Browse Items (16488 total)
Sort by:
-
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 -
Portable and Automated Healthcare Platform Integrated with IoT Technology
The diverse applications of Internet of Things (IoT), Artificial Intelligence (AI) in Bioelectronics - face recognition, pulse wave monitoring and insulin level measurements have been discussed in this chapter. This chapter also emphasizes on the IoT and its applications in biological sensors. The chapter focuses on IoT systems, such as neural networks and other channels that are integrated into a secure healthcare monitoring system in order to make the system operate as a smart model in healthcare sector that determines the priority based on health parameters gathered from the sensor nodes. In this work, the different approaches of IoT with distinct methodologies are also deliberated. 2024 Scrivener Publishing LLC. -
Statistical Data Analysis of Anticorrosion and Antifouling: Unveiling Insights from Performance and Trends
This chapter provides intended insights about the current antifouling and anticorrosion conventional coatings, each with a distinct quality, that fall short of fully satisfying the contemporary requirements of material preservation in a marine environment. To solve these issues and accomplish the required goals, a number of novel terminologies and procedures have been investigated and several recently published research works were referred to and briefly examined in order to provide appropriate conclusions. In order to explore new methodologies that provide total protection for materials from biofouling and corrosion in maritime environments, this study looks into the complex realm of antifouling and anticorrosion methods. 2024 Scrivener Publishing LLC. -
Photometric identification of objects from galaxy evolution explorer survey and sloan digital sky survey
We have used Galaxy Evolution Explorer (GALEX) and Sloan Digital Sky Survey (SDSS) observations to extract seven band photometric magnitudes for over 80 000 objects in the vicinity of the North Galactic Pole. Although these had been identified as stars by the SDSS pipeline, we found through fitting with model spectral energy distributions that most were, in fact, of extragalactic origin. Only about 9 per cent of these objects turned out to be mainsequence stars and about 11 per cent were white dwarfs and red giants collectively, while galaxies and quasars contributed to the remaining 80 per cent of the data. We have classified these objects into different spectral types (for the stars) and into different galactic types (for the galaxies). As part of our fitting procedure, we derive the distance and extinction to each object and the photometric redshift towards galaxies and quasars. This method easily allows for the addition of any number of observations to cover a more diverse range of wavelengths, as well as the addition of any number of model templates. The primary objective of this work is to eventually derive a three-dimensional extinction map of the Milky Way Galaxy. 2013 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Analysis of Fine Needle Aspiration Images by Using Hybrid Feature Selection and Various Machine Learning Classifiers
Women die of breast cancer most often worldwide. Breast tissue samples can be examined by radiologists, surgeons, and pathologists for evidence of this cancer. Fine needle aspiration cytology (FNAC) can be used to detect this cancer through a visual microscopic examination of breast tissue samples. This sample must be examined by a cytopathologist in order to determine the patient's risk of breast cancer. To determine if a tumor is malignant, the nuclei of the cells must be characterized by their chromatin texture patterns. A machine learning method is used in order to categorize FNA images into two classes, respectively Malignant and Benign. For detecting abnormalities, numerous feature collection methods and machine learning means are applied here. Using features extracted from the FNA image set, UCI machine learning datasets are used to validate the proposed approach. This paper compares three classification methodologies, namely random forests, Naive Bayes, and artificial neural networks, by examining their accuracy, specificity, precision, and sensitivity, respectively. With the ANN and PCA along with the Chi-square selection method, 99.1% of the classifiers are correctly classified. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A succinct analysis for deep learning in deep vision and its applications
