Volume 12 | Issue 4

 Abstract

The Staff Timetable Scheduling System is a comprehensive solution for managing staff timetables in educational institutions. It offers features for both teachers and administrators, including secure login, attendance marking, profile management, and timetable visibility. Administrators can create, manage, and optimize timetables via a centralized dashboard. Key functionalities include enforcing fixed time durations for attendance, managing leave applications, and automating library period allocations. The system aims to enhance transparency, communication, and productivity within educational institutions by promoting punctuality accountability, and equitable workload distribution. It fosters coordination and collaboration between teachers and administrators, ultimately helping institutions streamline scheduling processes and achieve academic objectives. Overall, By providing a user-friendly interface and intelligent scheduling algorithms, the system aims to enhance transparency, communication, and productivity within educational institutions. It addresses complexities in timetable management, promotes punctuality and accountability, and facilitates coordination between teachers and administrators, ultimately improving efficiency and academic success.

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 Keywords

Staff scheduling system, Educational Institutions, Effective resource utilization, Equitable Workload Distribution, Academic Success.

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 Abstract

This study has been undertaken to investigate the determine the performance of concrete with partial replacement of coarse aggregate with broken roof tiles and fine aggregate with bone waste. The broken roof tiles waste was varied from 0% to 50% for coarse aggregate and bone waste was constant at 4% for fine aggregate in M20 mix. Flexural tensile strength test was conducted to determine the strength of beam. As per the result analysis the maximum strength was obtained at 30% of partial replacement. This project ensures an eco-friendly and cost-efficient nature of the proposed replacement, aiming to sustainable construction practices.

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 Keywords

Bone waste, Broken roof tiles, flexural tensile strength, eco-friendly, sustainable

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Hand gesture recognition on video calls has emerged has shown promise as a to enhance user interaction and communication experiences in remote collaboration scenarios. The technique for real-time hand gesture recognition presented in this paper was created specifically for video conferencing applications. The suggested framework makes use of computer vision methods, including hand detection, tracking, and gesture recognition, to interpret hand movements made by participants during video calls.The system begins feeds from users' cameras and employs robust hand detection algorithms to identify hand regions within the frames. Subsequently, hand tracking algorithms are utilized to monitor the movement of detected hands across consecutive frames, enabling precise trajectory estimation. Models for machine learning, like convolutional neural networks (CNNs), are then employed to recognize specific hand gestures according to the tracked trajectories. To ensure seamless integration into video conferencing platforms, the system is optimized for real-time performance, minimizing latency and maximizing responsiveness. Furthermore, the proposed solution addresses challenges associated with varying lighting conditions, background clutter, and occlusions, thereby enhancing the obustness and dependability of gesture recognition .Experimental evaluations demonstrate the efficacy and efficiency of the proposed system in accurately interpreting a diverse range of hand gestures during live video calls. User studies further validate the usability and user satisfaction of the integrated gesture recognition feature, emphasizing its potential to revolutionize remote communication experiences.In conclusion, real-time hand gesture recognition contains enormous possibility for transformation. video communication, enabling intuitive and interactive interactions between participants in virtual environments. Prospective avenues for research encompass improving the system's adaptability to diverse hand gestures and exploring novel applications for gesture-based interaction in collaborative settings.

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 Keywords

Vision technique, Gesture Analysis, Sign Symbol Recognition ,Hand gesture ,Dumb Communication.Text and Voice

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 Abstract

Environmental justice and community activism have become central to addressing the disproportionate burden of environmental hazards and pollution borne by marginalized communities. Historically, marginalized populations, including low-income communities and communities of colour, have faced environmental injustices, such as exposure to toxic waste sites, air and water pollution, and inadequate access to green spaces and clean resources. This research paper aims to explore the principles of environmental justice, the role of community activism in advocating for environmental equity, and the challenges and opportunities for achieving environmental justice in contemporary society.

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 Keywords

Environmental Justice, Political Conflicts, Ecological Diversity, Sustainable Development.

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The Care bundle is a new concept in critical care, which is currently being promoted by the national health service modernization agency for critical care. It has been originated in North American and are described as best groups of evidenced based practice intervention. The theory behind this bundle is that when several evidenced based interventions are grouped together in a single protocol, which will improve patient outcome. The bundle approach to bestow/ deliver medical care has become popular in recent years. A care bundle is a group of intervention which when implemented will lead to a better outcome than performing intervention individually. It is a group of three to five evidenced based intervention, when performed together, will result in better outcome. It can be used to assure the minimum standards of care. They can be used as an audit tool to assess the delivery of intervention, but not to assess individual interventions.

