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Discrete financial in sentimental analysis using exploring patterns and trends
In todays rapidly evolving financial environment, its crucial for investors and decision-makers to effectively analyze stakeholder communications to gain valuable insights. This research conducts a comprehensive evaluation of a range of models that utilize machine learning, such as CNN (Convolutional Neural Network), LR (Logistic Regression), Doc2vec, and LSTM (Long Short-Term Memory), to determine their efficacy in interpreting investors sentiments and predicting business assessments and trading dynamics. The justification for preferring deep neural architectures compared to conventional data analysis lies in the challenge of handling extensive amounts of diverse and unorganized data. Deep learning techniques have shown impressive capacity in automatically detecting complex characteristics and unveiling concealed patterns within written records, rendering them well-suited for sentiment analysis in financial dialogue. This research questions the notion that depending exclusively on data from a solitary origin leads to persistently effective investment moves. In fact, stakeholder communication is impacted by numerous influential elements, leading to diverse sentiments and sentiments. Through our comparative assessment, we aim to illuminate how various deep learning models can adeptly capture the intricate nuances of sentiment within fiscal messaging. 2024, Taru Publications. All rights reserved. -
Investigating Salt-Finger Convection Under Time-Dependent Gravity Modulation in Micropolar Liquids
This paper investigates how gravity modulation affects salt-finger convection in a micropolar liquid layer confined between two parallel, infinitely long plates separated by a thin gap. The system is heated and has solute added from above. The study uses linear stability analysis to examine when and how salt-finger convection, driven by the salt-finger process, begins. To analyze this, the partial differential equations governing the system are solved numerically using normal mode analysis. The Venezian approach is applied to find the critical Rayleigh number and the solutal Rayleigh number, which are key to understanding the onset of convection. Also, the paper explores how different micropolar fluid parameterssuch as the coupling parameter, micropolar heat conduction parameter, couple stress parameter, and inertia parameteraffect the system when gravity modulation is present. It is found that gravity modulation can either stabilize or destabilize convection, depending on its frequency. At very high frequencies (approaching infinity), the effect of gravity modulation becomes minimal, having little impact on the convection process. The paper also examines the relationship between the critical Rayleigh number and the solutal Rayleigh number, which are related to heat and solute concentration, respectively. 2024 Wiley Periodicals LLC. -
Design of a new curve based cipher
This work aims to develop a model with curve based cryptographic scheme which supports Confidentiality and Authentication at low computing resources and equal security. T TARU PUBLICATIONS. -
EMPLOYEE ENGAGEMENT: ANTECEDENTS AND CONSEQUENCES
Employee engagement is the emotional connection and dedication that employees feel towards their organisation. It is a term used to describe how dedicated, enthusiastic and involved employees are towards/with their job and the organisation they work for. Antecedents are the variables that influence and contribute towards employee engagement, while consequences are the outcomes linked with employee engagement. Attrition of intellectual capital, disengagement with work, issues of conflict with students, lack of job satisfaction, etc. in the centres of higher education are becoming a burgeoning problem and constructive employee engagement is seen as the solution to these issues. The present study aims to examine the factors responsible for employee engagement as well as the outcomes that are derived due to effective implementation of employee-engagement practices. Data has been collected from 117 faculty members of higher-education institutions from South India using simple random sampling. Data has been analysed with the help of Excel, SPSS and AMOS, using statistical tools like T-test, ANOVA and SEM. The proposed model reflects strong positive association between antecedent variables like autonomy, rewards and recognitions, and fair and equitable treatment and employee engagement, and job satisfaction, organisational commitment and intention to stay as the outcomes of employee engagement. 2024 Published by Faculty of Engineering. -
I am lost in that reality, and I'm just playing the game: a qualitative study exploring gaming behaviour and its effect on young adults in India
