Browse Items (11810 total)
Sort by:
-
Global and Indian Perspectives on Russia-Ukraine War using Sentiment Analysis
In today's world, social media has become a platform through which people express their opinions and thoughts regarding various topics. Twitter is one such platform wherein people resort to expressing their opinions or portraying sentiments to the world. Today it has become easier to analyze mass opinion by using sentiment analysis. This paper investigates the ongoing Russia-Ukraine war by analyzing opinionated tweets, and it seeks to understand the sentiments from a global and Indian perspective. Operation Ganga was carried out to evacuate Indian citizens from the war-hit region. Multinomial Naive Bayes classifier classified the tweets into positive, neutral, and negative categories. The paper employed NRCLex for emotion classification and aspect-based sentiment analysis to divide opinions into aspects and determine the sentiment associated with each element. For the study, 4,31,857 tweets were extracted, and the results of sentiment analysis depict that 44.09% users had negative sentiments followed by 33.378% users expressing positive sentiment and remaining 22.53% people were neutral in their tweets. Fear, anger and sadness were amongst the top emotions expressed in the negative tweets whereas the positive tweets expressed trust and anticipation that the war would end soon. Operation Ganga was carried out to evacuate Indian citizens from the war-hit region. An analysis was performed on 1542 tweets that were obtained for Operation Ganga. 74.5% of the users had positive sentiments about Operation Ganga, whereas 16.67% and 8.5% had negative and neutral sentiments respectively. The people trusted this evacuation process resulting in more positive sentiments. Fear of losing near and dear ones and fear of safety was the topmost concern for Indians and leadership was one of the topmost aspects tweeted in the positive sentiments. Thus, the overall results depict that the common man does not prefer war and is fearful of the outcomes. The government should hear the voice of the common man and plan strategies and decisions considering the common man's sentiments. 2022 ACM. -
Occupancy Monitoring to Prevent Spread of COVID-19 in Public Places Using AI
The chapter aims to automate the counting of people for occupancy monitoring and send an alert email if the occupancy exceeds the defined threshold in case of restricted occupancy guidelines. The study aims to reduce the manual error, effort, and time for people counting and provide a tool for footfall analysis. We propose and implement an occupancy monitoring system by counting the number of people entering and exiting a building/room using cameras and machine learning (ML) algorithms. The Single Shot Detector (SSD) algorithm, which is based on the MobileNet architecture, is used. This project provides an effective process for execution using either a recorded video file or a live stream from a camera. As the system automates counting people, it reduces human effort and error. It provides accurate results on time. The project can be implemented anywhere using a laptop and a camera for capturing the video. Thus, it provides high portability of the project. The system can leverage pre-installed CCTV cameras and systems in colleges, malls, offices, etc. Thus, it requires less additional expenses and is economically friendly for the organization/decision-making authority. This chapter includes implications for various use cases such as ensuring adherence to COVID-19 guidelines by organizations, streamlining janitorial services, prevention of stampedes, improving indoor air quality, improving electricity efficiency, etc. This project fulfills an identified need to automate the people counting process and generate alerts accordingly. 2025 by Apple Academic Press, Inc. -
Neuro-technology and counselling
[No abstract available] -
An Impact of technology based constructivist teaching on acdemic achivement of IX standrad students of Bengaluru city
Modern education emphasizes on learner centered and joyful learning which is the newlineneed of the hour as well initiated by educationists and education commission. They opine that, children need to keep active throughout the teaching and learning process and encourage self-learning and independent learning. One such emerging practice is constructivist teaching. It has changed the educational practice and converted Passive Learner Centered Environment into Active Learner Centered Environment.Constructivism newlinebelieves that learning is not encouraged in zero ground but on previous experience and newlineprior knowledge. It is the beginning for construction of new knowledge. In the context of Indian school education, it is rightly accepted as one of the pedagogical practice in National Curriculum Framework 2005 and National Curriculum Framework for Teacher newlineEducation 2009. It is also duly adopted in school education and teacher education newlineprogramme of Karnataka. Apart from constructivist approach, technology and technology integration highly influence on present education system. Technology not used