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Bundle, CAUTI, CLABSI, SSI, VAP

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The task of producing accurate and contextually relevant textual descriptions from visual inputs has attracted a lot of attention in the field of image captioning. This research proposal uses the Flickr8k dataset as a baseline for evaluation to tackle this difficulty in the context of Tamil language. In order to better understand the relative advantages and trade-offs of each approach, the study examines three different methods for improving Tamil image captioning. This helps to clarify the challenges associated with non-English captioning assignments. Using Long Short-Term Memory (LSTM) networks and the Convolutional Neural Network (CNN) VGG16, the first method creates a baseline. This methodology leverages the sequential data processing capability of LSTM and the visual feature extraction skill of VGG16 to provide a basis for further comparisons. The paper then presents an attention-based encoder-decoder architecture that adds an attention mechanism to LSTM layers. During caption synthesis, this method dynamically selects pertinent image segments to increase caption accuracy. Assessment metrics are employed to evaluate the model's effectiveness in producing contextually accurate captions, including BLEU scores and attention plots. Finally, by combining Vision Transformers (ViTs) with Transformer Networks for image processing and sequence production, respectively, the research pushes the envelope of innovation. Using Transformer Networks' and ViTs' complementing strengths, this sophisticated approach aims to dramatically improve caption quality by understanding the complex relationship between visual content and spoken descriptions. The research clarifies the trade-offs related to striking a balance between model complexity, computing demands, and caption authenticity through extensive comparison analysis. Moreover, it highlights the linguistic complexities present in Tamil, a language distinguished by its intricate syntactic and morphological structure. This research adds to the expanding body of information on multilingual picture captioning and offers critical insights and guidance for future developments in linguistically sensitive captioning systems that can function well in a variety of linguistic and cultural situations.

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 Keywords

Tamil image captioning, comparative study, Flickr8k dataset, VGG16, LSTM, vision transformers, Transformer Networks, attention mechanism, BLEU, ROUGE and METEOR score

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In the fashion industry, finding well-fitting clothing that aligns with personal style preferences remains a significant challenge. Existing solutions, such as online shopping platforms, lack accurate prediction capabilities for how garments will fit for the individuals. To address this gap, GarbGenius proposes an innovative approach integrating Artificial Intelligence (AI) powered recommendation systems and Augmented Reality (AR) enhanced virtual try-on experiences. By leveraging user-image input submission and a comprehensive clothing datasets, GarbGenius will deliver personalized recommendations based on current fashion trends. Implementation involves developing user-friendly interfaces for user image input submission, clothing datasets, and algorithmic analysis of Vision Transformer (ViT) with recommendations, ensuring a seamless and immersive fashion wardrobe experience. Through iterative testing and maintenance, GarbGenius aims to empower users with confidence in exploring and embracing the latest trends tailored to their unique preferences.

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 Keywords

Artificial Intelligence (AI) powered recommendation systems, Augmented Reality (AR), fashion trends, clothing datasets, algorithmic analysis, Vision Transformer.

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 Abstract

Data Insight Pro is a comprehensive Tkinter-based graphical user interface (GUI) application designed to facilitate unstructured data management and enable users to extract meaningful insights from their datasets. It provides a user-friendly platform for uploading Excel or CSV files, performing data preprocessing tasks, conducting statistical analyses, and generating insightful visualizations. One of the key features of Data Insight Pro is its ability to preprocess data by handling missing values, removing duplicates, and detecting and handling outliers. This data cleaning process is crucial for improving data quality and enhancing the accuracy of subsequent analyses and visualizations. Additionally, the application incorporates a simple linear regression model to assess the impact of data preprocessing on model performance, calculating the accuracy before and after data cleaning. Users can download the cleaned dataset as a CSV file and the generated visualizations as PDF or PNG files, with the option to include log information for better documentation and reproducibility. Data Insight Pro aims to streamline the data exploration, analysis, and visualization process, empowering users to make informed decisions based on high-quality data and insightful representations.

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Keywords : Unstructured Data, Data Visualization, Data Preprocessing, Tkinter GUI, Unstructured Data Management, Linear Regression, Insights Extraction, Data Analysis.