The current study aims to explore gaming behaviour among young adults in India and its effects on their physical and emotional health, productivity, interactions, and social life. Data were collected from 12 avid gamers through in-depth semi-structured interviews. A thematic analysis was conducted to identify the global theme and organise themes from the data. The global theme was the exploration of contemporary gaming behaviour. The habit theme included the origin of the game, practice, and change over time, while the effects theme focused on the physical and emotional health, productivity, interactions, and social life of young adults who engaged in gaming. The findings suggest that gaming behaviour has become an established habit among young adults in India, significantly affecting various aspects of their lives. The study highlights the need for increased awareness of the potential negative consequences of excessive gaming and emphasises the importance of moderation in gaming. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Publication stress amongst scholars and faculties: a concern of mental health
Purpose: The purpose of this paper is to explore the impact of the seemingly entrenched culture of publish or perish on academics and lecturers mental health in academia. From an autoethnographic perspective, personal experiences of stress, anxiety and burnout are articulated and considered in terms of broader system issues within academia. Design/methodology/approach: Using personal reflections on publication pressure and combining that with the broader existing literature on mental health in academia, this paper, like the ones mentioned above, has been written with autoethnography as the research mode. Autoethnography is a research method that allows for profoundly exploring personal experiences but frames them in a broader academic context, thereby allowing for a qualitative analysis of academics mental health challenges. Findings: The pressure to publish in high-impact journals puts a person under a level of mental health stress that includes feeling anxious, feeling like an impostor and suffering from burnout. Therefore, this very unfitting competitive environment requires institutional support and strategies to mitigate the stress associated with publication. Originality/value: This paper offers an autoethnographic view of the mental health difficulties in academia, providing a firsthand account of the emotional toll of academic publishing. This paper fleshes out the burgeoning discourse surrounding mental health within higher education by connecting personal experiences with systemic issues, pointing to changes in culture and structure. 2024, Emerald Publishing Limited. -
Fragile pillars of food security: exploring the challenges of availability, accessibility, and quality for global food regime; [Pilares freis da seguran alimentar: explorao dos desafios de disponibilidade, acessibilidade e qualidade para o regime alimentar global]
Hunger is a critical issue impacting a greater part of the world. Food distribution systems are failing millions of people, thereby leading to the crisis of food security. Various international declarations, like the UDHR and the ICESCR, have designated food and basic nutrition as integral elements of human rights. Therefore, provision for adequate and affordable food to all has become the dominant value for relevant regulatory and policy regimes. The problem is particularly sensitive to war-like events as is revealed by troubling statistics emerging against the backdrop of Covid-19, Russia-Ukraine War, and Israel-Hamas crisis. All these recent events have increased global food security concerns. This paper evaluates the fragile nature of food security in its multidisciplinary dimension. The methodology undertaken is a combination of quantitative and qualitative analytical mechanisms to explore the multidimensional issues of food insecurity. A systematic approach has been taken to identify food availability, food accessibility, food utilization, and environmental vulnerability as the intrinsic obstacles to any regulatory intervention. In this context, the paper analyses challenges to food accessibility as the core problem of right to food and food sovereignty regimes. It signifies the connection between the structural notion of accessibility and the legal concept of right to food through food sovereignty. It analyses the causal linkages between the problem of food security and other fundamental policy challenges, like poverty, unemployment, and social inequality. The international nature of the crisis is also manifested in the classic developed-developing nations divide. It, consequently, highlights the structural inefficiencies of the powerful international bodies, like the WTO, the World Bank, and the IMF. The comprehensive nature of the problem is explored through the idea of food sovereignty, which signifies the cultural sensitivity of food and nutrition. Food insecurity is not merely a productivity problem. The paper, therefore, suggests a consumer-centric model of food distribution and accessibility as an optimal and practical model for public policy and regulations. 2024 Centro Universitario de Brasilia. All rights reserved. -
Enhancing the biodegradability and environmental impact of microplastics utilizing Eisenia fetida earthworms with treated low-density polyethylene for sustainable plastic management
