Browse Items (16488 total)
Sort by:
-
Plant- based Metabolites as Source of Antimicrobial Therapeutics: Prospects and Challenges
Plants are used as traditional medicines from ancient times to today as they are the largest living storehouses of bio- chemicals and pharmaceuticals known on Earth (Abdallah, 2011). The World Checklist of Vascular Plants (WCVP) database reported in April 2021 that there are 1,383,297 plant names with 996,093 plants identified at species level, constituting 342,953 accepted vascular plant species (Govaerts et al., 2021). Around 10% of the reported vascular plants are used as medicines (Salmer- Manzano et al., 2020). According to the MPNS, 33,443 species are recorded as being used for medicinal purpose (MNPS, 2021). Medicinal plants are those that have therapeutic properties which can pose pharmacological effect on the human or animal body (Namdeo, 2018). About 80% of the world's population depends on plant- based medicine for treatment of diseases (Okoye et al., 2014). The medicinal property of a plant is attributed to rich and diverse secondary metabolites (Allemailem, 2021). Secondary metabolites are intermediates or products of primary metabolism that are not involves directly in the growth and development of the plant (Jain et al., 2019). Plants generate secondary metabolites in response to stresses posed by biotic factors (bacteria, fungi, viruses, parasites, pests, weeds, and herbivore animals) and abiotic environmental factors (temperature, salinity, drought, UV radiation etc.) so as to adapt and survive in response to environmental stimuli during their life time (Yang et al., 2018). 2023 selection and editorial matter, Arti Gupta and Ram Prasad; individual chapters, the contributors. -
Experimental Investigation on Density and Volume Fraction of Void, and Mechanical Characteristics of Areca Nut Leaf Sheath Fiber-Reinforced Polymer Composites
Natural fiber-reinforced polymer composite is a rapidly growing topic of research due to the simplicity of obtaining composites that is biodegradable and environmentally friendly. The resulting composites have mechanical properties comparable to synthetic fiber-reinforced composites. In this regard, the present work is formulated with the objectives related to the development, characterization, and optimization of the wt% of reinforcements and the process parameters. The novelty of this work is related to the identification and standardization of the appropriate wt% of reinforcements and parameters for the processing of the areca nut leaf sheath fiber-based polymer composites for enhanced performance attributes. With this basic purview and scope, the composites are synthesized using the hand layup process, and the composite samples of various fiber compositions (20%, 30%, 40%, and 50%) are fabricated. The mechanical characteristics of biodegradable polymer composites reinforced with areca nut leaf sheath fibers are investigated in the present work, with a focus on the effect of fiber composition (tensile properties, flexural strength, and impact strength). The properties of composites are enhanced by combining the areca nut leaf sheath fiber and epoxy resin, with a fiber content of 50% being the optimal wt%. The Scanning electron microscopy (SEM) investigations also ascertain this by depicting the good interfacial adhesion between the areca nut leaf sheath fiber and the epoxy resin. The tensile strength of the composite specimen reinforced with 50% areca nut fiber increases to 44.6 MPa, while the young's modulus increases to 1900 MPa, flexural strength increases to 64.8 MPa, the flexural modulus increases to 37.9 GPa, and impact strength increases to 34.1 k J/m2. As a result, the combination of areca nut leaf sheath fiber reinforced epoxy resin shows considerable potential as a renewable and biodegradable polymer composite. Furthermore, areca nut leaf sheath fiber-reinforced epoxy resin composites are likely to replace petroleum-based polymers in the future. The ecosustainability and biodegradability of the composite specimen alongside the improved mechanical characteristics serve as the major highlight of the present work, and can help the polymer composite industry to further augment the synthetic matrix and fiber-based composites with the natural fiber-reinforced composites. 2022 B. A. Praveena et al. -
Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks
Software Defined Network (SDN) has been used in many organizations due to its efficiency in transmission. Machine learning techniques have been applied in SDN to improve its efficiency in resource scheduling. The existing models in SDN have limitations of overfitting, local optima trap and lower efficiency in path selection. This study applied Balancing Module (BM)-Spider Monkey Optimization (SMO)-Crow Search Algorithm (CSA) for multi path selection in SDN to improve its efficiency. The balancing module applies Gaussian distribution to balance between exploration and exploitation in the multi-path selection process. The Balancing module helps to escape local optima trap and increases the convergence rate. Deep Reinforcement learning is applied for resource scheduling in SDN. The Deep reinforcement learning technique uses the reward function to improve the learning performance, and the BM-SMO-CSA technique has 30 J energy consumption, where the existing models: DRL has 40 J energy consumption, and Graph-ACO has 62 J energy consumption. 2022 -
