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A Review of Algorithms for Mental Stress Analysis Using EEG Signal
Mental stress is an enduring problem in human life. The level of stress increases exponentially with an increase in the complexity of work life. Hence, it is imperative to understand the causes of stress, a prerequisite of which is the ability to determine the level of stress. Electroencephalography (EEG) has been the most widely used signal for understanding stress levels. However, EEG signal is useful only when appropriate algorithms can be used to extract the properties relevant to stress analysis. This paper reviews algorithms for preprocessing, feature extraction and learning, and classification of EEG, and reports on their advantages and disadvantages for stress analysis. This review will help researchers to choose the most effective pipeline of algorithms for stress analysis using EEG signals. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Synthesis and Characterization of Carbon Nanomaterial Derived from Anthracite
Among various storage devices, carbon based supercapacitors grabs the recent trends in the electronic devices. The present research work describes the synthesis of carbon nanomaterials derived from anthracite by using staudenmaier method. Anthracite was used as a precursor because of its high carbon content. The structural and chemical complex formation carried out by using XRD and FTIR confirms the formation of CNT's. The calculated value obtained from the XRD peaks confirms the formation of multilayer carbon nano-materials. The electrode was prepared by coating synthesized CNT on copper rod. The electrochemical performance of prepared working electrode was carried out by using cyclic voltammetric performance. Electrode characterization was performed for different scan rates 10, 20, 30 and 50 mV/sec in a potential window from-0.08 to 0.2V. The CV curves represents symmetric nature which imply that electrode material have stable capacitive process. 2019 Elsevier Ltd. -
Facile synthesis of nickel nanoparticles and its efficient dye degradation
The present investigation involves the synthesis of nickel (Ni) nanoparticles (NPs) by the chemical reduction of nickel chloride using hydrazine hydrate without the need for an inert atmosphere from an external source. The photocatalytic activity, structure and morphology of the NPs were studied by employing UV-Visible (UV-Vis) spectroscopy, powder X-ray diffraction (PXRD) and transmission electron microscopy (TEM). Degradation of methylene blue(MB) and rhodamine B(Rh-B) dyes using Ni NPs was investigated to see the feasibility in degrading these dyes from polluted water at low cost. Ni NPs showed a good photocatalytic activity of 84.1% under visible light for the degradation of MB when compared to Rh-B which showed an efficiency of 47.3 %. 2020 World Research Association. All rights reserved. -
Feature extraction of optical character recognition: Survey
Optical Character Recognition is still prevailing even after many decades of implementation. The challenges faced here are increasing day by day so as its applications. From Punched cards to Handwritten Text, from images to video, from uniform font to universal font, from English text to Global language, from researchers to visually handicapped are the transformations obtained from an era of the 1980s to 2010. This paper has covered the advancement of acknowledging the characters, how are features are extracted, various methodologies used and more importantly what is the use of OCR. Research India Publications. -
Electrochemical deposition for metal organic Frameworks: Advanced Energy, Catalysis, sensing and separation applications
The advent of metalorganic frameworks has gathered ever-increasing attention owing to their versatility, unparalleled porosity, tuneability, and rich topography. The need for an efficient synthetic method and the trending appeal for thin film MOFs has brought in huge data on electrochemical deposition techniques. Thin films have immense applications in the field of electronics (including energy devices such as batteries and supercapacitors), sensors, catalysis, and as liquid/gas separation devices. Here, the electrodeposition method requires no pre-treatment step, allows miniaturization, a homogeneous film with desirable thickness, and is observed to be an eco-friendly method. The limited number of articles focusing on the supremacy of the technique has motivated the authors to collectively summarize the scattered data. To limit the discussion to reasonable bounds, the article focuses on a critical comparison of electrodeposition techniques with other synthetic methods, and different types of electrodeposition methods, and familiarize them with the various electrodeposited MOF-composite designs. Finally, we discuss extensively the existing as well as future applications. This will encourage future researchers to exploit this electrochemical technique for designing & developing newer MOF films and similar next-generation materials which are energy-efficient, rapid, and accurate while in use. This review article hopes to list out significant advances in the area to the advantage of both commercial and academic aspects. 2023 Elsevier B.V. -
Factors Effecting on Work Values Towards Career Choices Among University Students