only for drill,practice, tutorial etc. but also for construction of knowledge. newlineIn this context, there is a need for technology integration in constructivist practice and to give new framework for learning, teaching as well as for learner centered education. newlineHence, this research is conducted to study An Impact of Technology Based Constructivist Teaching on Academic Achievement of IX Standard Students of Bengaluru City .The main objective of the study is to compare the effectiveness of Constructivist Teaching and Technology Based Constructivist Teaching on academic achievement of IX standard students in Social Science subject. newlineThe present study is experimental in nature newlinewith two equivalent group design. In this study purposive sampling technique is used. The sample comprised of 156 students studying in IX standard of two schools (Government newlineand Private School) of Bengaluru city affiliated to state board. -
Computational techniques for sustainable green procurement and production
Computational techniques are used to generate, solve, analyze, explain, or manage any simple or complex task. The use of environmentally responsible techniques to meet demand for resources, commodities, utilities, and services is known as green procurement. Computational technique in green procurement and production is one of the components of sustainable procurement, along with a commitment to social responsibility and good corporate behavior. Some solutions for this kind of issue are low-maintenance, energy-efficient, and long-lasting. Several experts and researchers provided their findings on the environmental impact of ICT with the use of computational techniques. Also, the importance of energy-efficient information technology for environmentally conscious and feasible information technology is a hot topic because a computer faces environmental challenges at every stage of its life, from development to use to disposal. Due to changing environmental conditions, corporations have prioritized carbon emissions in procurement and transportation, which have the highest carbon impact. To encourage potential suppliers to adopt environmentally friendly practices, green criteria should be introduced into public procurement. Environmentally friendly corporate practices and environmental conservation are considered significant tools through public procurement. Techniques for green procurement and production procedures have recently been correlated with the concept of computational techniques of green procurement and production, owing to the increased emphasis on the concept of computational approaches. For eco-friendly procurement and production operations, computational approaches are inculcated and presented in the same way that they are for green procurement and manufacturing. From this perspective, this chapter presents a methodology for merging computational techniques into green procurement and production in public procurement in the form of green computing. 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies. -
Application of smart manufacturing in business
The application of machine learning to production is becoming a chief objective for businesses all around the world. Smart product-service systems enable digital business model innovation by merging digitized product and service components. The life cycle that comes with the realization of customer value is a critical component of these industrial solutions and manufacturing industry is undergoing significant changes as a result of digitalization and automation. As a result, smart services, or digital services that generate value from product data, are gaining popularity. Customers may now contribute in greater numbers in product design during the design process. Giving more people access, on the other hand, increases the security vulnerabilities associated with cloud manufacturing. Smart Manufacturing is one of the technology-driven approach to manufacturing that uses network-connected machines to monitor the process. Smart manufacturing has the ability to be used in a variety of ways, including putting sensors in manufacturing machines and collecting data on their operating state and performance. Thus, the main purpose here is to find ways to improve and automate production performance. This conceptual paper attempts to give a view of how a smart intelligence system may be used in business and how individuals and organizations can produce value. 2023 Author(s). -
Investigating stock market efficiency in India
International Journal of Computer Application & Management, Vol. 3, Issue 3,pp.45-48 ISSN No. 2231-109 -
Exploring Socio-Variational Patterns in Indian Adolescents Lexical Diversity: Insights for Education