CNN-based Indian medicinal leaf type identification and medical use recommendation
Medicinal leaves are playing a vital role in our everyday life. There are an enormous amount of species present in the world. Identification of each type would be a tedious task. Using image processing technology, we can overcome this problem by providing computer vision with the help of a convolution neural network (CNN). The objective of this research is to find out the best CNN model that helps in classifying the plant leaf species and identifying its category. In this research work, the proposed basic CNN model consisting of four convolution layers uses ten different medicinal leaf species each belonging to two categories providing an accuracy of 96.88%. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
Chaos and control in a fractional-order financial model: a non-local dynamical approach
The idea of financial resources is all encompassing and crucial to every facet of human existence; it also has indirect relationships to people, communities, cities, and nations. Researchers are interested in this topic since it has significant value for a societys progress. This investigation devotes to the analysis of non-local effects of the fractional finance system. In order to make sure the system is well-posed, the boundedness has been examined. Furthermore, the stability analysis investigation confirms the unstable state of the system. Additionally, we show how to use Lyapunov exponents and bifurcation parameter analysis to determine the appropriate range where the system is more chaotic. Using Picards operator, we investigated the existence and uniqueness of the solutions and showed that the system under consideration had two unstable equilibrium points. By using the active control approach, we provide the necessary circumstances for fractional finance systems to synchronize as well as control functions to manage chaos in the considered system, so that we can record the observations. To illustrate the numerical simulations for different parameter values of the finance system, the fractional Eulers method is used, and the chaotic behaviors are captured in figures. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Time Series Forecasting of Stock Market Volatility Using LSTM Networks
Forecasting stock market volatility is a pivotal concern for investors and financial institutions alike. This research paper employs Long Short-Term Memory (LSTM) networks, a potent class of recurrent neural networks, to predict stock market volatility. LSTM networks have proven adept at capturing intricate temporal dependencies, rendering them a fitting choice for time series data analysis. We commence by elucidating the notion of stock market volatility and its profound significance in financial decision-making. Traditional methodologies, such as GARCH models, exhibit shortcomings in deciphering the convoluted dynamics inherent in financial time series data. LSTM networks, with their capacity to model extended temporal relationships, present an encouraging alternative. In this study, we assemble historical stock price and trading volume data for a diverse array of assets, diligently preprocessing it to ensure its aptness for LSTM modeling. We systematically explore various network architectures, hyperparameter configurations, and input features to optimize the efficacy of our models. Our empirical investigations decisively underscore the supremacy of LSTM networks in capturing the subtleties of stock market volatility compared to conventional techniques. As the study progresses, we delve deeper into the complexities of LSTM network training, leveraging advanced techniques such as batch normalization and dropout to fortify model resilience. Moreover, we delve into the interpretability of LSTM models within the context of stock market forecasting. 2023 IEEE. -
Machine Learning for Smart School Selection and Admissions
Choosing the best school for their kid is an important choice that parents must make, and it is sometimes stressful and unsure. Machine learning is a potential way to improve and streamline the admissions and school selection process in the current digital era. This study investigates the use of machine learning methods in the context of selective admissions and smart school selection. We propose a user-friendly, web-based tool in the early phases of our study that helps parents and guardians locate the ideal school for their kid by using machine learning algorithms. To provide individualized school recommendations, the platform gathers and analyses a range of data, such as extracurricular activity participation, academic achievement, regional preferences, and school reputation. This makes choosing a school easier and supports parents in making wise choices. This paper's second section explores the technical details of the machine learning techniques used, going into the nuances of feature selection, data preparation, and model assessment. We also draw attention to the difficulties and moral issues - such as maintaining impartiality and avoiding bias - that come with using machine learning to school selection. 2023 IEEE. -