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The study explores the dynamics of influential marketing in beauty industry, with a specific focus on Instagram as a pivotal channel in increasing brand awareness. The purpose of this study is to investigate the correlation between PR and Influential Marketing to ascertain why Instagram is the most suitable and effective social media platform for raising brand awareness of beauty brands. Additionally, the study aims to explore how beauty influencers increase brand recognition and determine if Millennials or Gen Z are more likely to engage with beauty-related influencer marketing. This study employs a Mixed-Method approach by conducting a structured survey to get insights from individuals who are well-versed in the beauty industry's Instagram influencers. In order to provide a robust data set from individuals actively engaging with Instagram beauty influencers, purposive sampling is used to identify individuals with relevant experience or knowledge. The expected results of this study should deepen our comprehension of Instagram's potential as a platform for influencer marketing of beauty sector.

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 Keywords

Influential marketing, beauty sector, Instagram, Consumer recognition, Brand awareness, Beauty influencers, Public relations, Millennials, Gen Z

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The goal of this project is to increase the accuracy of stock market predictions by combining machine learning techniques with sentiment analysis. This study examines the combination of sentiment analysis and machine learning approaches to increase the accuracy of stock market predictions, making use of the enormous volumes of textual data that are available from several sources, such as social media and financial news. We use sophisticated machine learning techniques to use the large volumes of textual data that are available from several sources, such as social media and financial news, in addition to traditional numerical data. By capturing the complex relationship between market sentiment and quantitative data, our method seeks to improve the predictive power of our models.We show how well our methodology works for precisely predicting changes in the stock market through extensive data preparation, feature engineering, and model construction. Sentiment analysis integration offers insightful information on the dynamics of market sentiment, giving analysts and investors a more comprehensive grasp of market behavior. The field of stock market prediction is advanced by this research by utilizing cutting-edge methods that integrate textual and numerical data analysis.

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 Keywords

Machine Learning, sentimental analysis ,Qlstm, Training and Testing, Dataset Expansion.

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 Abstract

Kasaba is a tribal community living in the Nilgiris. It has got its own linguistic system and it has been studied by many linguists viz. Zvelebil,K (1971) Shanmugam S.V(1974) ChidamparanatanPillai (1976) and so on. But, in modern days, the language of Kasaba tribe gets changed because of the impact of urbanization, modernization, social changes and modern communication network. That leads to loss of age old linguistic entities and its originality: So, Kasaba language has to be studied sociolinguistically, for protecting and preserving it as no effort in this regard it has been carried out so far. This paper, thus, tries to understand the sociology of Kasaba language, the attitudes of Kasaba tribes towards their mother tongue, the linguistic situation prevail in their living environments (both in settlement and in main-stream region), their understanding over their language and the level of bilingual competence they have in their mother tongue, and reginal language (Tamil). Around 40 informants representing some social unvaribles viz. gender, age, education and floatingness, have been approached and their views have been elicited and through a questionnaire the corpus have been analysed qualitatively and quantitatively, for drawing inferences.

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 Keywords

Attitude, Bilingual, Linguistic situation, Competence, Qualitative, Quantitative

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This study investigates the relationship between self-efficacy and academic achievement in adolescents. The sample consisted of 1000, 9th grade students in Guntur District, Andhra Pradesh, India. Data was collected through self-reported questionnaires on self-efficacy and academic performance. The findings reveal that adolescents in the study generally have an above-average level of self-efficacy. There is a significant difference in self-efficacy between genders, with females scoring higher than males. However, there is no significant difference in self-efficacy based on locality (rural vs urban) or type of school management (government vs private). Finally, the study finds a significant positive correlation between self-efficacy and academic achievement. This suggests that students with higher self-efficacy tend to perform better academically.

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SELF-EFFICACY

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This paper conducts a comprehensive analysis of rainfall patterns and their hydrological implications in Kolkata, West Bengal, India, spanning the monsoon period from 2011 to 2020. The study employs data from the Centre of Hydro technology and Remote Sensing (CHRS) and the India Water Resources Information System (India-WRIS). Advanced geospatial tools, including ArcGIS, ArcMap, and HEC-HMS, are utilized, coupled with BHUVAN's land-use and land-cover data, and the curve number from TR-55 by the USDA Soil Conservation Service (SCS), now Natural Resources Conservation Service (NRCS), serving as a basis for estimating runoff potential based on land use and soil type. The study focuses on the analysis of rainfall variability, identification of precipitation trends, and assessment of hydrological responses to rainfall events. Additionally, HEC-HMS is employed for hydrological modeling, simulating runoff and streamflow dynamics under varying rainfall scenarios. The findings reveal that the peak discharge in most subbasins and reaches around Kolkata occurred in 2014, with some experiencing peak discharge in 2015. During this period, peak discharge reached approximately 1000 - 1300 m3/s in Reaches 21, 18, 31, 30, 28, 27 and 26 (generated in HEC-HMS) with a recorded rainfall of 374.12 mm in July 2014 in the city and also the city's basin registered a peak discharge rate of 50.3 m3/s in the same time. Analyzing recent trends, it is observed that the city typically receives rainfall ranging between 300 mm to 400 mm, suggesting a consistent peak discharge of maximum of 50m3/s during each monsoon season.

