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Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets
Covid-19 pandemic has affected the lives of people across the globe. People belonging to all the sectors of the society have faced a lot of challenges. Strict measures like lockdown and social distancing have been imposed several times by governments throughout the world. Universities had to incorporate the online method of teaching instead of the regular offline classes to implement social distancing. Online classes were beneficial to most of the students; at the same time, there were many difficulties faced by the students due to lack of facilities to attend classes online. Students faced a lot of challenges, and a sense of anxiety was prevalent during the uncertain times of the pandemic. This research article analyzes the stress among students considering the tweets across the globe related to students stress. The algorithms considered for classification of tweets as positive or negative are support vector machine (SVM), bidirectional encoder representation from transformers (BERT), and long short-term memory (LSTM). The accuracy of the abovementioned algorithms is compared. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sentiment Analysis On Covid-19 Related Social Distancing Across The Globe Using Twitter Data
Covid 19 pandemic has devastated the lives of several people across the globe. Social distancing is considered a major preventive measure to stop the spread of Covid 19. The practice of social distancing has caused a sense of loneliness and mental health problems in society. The aim of this study is to consider global tweet data with social distancing keywords for analyzing the sentiments behind them. Classification of tweets as positive or negative is carried out using Support Vector Machine and Logistic Regression. The Electrochemical Society -
Subjectivity analysis using social opinion mining on stress and strain during covid 19 pandemic
The psychological health of several people across the globe has been under great risk newlineas a result of the COVID-19 pandemic that shook the entire world. The ubiquitous newlinepandemic had created a tectonic shift in everyone s life. The lives of people have newlineundergone a severe transition with strict measures like lockdown and social distancing newlineimposed by governments of several countries to stop the spread of the viral infections. newlineCoping through the adverse situation has been quite onerous causing stress among the people. The transition from normal life to a life filled with several restrictions has newlinebeen stressful and strenuous. A state of emotionally or physically being tensed can be newlineconsidered as stress. Stress can cause frustration, depression, nervousness and other mental health issues. Stress also leads to strain. Social media networking sites like newlineX(Earlier Twitter) and Facebook have emerged to become popular. During the times of lockdown and social distancing the social media networking sites have been a great newlineplatform for expressing opinions, exchange of ideas and thoughts. People have expressed their stressful situations and coping mechanisms through tweets , Facebook newlineposts and several other social media sites during the pandemic. The underlying stress newlineand strain of a person can be analyzed through the posts shared by the person through the social media sites. Early detection of the prevalence of the stress and strain is important, as medical help can be sought quickly and the person affected can be back to normalcy. Subjectivity analysis is the study that deals with analyzing the emotions, feelings, attitudes and polarity of opinions considering any subject matter. newlineThe present research focuses on subjectivity analysis through social opinion mining newlineduring the COVID-19 pandemic. Social opinion mining incorporates Natural Language Processing and Computational Linguistics that identifies the subjectivity across the posts of social media.