Exploring Explainable Artificial Intelligence for Transparent Decision Making
Artificial intelligence (AI) has become a potent tool in many fields, allowing complicated tasks to be completed with astounding effectiveness. However, as AI systems get more complex, worries about their interpretability and transparency have become increasingly prominent. It is now more important than ever to use Explainable Artificial Intelligence (XAI) methodologies in decision-making processes, where the capacity to comprehend and trust AI-based judgments is crucial. This abstract explores the idea of XAI and how important it is for promoting transparent decision-making. Finally, the development of Explainable Artificial Intelligence (XAI) has shown to be crucial for promoting clear decision-making in AI systems. XAI approaches close the cognitive gap between complicated algorithms and human comprehension by empowering users to comprehend and analyze the inner workings of AI models. XAI equips stakeholders to evaluate and trust AI systems, assuring fairness, accountability, and ethical standards in fields like healthcare and finance where AI-based choices have substantial ramifications. The development of XAI is essential for attaining AI's full potential while retaining transparency and human-centric decision making, despite ongoing hurdles. 2023 EDP Sciences. All rights reserved. -
Advanced Technological Improvements in Making of Smart Production Using AI and ML
The necessity for adaptation and creativity in the manufacturing sector demonstrates the importance of sustainable manufacturing by the merging of advanced technologies. To encourage sustainability, a global view on the integration of smart manufacturing procedures is important. Artificial intelligence (or AI) has appeared as a crucial factor in achieving environmentally conscious manufacturing, with methods like the use of machine learning (ML) getting popularity. This study carefully studies the scientific papers related to the usage of AI and ML in business. The emergence of Industry 4.0 as a whole has positioned machine learning (ML) and artificial intelligence (AI) as drivers for the smart industry change. The study categorizes material based on release year, writers, scientific field, country, institution, and terms, applying the Web of Biology and SCOPUS databases. Utilize UCINET alongside NVivo 12 software, thereby the analysis covers empirical studies on machine learning (ML) and artificial intelligence (AI) via 1999 until the present, showing their growth before and after the start of Industry 4.0. Notably, the USA displays a substantial addition to this area, with a noticeable surge in desire following the rise of Industry 4.0. 2024 IEEE. -
Utilizing Transforming Portfolio Management Through Automation Using Advanced Deep Reinforcement Learning Algorithms for Optimized Investment Strategies
This paper focuses on the future possibility of enhancing the applications of DRL in autonomously managing a portfolio for better investment plans. Having used past financial data and a highly developed case of DRL, the proposed system shows better performance compared to conventional investment strategies and indices. This process includes data gathering from the financial databases, the steps of preprocessing and feature extraction, and the use of the DQN structure. After that, the system's training and validation are done by a finite portion of real-world data and a large number of synthesized data to improve stability. The result shows that the new method achieves superior cumulative return, Sharpe ratio, maximum drawdown, and annualized volatility; therefore, it suggests that the proposed system can flexibly predict the fluctuating stock market trends and make appropriate investment decisions. Thus, the present research adds importance to the use of DRL in improving return potential and risk management in portfolio management. Thus, this study adds to the existing literature and practice by allowing for the automation of the optimization and testing for investment solutions at a larger scale, while opening up opportunities for future developments in the application of financial technology and investment tools. 2025 IEEE. -
Optimizing Retail Operations with Big Data-Driven Insights: From Inventory Management to Personalized Marketing