Following the COVID-19 pandemic, vast data emerged regarding the plummeting literacy and readability levels among Indian adolescents, posing a challenge to address in its present condition of a vastly heterogeneous socio-demographic environment. This study is grounded in Bourdieu and Passeron's (1977) theory, which acknowledges schools as places with societal relevance that perpetuate social inequality. This implies the need to formulate robust policies to address educational inequalities. To this extent, the researchers used an exploratory design to evaluate lexical diversity by purposively sampling 100 volunteer teenagers across three schools. In addition to the data received from school officials, survey questionnaires collected socio-economic information (age, gender, area of stay, socio-economic scale [SES], and school type). The authors used the Kuppuswamy SES scale (2022) to determine socio-economic scale measures, as well as the calculation of Lexical Diversity scores through the computational open-source software TextElixir. The findings reveal that age and gender do not affect lexical diversity. However, school type, SES, and area of stay significantly affect adolescents from the lower social class, who need targeted interventions to bridge gaps of educational inequity. This study addresses the limitations of previous correlational studies by offering educational insights to ensure educational equity amidst prevalent social class inequalities. Authors. -
Lexical Richness of Adolescents Across Multimodalities: Measures, Issues and Future Directions
Lexical Richness (LR) is a scarcely researched subject in India. The objective of this paper is twofold: (i) To statistically inquire whether LR varies across three multimodalities: visual-only, audio-only, and audio-visual; and (ii) To see which of the two measures of LR (MATTR and Guiraud) is independent of text length and is best suited for short oral productions. 270 students across three types of schools were examined, out of whom 100 willingly completed all three oral tasks. The students were asked to retell the stories transacted in each modality in their own words. Randomization of sampling is done to mitigate the confounding modality bias. Additionally, the genre and parts of the storyline in each modality are similar. The students oral speech samples were recorded, transcribed and analyzed on WordCruncher and TextElixir software. The results revealed that there is statistically significant variance among the modalities. Furthermore, the Moving Average Type Token Ratio (MATTR) is seen to be independent of text length compared to Index of Guiraud. This study also throws light on the observations made during the study, pertinent issues in the field of education, and future directions for research on LR. 2023 IUP. All Rights Reserved. -
Empowering Adolescent Emergent Readers in Government Schools: An Exploration of Multimodal Texts as Pathways to Comprehension
This exploratory study, which was part of a larger investigation into multimodality, looked at the comprehension levels of 62 Grade 8 students from two government schools who were identified as emerging readers out of a group of 118 students. Through observations and interactions with teachers and students, the potential for multimodal texts to enhance comprehension was highlighted. The study specifically compared the effectiveness of a digital comic (Text A) and an audio-visual text (Text B) in enabling comprehension among these emergent readers. Participants were instructed to narrate the content and share their interpretations of these texts, with their responses recorded and analyzed. Feedback revealed a marked preference for Text B among 45 of the 62 emergent readers assessed. Employing theoretical frameworks related to comprehension, language production, multimodality, and task structure, this research concentrated on the subset of 45 students who favored Text B. The findings underscore the importance of aligning instructional materials with students preferred learning modalities, suggesting that such alignment enhances comprehension. The study proposes a refined approach to literacy education policy, advocating for the inclusion of diverse modalities to better meet the varied learning needs of students. 2024 Association of Literacy Educators and Researchers. -
Optimizing Food Production with a Sustainable Lens: Exploring Blockchain Technology in Raw Plant Materials and Organic Techniques in Achieving Sustainable Development Goals
Amidst a rising population and mounting environ- mental concerns, India seeks a transformative approach to ensure food security and sustainable agriculture by 2030, as outlined in Sustainable Development Goal 2 (SDG 2). This research explores the immense potential of organic farming methods and raw plant materials to unlock this vision. Plants have a wealth of unrealized potential that extends beyond their conventional functions. The study looks at how different plant parts, like branches, leaves, stems, and even "waste"materials, can be used in a variety of ways to increase self-sufficiency, lessen environmental impact, and access renewable resources. Case studies from across the globe highlight this potential, highlighting the many advantages for the environment and communities. Additionally, the study investigates the innovative use of blockchain technology to promote a more transparent and resilient agricultural environment in India. Imagine blockchain-powered climate-smart practices, safe and transparent transactions, and precision agriculture led by sensor