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SCS, HEC-HMS, TR-55, Hydrological Modelling, Peak Discharge

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The advent of Internet of Things (IOT) technology has revolutionized the poultry farming industry by enhancing productivity, optimizing resources, improving efficiency, and reducing production costs. This article explores the potential of IOT sensors in the field of poultry farming and provides insights into the future of automation. Various parameters such as humidity, temperature, gas levels, dust, and pesticide/insecticide quantities are monitored using a network of sensors, enabling comprehensive data collection and facilitating early fault detection and diagnosis. A Decision Support System (DSS) serves as the central operating system that governs and coordinates all activities within the poultry farm. This intelligent system utilizes the data collected by the sensors to provide real-time analysis, predictions, and recommendations, empowering farmers to make informed decisions for optimal farm management. By leveraging IOT technology, the DSS enables precise control over environmental conditions, feed distribution, and disease prevention, ensuring healthier and more productive poultry. This article also addresses the specific challenges faced by poultry rose farming and highlights the latest IOT-based solutions that are both intelligent and sustainable. The proposed model presented in this article is tailored to adapt to the changing environmental conditions, effectively redefining the concept of sustainability in poultry farming. By incorporating IOT technology, farmers can achieve significant improvements in resource utilization, minimize waste, and enhance the overall sustainability of their operations. The integration of IOT sensors and the DSS offers numerous benefits, including increased operational efficiency, reduced costs, improved animal welfare, and enhanced product quality. The automated monitoring and control provided by IOT systems streamline farm management processes and enable proactive decision- losses. While initial setup costs may be incurred, the long-term benefits making. Additionally, by harnessing the power of data analytics and machine learning, the DSS can provide valuable insights into trends, patterns, and correlations, further optimizing poultry farming practices.

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 Keywords

IoT, DSS, Poultry farming

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 Abstract

The Human-Computer Interaction application discussed in this research paper focuses on cursor control using facial movements captured through a standard webcam. This innovative system operates without the need for any wearable hardware or sensors, relying solely on the analysis of the user's facial gestures. The core functionality revolves around predicting facial landmarks, particularly eye blinks and mouth movements, within a video stream. Various actions correspond to specific facial movements: opening the mouth activates or deactivates mouse control, winking the right eye triggers a right-click function, winking the left eye initiates a left-click action, squinting detects the activation or deactivation of scrolling, and head movements (pitch and yaw) control scrolling or cursor movement. The system relies on facial landmarks to derive features that enable the detection of these actions. For instance, eye aspect ratio (EAR) helps identify winks and blinks, while mouth aspect ratio (MAR) indicates whether the mouth is open or closed, essential triggers for mouse control. This model primarily focuses on analyzing two main factors: Eye Aspect Ratio (EAR) for detecting winks and blinks, and Mouth Aspect Ratio (MAR) to determine the status of the mouth--whether it's open or closed. These ratios serve as crucial indicators for interpreting various facial gestures that, in turn, trigger specific mouse control actions.

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 Keywords

Human-Computer Interaction (HCI),Facial Movements, Cursor Control, Assistive Technology, Webcambased Interaction, Disability Accessibility, Facial Landmarks Prediction, Eye-Aspect-Ratio(EAR), Mouth-Aspect-Ratio (MAR), Facial Expression Tracking, Eye Blink Detection ,Wink Detection, Head Movements, Gaze Estimation Face Detection Models, Landmark Detection Models, Head Pose Estimation Models, Computer Accessibility, Facial Gesture Control, Eye Tracking Technology