In this paper, we look into the role of big data analytics in the strategic transition of retail businesses particularly on inventory management, supply chain and marketing. Using such technologies as big data and machine learning, retailers can find new patterns within such information that can lead to improved efficiency, and satisfaction of consumers. The study also shows noteworthy performance gain in areas of stock out and overstock, inventory Turns and delivery correctness. Even more, the approach to the customer targeting, that stemmed from the principles of the customer segmentation and recommendation systems, led to the growth of the conversion, customer loyalty, and customer lifetime value. The research evidence suggests that information-based management strategies contribute to organizational performance and sustained competitive advantage of firms operating in the retail sector. Issues like data integration, privacy and infrastructural, components are also addressed and hence making it easy form the basis of any future learning and trying out on the real life challenges. The current research focuses on the significance of big data for designing growth and innovation strategies in the changing retail environment. 2025 IEEE. -
A SYSTEMATIC RESEARCH REVIEW AND META-ANALYSIS OF ENVIRONMENTAL SCIENCES AND MANAGEMENT MODELS
This research advances the comprehension of the processes behind individuals' environmentally friendly behavior using a comprehensive approach. A questionnaire addressed intrapersonal, motivational, relationships, and educational aspects, with environmental science as the primary catalyst for green behavior within a complete theoretical structure.The method is the CADMIACA approach, which is founded on Comprehensive Action Determining Modeling (CADM), together with various Motivational and Interpersonal (MI) theories and the Activity Competence Algorithm(ACA). This framework encompasses various control factors relevant to comprehensively characterizing the factors influencing environmentally friendly behavior, including climate change, energy conservation, recycling, sustainable buying, and contamination.The findings were gathered in the A Coru metropolitan region to experimentally evaluate the causal relationships among the parameters that formed the framework utilizing Structural Equation Modeling (SEM). Findings show that environmentalscience serves as an effective instrument for fostering eco-friendly behavior among residents. The extensive CADMIACA model aligns well with the information since all components incorporated in the framework (intrapersonal, inspiring, social, and institutional) are pivotal in shaping green conduct.Environmental instruction and intrapersonal variables emerged as the primary predictors of green conduct, but social and motivational variables were less prevalent in influencing such behavior. The findings suggest that human conduct plays a vital role in environmental protection. 2025, Rotherham Academic Press Ltd. All rights reserved. -
Smart Steering Wheel for Improving Drivers Safety Using Internet of Things
Nearly 3700 people every day die on the worlds roads in collisions with trucks, cars, buses, motorcycles, bicycles, or pedestrians.The cause of accidents is drowsiness, drunk driving, breaking the speed limit, Driver health issue and rash driving. The most concept of this venture is to avoid the street mishap so we are utilizing liquor location sensor, eye flicker sensor, over speed control sensor, temperature sensor, beat sensor. To detect drowsiness, speed of the vehicle, drivers health, alcohol consumed by the driver, and rash driving status the model is installed with sensors in steering wheel and camera. The sensors will detect the physical condition of the driver and the camera module will take the live recording of the drivers face part to detect the drowsiness. Simple but effective strategies are used to improve the baseline detection/tracking algorithm and the eye-state classification algorithm, and the results are tabulated to increase the systems dependability and accuracy. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Tadpole domination number of graphs
The graph obtained by joining cycle Cm to a path Pn with a bridge is called Tadpole graph denoted by Tm,n. A subset D of V (G) is said to be a tadpole dominating set of G if D is a dominating set and the set of vertices of D spans a tadpole graph Tm,n where m ? 3, n ? 1. In this paper, we find the tadpole domination number of cartesian product of certain graphs, namely, paths, cycles and complete graphs. Also the tadpole domination number for the Mycielskian of cycles and paths is obtained. 2023 World Scientific Publishing Company. -
The s-shunt non-intersection graph of a graph
For an integer s ? 1, an s ? arc in a graph G is a sequence of (s + 1) vertices (v1,v2,,vs,vs+1) of G such that for all 1 ? i ? s, vi ? vi+1, and for all 1 ? i ? s ? 1, vi?vi+2. A non-intersection graph of the set of all s-arcs on distinct vertices of G, that can be shunted onto some other s-arc on distinct vertices of G, has been introduced. Basic properties based on the order, size and the degree of an arbitrary vertex of the non-intersection graph of a graph defined are obtained. Additionally, certain properties pertaining to the connectedness of the same are discussed. 2026 World Scientific Publishing Company. -