Low-density polyethylene (LDPE) is widely used in food packaging and agricultural mulching, but its disposal generates macro, meso and microplastics that infiltrate the food chain and carry harmful substances. The present study aimed to improve remediation strategies for soils contaminated with LDPE and enhance the survivability of Eisenia fetida. The study dissolved LDPE in trichloroethylene and treated it with starch, hydrogen peroxide, nitric acid and acetic acid, initiating thermo-oxidative reactions. The treatment decreased LDPE's crystallinity index from 48.48% to 44.06% (single treatment), 44.06% to 40.02% (double treat-ment) and 40.02% to 32.98% (triple treatment), achieving a 15.5% reduction in crystallinity. LDPE microplastics with 40.02% crystallinity showed lower mortality rates in Eisenia fetida earthworms compared to those with 44.06% and 32.98% crystallinity and untreated LDPE. When introduced to E. fetida, microbiota in the earthworm casts included unidentified species from Pseu-domonas and Zoopagomycota, known polyethylene degraders. Microbial analysis of treated LDPE microplastics showed changes in gut microbiota, including potential degraders from Aeromonas and Malassezia restricta. XRD (X-ray diffraction techniques analyses) and FTIR(Fourier Transform Infrared Spectroscopy) analyses provided insights into distinct LDPE degradation patterns, identifying hydroxyl and carboxylic groups as functional groups. The study also investigated the ability of altered mi-croflora with treated microplastics to degrade LDPE, favouring decreased earthworm mortality rates. The crystallinity index of treated polyethylene further reduced from 40.02% to 23.58% after 21 days of exposure to E. fetida. This research advances the understanding of oxidised plastics' ecological impacts and will help to develop environmentally sustainable and biodegradable LDPE. Author (s). -
Exploring Socio-Political Factors and Quality of Life Among LGBT Individuals in India
The quality of life of queer individuals in India is a result of a complex sociopolitical climate which is what this study aims to explore through qualitative methodology. Previous research has explored the social factors that impact the wellbeing of LGBT individuals in western countries, while the impact of politics on the wellbeing of marginalized groups is still largely unexplored. Through thematic analysis, this study found that family support and peer networks are the two most important social structures that determine the quality of life of LGBT emerging adults in India, whereas the impact of politics on wellbeing depends on the level of political awareness of the participants and their socio-political privilege in terms of caste, class and gender. However, there were significant differences in the relevant factors that affect the quality of life for cisgender and transgender participants which leaves room for further research. The findings indicate intra-community conflicts and changing dynamics within the community, and there needs to be extensive research on understanding the intersectionality of different identities within the community and their impact on the lives of queer individuals. 2024 Taylor & Francis Group, LLC. -
The Impact of Computer-Mediated Communication on Relationships and Social Interactions
Computer-mediated communication (CMC) has profoundly changed how we express or connect in the modern world. Various virtual platforms, like Instagram, WhatsApp, and online games, have transformed how we communicate, and there is an overlap between the virtual and the physical world. This reflective study uses a comprehensive literature synthesis to examine the transforming nature of CMC on relationships and socialization patterns. The findings emphasize the importance of a holistic approach to understanding technology in interpersonal communication. Through this study, we attempt to mitigate the potential harms of excessive internet use through digital literacy, reflecting on online interactions and mindfulness in using the medium, especially for school-age children. The main takeaway from this reflective research is that when using technology for communication, one should practice equality and fairness across the board. Both the real and virtual worlds operate on the same principles of similarity and social exchange to create relationships, even though these theories are based on traditional offline relationships. 2024 Taylor & Francis Group, LLC. -
Trusted explainable AI based implementation for detection of neurodegenerative disorders (ND)