The pandemic effect of COVID-19 triggered a global recession in the year 2020. The unpredictability that surrounds the coronavirus is the most challenging problem that many people must confront, particularly in terms of making decisions regarding their careers, considering the significant shift in employment opportunities. The purpose of this research is to investigate the influence anxiety and the Covid-19 pandemic have on work values and the reality of career choices among university students. A quantitative research methodology was applied to 110 respondents from a nearby institution to achieve the study's objective. This was done through online surveys and the snowball sampling technique. In order to acquire the findings, a data analysis using SPSS and PLS-SEM was carried out. It is evident from the study's findings that students work values are impacted by anxiety and the COVID-19 pandemic. Moreover, the findings support the hypothesis that anxiety and the COVID-19 pandemic influence students employment decisions. The findings of the study provide insight into the body of knowledge. The influence of anxiety and the COVID-19 pandemic on current work values among university students about career choices are examined, and recommendations are made to various stakeholders, such as policymakers, university management, and career counselors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Thermoluminescence (TL) studies of quartz sample collected from Meghalaya for radiation dosimetry
This study focuses on the investigation of the TL behavior of the quartz sample that was collected from Khonglah, West Jaintia Hills District in Meghalaya. The sample was crushed into powdered sizes (10100 m) and heated to reset the trap structures prior to irradiation. Characterization techniques, such as XRD and EDS, were performed to analyze the structure and elemental composition of the prepared sample. The sample shows a trigonal structure with a hexagonal lattice arrangement. Intrinsic defects and impurity centers, such as Al, Mg, and Mn, were present in the sample. TL glow curve analysis was performed after gamma irradiation with different doses (1070 Gy). With a gamma dose of 50 Gy, the sample showed a well-resolved glow curve with four intense peaks at 201C, 238C, 302C and 350C. Deconvolution of the peaks was done by using CGCD software and calculation of different trap parameters like the order of kinetics (b), activation energy (E), and the frequency factors (s) of the fitted glow curve was performed by Peaks shape method. These results suggested that natural quartz from Meghalaya exhibits promising dosimetric characteristics due to its good TL response with gamma dose and can serve as a reliable and cost-effective material for personal and environmental radiation monitoring. 2026 Informa UK Limited, trading as Taylor & Francis Group. -
Computational and experimental investigation on biological and photophysical properties of high yielded novel aryl-substituted pyrazolone analogue
A series of new aryl-substituted pyrazolone derivatives 5(a-h) were synthesised via the Baylis-Hillman acetate reaction with pyrazolones and tested for antifungal, antibacterial, and antioxidant properties in vitro. Among the tested molecules 5d and 5e show good in vitro antifungal and antibacterial activities due to the presence of fluorine, which enhances the absorption rate by increasing lipid solubility and improves the pharmacological activity. It is also evident from the results obtained from structure-activity relationship (SAR) studies. Further, the photophysical properties of synthesized compounds were theoretically estimated using the ab-intio technique. The ground state optimization and HOMOLUMO energy levels are calculated using the DFT-B3LYP-6-311 basis set. Using the theoretically estimated HOMOLUMO value, global chemical reactivity descriptor parameters are estimated, and the result shows that compounds 5d and 5e have a higher electronegative and electrophilicity index than other molecules. Overall results suggest that, fluorine substituted pyrazolone derivatives show good photophysical, SAR, and biological properties. 2022 Elsevier B.V. -
Photophysical and In Vitro-In Silico Studies on Newly Synthesized Ethyl 3-((3-Methyl-1-phenyl-1H-pyrazol-5-yl)oxy)-2-methyleneheptanoate
Abstract: In the present work, the aryl-substituted pyrazolone derivative ethyl 3-((3-methyl-1-phenyl-1H-pyrazol-5-yl)oxy)-2-methyleneheptanoate (ETT) has been synthesized by the reaction of Baylis-Hillman acetate with pyrazolones and screened for their in vitro antifungal, antibacterial, and antioxidant properties. The molecule shows good in vitro antifungal and antibacterial activities due to the presence of pentane, which enhances the absorption rate by its increased lipid solubility and improves the pharmacological activity. It is also evident from the results obtained from structure-activity relationship (SAR) studies. In silico studies were conducted on the synthesized molecule, examining its interactions with DNA Gyrase, Lanosterol14 alpha demethylase, and KEAP1-NRF2 proteins. The results revealed strong binding interactions at specific sites. Further, the photophysical properties of synthesized compounds were theoretically estimated using the ab-intio technique. The ground state optimization, dipole moment, and HOMOLUMO energy levels are calculated using the DFT-B3LYP-6-31G(d) basis set. Using the theoretically estimated HOMOLUMO value, global chemical reactivity descriptor parameters are estimated, and the result shows the synthesised molecule has a highly electronegative and electrophilic index. NBO analysis proved the presence of intermolecular ON.H hydrogen bonds caused by the interaction of the lone pair of oxygen with the anti-bonding orbital. The results suggest that pentane-substituted pyrazolone derivatives show good photophysical and biological applications. Pleiades Publishing, Ltd. 2024. -
