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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. -
Feature Fusion Classification for Emotional Intelligence Using Peripheral Signals
Real-time emotion identification is an innovation in the field of humancomputer interaction, which is an essential and challenging task. The existing studies methods for identifying emotions include face, audio, and physiological signals. The study aims to develop a model for emotion classification to identify and interpret human emotions through skin temperature, respiration, and plethysmography. The study also includes analyzing and interpreting emotional states through ensemble models. The classification is based on the frequency domain signal components extracted using the Fast Fourier Transform (FFT), such as amplitude and frequency, to identify emotional states. Ensemble-based machine learning algorithms such as XGBoost and LGBM achieved the highest accuracy in classifying various emotional states. The study involves unimodal and ensemble methods to analyze the signals. The comparative classification rate of unimodal results with ensemble shows that it is the highest at 85.99%, achieved for sad emotions by XGBoost. Fusing modules like respiration, skin temperature, and plethysmography maintains the accuracy level for all four emotions. The unimodal temperature has the highest accuracy of 86.1% for calm, whereas the fusion model has maintained accuracy for all the emotional states. The feature amplitude is the most promising feature for the classification method, which attains an average of 83.2% for XGBoost. The applications enhance user experiences and contribute valuable help in psychology, mental health care, and HumanComputer Interaction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Impact of chitosan and chitosan nanoparticles on seed germination: probabilities and prospects
Agriculture is the science and practice of making plants and animals for the food for animals, and humans at large. It is used not only for food but also for other needs like wool, silk, leather, etc and now we start to use these agricultural practices for making plants and animals as bioreactors for making valuable pharmaceuticals. But, the main segment of agriculture is related to the cultivation of plants. Even though many types of plant propagation systems are available, the use of seeds is considered to be the most commonly used method in many crop varieties. The productivity of the crops is largely depending on healthy seedling production. There are many methods including the traditional methods to improve seedling development. Recent scientific developments help in the development of better seedlings and thereby the crop yield. Chitosan and its nanoparticles are being used in agriculture in different aspects starting from seed germination and seedling development apart from the use in controlled release of the nutrients. In the present chapter, the use of chitosan and chitosan nanoparticles in agriculture especially in the stages of seed germination and seedling development in different crop varieties is discussed. 2022 Elsevier Inc. All rights reserved. -
Therapeutic profiling of Saraca indica bark oil silver nanoparticles: Bioactivity and cytocompatibility in human keratinocyte (HaCaT) cells
This study explores the potential of silver nanoparticles synthesized from Ashoka (Saraca indica) bark oil, which has properties as a natural therapeutic agent. The silver nanoparticles (Ag-NPs) were produced using a green synthesis method from the Saraca indica bark oil and characterized through UV-Vis spectrophotometry, FTIR, and SEM techniques. Fungal infections are mainly caused by Candida spp., especially Candida albicans, which significantly contributes to diseases like candidiasis. The antifungal and antibacterial activities were tested against Candida albicans and Bacillus subtilis. Using the disk-diffusion method, different concentrations of Ag-NPs were evaluated and compared with fluconazole and streptomycin. Results showed that the inhibition zones were concentration-dependent, with a maximum inhibition zone of 21.751.768 mm, 21.751.06 mm at 100 g/mL against C. albicans and B. subtilis. The DPPH assay showed 62.17 % antioxidant activity at 80 g/mL, and IC?? values were 36.43 g/mL for AO-Ag NPs compared to 26.88 g/mL for crude oil. The increasing resistance to antifungal drugs and limited effective treatments highlight the need for alternatives. The DPPH antioxidant assay confirmed the nanoparticles free radical scavenging ability, indicating antioxidant potential. An albumin denaturation anti-inflammatory assay revealed notable inhibition by the nanoparticles compared to Ascorbic acid. Cytotoxicity was assessed on human keratinocyte (HaCaT) cells, showing dose-dependent cytocompatibility, with > 90 % viability at lower concentrations and 12.31 1.62 % viability at 100 g/mL. Compared to crude bark oil and positive controls, the nanoparticles exhibited enhanced bioactivity with reduced cytotoxicity to normal skin cells. Morphological observations also suggested apoptosis, possibly linked to ROS-mediated oxidative stress pathways. Overall, this research indicates that Saraca indica-silver nanoparticles are cost-effective, eco-friendly, and biocompatible, with antimicrobial, antioxidant, anti-inflammatory, and low cytotoxic properties. These properties support their potential use in developing nanomedicine treatments for infections and inflammation. 2025 The Authors -
Diazanorbornene: A Valuable Synthon towards Carbocycles and Heterocycles
Desymmetrization of meso compounds is well recognized as a powerful method for delivering biologically relevant molecular skeletons in a few synthetic steps. Heterobicyclic olefins are a class of meso compounds which exhibit exceptional reactivity due to their high ring strain originating from the unfavorable bond angles and eclipsing interactions. Extensive research was carried out towards the synthetic transformations of oxa-, aza-, and diazanorbornenes/norbornadienes for the synthesis of a wide variety of carbocycles and heterocycles in a single step, most importantly in a stereo- and chemo-selective manner. This review summarizes the relevant aspects of diazanorbornene reactivity which will inspire the synthetic community for exploiting these highly strained bicyclic systems for the creation of extensive libraries of novel structurally and biologically interesting molecules. The review is divided into several sections based on the type of reactions that diazanorbornenes are subjected to. 2020 Wiley-VCH GmbH