The potential of explainable artificial intelligence (XAI) in detection of neurodegenerative disorders (ND) holds great promise in the field of healthcare. These diseases interfere with the daily functioning and independence of a person. The current studies lack in highlighting the aspect of explainability in their predictions and the various algorithms cannot provide any plausible explanations for their predictions making it difficult for medical professionals to place trust in their findings. Thus, the proposed framework aims to bridge this gap by exploring the development of a trustworthy framework for XAI-based ND detection, focusing on key aspects that can significantly impact its effectiveness and acceptance. The framework makes use of Trust-based SHAP (SHapley Additive exPlanations) values in classification. By computing trust values, the framework ensures more reliable predictions and increases interpretability, instilling confidence in clinicians and patients. The results show that with the inclusion of the trust-driven framework, the accuracy of the algorithm increased from 93.33% in the normal circumstances to 98.21%, highlighting the efficacy of the framework as compared to the other works. This shows that a trustworthy framework for XAI-driven ND detection can reshape care by enabling early detection, personalized treatment plans and enhancing decision-making process. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database
In the rapidly evolving field of education, a semantic search engine is essential to efficiently retrieve knowledge experts data. Universities and colleges continuously generate a vast amount of educational and research data. A semantic search engine can assist students and staff in efficiently searching for required information in such a big data pool. The existing systems have limitations in providing personalized recommendations that align with the individual learning objectives of students and scholars, thus hindering their educational experience. To address this, this paper proposed a KMSBOT. This novel recommendation system effectively summarizes academic data and provides tailored information for students, research scholars, and faculty, enhancing educational experiences. This paper meticulously details the development of KMSBOT, which comprises a neo4j-based knowledge graph technique, the NLP method for data structuring, and the KNN machine learning model for classification. The system employs a three-module approach, utilizing data structuring, NLP processing, and semantic search engine integration. By leveraging Neo4j, NLTK, and BERT in Python, this proposed work ensures optimal performance metrics such as time, accuracy, and loss value. The proposed solution addresses traditional recommendation systems limitations and contributes to a brighter future, improving user satisfaction and engagement in academic environments. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Design of automatic follicle detection and ovarian classification system for ultrasound ovarian images
Polycystic Ovary Syndrome (PCOS) is a common reproductive and metabolic disorder characterized by an increased number of ovarian follicles. Accurate diagnosis of PCOS requires detailed ultrasound imaging to assess follicles size, number, and position. However, noise often needs to be improved on these images, complicating manual detection for radiologists and leading to potential misidentification. This paper introduces an automated diagnostic system for integration with ultrasound imaging equipment to enhance follicle identification accuracy. The system consists of two main stages: preprocessing and follicle segmentation. Preprocessing employs an adaptive Frost filter to reduce noise, while follicle segmentation utilizes a region-based active contour combined with a modified Otsu method. Unlike the conventional Otsu method, where the threshold value is selected manually, the modified Otsu method automatically selects initial threshold values using an iterative approach. After segmentation, features are extracted from the segmented results. An SVM classifier then categorizes the ovarian image as normal, cystic, or polycystic. Experimental results demonstrate that the proposed methods Follicle Identification Rate is 96.3% and the False Acceptance Rate is 2%, which significantly improves classification accuracy, highlighting its potential advantages for clinical application. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Predicting Consumers' Usage Intention Towards User-Generated Content: A Hybrid SEM-ANN Approach
With the change in the communication pattern, end-users are engaging in creating content and refer-ring to the content created by other users while making purchase decisions. This research aims at modelling factors affecting consumers' usage intention (UI) towards user-generated content (UGC) using Need for Cogni-tion (NfC) as a moderator of the proposed relationships. The factors affecting consumers' UI involve perceived usefulness (PU), source credibility (SC), information quality (IQ) and NfC. Further, a novel attempt has been made by using the neural network approach to assess the predictive accuracy of the model. A structured ques-tionnaire was used to collect data from 298 consumers through a survey. Data were analysed using two-stage structural equation modelling (SEM) and artificial neural network (ANN). All the independent variables viz., PU, SC, IQ and NfC significantly affect attitude towards UGC, which in turn affects UI. Results of multi-group anal-ysis and a series of chi-square difference tests reveal that a NfC significantly moderates the relationship be-tween PU and attitude, as well as that between SC and attitude. The root mean square error values from the neural network analysis suggest that the models show good predictive accuracy. This study provides a novel assessment of the usage of a hybrid SEM-ANN approach for understanding of UGC by incorporating NfC as a moderator in shaping consumers' attitudes and intentions to use UGC. 2024 World Scientific Publishing Co. -