NSS-ML: a Novel spectrum sensing framework using machine learning for cognitive radio IoT networks
A key component of cognitive radio systems is spectrum sensing, which reduces coexistence problems and maximises spectrum efficiency. However, the introduction of multiple situations with distinct characteristics brought about by 5G communication presents problems for spectrum sensing to support a wide range of applications with high performance and flexible implementation. Inspired by these difficulties, a new method with a multi-layer extreme learning machine optimised for bats is presented in this study. This technique makes use of a variety of input vectors, such as channel ID, energy, distance, and received signal intensity, to enhance user categorization and sensing capabilities. Moreover, we compare the proposed method with the state-of-the-art spectrum sensing approaches in order to evaluate its effectiveness in 5G situations, especially in healthcare applications. Evaluation metrics including channel detection probability, sensitivity, and selectivity are carefully examined. The findings unequivocally prove the suggested spectrum sensing approachs superiority over current methods and highlight its potential for smooth incorporation into a variety of 5G applications. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Deep Learning-Based Consolidated Disease Classification in Health Data Management
Healthcare data management is critical for ensuring comprehensive and high-quality medical treatment. Sensitive patient data management has a potential new option thanks to blockchain technology. However, existing blockchain-based healthcare data management systems face challenges in scalability, integration, and regulatory compliance. To address these issues, a novel blockchain-based healthcare data management system has been proposed to provide a secure, decentralized, and interoperable platform for managing sensitive patients medical information. Proposed approach involves collecting comprehensive health measurements from patients using wearable sensors and ensuring the security and integrity of patient data through robust user verification protocols. Artificial Neural Networks (ANNs) are employed to consolidate disease symptoms, enhancing the efficiency and accuracy of data analysis. The results and comparative analysis showcase the efficiency of the proposed method in terms of precision, accuracy, recall, search accuracy and F1-score. The accuracy of the proposed method is improved by 12.9%, 6.07%, and 14.28% when related to the existing ACTION-EHR, BSDMF, and BlockMedCare techniques respectively. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Food Security and Its Impact on Society: Cases of Developing World
Food security is a matter of concern in the twenty-first century as is evident from the importance given to it in the United Nations Sustainable Development Goals. Despite attempts to address food scarcity concerns at global conventions such as the World Food Summit of 1996, food remains scarce. Scholars further suggest that though food scarcity is a global issue, its roots and impact is local. Consequently, a study of food must study the major challenges that converge to undermine food security worldwide including conflicts, climate change, global policies and in recent times even the Covid 19 pandemic. However, at a fundamental level food scarcity is the by-product of not just a legacy of past failures to build more just, sustainable, and resilient food systems, but rather a by-product of our inability to be responsible and sustainable consumers. This chapter highlights that despite surplus food production, developing nations often face food insecurity owing to the diversion of food towards developed nations. These nations, instead of sharing global resources (including food and agricultural labour), often contribute towards the global food crisis. Moreover, some of these developed nations engage in an industrialised system of food produc-tion which might meet the nations food requirements but are not sustainable modes of production and pose a serious threat to the environment. Nevertheless, the indis-cretions of the developed nations affect the developing nations economically as well as socially. As social outcasts, marginalised communities and individuals within the developing world are worst affected. As a result, this chapter offers insight into the social struggle brought on by inaccessibility to food. The chapter further suggests that addressing concerns of food security is not only a matter of addressing the inequalities manifest in the production, distribution and consumption of food but also learning to be responsible and sustainable consumers. Simply stated, the chapter recommends connecting SDG 2 with SDG 12. This chapter would also include the position of India in GHI, the Ukraine crisis and its aftermath in various developing countries, the earthquake in Turkey and how it affects the food security, and a few instances from Africa to highlight the concepts of food security and its correlation with sustainability of any society. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Sustainable consumption: Barriers and catalysts for green purchase
