makers EBP (Evidence, Based Policy) advice. 2025 IEEE. -
Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance Applications
The activity of the object in question is alerted directly upon completion of an effective object tracking. Dependent on hardware support or not, a strong object tracking protocol is required for a precise object tracking application. According to these methods, tracking an object accurately within a predetermined processing time window required a significant amount of computer complexity. In contrast, a variety of quality-degrading elements, including occlusion, shifting lighting, shadows, and so on, have an adverse effect on tracking. All of these tracking shortcomings will be fixed by a revolutionary residual network based on loss operator and anchor creation. Detection of object has concerns that rely on the process of feature extraction to afford efficient quality. For this purpose a model called ResNet has been used that comprises thirty layers and hence named as Resnet-thirty. These networks are a type of Convolutional Neural Network (CNN) that contain residual connections among various layers. The various merits of these connections is the network has the capability to learn the features of global, local and intermediate in parallel. As such, the system is robust against changes in lighting. These variations in light were understood in terms of tracking objects within a changing background. The proposed work uses MOT datasets. This dataset comprises of MOT 15, MOT16, MOT17 and MOT20. The results have been found by using these datasets. Hence, it evidently outperforms in terms of precision, recall, MOTA, IDF, MOTP, SAIDF and F1 measure to track the objects. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. -
Improved Henon Chaotic Map-based Progressive Block-based visual cryptography strategy for securing sensitive data in a cloud EHR system
The core objective of secret sharing concentrates on developing a novel technique that prevents the destruction and leakage of original data during the distribution and encoding processes. Progressive Visual Cryptography (VC) is considered for the potential over the traditional VC schemes since the former does not require and does not suffer from the limitations of requiring a minimum number of participants during the process of encryption and sharing. The chaotic map-based Progressive VC is superior in facilitating predominant secrecy under sharing and encryption. In this paper, an Improved Henon Chaotic Map-based Progressive Block-based VC (IHCMPBVC) scheme is proposed to prevent the leakage and destruction of sensitive information during an exchange and encryption. This proposed IHCMPBVC technique uses the merits of Henon and Lorentz maps for effective encryption since it introduces the option of deriving non-linear behavior that results in sequence generation that covers the complete range with proper distribution in order to minimize the degree of leaks in sharing. The simulation results of the proposed IHCMPBVC technique investigated using entropy, PSNR, and Mean Square Error were improved at an average rate of 27%, 23%, and 31%, predominant to the baseline VC approaches considered in the comparison. 2022 The Authors -
Paraquat associated stomatitis: A forensic marker of exposure intent and prognosis
Introduction: Paraquat-associated stomatitis (PAS) is a hallmark of paraquat poisoning but its potential as an intent-specific or prognostic marker remains unexplored. This study investigated whether PAS patterns differ between accidental and suicidal exposures. Methods: A pooled, individual case analysis of 170 paraquat poisoning cases from 122 publications was conducted following PRISMA guidelines. Data on demographics, exposure intent, and detailed oral lesion characteristics (morphology, pattern, severity) were extracted. Statistical analyses identified predictors of intent and prognosis. Results: Oral lesions were present in 78.8% of cases. While the overall prevalence of PAS did not differ between accidental (85.1%) and suicidal (83.9%) intents, lesion morphology was a key differentiator. Widespread ulcers were strongly associated with suicidal ingestion (aOR=6.37; 95% CI 1.9520.78; P=0.002), independent of lesion severity or WHO grade. Conversely, focal but objectively more severe ulcers characterized accidental exposures. Lesion morphology, not severity, distinguishes intent. Other independent predictors of suicidal intent included female sex (aOR=3.40; P=0.031), oliguria (aOR=4.21; P=0.006), and delayed treatment (aOR=8.39; P=0.023). Paradoxically, accidental exposures were associated with significantly higher mortality (68.9%) compared to suicidal cases (39.8%) (P=0.001). The absence of PAS reliably indicated a non-oral exposure route (P<0.001). Discussion: This work introduces the novel concept of intent-specific PAS morphology, establishing ulcer pattern as a forensic marker. It is demonstrated, for the first time, that widespread lesions objectively indicate suicide, while focal, severe ulcers signify accidental exposurea finding linked to a paradoxical mortality risk. This intent-specific signature could provide clinicians with a rapid, objective tool for stratification. For medicolegal investigations, it qualifies PAS as crucial physical evidence to clarify disputed histories, advocating for its systematic documentation in clinical and autopsy practice as a new standard in forensic toxicology. 2026 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Effect of Short Glass Fiber Addition on Flexural and Impact Behavior of 3D Printed Polymer Composites
Fused deposition modeling (FDM), one of the most widely used additive manufacturing (AM) processes, is used for fabrication of 3D models from computer-aided design data using various materials for a wide scope of applications. The principle of FDM or, in general, AM plays an important role in minimizing the ill effects of manufacturing on the environment. Among the various available reinforcements, short glass fiber (SGF), one of the strong reinforcement materials available, is used as a reinforcement in the acrylonitrile butadiene styrene (ABS) matrix. At the outset, very limited research has been carried out till date in the analysis of the impact and flexural strength of the SGF-reinforced ABS polymer composite developed by the FDM process. In this regard, the present research investigates the impact and flexural strength of SGF-ABS polymer composites by the addition of 15 and 30 wt % SGF to ABS. The tests were conducted as per ASTM standards. Increments in flexural and impact properties were observed with the addition of SGF to ABS. The increment of 42% in impact strength was noted for the addition of 15 wt % SGF and 54% increase with the addition of 30 wt % SGF. On similar lines, flexural properties also showed improved values of 44 and 59% for the addition of 15 and 30 wt % SGF to ABS. SGF addition greatly enhanced the properties of flexural and impact strength and has paved the path for the exploration of varied values of reinforcement into the matrix. 2023 The Authors. Published by American Chemical Society -
Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data
A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Enhanced Channel Division Method for Estimation of Discharge in Meandering Compound Channel
Accurate prediction of shear force distribution along the boundary in open channels is a key to the solution of numerous hydraulic problems. The problem becomes more complicated for meandering compound channels. A model is developed for predicting the percentage of shear force at the floodplain (%Sfp) of two-stage meandering channels using gene-expression programming (GEP) by considering five dimensionless parameters viz. the width ratio, relative depth, sinuosity, bed slope, and meander belt width ratio as the inputs in the model. Basing on the %Sfp, the apparent shear force along the division lines of separation in compound channels is selected for discharge calculation using the conventional channel division methods. An Enhanced Channel Division Method (ECDM) is introduced to calculate discharge by assuming interface line at main channel and floodplain junction. A modified variable-inclined (MVI) interface is suggested having zero apparent shear determined from flow contribution in the main channel and floodplain. The MVI interface is further used to calculate discharge in the meandering compound channels. Performance of the GEP model is tested against other analytical methods of calculating %Sfp. Error between the observed and calculated discharges using the MVI interface is found to be the minimum when compared to other interface methods. The enhance channel division method is successfully applied for validating the two available overbank discharge values for the river Baitarani at Anandapur (drainage area of 8570 sq. km), giving the minimum errors of 0.31% and 1.02% for flow depths of 7.5m and 8.63m, respectively. 2020, Springer Nature B.V. -
Assessment of Shear Stress Distribution in Meandering Compound Channels with Differential Roughness Through Various Artificial Intelligence Approach
Accurate prediction of shear stress distribution along the boundary in an open channel is the key to solving numerous critical engineering problems such as flood control, sediment transport, riverbank protection, and others. Similarly, the estimation of flow discharge in flood conditions is also challenging for engineers and scientists. The flow structure in compound channels becomes complicated due to the transfer of momentum between the deep main channel and the adjoining floodplains, which affects the distribution of shear force and flow rate across the width. Percentage sharing of shear force at floodplain (%Sfp) is dependent on the non-dimensional parameters like width ratio of the channel (?) , relative depth (?) , sinuosity (s) , longitudinal channel bed slope (So) , meander belt width ratio (?) , and differential roughness (?). In this paper, various artificial intelligence approaches such as multivariate adaptive regression spline (MARS), group method of data handling Neural Network (GMDH-NN), and gene-expression programming (GEP) are adopted to construct model equations for determining %Sfp for meandering compound channels with relative roughness. The influence of each parameter used in the model for predicting the %Sfp is also analyzed through sensitivity analysis. Statistical indices are employed to assess the performance of these models. Validation of the developed %Sfp model is performed for the experimental observations by conventional analytical models; to verify their effectiveness. Results indicate that the proposed GMDH-NN model predicted the %Sfp satisfactorily with the coefficient of determination (R2) of 0.98 and 0.97 and mean absolute percentage error (MAPE) of 0.05% and 0.04% for training and testing dataset, respectively as compared to GEP and MARS. The developed model is also validated with various sinuous channels having sinuosity 1.343, 1.91 and 2.06. 2021, The Author(s), under exclusive licence to Springer Nature B.V. -
Determination of Discharge Distribution in Meandering Compound Channels Using Machine Learning Techniques
Accurate flow rate prediction is essential to analyze flood control, sediment transport, riverbank protection, and so forth. The flow rate distribution becomes even more complicated in compound channels due to the momentum transfer between different subsections across the width of the channel. Conventional channel division methods estimate flow distribution at the main channel and floodplains by assuming a division line with zero apparent shear stress. The article attempts to develop a model to calculate the percentage of discharge in the main channel (%Qmc) using techniques such as Group Method of Data Handling - Neural Network (GMDH-NN) and gene-expression programming (GEP) by incorporating the effects of various geometric and hydraulic parameters. The paper proposes a modified channel division method with a variable-inclined interface, with zero apparent shear force distribution at the channel subsections according to the statistical indices employed to assess these models' performance in predicting %Qmc. This variable-inclined interface changes its slope according to the channel parameters. The model's effectiveness is verified by validating with experimental observations by conventional analytical methods. 2021 American Society of Civil Engineers. -
Cybernetic Intelligence in Intellectual Property Law: AI-Driven Innovation, Detection, and Ethical Challenges
This paper explores the transformative role of cybernetic intelligence in intellectual property (IP) law, including its integration in critical areas, such as registration, intellectual property infringement detection, innovation protection, and dispute resolution. IP processes are being reshaped by the introduction of cybernetic systems that improve process efficiency and accuracy in registration and by advanced algorithms that revolutionise the detection of violations. In particular, the paper explores the ethical challenges raised about AI driven innovation like ownership of work created by AI, biases of automated systems and risking monopoly. Further, it analyses the potential of cybernetic tools for dispute resolution that blends data knowledge with Peoples judgement to the point of fairness. The paper emphasises ethical, legal implications, proposing robust AI regulatory frameworks, transparency and accountability when deploying AI.