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A Perspective on Challenges and Opportunities of Supply Chain Management
Global Journal of Arts and Management Vol. 2, No. 3, pp. 227 - 231, ISSN No. 2249-2658 -
Stocks and throughput Accounting on Material Management and its Impact on Cost Management
Global Journal of Arts and Management, Vol. 2, No. 3, pp. 244-246, ISSN No. 2249-2658 -
An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model
Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%. 2024 IEEE. -
Nouveau shoppers buying behavior pattern and perception towards luxury brands
The customer perception towards purchasing luxury brands has various psychological patterns and the behviour towards purchasing such brands differs accordingly. The main objective of the study is to map the nouveau shoppers mind-set towards shopping malls and to analyze the buying behavior pattern and perception towards luxury brand on shopping malls. For this purpose a sample of 130 was collected from the respondents were percentage analysis, descriptive statistics, Kruskall Wallis test and Oneway anova were used as tools to analye the data. The conclusion is that shopping malls have higher potentiality to pull the customers to visit their places but the conversion of making every customers purchasing in the mall is based on various factors of each individual shops. The conversion towards making the consumers purchasing the products can be done to attractive displays and understanding the mindset of modern shoppers towards various products and brand. 2020 Webology Center. -
Smart Home Systems Using Wireless Sensor Network - A Comparative Analysis
International Journal of Computer Engineering & Technology, Vol-3 (3), pp. 94-103. ISSN-0976-6367 -
Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living
Activity recognition(AR) is a popular subject of research in the recent past. Recognition of activities performed by human beings, enables the addressing of challenges posed by many real-world applications such as health monitoring, providing security etc. Segmentation plays a vital role in AR. This paper evaluates the efficiency of Area-Based Segmentation using different performance measures. Area-Based segmentation was proposed in our earlier research work. The evaluation of the Area-Based segmentation technique is conducted on four real world datasets viz. Aruba17, Shib010, HH102, and HH113 comprising of data pertaining to an individual, living in the test bed home. Machine learning classifiers, SVM-R, SVM-P, NB and KNN are adopted to validate the performance of Area-Based segmentation. Amongst the four chosen classification algorithms SVM-R exhibits better in all the four datasets. Area-Based segmentation recognise the four test bed activities with accuracies of 0.74, 0.98, 0.66, and 0.99 respectively. The results reveal that Area based segmentation can efficiently segment sensor data stream which aids in accurate recognition of smart home activities. 2019 Procedia Computer Science. All rights reserved. -
Ambient monitoring in smart home for independent living
Ambient monitoring is a much discussed area in the domain of smart home research. Ambient monitoring system supports and encourages the elders to live independently. In this paper, we deliberate upon the framework of an ambient monitoring system for elders. The necessity of the smart home system for elders, the role of activity recognition in a smart home system and influence of the segmentation method in activity recognition are discussed. In this work, a new segmentation method called area-based segmentation using optimal change point detection is proposed. This segmentation method is implemented and results are analysed by using real sensor data which is collected from smart home test bed. Set of features are extracted from the segmented data, and the activities are classified using Naive Bayes, kNN and SVM classifiers. This research work gives an insight to the researchers into the application of activity recognition in smart homes. Springer Nature Singapore Pte Ltd. 2019. -
Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow
As there has been a rise in the usage of in silico approaches, for assessing the risks of harmful chemicals upon animals, more researchers focus on the utilization of Quantitative Structure Activity Relationship models. A number of machine learning algorithms link molecular descriptors that can infer chemical structural properties associated with their corresponding biological activity. Efficient and comprehensive computational methods which can process huge set of heterogeneous chemical datasets are in demand. In this context, this study establishes the usage of various machine learning algorithms in predicting the acute aquatic toxicity of diverse chemicals on Fathead Minnow (Pimephales promelas). Sample drive approach is employed on the train set for binning the data so that they can be located in a domain space having more similar chemicals, instead of using the dataset that covers a wide range of chemicals at the entirety. Here, bin wise best learning model and subset of features that are minimally required for the classification are found for further ease. Several regression methods are employed to find the estimation of toxicity LC50 value by adopting several statistical measures and hence bin wise strategies are determined. Through experimentation, it is evident that the proposed model surpasses the other existing models by providing an R2 of 0.8473 with RMSE 0.3035 which is comparable. Hence, the proposed model is competent for estimating the toxicity in new and unseen chemical. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Financial analytical usage of cloud and appropriateness of cloud computing for certain small and medium-sized enterprises
