Volume 12 | Issue 4 |

 Abstract

Abstract: A tiny green box will appear in the center of the screen as the computer's webcam records footage of the person seated in front of the device. The objects displayed in that green box will be processed by the code, and if it matches, a red boundary will appear, indicating that the object has been recognized by the computer. The mouse pointer can then be moved by dragging the object. This will contribute to the computer's security as well as creating a virtual computing environment. In place of various objects, one hand gesture will be used to move the cursor here; a different gesture will be used for a right click and a different one for a left click. Similarly, a simple gesture can perform keyboard functions virtually that may have been performed on a physical keyboard. When the recognized gesture is noticed, a red border appears; otherwise, the box will just display a green box.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Haar Cascade Algorithm Virtual Mouse, Virtual Keyboard, Computer Vision

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project investigates the synthesis and characterization of tin sulfide (SnS) and zirconium oxide (ZrO2) nanomaterials with the aim of exploring their potential application in photovoltaic devices. Tin sulfide has garnered attention as a promising absorber material for thin-film solar cells due to its suitable band gap and earth-abundant constituents, while zirconium oxide nanoparticles exhibit desirable properties such as high refractive index and optical transparency, making them attractive for various optoelectronic applications.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

The synthesis of SnS and ZrO2 nanoparticles is carried out using facile and scalable methods, including solvothermal, hydrothermal, or chemical precipitation routes, tailored to control the size, morphology, and crystallinity of the nanomaterials. The structural, morphological, and optical properties of the synthesized nanoparticles are characterized using techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-r

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Anuja Chauhan is a renowned Indian author, advertiser, and screenwriter, celebrated for her contributions to both literature and advertising. Born in Meerut, Uttar Pradesh, she spent her childhood in various cantonment towns across North India due to her father's service in the Indian Army. Chauhan pursued her education in New Delhi, completing her schooling at Army Public School, Sophia Girls Convent, and Delhi Public School, Mathura Road. She holds a bachelor's degree in economics from Miranda House, Delhi University, and a postgraduate diploma in mass communication from the Royal Melbourne Institute of Technology. Her career in advertising began in 1993 when she joined JWT (J. Walter Thompson), where she created iconic catchphrases for Pepsi Cola, India, such as "Yeh Dil Maange More!" and "Oye Bubbly." By 2003, at the age of 33, she became one of the youngest vice-presidents at JWT. Chauhan has been recognized as one of the hottest creative directors in India and has featured in The Economic Times supplement Brand Equity's list. As an author, Chauhan is best known for her novels "The Zoya Factor" (2008), "Battle For Bittora" (2010), "Those Pricey Thakur Girls" (2013), "The House That BJ Built" (2015), "Baaz" (2017), "Club You To Death" (2021), and "The Fast and the Dead" (2023). Her writing often combines romance with humor and social commentary, resonating with a wide range of readers. In 2010, she resigned from her executive role at JWT to focus on her writing career. Despite leaving her full-time role, she continues to consult in advertising and has served on the One Show Jury in 2011 as the only Indian member. Anuja Chauhan's personal life is as vibrant as her professional one. She is married to Niret Alva, a television presenter and producer, and they have three children. Her older sister, Nandini Bajpai, is also an author. Chauhan's work has left a significant mark on both the advertising and literary worlds, making her a prominent figure in contemporary Indian culture.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Indian Author , Advertiser , social commentator

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study has been undertaken to the development of a deep learning-based system for predicting tomato leaf diseases, which are particularly significant in the agricultural industry due to the crop's high commercial value and contribution to food seThis study has been undertaken to the development of a deep learning-based system for predicting tomato leaf diseases, which are particularly significant in the agricultural industry due to the crop's high commercial value and contribution to food security. The proposed system utilizes a deep convolutional neural network (CNN) with transfer learning. The system determines plant health and predicts the specific disease if presentcurity. The proposed system utilizes a deep convolutional neural network (CNN) with transfer learning. The system determines plant health and predicts the specific disease if present

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep learning, Convolutional Neural Network (CNN), Transfer learning,Image Preprocessing, Tomato leaf disease, Early detection, Web application, Agricultural productivity

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Fake news significantly impacts our social lives, especially in the political sphere. Fake news identification is an exciting subject of study but has presented some challenges due to the limited tools (e.g., datasets published in literature). External knowledge has been incorporated recently to offer helpful background information and aid in detecting news. However, current knowledge-enhanced efforts include directly incorporating all knowledge contexts via static entity embeddings, which may lead to noisy and irrelevant knowledge. Our proposed Hybrid System will obtain the best results. Our System detects fake news using Term Frequency, Term Frequency Inverted Document frequency and Kullback Leibler Divergence (TF-TDF-KLD). Our hybrid System is more efficient than the existing one, with 92.5 % accuracy.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fake News Detection, TF-IDF-KLD, feature extraction, social lives