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 Abstract

The mental disorders are considered to be occurring at the age of 10 to 29 years in regards to approximately 10 to 20%. The factors that have been protective for the prevention of mental disorders in this age group have been the self-esteem, relationship into personally as well as the support from a social perspective. The main purpose that is behind this study was for the determination of relationship in between the protective factors as well as the impact of the dysfunction on to the interpersonal relationship as well and self-esteem in regards to the young adults. These researches have been employing a questionnaire design method. 100 people were selected from age group of 18 to 24 years and the data has been collected with five sections of the questionnaire, demographic data, self-esteem scale, index regarding the interpersonal relationship, mental health continuum in the short form. Self-esteem has been considered as one of the global attitudes towards in the perspective of each and everyone please stop it has been representing the perspective of one person in relation to the values. Although some are having believe that the importance in terms of self-esteem has been inflating. It has been also appearing in the form of the souls for the purpose of the relational type of outcomes. Individuals that are having very high self-esteem and are tender for having much more support socially as well as from the side of their friends as well as they will be tending to be much more satisfied inside their relationship. The findings have been showing that the people belonging to this age group will be having different types of attachment towards their parents which are having specific type of features in regards to self-esteem as well as it specifically in terms of their interpersonal relationships. There is herself in concluding that the system in regards to any attachment with their respective parents has been very necessary for the development of psychological well-being of each and every individual adolescent.

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 Keywords

self-esteem, family interpersonal relationship, young adults, etc.

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 Abstract

bikaner share me schooliy siksha ke vikash me sankhyiki vishleshan avm mulyankan

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bikaner share me schooliy siksha ke vikash me sankhyiki vishleshan avm mulyankan

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Floods pose significant risks to human lives and infrastructure worldwide, necessitating accurate prediction and timely response strategies. This study proposes an Artificial Intelligence (AI) and Machine Learning (ML) approach to predict flood intensity and evaluation human headcount in flood-affected areas. Leveraging historical flood data, atmospheric parameters, terrain features, and satellite imagery, our model employs advanced AI algorithms, including deep learning neural networks and ensemble methods. Firstly, flood intensity prediction is addressed using a amalgamation of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to evaluate spatiotemporal patterns and predict flood levels with high exactness. Secondly, human headcount estimation utilizes a hybrid model incorporating support vector machines (SVMs) and random forest algorithms trained on socio-demographic data, population densities, and real-time information from social media and disaster retort agencies. The proposed AI/ML framework offers several advantages, including real-time monitoring, early caution systems, and efficient resource allocation during flood events. Validation against historical flood incidents establishes the model's robustness and effectiveness in predicting flood intensity and estimating human headcount. Integration of this technology into prevailing disaster management systems can enhance preparation, response, and recovery efforts, ultimately modifying the adverse impacts of floods on communities and organization.

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Flood intensity, Human headcount estimation, Spatiotemporal patterns, Real-time monitoring, Early warning systems, Real-time monitoring, Convolutional Neural Networks (CNNs).

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Since ancient times, the medicinal herb Terminalia arjuna has been utilized in traditional medicine to treat a variety of illnesses and disorders. This study used contemporary medication techniques to empirically validate the folkloric method. Examining the pharmacological properties of the native medicinal plant Terminalia arjuna was the primary goal of this review. Given that green medicine is healthier than manufactured goods, medicinal plants have been identified as a significant source of therapeutic compounds to treat human illness. Vagbhata (c. 7th century CE) brought the Arjuna into Ayurveda as a remedy for cardiac ailments. Traditionally, a milk decoction is made of it. Arjuna was used for thousands of years as a medicinal herb; in the ancient Indian Vedas, Vagbhata describes Arjuna's use for treating wounds, hemorrhages, and ulcers, topically administered as a powder.

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 Keywords

Flavonoids, Triterpenoids, Terminalia arjuna, Cardiovascular diseases, and Coronary prevention.

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 Abstract

Research introduces "EtherDrug," a decentralized Pharmaceutical Supply Chain Management System leveraging Ethereum blockchain technology to combat counterfeit drugs and pharmaceutical trafficking. By utilizing a technology stack comprising Ethereum, MetaMask, Ganache, Truffle, Solidity, HTML, and CSS, the project orchestrates a systematic approach, beginning with node registration and order placement with Medicine ID and quantity specifications. The system meticulously controls each transaction, allowing individual nodes to accept or reject orders, while real-time tracking provides precise updates on the location and timestamp of medicines within the supply chain. This innovative solution not only addresses the immediate need for transparency and security in drug supply chains but also contributes to the broader discourse on blockchain's application in ensuring pharmaceutical quality, safety, and efficacy, offering a practical solution to industry-wide challenges.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

EtherDrug, Ethereum Blockchain Technology, Counterfeit Drugs, Pharmaceutical Trafficking.

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Creative Commons Attribution 4.0 and The Open Definition