Volume 12 | Issue 4 |

 Abstract

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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 Keywords

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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 Keywords

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

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 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 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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 Keywords

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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