The complexities of home and belonging in the Gulf-Malayalee experience: a close reading of Salim Ahameds Pathemari (2015)
This paper explores the interaction between home, belonging, and migration by closely reading Salim Ahameds 2015 Malayalam film, Pathemari. The paper briefly traces migration history from Kerala to the Gulf and its impact on Keralas housing boom, influencing its socioeconomic and cultural landscape. Through this, the paper examines how Gulf Malayalees navigate the multifaceted and contested concept of home despite being physically and emotionally displacedthe paradox of belonging and unbelonging, in their attempts to secure a material home while working as blue-collared Malayalee migrants in the Gulf. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Intention to use fintech services: An investigation into the moderation effects of quality of internet access and digital skills
This paper aims to investigate the moderating influence of the quality of access to internet and digital skills on the factors that influence the intention to use fintech services among the young working population in India. We use the Theory of Planned Behavior to examine the intention to adopt financial technology in a rapidly technologically transformative Indian landscape. We conducted an empirical investigation on 324 young workers in India using the survey method. The TPB model's relevance in an Indian context is validated. Attitude, perceived behavioral control, and subjective norms together accounted for 48.7% of the variation in the intention to use fintech services. The quality of internet access significantly moderated the positive effect of young workers' attitudes on their intention to use fintech. Digital skills significantly moderated the positive effects of attitude and perceived behavioral control on intentions to use fintech services. India is considered a very fast adopter of digital technology. In India, the use of electronic channels in financial service delivery is on the rise. With the wide geographic dispersion and huge population, the quality of internet access and digital skills can influence the intention to use fintech services. There can be vast differences in the behavioral mindset of people in a developing country like India compared to that of a developed one regarding the use and adoption of digital platforms for accessing financial services. Developers and regulators must adopt approaches and policies that consider these behavioral factors. This paper examines the Theory of Planned Behaviour in the context of a rapidly transforming behavioural context in India with the adoption of technology-based financial services. The importance of quality internet access and digital skills as factors moderating the adoption of technology is examined in this paper, unlike many previous studies. 2024 Conscientia Beam. All Rights Reserved. -
Inter-State Migration, Footloose Labour and Accessibility to Health Care: An Exploration among Metro Workers of a Camp in Bengaluru
The neoliberal political economy that India adopted in 1991 has brought in huge Foreign Direct Investments, which has led to a perceptible increase in the number of migrants in the major cities of India due to various structural reasons in their place of origin and rapid developmental activities in the cities. Bengaluru has the second largest migrant population after Mumbai, and as per the labour department of the government of Karnataka; there are more than 65 lakh migrant workers in Karnataka, who are involved in various developmental projects, including the metro railway project in Bangalore. Even though the Karnataka Building and Other Construction Workers Welfare Board (KBOCWWB) offers certain social security, including health care for registered migrants, they must wait more than a year to get these benefits. With privatisation and increased out-of-pocket expenditure for health related issues, the migrants face a major hurdle in surviving at the migrated workplaces. Many of them are unaware of welfare boards, and the number of migrants who are registered with them is very small. This paper aims to understand the accessibility of health facilities for migrant workers working in the Bengaluru Metro Project. This research will understand the legal, economic and psychological aspects related to the health status of migrant workers through qualitative study. The study used in-depth interviews to elicit responses from selected inter-state migrant workers to understand their access towards health facilities. The thematic analysis of the interview transcripts revealed a substantive gap in workers access to health facilities. The unregulated working conditions have added more stress to the workers, and due to poverty and unemployment back home, these hurdles are not forcing them to go back. More awareness creating interventions from the government can transform their lives. (2024), (University of Duisburg). All rights reserved. -