This chapter examines factors influencing green purchasing in both developed and developing nations, addressing catalysts and barriers. Key obstacles include price sensitivity, socioeconomic disparities, lack of standardized eco- labeling laws, and ingrained cultural beliefs. It highlights the roles of government regulations, corporate social responsibility (CSR), environmental awareness, and sustainable packaging in shaping consumer behavior. The study explores knowledge gaps, financial limitations, and cultural influences that affect sustainable choices. Strategies such as eco- labeling, tiered pricing, and financial incentives are analyzed, alongside CSR's impact on consumer trust. Emerging trends like post- COVID green consumption, blockchain for eco- label transparency, AI- driven marketing, and circular economy practices are also discussed. Ultimately, the chapter proposes an integrated framework combining technological, cultural, and economic perspectives to enhance sustainability. 2025, IGI Global Scientific Publishing. All rights reserved. -
Artificial Intelligence in Fostering Sustainable Development
Sustainable development is vital to mankind. The world is finding a growing effort of Artificial Intelligence (AI) towards sustainability, and we made an attempt to address the development in sustainability using AI systems. Sustainable development has three pillars of sustainability (i.e. social, economic, environment), and as such, the pillars of sustainable AI. The entire Life cycle of AI products can foster change in the movement of sustainability from which greater integrity and social justice can be achieved. Sustainable AI helps us to address the whole socio-technical system more than AI applications. This paper tried to address the positive impacts of AI on sustainable indicators in terms of Environmental, Societal and Economy factors. This paper is prepared to make readers, policymakers, AI ethicists and AI developers to inspire and connect with the environment for the current and future generations as there are few AI costs to be made compatible with the environment. 2023 American Institute of Physics Inc.. All rights reserved. -
Unveiling the Future: Exploring Stock Price Prediction in the Finance Sector through Machine Learning and Deep Learning - A Comprehensive Bibliometric Analysis
The investigation of predicting share prices is a captivating and beneficial area of study within the realm of economic research. precise projections and findings can potentially benefit shareholders by reducing the risk of making suboptimal investment selections. The objective of this investigation is to examine the present state of research pertaining to the prognostication of share price predictions through the utilization of Machine Learning (ML) and Deep Learning techniques. The present study examined the existing body of scientific works on methods involving DL and ML in the context of predicting the value of stocks. This study presents a comprehensive overview of research trends, methodologies, and applications in a particular field by conducting a bibliometric analysis of publications indexed in the Scopus database. Drawing from the presented data, recommendations for optimal methodologies can be formulated. The data was visually represented through the utilization of the R programming language and Vos Viewer software. The investigation additionally discerns the primary authors, institutions, and nations that are making contributions to this particular field of research. The outcomes of this investigation possess the potential to guide future research trajectories and offer significant perspectives for professionals and policymakers who are keen on utilizing machine learning and deep learning in the financial sector. 2024 IEEE. -
Development and Validation of a Framework to Identify High Potential Employees in Organizations
The present study aimed to develop and validate a multidimensional framework for identifying high-potential employees (hi-pots) to support succession planning and leadership development in organizations. A sequential exploratory mixed-methods approach was employed. In the qualitative phase, semi-structured interviews with seven organizational leaders were conducted to identify key traits and behaviors of hi-pots. The subsequent quantitative phase involved 276 managerial employees who responded to a newly developed measurement scale. The data were subjected to statistical validation to reinforce the proposed model. The validated framework comprises three dimensions: Foundation, growth, and career. The foundation dimension includes inherent traits such as optimism and sociability. The growth dimension, encompassing promotability, adaptability, and proactiveness, showed the strongest predictive power for leadership readiness. The career dimension involves performance-based competencies like technical proficiency and time management. Among these, the growth dimension emerged as the most influential for leadership potential. Organizations can utilize this framework for structured talent identification, improving leadership pipelines, and strategic HR planning. 2026 Econjournals. All rights reserved. -