data. Water-efficient irrigation, environmentally friendly pest control, and strong traceability systems are all part of this vision, which aims to strengthen the Indian agricultural sector's resilience. The study suggests a framework of customized policy recommendations centered on non-losable farming methods in recognition of the need for wider implementation. This framework, created especially for the Indian context, supports the promotion of agrotourism, improved education and extension services, accessible financial risk management tools, and the smart redistribution of subsidies. The research highlights the transformative potential of this approach by highlighting the many benefits of these practices, including the environmental (less water use, increased biodiversity, improved soil health, and carbon sequestration), social (better community resilience, food security, farmer income, preservation of cultural heritage, equitable trade), and economic (premium market access, lower input costs, and higher yields) gains. In the end, this research offers a strong plan of action for India to greatly advance SDG 2 and create a more sustainable future for all of its people. A food system that feeds people and the environment can be developed by carefully using organic farming methods and unprocessed plant resources in conjunction with successful legislative initiatives. 2024 IEEE. -
Cardless Society: Assessing the Role of Cardless ATMs in Shaping the Future of Financial Transactions
The ubiquitous ATM faces a critical crossroads in a world where the digital pulse is becoming more and more ingrained. The sound of plastic clicking, which used to be a comforting symbol of financial independence, is becoming less audible in the background noise of near-field communication and the Erie silence of digital scans. This study goes beyond the physical card and explores the unexplored world of cardless ATM technology, where security, convenience meet and innovation completely reimagines the process of getting cash. The meticulous analysis and potential use of technology can completely twist the dynamic rhythm of this world. 2024 IEEE. -
Sub-Optimization based Random Forest Algorithm for Accurate and Efficient Land use and Land Cover Classification using Landsat Time Series Data
The land use and land cover (LULC) play an essential role to investigate the impacts of environmental factors and socio-economic development in the Earth's surface. Extracting the hidden information from the remote sensing images in the observed earth environment is the challenging process. In this research, implemented a model that uses Landsat data to investigate the LULC changes. Utilized the Landsat 5,7 and 8 as inputs for the 1985 to 2019 by Google Earth Engine (GEE) is applied for the robust classification. This paper proposed a Sub-forest optimization based Random forest (SO-RF) classifier with faster diagnosis speed for LULC classification. Moreover, to increase the multispectral Landsat band's resolution from 30 m to 15 m, the pan-sharpening algorithm is utilized. In addition, analyzed the various image configurations grounded numerous spectral indices and other supplementary data such as land surface temperature (LST) and digital elevation model (DEM) on final classification accuracy. The proposed SO-RF produced higher accuracy (0.97 for kappa, 96.78% Overall accuracy (OA), 0.94 for f1-score) than Copernicus Global Land Cover Layers (CGLCL) map and state of art methods like K-Nearest Neighbor (KNN), Decision Tree (DT), and Multi-class Support Vector machine (MSVM). 2024 IEEE. -
Big Data Analytics: A Trading Strategy of NSE Stocks Using Bollinger Bands Analysis
The availability of huge distributed computing power using frameworks like Hadoop and Spark has facilitated algorithmic trading employing technical analysis of Big Data. We used the conventional Bollinger Bands set at two standard deviations based on a band of moving average over 20 minute-by-minute price values. The Nifty 50, a portfolio of blue chip companies, is a stock index of National Stock Exchange (NSE) of India reflecting the overall market sentiment. In this work, we analyze the intraday trading strategy employing the concept of Bollinger Bands to identify stocks that generates maximum profit. We have also examined the profits generated over one trading year. The tick-by-tick stock market data has been sourced from the NSE and was purchased by Amrita School of Business. The tick-by-tick data being typically Big Data was converted to a minute data on a distributed Spark platform prior to the analysis. 2019, Springer Nature Singapore Pte Ltd. -
Marine brown algae (Sargassum wightii) derived 9-hydroxyhexadecanoic acid: A promising inhibitor of ?-amylase and ?-glucosidase with mechanistic insights from molecular docking and its non-target toxicity analysis