Further Study on the s-Shunt Intersection Graph of a Graph
For an integer s ? 1, an s-arc in a graph G is a sequence of (s + 1) vertices (v1, v2, , vs+1) of G such that any two consecutive vertices are adjacent in G and vi ? vi+2; 1 ? i ? s ? 1. Certain structural properties of an intersection graph defined on the set of all s-arcs on distinct vertices of a graph G, that can be shunted onto another s-arc on distinct vertices of G, known as the s-shunt intersection graph of G is studied. 2026, SINUS Association. All rights reserved. -
The (Pk,I) Transformation Graph of a Graph
A study on graphs that are derived from graphs based on the intersection of all k-paths of a graph G is initiated. The (Pk,I) transformation graph of paths, cycles, Pn?pK1 and Cn?pK1 is discussed. Also, certain structural properties of the newly constructed graph are also discussed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Kannada script recognitions from scanned book cover images
Text extraction from the images plays a vital role in providing valuable information. Text extraction from images is still a challenging area specially extracting text from regional scripts of India like Kannada, Malayalam etc. Most of the times the images contain complex background then the cropping of text becomes even more challenging for extracting features. The input image is a scanned document images of Kannada book cover which is scanned with flatbed scanner of 400dpi resolution. The data sets are created by dividing the original images into number of varied size of blocks. Both spatial and frequency features are extracted for classifying images. This paper aims at recognizing the scanned images block which contains text or not by using multiple feature approach. The classification is analysed using Multilayer perceptron, Kstar and KNN. Experiments are performed on different sets of scanned documents of text cover images. Compare to all the classifiers KNN has given the encouraging results. Research India Publications. -
A Comprehensive Study On Detection Of Emotions Using Human Body Movements: Machine Learning Approach
Identifying emotions from human beings is the most challenging area in artificial intelligence. There are different modules used to identify emotions like speech, face, EEG, Physiological Signals, and body movement. However, emotional recognition from body movement is the need of time. The review focuses on identifying various emotions with the help of the full-body movement model and the parts-based model. The aim of the survey is to identify the recent work done by the researchers with the help of full-body movements and body parts-based models. Recently, little research has been done on the identification of emotions using body movements, but most of the time it has succeeded to some extent. Identifying various human emotions using body movements is a really very challenging task. This research work discovers that the various popular machine learning algorithms like Support Vector Machines, Neural Networks, and convolutional neural networks are majorly used to identify basic emotions. 2023 American Institute of Physics Inc.. All rights reserved. -
Bimodal Classification for Emotional Intelligence Using Peripheral Signals
Innovation in the field of humancomputer interaction involves analyzing users real-time emotions, which stands to be an essential and challenging task as they can be easily controlled or faked. Methodologies for analyzing emotions in existing studies include facial, audio, and physiological signals. The primary objective is to develop a model for emotion classification that can accurately identify and interpret human emotions through skin temperature, respiration, and plethysmograph. The aim was to analyze ensemble models that accurately discern and interpret emotional states. The emotional states were classified based on the frequency domain signal components extracted using Fast Fourier Transform (FFT), such as amplitude and frequency. Ensemble-based machine learning algorithms such as XGBoost and LGBM achieved the highest accuracy in classifying various emotional states. The study involves unimodal and bimodal analysis of the signals. The comparative classification rate of bimodal results is the highest for calm, with 85.5%, by combining a plethysmograph and temperature. Whereas the bimodal results with respiration and skin temperature maintain the accuracy level for all four emotions. The results also convey the significance of plethysmograph and temperature for a high classification rate of happiness and emotion, whereas respiration has improved the classification rate of anger and sadness. The potential applications include enhancing user experiences and contributing valuable insights into mental health care, humancomputer interaction, and recommendation systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.