Introduction: Deep learning methodologies can achieve forefront results on testing deep vision issues, for instance, picture portrayal, an object area, face affirmation, Natural Language Processing, Visual Data Processing and online life examination. ConvNet, Stochastic Hopfield network with hidden units, generative graphical model and sort of artificial neural network castoff to absorb competent information coding in an unproven way are deep learning plans used in deep vision issues. Objection: This paper gives a succinct survey of without a doubt the most critical Deep learning structures. Deep vision assignments, for instance, object revelation, face affirmation, Natural Language Processing, Visual Data Processing, web-based life examination and their utilization of this task are discussed with a short record of the historic structure, central focuses and impairments. Future headings in arranging Deep learning structures for Deep vision issues and the troubles included are analysed. Method: This paper consists of surveys. In Section two, Deep Learning Approaches and Changes are audited. In section three, we tend to portray the uses of Applications of deep learning in deep vision. In Section four, Deep learning challenges and directions are mentioned. At long last, Section five completes the paper with an outline of the results. Results and Conclusion: Though deep learning can recall a huge proportion of data and info, its feeble cognitive and perception of the data makes it a disclosure answer for certain applications. Deep learning despite everything encounters issues in showing various erratic facts modalities at the equal period. Multimodal profound learning is an extra notable heading in progressing deep learning research. IJCRR. -
Discussion on ostracised transgender individuals and entrepreneurship through review of literature
Transgender individuals are the most deteriorated individuals in society. They face a wide range of trodden lives and setbacks in their everyday life. They encounter challenges and difficulties from the time they violate the social norms, they are also humiliated from their biological families and are sent to live a life of their own. In India, the Mughal period was termed to be the golden years for transgender individuals. It was after colonisation and implementation of the Criminal Tribes Act 1871 transgender individuals were treated brutally and eventually begging and sex work became their only source of income. Alongside, entrepreneurship proved to be a success factor as it brought the shunned women into the mainstream society. Thereby, entrepreneurship increases social capital and thus encourages transgenders in job creation activities. Despite a dire situation, there are transgender individuals who have faced all odds and have proved to set benchmarks in the society in varied fields. There are sporadic transgender individual entrepreneurs in the country who have paved their way into the entrepreneurial world, which is an important area to be explored. The study focuses on literature relating to transgender individuals, challenges faced by transgender individuals, entrepreneurial motivations and also transgender entrepreneurs. 2020 SERSC. -
Sartrean Insights on Understanding the Repercussions of Rape Trauma in the Gripping Narratives of Roxanne Gay and Neesha Arter
Women have been subjugated to violence from time immemorial. One of the most horrific forms of violence is sexual violence and rape. Their voice was not heard until the rise of second wave feminism which began around 1970. Women started to write about their experiences in the form of memoirs to bring to light the atrocities of rape and the implications of trauma and its impact. Sexual assault inflicts profound psychological and emotional wounds that give rise to a condition referred to as Rape Trauma. Rape Trauma Syndrome includes of a wide range of physical and psychological signs such as insomnia, nightmare, flashbacks, anxiety, and depression and so on and they last for a long period of time in one?s life. The research uses two memoirs written by American women, Roxanne Gay?s Hunger: A Memoir of my Body (2017) and Neesha Arter?s Controlled: The worst Night of my Life and its Aftermath (2015). The research uses Sartre?s perspective on embodiment, freedom, self to analyse rape trauma. The research uses Jean Paul Sartre?s concept to analyse the immense effects of rape trauma on the lives of the two women as documented in their memoirs. Using the framework provided by Constance L. Mui, the research delineates how rape trauma destroys the fundamental project of the protagonists and how rape trauma annihilates an individual from her own body and isolates her from the world. 2024 Sciedu Press. All rights reserved. -
Enhancing Workplace Efficiency with The Implementation of the Internet of Things to Advance Human Resource Management Practices
Improving human resource management via the use of the Internet of Things (IoT) is the focus of this research. The primary objective is to enhance productivity in the workplace. The researchers utilized a mix of qualitative (describing) and quantitative (numbers-based) techniques to collect and analyses data. This research shows that key HR KPIs are positively affected by using IoT in HRM. Businesses utilizing IoT for real-time monitoring have better operations and more engaged employees. The study found that state-of-the-art technology, extensive training, and effective change management were needed to overcome people's security concerns and unwillingness to change. The Internet of Things may transform HRM and corporate operations, according to study. According to study, companies should invest in people-focused technologies and services. It emphasizes creating a workplace that embraces new technology while prioritizing security and privacy. In conclusion, the study's results may help organizations navigate HRM and the IoT's changing terrain. It suggests linking HR and technology to improve workplace flexibility and efficiency. 2024 IEEE. -