Jeopardized glucose hemostasis leads to cronic metaboic disorder like Diabetes mellitus and it is predicted to occur in ?700 million people in the coming 20 years. Our study aims to isolate Palmitic acid (C16H32O3), 9-Hydroxyhexadecanoic acid metabolite from Sargassum wightii to inhibit alpha-amylase and alpha-glucosidase to reduce postprandial hyperglycemia and decline the risk of diabetes. High docking score of palmitic acid with both ?-amylase and ?-glucosidase is observed in in-silico molecular docking analysis, in comparison to commercially available drug acarbose. The three hydrogen bond in palmitic acid interacts with the important amino acids like Arg195, Lys200 and Asp300 in Glide XP docking mode for alpha-amylase. For ?-glucosidase, quantum-polarized ligand docking (QPLD) was used with similar three hydrogen bond interactions. Both docking studies showed significant binding interaction of palmitic acid with ?-amylase (?5.66 and ?5.14 (Kcal/mol)) and with ?-glucosidase (?4.52 and ?3.51(Kcal/mol)) with respect to the standard, acarbose docking score. The bioactive palmitic acid isolated from the brown alga, Sargassum wightii is already seen to inhibit digestive enzyme with non-target property in Artemia nauplii and zebra fish embryos. Further studies are required to investigate its role in in vivo antidiabetic effects due to its non-toxic and digestive enzyme inhibitory properties. It can be recommended in additional pharmaceutical studies to develop novel therapeutics to manage diabetes mellitus. 2023 SAAB -
One-Pot Synthesis of Silver Nanoparticles Derived from Aqueous Leaf Extract of Ageratum conyzoides and Their Biological Efficacy
The main objective of the present research work is to assess the biological properties of the aqueous plant extract (ACAE) synthesised silver nanoparticles from the herbal plant Ageratum conyzoides, and their biological applications. The silver nanoparticle syntheses from Ageratum conyzoides (Ac-AgNPs) were optimised with different parameters, such as pH (2, 4, 6, 8 and 10) and varied silver nitrate concentration (1 mM and 5 mM). Based on the UVvis spectroscopy analysis of the synthesised silver nanoparticles, the concentration of 5 mM with the pH at 8 was recorded as the peak reduction at 400 nm; and these conditions were optimized were used for further studies. The results of the FE-SEM analysis recorded the size ranges (~3090 nm), and irregular spherical and triangular shapes of the AC-AgNPs were captured. The characterization reports of the HR-TEM investigation of AC-AgNPs were also in line with the FE-SEM studies. The antibacterial efficacies of AC-AgNPs have revealed the maximum zone of inhibition against S. typhi to be within 20 mm. The in vitro antiplasmodial activity of AC-AgNPs is shown to have an effective antiplasmodial property (IC50:17.65 ?g/mL), whereas AgNO3 has shown a minimum level of IC50: value 68.03 ?g/mL, and the Ac-AE showed >100 ?g/mL at 24 h of parasitaemia suppression. The ?-amylase inhibitory properties of AC-AgNPs have revealed a maximum inhibition similar to the control Acarbose (IC50: 10.87 ?g/mL). The antioxidant activity of the AC-AgNPs have revealed a better property (87.86% 0.56, 85.95% 1.02 and 90.11 0.29%) when compared with the Ac-AE and standard in all the three different tests, such as DPPH, FRAP and H2O2 scavenging assay, respectively. The current research work might be a baseline for the future drug expansion process in the area of nano-drug design, and its applications also has a lot of economic viability and is a safer method in synthesising or producing silver nanoparticles. 2023 by the authors. -
Structural and antibacterial assessment of two distinct dihydroxy biphenyls encapsulated with ?-cyclodextrin supramolecular complex
?-Cyclodextrin plays a vital role in biological application because it can enhance the stability and solubility of the guest molecules in the supramolecular inclusion complexes. Moreover, the ?-Cyclodextrin inclusion complex has control-releasing behavior and lower toxicity than bare guest molecules. To improve the solubility and stability properties of two structurally different fluorescent guest molecules, namely 2,2?-dihydroxy biphenyl and 3,3?-dihydroxy biphenyls, they involve the ?-Cyclodextrin inclusion complex process. Optical measurements clearly described the efficient binding through the changes in the absorbance and emission intensities of guest molecules in the presence of ?-Cyclodextrin. The Job's plot from absorbance measurements reveals the 1:1 stochiometric ratio of binding of guests and the ?-Cyclodextrin host. The FT-IR spectra of the solid complex show the characteristic stretching and bending vibrations from both the guests and the host molecule. The 1HNMR spectra of the inclusion complex promote downfield shifting of guest molecule protons upon binding with the ?-Cyclodextrin host. The solid complex prepared using the solution method exhibits superior antibacterial activity against both gram-positive and gram-negative bacteria compared to the kneading and physical mixing methods. 2024 -
Cricket Shot Classification with Deep Learning: Insights for Coaching and Spectator Experience Enhancement