The term "cloud computing"refers to a novel approach of providing useful ICTs to consumers over the internet on an as-needed and pay-per-usage basis. Businesses may streamline internal processes, increase contact with customers, and expand their market reach with the aid of cloud computing, which provides convenient and inexpensive access to cutting-edge information and communication technologies. Developing economies like India's present unique problems for small and medium-sized businesses (SMEs), such as a lack of funding, an inadequate workforce, and inadequate information and communication technology (ICT) use. Various advantages offered by current information and communication technology solutions are unavailable to SMEs because of these limitations. If small and medium-sized enterprises (SMEs) are seeking to enhance their internal operations, communication with customers and business partners, and market reach using current information and communication technology (ICT) solutions, cloud computing might be a good fit for them. Therefore, SMEs are particularly well-served by cloud computing. Companies with a lack of capital, personnel, or other resources to deploy and use appropriate ICTs may greatly benefit from cloud computing, and the public cloud in particular. 2024 Author(s). -
Design and performance analysis of eight channel demultiplexer using 2D photonic crystal with trapezium cavity
In this work, an eight-channel dense wavelength division multiplexing demultiplexer is designed with a 2D photonic crystal triangular lattice. The proposed demultiplexer consists of a centre bus waveguide, an isosceles trapezium resonant cavity, and an eight-circular ring cavity (CR1, CR2, CR3, CR4, CR5, CR6, CR7, and CR8). The point defect resonant cavity consists of seven rods to drop different wavelengths from eight cavities, each of eight drop waveguides. The design is very simple to realise. The finite difference time domain and plane wave expansion method methods were used to analyse the proposed designs band structure and transmission spectrum. The resonant wavelengths are 1.5441 ?m, 1.5443 ?m, 1.544 49 ?m, 1.5447 ?m, 1.5449 ?m, 1.5451 ?m, 1.5453 ?m, and 1.5455 ?m respectively. The proposed device provides a high-quality factor, transmission efficiency, and low crosstalk. The devices footprint is 490.0 ?m2, which can be easily incorporated into photonic integrated circuits. 2023 IOP Publishing Ltd. -
Performance Evaluation of Transfer Learning VGG16 in Handwritten Text Using Word Beam Search and Language Model
This study evaluates the performance of transfer learning using the VGG16 model for handwritten text recognition, integrating Word Beam Search decoding and language modeling techniques. The VGG16 model, pre-trained on large-scale datasets, serves as a feature extractor for handwritten text images, capturing intricate patterns and structures inherent in handwriting. To convert these visual features into textual information, the system employs a Recurrent Neural Network (RNN) trained with the Connectionist Temporal Classification (CTC) loss function, producing a matrix of character probabilities for each time-step. The Word Beam Search algorithm is utilized for decoding these probabilities into coherent text, effectively constructing recognized text by referencing a predefined dictionary and addressing challenges such as arbitrary character strings and varying handwriting styles. The integration of language models incorporates context which further sharpens the output and improves precision and trustworthiness of recognition systems. Experimental results demonstrate that this combined approach significantly improves recognition performance, highlighting the efficacy of transfer learning and advanced decoding strategies in handwritten text recognition. This involves analyzing its effectiveness across various datasets. Transfer learning leverages pre-trained models, like VGG16, to address challenges such as limited labeled data and extensive training times. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
Activity recognition using machine learning techniques for smart home assisted living
The statistical survey by United Nations Department of Economic and Social Affairs/Population Division says, quotglobally the number of persons aged 60 and above is expected to be more than double by 2050 newlineand more than triple by 2100quot. Especially in India, 9.5 percent of the population comprises of elders above 60 years. This may reach 22.2 percent in 2050 and 44.4 percent in 2100. On one side, the population of newlineelders are gradually increasing and on the other side there is a challenge to take care of the wellbeing of the elders when they are living alone. Smart home assisted living system can address these problems. Smart newlineHome Assisted living System is one among the growing research areas in smart computing. Advances in sensing, communication and ambientintelligence technologies created tremendous change in smart living newlineenvironment. The development in technology made smart home to support elders, disabled persons and the needy person. newlineActivity recognition is a growing technology in recent research and it plays a vital role in smart home assisted living system. Activity Recognition is a more dynamic, interesting, and challenging research newlinetopic in different areas like Ubiquitous Computing, Smart Home Assisted Living, Human Computer Interaction (HIC) etc. It provides solution to various real-time, human-oriented problems like elder care and health newlinecare. newlineIn order to address the issue on providing support on elder care this research proposes a machine learning based activity recognition model and an enhanced communication protocol for a smart home system, which are collaborated for designing the architecture of a smart home assisted living system. This system consists of three sub phases viz., data acquisition, monitoring system, and tracking system. -
Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow
As there has been a rise in the usage of in silico approaches, for assessing the risks of harmful chemicals upon animals, more researchers focus on the utilization of Quantitative Structure Activity Relationship models. A number of machine learning algorithms link molecular descriptors that can infer chemical structural properties associated with their corresponding biological activity. Efficient and comprehensive computational methods which can process huge set of heterogeneous chemical datasets are in demand. In this context, this study establishes the usage of various machine learning algorithms in predicting the acute aquatic toxicity of diverse chemicals on Fathead Minnow (Pimephales promelas). Sample drive approach is employed on the train set for binning the data so that they can be located in a domain space having more similar chemicals, instead of using the dataset that covers a wide range of chemicals at the entirety. Here, bin wise best learning model and subset of features that are minimally required for the classification are found for further ease. Several regression methods are employed to find the estimation of toxicity LC50 value by adopting several statistical measures and hence bin wise strategies are determined. Through experimentation, it is evident that the proposed model surpasses the other existing models by providing an R2 of 0.8473 with RMSE 0.3035 which is comparable. Hence, the proposed model is competent for estimating the toxicity in new and unseen chemical. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Mediating Role of Mathematics and Science Engagement in the Relationship between Attitude toward STEM Education and Subjective Well-being of Adolescents
Science, technology, engineering, and mathematics (STEM) education has become a focal point of global discussions in the field of education. It emphasizes an interdisciplinary approach to learning. Subjective well-being of adolescents is characterized as joy to learn, close connectedness in schools, perception of the purpose of education, and the estimation of academic efficiency. This study investigates the mediating role of mathematics and science engagement in the relationship between the attitude toward STEM education and subjective well-being of school students in Kerala. Drawing upon theoretical frameworks from psychology, education, and sociology, this study employs a quantitative approach to data collection and analysis. A sample of 363 secondary and senior secondary students was administered standardized survey tools, measuring attitudes toward STEM education, subjective well-being, and their engagement in mathematics and science classes. Regression and mediation analyses resulted in indicating the positive, mediating effect of mathematics and science engagement in the relationship between the attitude toward STEM education and subjective well-being. Practically, the study suggests that educators should foster positive STEM attitudes through engaging teaching techniques and hands-on activities. Cultivating a positive STEM culture in schools can contribute to students well-being and equip them for future success in STEM fields. 2025 International Council of Associations for Science Education (ICASE). All rights reserved. -
A Privacy-Preserving Federated Learning Protocol for Secure Analytics of IoT Sensor Data Using Homomorphic Encryption
The proliferation of Internet of Things (IoT) devices has led to massive amounts of sensitive data generation, making data security a paramount concern. Existing methods often struggle with protecting heterogeneous IoT data efficiently, particularly during model training and communication. In this work, we propose a federated learning framework integrated with secure encryption mechanisms to safeguard IoT data during model training and aggregation. Each client device trains a local model using its own sensor data, encrypts the model parameters, and sends them to a server. The server aggregates the encrypted models and sends back the global model for decryption by the clients, ensuring data privacy throughout the process. The proposed framework reduces the unauthorized access risks and also the experimental results demonstrate that the model results in an accuracy of 92% during prediction tasks. The system's encryption overhead was minimal, with only a 7.5% increase in computation time compared to unencrypted federated learning methods. Future work will focus on optimizing the encryption techniques for resource-constrained IoT devices and exploring adaptive security mechanisms powered by machine learning to detect emerging threats dynamically. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Building a sustainable brand in textile industry
The textile industry is at a pivotal moment where integrating sustainability into brand strategies is not merely an ethical obligation but a competitive necessity. This study highlights three key approaches-adoption of circular economy principles, implementation of blockchain technology for supply chain transparency, and the use of innovative, sustainable materials-that are shaping the future of sustainable brand development in the textile sector. -
Automated Classification of Medicinal Plants Using Lightweight Deep Learning and Transfer Learning