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project focuses on leveraging business intelligence methods to conduct sentiment analysis on mental health-related data, aiming to provide valuable insights into individuals' emotional well-being. By analyzing text-based interactions from various sources like social media, forums, and chat logs, sentiment analysis models categorize text into positive, negative, or neutral sentiments. Emotion detection algorithms further identify specific emotions such as sadness, happiness, or anxiety. Business intelligence tools and dashboards are utilized to visualize and present sentiment analysis results, enabling stakeholders like mental health professionals, policymakers, and researchers to monitor online sentiments in real-time. Predictive models are also developed to forecast mental health trends using sentiment analysis data, facilitating early intervention and resource allocation. By applying business intelligence techniques to analyze mental health data, this project aims to enhance understanding of mental health trends, support timely interventions, and promote overall well-being. The insights generated can aid individuals, healthcare providers, and policymakers in making informed decisions and delivering targeted support for mental health challenges.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Business Intelligence, Depression Detection.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The financial industry has experienced a significant surge in data availability and computational power, leading to a transformative shift in credit risk assessment methodologies. This project endeavors to leverage machine learning techniques to refine the accuracy and efficiency of credit risk analysis. The primary objective is to develop predictive models that can effectively evaluate the creditworthiness of individuals and businesses, thereby facilitating lending institutions in making informed decisions. To achieve this end, a diverse set of financial, demographic, and behavioral data from various sources, such as credit reports, income statements, and payment histories, is collected. The collected data is pre-processed to handle missing values and outliers, and feature engineering techniques are employed to extract pertinent information for modeling. A combination of supervised and unsupervised machine learning algorithms is utilized to build predictive models that can classify borrowers into different risk categories. The analysis involves the evaluation of multiple machine learning algorithms, such as logistic regression, k-nearest neighbors, decision trees, and neural networks, to identify the most suitable models for credit risk prediction. The performance of these models is assessed using various evaluation metrics, including accuracy, precision, recall, and receiver operating characteristic (ROC) curves. The project also incorporates ongoing monitoring and model updating to adapt to evolving economic conditions and changing borrower behaviors. By implementing a sturdy framework for credit risk analysis using machine learning, this project aims to provide financial institutions with a powerful tool for optimizing their lending practices and managing credit risk effectively.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Financial Industry, Data availability, Computational Power, Credit Risk Assessment Methodologies, Machine Learning Techniques, Credit Reports, Creditworthiness

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Heart Vigilance presents a web application comprising three integral modules: a prediction model, an Optical Character Recognition (OCR) model, and a chatbot. This research focuses on recognizing the presence of heart disease in individuals by using these seven key parameters(Age, Sex, Chest pain type, BP, Cholesterol, FBS over 120, EKG results,Max HR, Exercise Angina, ST depression, Slope of ST, Number of Vessels Fluro, Thallium, BMI), validated in consultation with a cardiologist. Various prediction algorithms, including logistic regression, decision tree, and random forest, were employed and assessed for accuracy, with random forest demonstrating the highest accuracy at approximately 87%. The OCR model, leveraging technology from nano nets OCR, aids in extracting parameter information from patients' lab reports, reducing potential human errors. Additionally, a GPT-4-based chatbot is integrated into the application to provide responses to basic inquiries regarding heart disease and patient care. The amalgamation of these modules constitutes the Heart Vigilance application, designed to enhance heart disease prediction and awareness.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine learning, Chatbot, OCR, Heart disease Prediction

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Inula racemosa, also known as Pushkaramula, is a well-documented medicinal plant in India, belonging to the Asteraceae family. This review article aims to comprehensively gather and examine scientific research on the ethnopharmacology, phytochemistry, and bioactivity of I. racemosa, encompassing its isolated phytoconstituents and extracts. Traditionally, the plant has been utilized for its anti-inflammatory, cardiovascular, hypoglycemic, antianginal, analgesic, and antibacterial properties, which have been validated through experiments on various animal models. Furthermore, the plant's distribution, physical characteristics, and traditional uses are emphasized, highlighting its potential for further exploration and utilization in the field of pharmacology. The diverse range of pharmacological effects exhibited by Inula racemosa, particularly the bioactivities of alantolactone and isoalantolactone, underscores the plant's importance and the possibility of discovering novel chemical compounds for further application.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Inula racemosa, ethnopharmacology, phytochemistry, bioactivity.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract:There search aims to create and assess an herbal anti-acne gel using ingredients such as green tea leaves, nutmeg, and guava leaves. Additionally, the study investigates the presence of chemical constituents in plant extracts. The gel formulation includes carbopol-934, methyl paraben, propylparaben, polyethylene glycol and triethylamine. From the stability study results, it is evident that the formulation remains stable, with no significant changes in pH, viscosity, spread ability, consistency. The various formulations (F1 to F3) were prepared by adjusting quantities, and the selected batches were thoroughly evaluated. Notably, different concentrations of herbal extracts exhibited varying pH levels, viscosities, and spread ability.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

polyherbal , anti oxidant , anti microbial , nutmeg, Green tea leaves , guava leaves

  License

Creative Commons Attribution 4.0 and The Open Definition