The cricket field has undergone significant transformations owing to recent technological advancements, particularly in countries like India. Technology has been used to determine projected scores, chances of winning, run rates, and many more parameters. This study centers on employing Deep Learning in cricket, focusing on the classification of different types of shots played by batsmen to aid in creating coaching strategies and enhancing the spectator experience. The proposed model uses a dataset of cricketing shots generated by collecting images from the internet, comprising 5781 images of 7 distinct shot types played by batters. The VGG-16, VGG-19, and RestNet-50 model architectures were trained for the classification task, with the best result obtained from VGG-16. Pre-processing tasks, such as scaling, augmentation, etc., were performed on the images before classification. Subsequently, 85% of the total images were used to train the model and for testing, rest 15% of images, resulting in an accuracy of 96.50% from VGG-16, 92% from VGG-19, and 78% from RestNet-50. 2024 IEEE. -
New Paradigm of Marketing-Financial Integration Modelling for Business Performance: An IMC Model
When it comes to the provision of financial services, the integrated marketing communication (IMC) process is crucial in the creation and maintenance of client-provider bonds. This research presents a literature assessment on the theoretical basis for using marketing communication tools in the provision of financial services. This research is an attempt to bolster the little theoretical literature on the effectiveness of marketing communication techniques in the provision of financial services. Financial service providers use marketing communication as a channel for two-way exchanges with their clientele, with the ultimate goal of maximising the benefits their customers bring to the company. When it comes to providing financial services, an organisations success hinges on its ability to effectively manage its relationships with both current and potential consumers. As a result, it is important for practical reasons to be guided by well-defined marketing communications goals to identify the extent of usage and within the constraints of available resources. In this regard, businesses are free to establish specific communications objectives in accordance with their unique situations to direct the implementation of their IMC plan. This study aims to find out an impact of financial integration with IMC on business performance. This study is descriptive in nature. Primary data is collected with the help of questionnaire. The study finds that the financial integration in the IMC model has a statistically significant impact on business success. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Computational Chemical Property Prediction and Anticancer Simulation of Heterocyclic Molecules
The Density Functional Theory (DFT) technique is popularly employed in establishing organic molecules' structural properties and reactivities. The B3LYP hybrid functional with the basis set 6-311G++(d,p) is utilised in the computational calculations with Gaussian 09W software. The DFT studies include energy minimisation (geometry optimisation), frontier molecular orbitals (FMO) analyses, theoretical UV spectral computation, natural bond orbital (NBO) evaluation, Topological analyses using Multiwfn 3.8 software are performed to evaluate the Pauli repulsion in atomic orbitals (as shown by ELF (Electron Localisation Function) maps), the areas of strong and weak pi-delocalisation in the molecules (as depicted in LOL (Localised Orbital Locator) maps) and the weak non-covalent intra-molecular interactions (as indicated in colour maps of RDG (Reduced Density Gradient)). Pharmacological evaluation is performed using SwissADME, ADMETLab 2.0, and PreADMET online tools. Molecular docking is performed using AutoDock Tools 1.5.6 with select anticancer target proteins to predict the bioactivity potential of the title molecules. The molecules studied in the work include a spiro compoun d, spiro[1H-indole-3,2-3H-1,3- benzothiazole]-2-one, a 2(3H)-furanone, 3,3,5-triphenylfuran-2(3H)-one, and a benzo[d]imidazole, 5,6-dichloro-1-cyclopentyl-2-(methylsulfinyl)-1H- benzimidazole. In addition, comparative studies are performed on the structure and reactivity of spirobrassinin derivatives, spirocyclic isatin-derivative analogues, and 3(2H)-furanones, and these three classes of molecules have already been predicted to possess anticancer properties in vitro. Interesting properties emerge in the preliminary theoretical investigations for these molecules, particularly in the FMO, the NLO and the molecular docking studies. The theoretical studies explore the reactivity, structure, and stability of the molecules under study, and biological evaluation examines their potential as lead compounds for cancer therapeutics. These studies can be further extended to include experimental validation and in vitro and in vivo tests to confirm further the efficacy of the anticancer action as well as the potential toxicity of the compounds. The theoretical investigations provide a database of information that could be useful for experimental scientists and medicinal chemists who primarily focus on drug design and discovery in their research so that they can narrow down the number of possible lead compounds from the vast chemical space of organic compounds that possess drug-like characteristics.