Enhancing CNN Weights for Improved Routing in UAV Networks for Catastrophe Relief with MSBO Algorithm
UAVs have become key in various applications lately, from catastrophe relief to environmental monitoring. The plan of powerful and reliable directing protocols in UAV networks is seriously hampered by the dynamic and habitually eccentric mobility patterns of UAVs. This study proposes a novel technique to beat these challenges by utilizing the Modified Smell Bees Optimization (MSBO) algorithm to upgrade the weights of CNNs. This studys principal objective is to further develop UAV network routing decisions by using CNNs ability for design recognition and the Modified SBOs optimization abilities. Our methodology comprises of randomly relegating CNN weights to a populace of bees at start, evaluating their wellness by fitness of directing performance, and iteratively fine-tuning these weights utilizing local and global search procedures got from bee searching. Broad simulations and performance evaluations show that our recommended approach incredibly expands the general dependability of UAVs, brings down communication latency, and improves directing productivity. Future exploration in UAV network improvement gives off an impression of being going in a promising direction with the integration of CNNs for pattern recognition and the Modified SBO for weight enhancement. In addition to progressing UAV routing conventions, this work sets out new open doors for machine learning applications of bio-inspired optimization algorithms. 2024 River Publishers. -
Extended Slash Modified Lindley Distribution to Model Economic Variables Showing Asymmetry
This article introduces a novel probability distribution to model economic variables with high kurtosis and heavy tails showing a decreasing trend. From a mathematical viewpoint, it corresponds to the distribution of the ratio of two independent random variables, one with the modified Lindley distribution and another with the beta distribution. In some sense, it can be described as an extended three-parameter version of the Lindley distribution that has the ability to model data with high kurtosis. After presenting this distribution in more in-depth details, a comprehensive analysis is given, including its associated functions, moments, skewness, and kurtosis characteristics. Furthermore, a parametric estimation work is carried out. A simulation approach is used to validate the performance of the obtained estimates. The applicability of the proposed distribution is demonstrated by fitting real-world data into various socioeconomic scenarios. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Fluorescence bioimaging applications of europium-doped strontium aluminate nanoparticles
Fluorescence bioimaging is widely used for physiological studies to visualise intercellular molecular events due to its highly selective, sensitive, and non-destructive nature. However, its application in in vivo live imaging is often limited by the scarcity of biocompatible fluorescent probes possessing optimal properties. Our study focuses on developing europium-based nanoparticles for in vivo bioimaging, especially imaging of plants. Eu-doped strontium aluminate nanoparticles were synthesised through a conventional solid-state reaction. Structural characterisation of samples using XRD confirmed the prevalence of SrAl2O4 as the prominent phase. The FTIR spectrum, SEM and TEM images were recorded for further characterization. Photoluminescence studies showed orange red emission of sample. The antibacterial activity of the nanophosphors was studied, demonstrating no antibacterial activity against Escherichia coli and Pseudomonas aeruginosa. Furthermore, in vitro cytotoxicity studies conducted using Neuro-2A cells showed no indications of cytotoxicity associated with europium doped strontium aluminate nanoparticles. When incorporated into the plant tissue culture medium, these nanoparticles were found to have no effect on seed germination and plant growth, and it demonstrated no phytotoxicity. Imaging studies have shown the uptake of nanoparticles by plants and their subsequent transport through the vascular system. Our results emphasise the direct integration of nanophosphors into plant tissues from the growth medium, eliminating the necessity for traditional staining methods in fluorescence bioimaging. Incorporation of nanophosphors into living organisms holds promise for non-invasive and long-term fluorescence imaging, with potential applications in biological studies and diagnostics. The outstanding fluorescence properties and biocompatibility of europium doped strontium aluminate nanoparticles broaden its potential for various applications in fluorescence bioimaging. 2024 Elsevier Ltd and Techna Group S.r.l.