Statistic analysis of IPL match score and winner inning wise using machine learning algorithms
This study explains the statistical analysis of cricket match score prediction using machine learning. According to recent changes in data science and sports, the use of sports-based machine learning and data mining shows the importance of process in outcome performance and prediction. The scope of this research paper is to evaluate current measurements used in the previous work to understand the estimation the ways used to model and analyze data and characterize the variables that govern performance using statistical methods. Actually, this research article will present a reliable statistical tool for data analysis using machine learning algorithms. At present, sports organizations produce enough statistical information on every player, team, match, and season for particular related sports. The first sports researchers were thought to be experts, coaches, team managers, and analysts. Sports organizations want to do statistical analysis of player from their previous data stored on their database using different data mining and machine learning algorithms. Sports data helps coaches and managers in many ways, such as predicting results, analyzing player performance, and skills, and evaluating strategies. Forecasts help managers and organizations make decisions to win teams and competitions. The current evaluation of research shows that primary studies of data mining systems can predict outcomes and evaluate the strengths and weaknesses of each system. Statistical analyses are made for each match for result predictions. Although in many respects this application is very limited. These are prime factors which important to examine machine learning algorithms in these situations to see if the application can give the nearest results in analysis. This research aims to give solutions that will help to make predictions more accurate and precise than previous methods, using more accurate data and machine learning. 2024, Taru Publications. All rights reserved. -
A Cognitive Architecture Based Conversation Agent Technology for Secure Communication
This paper outlines a multi-agent system-based approach to provider selection. Suppliers in the supply chain are different and the demand and supply levels are high. Buy agents will find the right supply agent in our approach. First, the multi-layer classification system is used to rationally arrange and overall selection on suppliers and buyers. Secondly, the purchase information is organized by the supplier agent to improve device performance. The assessment process is then used to select the suppliers initially. In addition to selecting the correct provider and maximizing the value of the purchaser, the time negotiating mechanism is implemented. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Role of Green Human Resource Management Practices on Sustainable Performance of the Service Sector Organizations
The prominence revolution took place in the 1980s, and then the Green HRM practises movement blew over the sustainable development literature in the 1990s. This represents an excellent practice because it newlineincorporates sustainable development principles into the operational processes newlineof the human resources department. According to Mishra (2017), Green HRM is a tactical instrument for arranging human resource competences and promoting ideas that motivate businesses to embrace sustainable strategies. newlineThe term human resource management (HRM) is used to describe a set of practices, procedures, and methods that are focused on managing and optimizing the performance of personnel within a business, thereby providing the organization with a competitive advantage. Despite the fact that green human resource management has grown to be a major source of concern, India has only relatively handful of first-hand studies done in this area. While conventional HRM concentrates on functions to provide an organisation a competitive edge, green HRM places an emphasis on the capture and retention of green efforts to meet corporate objectives. newlineGreen HRM in India is plagued by a variety of issues. The study has chosen a few traits from the research at hand that may be utilised to create Green HRM practices for sustainable development. It stands to reason for contemporary businesses to take environmental responsibility seriously (Jackson, Renwick, Jabbour and Camen, 2011). Numerous studies show that environmental newlineinitiatives increase production and provide companies a competitive edge (Bombiak and Marciniuk-Kluska, 2018). Although organizational solutions quotmoved beyond pollution control and the mitigation of environmental degradationquot, Saeed, Jun, Nubuor, Priyankara and Jayasuriya (2018) have highlighted its significance in the New Millennium Era. -
-
A Fractional Atmospheric Circulation System under the Influence of a Sliding Mode Controller