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Subjectivity Analysis Using Social Opinion on Stress and Strain During Covid-19 Pandemic
The psychological health of several people across the globe has been under great risk newlineas a result of the COVID-19 pandemic that shook the entire world. The ubiquitous newlinepandemic had created a tectonic shift in everyone s life. The lives of people have newlineundergone a severe transition with strict measures like lockdown and social distancing newlineimposed by governments of several countries to stop the spread of the viral infections. newlineCoping through the adverse situation has been quite onerous causing stress among the people. The transition from normal life to a life filled with several restrictions has newlinebeen stressful and strenuous. A state of emotionally or physically being tensed can be newlineconsidered as stress. Stress can cause frustration, depression, nervousness and other mental health issues. Stress also leads to strain. Social media networking sites like newlineX(Earlier Twitter) and Facebook have emerged to become popular. During the times of lockdown and social distancing the social media networking sites have been a great newlineplatform for expressing opinions, exchange of ideas and thoughts. People have expressed their stressful situations and coping mechanisms through tweets , Facebook newlineposts and several other social media sites during the pandemic. The underlying stress newlineand strain of a person can be analyzed through the posts shared by the person through the social media sites. Early detection of the prevalence of the stress and strain is important, as medical help can be sought quickly and the person affected can be back to normalcy. Subjectivity analysis is the study that deals with analyzing the emotions, feelings, attitudes and polarity of opinions considering any subject matter. newlineThe present research focuses on subjectivity analysis through social opinion mining newlineduring the COVID-19 pandemic. Social opinion mining incorporates Natural Language Processing and Computational Linguistics that identifies the subjectivity across the posts of social media. -
A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection
The rapid proliferation of the Internet of Things (IoT) has engendered substantial security apprehensions, chiefly due to the emergence of botnet attacks. This research study delves into the realm of Intrusion Detection Systems (IDS) by leveraging the IoT23 dataset, with a specific emphasis on the intricate domain of IoT at the network's edge. The evolution of edge computing underscores the exigency for tailored security solutions. An array of statistical methodologies, encompassing ANOVA, Kruskal-Wallis, and Friedman tests, is systematically employed to illuminate the evolving trends across multiple facets of the study. Given the intricacies entailed in feature selection within edge environments, Chi-square analyses, Recursive Feature Elimination (RFE), and Lasso-based techniques are strategically harnessed to unearth meaningful feature subsets. A meticulous evaluation encompassing 19 classifiers, meticulously selected from both machine learning (ML) and deep learning (DL) paradigms, is rigorously conducted. Initial findings underscore the potential of the Gated Recurrent Unit (GRU) model, especially when coupled with intrinsic lasso-based feature selection. This promising outcome catalyzes the formulation of an ensemble approach that harnesses multiple LassoCV models, aimed at amplifying feature selection proficiency. Furthermore, an optimized ResNet-GRU model emerges from the fusion of the GRU and ResNet architectures, with the objective of augmenting classification performance. In response to mounting concerns regarding data privacy at the edge, a resilient federated learning ecosystem is meticulously crafted. The seamless integration of the optimized ResNet-GRU model into this framework facilitates the employment of FedAvg, a widely acclaimed federated learning methodology, to adeptly navigate the intricacies associated with data sharing challenges. A comprehensive performance evaluation is undertaken, wherein the ResNet-GRU model is benchmarked against FedAvg and a diverse array of other federated learning algorithms, including FedProx and Fed+. This extensive comparative analysis encompasses a spectrum of performance metrics and processing time benchmarks, shedding comprehensive light on the capabilities of the model. (2023), (Science and Information Organization). All Rights Reserved. -
Enhancing IoT Security Through Deep Learning-Based Intrusion Detection
The Internet of Things (IoT) has revolutionized the way we interact with technology by connecting everyday devices to the internet. However, this increased connectivity also poses new security challenges, as IoT devices are often vulnerable to intrusion and malicious attacks. In this paper, we propose a deep learning-based intrusion detection system for enhancing IoT security. The proposed work has been experimented on IoT-23 dataset taken from Zenodo. The proposed work has been tested with 10 machine learning classifiers and two deep learning models without feature selection and with feature selection. From the results it can be inferred that the proposed work performs well with feature selection and in deep learning model named as Gated Recurrent Units (GRU) and the GRU is tested with various optimizers namely Follow-the-Regularized-Leader (Ftrl), Adaptive Delta (Adadelta), Adaptive Gradient Algorithm (Adagrad), Root Mean Squared Propagation (RmsProp), Stochastic Gradient Descent (SGD), Nesterov-Accelerated Adaptive Moment Estimation (Nadam), Adaptive Moment Estimation (Adam). Each evaluation is done with the consideration of highest performance metric with low running time. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