The identification of medicinal plants plays a pivotal role in traditional medicine, biodiversity conservation, and rural healthcare. Conventional manual identification methods are often time-consuming and error-prone, particularly when differentiating between morphologically similar species or plants at varying growth stages. Recent developments in deep learning, especially convolutional neural networks (CNNs) with transfer learning, have emerged as robust solutions for image-based classification tasks, offering efficiency and high accuracy with limited computational resources. The proposed framework employs a carefully structured deep learning pipeline integrating advanced preprocessing, lightweight architecture design, and domain-adaptive transfer learning. A large real-world dataset of 20,109 medicinal leaf images across 99 classes was standardized through resizing, normalization, and categorical encoding, followed by targeted data augmentation and class-weight balancing to address inter-class similarity and dataset imbalance. A key methodological novelty lies in the use of MobileNetV3 with an optimized transfer-learning strategy, leveraging its inverted residual blocks, Squeeze-and-Excite modules, and hard-swish activation to enhance texture-, venation-, and contour-based feature extraction in plant leaves. Unlike existing plant-recognition studies that rely on heavier CNNs, our approach introduces a computationally efficient, low-latency model specifically tailored for mobile and embedded deployment. Experimental results demonstrate that the proposed MobileNetV3-based model achieved a classification accuracy of 92.88%, with macro- and weighted-average F1-scores of 0.85 and 0.86, respectively. Precision and recall values across most classes ranged between 0.80 and 0.95, confirming the models reliability in differentiating species. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
Evaluation of phytoconstituents of Triticum aestivum grass extracts on nutritional attributes, antioxidant, and antimicrobial activities against food pathogens with molecular in silico investigation
The plant-based medicine and diet is gaining importance in recent days. The consumption of Triticum aestivum grass in the form of juice and tablets is increasing among common people. The present study elaborates on the nutritional, antioxidant, and antimicrobial potential of a nongenetically modified type of T. aestivum grass, along with the evidence of molecular docking studies. The T. aestivum grass extracts like decoction, aqueous, ethanol, and chloroform were subjected to preliminary phytochemical tests, quantitative estimation, antioxidant analysis, and antimicrobial activity determination. The ethanolic extract that had good antioxidant and antimicrobial potential was subjected to gas columnmass spectroscopy (GCMS) analysis and the compounds identified were docked against the antioxidant and antimicrobial receptors. The decoction and aqueous extracts performed well in preliminary qualitative tests with the presence of most of the phytochemicals tested. The decoction, aqueous, and ethanolic extracts possessed good concentrations of the phytochemicals. The decoction had about 210.839.16 and 154.160.33mg/g of carbohydrates and proteins, respectively, while the aqueous extract had about 10.910.08mg/g of amino acids and the ethanolic extract had about 52.51.4mg/g of phenolic content, which were the highest concentration of the phytochemicals observed among the extracts. Along with phytochemical potential, good antioxidant potential in the DPPH and ABTS by decoction as well as ethanolic extract with nearly 40 and 90% inhibition, respectively, and in FRAP by aqueous extract with maximum OD value. The ethanolic extract exhibited the best inhibition potential against the Staphylococcus aureus about 281mm, Pseudomonas aeruginosa with 202mm, Bacillus cereus at 201mm by the ethanolic extract at 200?g concentration, and Aspergillus fumigatus and A. niger at 150mm by the aqueous extract at 200?g concentration. The GCMS analysis revealed the presence of terpenoids, alkaloids, and phenols, which on docking had highest binding capacity toward the antioxidant and antimicrobial receptors. 2023 The Authors. Food Frontiers published by John Wiley & Sons Australia, Ltd and Nanchang University, Northwest University, Jiangsu University, Zhejiang University, Fujian Agriculture and Forestry University. -
Dietary Plants, Spices, and Fruits in Curbing SARS-CoV-2 Virulence
Patients with coronavirus disease 2019 (COVID-19) infection can suffer from a variety of neurological disorders; therefore, there is a demand to investigate specific treatments. As a part of this endeavor, academic databases related to clinical, neuropathological, and immunological biomarkers were examined for searching promising drugs to treat neurological disorders in the COVID-19 group. Also, the neuroprotective potential of herbs for patients with post-COVID-19 has been evaluated using PubMed, MEDLINE, Scopus, EMBASE, Google Scholar, EBSCO, Web of Science, Cochrane Library, WHO database, and ClinicalTrials.gov. The terms used for the Boolean search were Indian herbs and neuroprotective potential, post-COVID-19 symptoms, and so on. Based on our knowledge, nervous system immunity is an inherent characteristic of the nervous system because it is highly immunologically active. It was found that patients infected with COVID-19 often experience neurological symptoms such as muscle pain, headaches, confusion, dizziness, and loss of smell and taste. The most commonly used herbs for neurological disorders are Bacopa monnieri, Mucuna pruriens, Withania somnifera, Acorus calamus, Phyllanthus emblica, Blumea balsamifera, Asparagus racemosus, Cannabis sativa, Convolvulus prostratus, Swertia chirata, Vitex negundo, Nyctanthes arbor-tristis Linn, Centella asiatica, Curcuma longa, Ocimum tenuiflorum. It is widely recognized that herbal drugs have the potential for treating neurological diseases such as Parkinsons, Alzheimers, and cerebrovascular diseases in COVID-19 patients. However, clinical trials are still limited. The suitability of drugs depends on the investigation of biomarkers and pathobiological mechanisms. Thus, it is necessary to use modern scientific approaches and technologies to conduct comprehensive mechanistic studies to understand the therapeutic potential of herbs for neurological disorders associated with the SARS-CoV-2 infection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.