The earths surface is heated by the large-scale movement of air known as atmospheric circulation, which works in conjunction with ocean circulation. More than (Formula presented.) variables are involved in the complexity of the weather system. In this work, we analyze the dynamical behavior and chaos control of an atmospheric circulation model known as the Hadley circulation model, in the frame of Caputo and CaputoFabrizio fractional derivatives. The fundamental novelty of this paper is the application of the Caputo derivative with equal dimensionality to models that includes memory. A sliding mode controller (SMC) is developed to control chaos in this fractional-order atmospheric circulation system with uncertain dynamics. The proposed controller is applied to both commensurate and non-commensurate fractional-order systems. To demonstrate the intricacy of the models, we plot some graphs of various fractional orders with appropriate parameter values. We have observed the influence of thermal forcing on the dynamics of the system. The outcome of the analytical exercises is validated using numerical simulations. 2022 by the authors. -
Thematic Mutual Funds: Performance and Benchmark Analysis Leveraging AI in Futuristic New-Age Investment Themes
Young investors love thematic mutual funds. By 2022, AUM will reach 1.73 lakh crore. These funds have a great possibility of long-term growth since they are timing the market and investing wisely. This research uses AI-driven analytics to examine a select thematic mutual fund that has done well despite risk, diversity, and market volatility concerns. Five actively managed themes were chosen from various fund firms. Infrastructure, ESG, Technology, Banking and Financial Services, and Pharma & Healthcare. We evaluated the best strategies in each topic using qualitative and AI-enhanced quantitative success metrics, including Alpha, Beta, Standard Deviation, Rolling Return, and Sharpe Ratio. We compared themes to key metrics to evaluate their performance over five years (2018-2022). Themed mutual funds perform well for future savings. In particular, AI-driven asset selection algorithms that were market-appropriate outperformed other purchasers. The findings demonstrate the importance of economic cycle-based themes and clever distribution strategies to maximize profits. This research illuminates investment choices and illustrates how AI-enabled active investing might improve market forecasts, volatility, and returns in themed mutual funds. 2025 IEEE. -
Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study
This study analyzes three distinct convolutional neural network (CNN) models, ResNet, Parallel CNN, and VGG16, for object classification using the Star-Galaxy Classification dataset. The dataset comprises a vast collection of celestial object images, including galaxies, stars, and quasars. The effectiveness of each CNN model is evaluated based on accuracy, a commonly used performance metric. The results reveal that the Parallel CNN model achieved the highest accuracy of 90.08% in classifying celestial objects, followed by VGG16 with an accuracy of 86%, and ResNet with an accuracy of 83%. Specifically, the Parallel CNN model demonstrates superior performance in classifying galaxies and stars. These findings provide valuable insights into the strengths and weaknesses of each model for this specific classification task, guiding the development of more effective CNN models for similar applications in cosmology and other fields. This research contributes to the growing literature on CNN models' application in astronomy and underscores the importance of selecting appropriate models to achieve high accuracy in object classification tasks. The study's insights can be utilized to inform the development of more effective CNN models for similar tasks and facilitate advancements in astronomical research. 2023 IEEE. -
Impact of AI Technology Disruption on Turnover Intention of Employees in Digital Marketing
The rapid integration of AI technology into the digital marketing sector has prompted a need to understand its effects on employee perspectives and behaviors. This study investigates how AI adoption influences job insecurity, turnover intention, and job mobility among digital marketing professionals. Addressing concerns about AI rendering roles obsolete is crucial for fostering a supportive work environment. Turnover intention, influenced by AI adoption and potential job dissatisfaction, offers insights into employees' commitment to the industry. Job mobility, influenced by growth prospects and alignment with AI-driven workplaces, sheds light on career aspirations. Our study involving 303 employees of digital marketing industry in India reveals that AI disruption significantly impacts turnover intention, with job insecurity mediating this effect. Additionally, mistreatment by superiors increases turnover intention. Overall, this research underscores the profound impact of AI technology on employees' attitudes, behaviors, and career decisions in digital marketing, providing valuable insights into their perceptions and engagement 2024 IEEE.