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LiST: A Lightweight Framework for Continuous Indian Sign Language Translation
Sign language is a natural, structured, and complete form of communication to exchange information. Non-verbal communicators, also referred to as hearing impaired and hard of hearing (HI&HH), consider sign language an elemental mode of communication to convey information. As this language is less familiar among a large percentage of the human population, an automatic sign language translator that can act as an interpreter and remove the language barrier is mandatory. The advent of deep learning has resulted in the availability of several sign language translation (SLT) models. However, SLT models are complex, resulting in increased latency in language translation. Furthermore, SLT models consider only hand gestures for further processing, which might lead to the misinterpretation of ambiguous sign language words. In this paper, we propose a lightweight SLT framework, LiST (Lightweight Sign language Translation), that simultaneously considers multiple modalities, such as hand gestures, facial expressions, and hand orientation, from an Indian sign video. The Inception V3 architecture handles the features associated with different signer modalities, resulting in the generation of a feature map, which is processed by a two-layered (long short-term memory) (LSTM) architecture. This sequence helps in sentence-by-sentence recognition and in the translation of sign language into text and audio. The model was tested with continuous Indian Sign Language (ISL) sentences taken from the INCLUDE dataset. The experimental results show that the LiST framework achieved a high translation accuracy of 91.2% and a prediction accuracy of 95.9% while maintaining a low word-level translation error compared to other existing models. 2023 by the authors. -
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Fabrication of electrochemical sensors for pharmaceuticals and biologically significant molecules
Newer properties of electrochemical sensors for various target molecules are being developed in continuum. Such sensors have attracted a lot of attention due to their simplicity, high sensitivity and trace-level detection of analytes in real samples. Sensor is a system that on stimulation by any form of energy undergoes change in its own state which helps to analyze the stimulant qualitatively and quantitatively. In the thesis studies presented, we have also described the development of electrochemical sensors for the determination of pharmaceuticals and biologically significant molecules. This can be achieved by modifying newlineelectrodes by electrochemical method. Electrode modifiers like metal nanoparticles dispersed on conducting polymers and carbon nanospheres were employed for modification of carbon fiber paper working electrode substrate. These modified electrodes were physicochemically characterized by X-ray diffraction (XRD), Field emission scanning newlineelectron microscopy (FESEM) with energy-dispersive X-ray spectroscopy (EDS), Transmission electron microscopy (TEM), Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy and X-ray photoelectron newlinespectroscopy (XPS) and electrochemically characterized using Cyclic voltammetry and Electrochemical impedance spectroscopy (EIS). newlineThe modified electrodes have exhibited remarkable electrocatalytic behaviour towards oxidation or reduction of chosen analytes. These modified electrodes were used as electrochemical sensors after optimization of experimental conditions. Under optimal conditions, the sensors have displayed significantly an ultra-low level detection limit with wide linear response and high selectivity towards analyte in the newlinepresence of other interfering substances. newlineThe ultrasensitivity and reliability of the fabricated sensors towards analyte of interest were effectively determined in real samples. -
Prevention of Child Sexual Abuse : A Protection Motivation Theory-Based Intervention for Mothers of Preadolescents
Child sexual abuse (CSA) is a growing concern in the world. Prevention of CSA in India is challenging due to deep rooted traditional values and beliefs. Sex and related matters newlineare difficult topics for parents to discuss. Lack of parental awareness leads to increased newlinerisk for CSA. Maternal care is the most influential aspect of child rearing and they need information and skills to educate children on sexual abuse. The literature review was based on Bloom s taxonomy for academic writing. The need for systematic and evidencebased approach in primary prevention was identified. The aim of this study was to test the efficacy of Protection Motivation Theory (PMT)-based psycho education program in enhancing mothers knowledge, attitude and sense of parental competence among mothers. An interactive mixed-method design embedding quantitative and qualitative methods selected 72 mothers as participants from Kannur, Kerala. Mothers aged between 30-40 years who had preadolescent children (8-12 years) were assigned to control and experimental group. A facilitator s psycho education manual was developed embedding PMT constructs for the intervention. The quantitative results indicated significant differences between the groups for CSA knowledge and attitude. The impact of the intervention was moderate to high. The qualitative results indicated the benefits of intervention. Mothers have overcome communication blocks, misconceptions regarding CSA education are cleared, are aware of risks and warning signs and are confident to deal with CSA disclosure. The involvement of mothers in the prevention program was found to be effective in this study. The findings of this study have important implications for developing theory- based interventions for CSA prevention. The application of systematic evidence-based interventions promotes active engagement of participants for applying the learnt skills effectively. The culturally sensitive issues like CSA needs more contextual understanding of the problems to find effective solutions. -