A Novel Approach to Packet Dropping and Malicious Attack Detection using Ensemble Techniques
Packet-dropping attacks interrupt data transfer while damaging security protocols, which create a threat to wireless Sensor Networks and Mobile Ad Hoc networks. This paper examines packet-dropping detection methods as well as security attack identification since these threats represent significant risks to networks such as Wireless sensor networks and Mobile Ad Hoc Networks. The research paper utilized a dataset from Kaggle for network traffic analysis, which classified packets through their behaviors as either abnormal or normal. The detection employed a stacking classifier with logistic regression as the meta-classifier and Support Vector Machine, Gradient Boosting, and K-Nearest Neighbour as its main constituents. The analysis model showed high detection rates for packet-dropping incidents, reaching 93.5%, and for malicious attacks, reaching 98.2%, based on the experimental test results. The obtained data shows that stacking models show stable reliability levels above traditional approaches. Ensemble learning proves effective for discovering cyber threats through results that reduce the number of incorrect detections. The stacking classifier functions as a dependable framework for developing security measures required to protect computer networks from modern-day threats. 2025 IEEE. -
Thermorheological and magnetorheological effects on Marangoni-Ferroconvection with internal heat generation
Marangoni convectiveinstability in a ferromagnetic fluid layer in the presence of a spatial heat sourceand viscosity variation is examined by means of the classical linear stability analysis. The higher order Rayleigh-Ritz technique is used to compute the critical Marangoni number. The effective viscosity of the ferromagnetic liquid is taken to be a quadratic function of both the temperature and magnetic field strength. It is shown that the ferromagnetic fluid is significantly influenced by the effect of viscosity variation and is more prone to instability in the presence of heat source compared to that when viscosity is constant. On comparing the corresponding results of heat source and heat sink it is found that heat sink works in tandem with the effect of viscosity variation if magnetic field dependence of viscosity dominates over temperature dependence. If the temperature dependence of viscosity dominates, the effects of viscosity variation and heat sink are mutually antagonistic. Published under licence by IOP Publishing Ltd. -
Bard-Taylor ferroconvection with time-dependent sinusoidal boundary temperatures
The combined effect of centrifugal acceleration and time-varying boundary temperatures on the onset of convective instability of a rotating magnetic fluid layer is investigated by means of the regular perturbation method. A perturbation expansion in terms of the amplitude of applied temperature field is implemented to effectively deal with the effects of temperature modulation. The criterion for the threshold is established based on the condition of stationary instability manifesting prior to oscillatory convection. The modulated critical Rayleigh number is computed in terms of Prandtl number, magnetic parameters, Taylor number and the frequency of thermal modulation. It is shown that subcritical motion exists only for symmetric excitation and the destabilizing effect of magnetic mechanism is perceived only for asymmetric and bottom wall excitations. It is also delineated that, for bottom wall modulation, rotation tends to stabilize the system at low frequencies and the opposite is true for moderate and large frequencies. Furthermore, it is established that, notwithstanding the type of thermal excitation, the modulation mechanism attenuates the influences of both magnetic stresses and rotation for moderate and large frequencies. Published under licence by IOP Publishing Ltd. -
On the influencing facets of infant mortality in Karnataka: A study based on birth orders
The infant mortality rate (IMR) is used to assess the overall physical health of any community. Reducing this and spreading awareness among people can improve the well-being of society. In India, IMR is high due to the complex and challenging health policies and increased population, but various socio-economic and demographic factors play a significant role in determining the infant mortality rate. This study majorly focuses on identifying the factors influencing infant mortality, and a model has been proposed to estimate the likelihood of an infant's survival in Karnataka. For the empirical analysis, data has been taken from the National Family Health Survey-4 (2015-16), India. It is found that mothers' education and female literacy are the most significant factors affecting the IMR irrespective of the birth order. It is also found that the various socio-economic and demographic factors do not have a significant influence on the survival status of an infant as the birth order increases. Other factors like preceding birth interval, wealth index, caste, and religion also influence infant mortality. Hence, it is suggested that parents should have access to quality education and health facilities near their place of residence to reduce infant mortality at each order of birth. 2025 Author(s).