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A Posthuman Analysis of Human - Machine Relationship in Select American Science Fiction Films
The research A Posthuman Analysis of Human Machine Relationship in Select American Science Fiction Films attempts to foreground the emerging posthuman scenario brought about by the explosion of Artificial Intelligence (AI) in contemporary life by analysing the posthuman representations achieved by depicting AI characters and their relationship with humans in the select American science fiction films. The primary texts for the study are Stephen Spielberg s AI: Artificial Intelligence (2001), Spike Jonze s Her (2013), Mathew Leutwyler s Uncanny (2015), and Drake Doremus Zoe (2018). The research analyses the posthuman newlinerepresentations in the select films using the methodological framework of philosophical posthumanism of Francesca Ferrando with its constituent elements of post-humanism, post-anthropocentrism, and post-dualism. The term posthuman in philosophical posthumanism refers to the critique of the notion of human preserved by the Western humanistic traditions. The three constitutive elements of philosophical posthumanism, namely, post-humanism, postanthropocentrism, and post-dualism, offer a revisit of the notion of human propagated by Western humanistic traditions and offer a renewed worldview of being human in the contemporary technocentric society where nonhuman agency is being widely newlinerecognized. From an epistemological perspective, this research adds to the evolving posthuman discussions, providing a new dimension to what it means to be a human and challenging the age-old assumptions about the human condition. -
A study on the self-esteem and social relations of adolescents with learning disability /
According to World Health Organization Learning disability is a state of arrested or incomplete development of mind. The most common learning disabilities are Dyslexia, Dyscalculia Dysgraphia, Auditory and Visual Processing Disorders and Nonverbal Learning Disabilities. Adolescents with learning difficulty have trouble expressing their feelings, calming themselves down, and reading non-verbal cues which can lead to difficulty in the classroom and with their peers. In India approximately 13 to 14 per cent of all school children suffer from learning disorders (Sadaket 2009). The social relationships of the adolescents with learning disability have a positive influence on their self esteem. It helps them to maintain a constructive relationship with their peers, teachers and parents. The study was focused to know the relationship of self esteem and social relation in adolescents with learning disability. The study was conducted among all the Adolescents with Learning Disability, in an alternative school in Bangalore. The size of the sample was 50 which include both genders and the sampling design was purposive sampling. -
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A Study on Indian Foriegn Exchange Market Efficiency - Application of Random Walk Hypothesis
International Journal of Research in Computer Application & Management Vol. 2, Issue 10, pp. 138-142, ISSN No. 2231-1009 -
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The role of internal control and firm specific characteristics on firm value
Firm value is considered as a vital aspect in analysing a company s financial health. It is the total value of a company. This study determines the role of firm-specific characteristics such as firm size, firm age, newlineliquidity, firm complexity, board independence, institutional ownership, newlinenon-performing assets, annual volatility of stock returns, leverage and internal control represented by Enterprise Risk Management (ERM) and Big4 auditor on the firm value measured using Tobin s Q, Return On Equity (ROE) and Return On Assets (ROA). This proposition was addressed with the sound statistical investigation of 127 companies listed in the NSE financial services and manufacturing sectors by utilising annual newlinepanel data for 11 years from 2007-17. Regression results indicated that in the financial services sector, the purchasers consider firm size, firm age, liquidity, the volatility of stock returns and non-performing assets. ROA shows that the management has to focus on firm size, firm age and volatility of stock returns. ROE informs that the investors will look into newlinefirm size, firm age, institutional ownership, non-performing assets, leverage, firm complexity and volatility of stock returns. Whereas in the manufacturing sector, the purchasers focus on adoption of ERM, firm size, firm age and liquidity. ROA showed that management has to give importance to ERM, firm size, firm age, firm complexity, liquidity, and leverage. ROE revealed that the investors look into firm size, firm newlinecomplexity, liquidity and leverage. These findings are of particular interest newlineto investors, researchers and practising managers in the financial services and manufacturing sector. -
The nexus between demogaphics and investment behaviour /
Asian Journal Management, Vol.8, Issue 2, pp.361-369, ISSN: 2321-5763 (Online) 0976-495X (Print).