Volume 12 | Issue 4

 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.

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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

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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.

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Indian Author , Advertiser , social commentator

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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

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

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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.

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 Keywords

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

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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.

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Business Intelligence, Depression Detection.

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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.

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 Keywords

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

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 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.

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Machine learning, Chatbot, OCR, Heart disease Prediction

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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.

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Inula racemosa, ethnopharmacology, phytochemistry, bioactivity.

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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.

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 Keywords

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

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Customer segmentation is a fundamental strategy in marketing, enabling businesses to tailor their products, services, and communication to specific customer groups. This paper presents an innovative approach to customer segmentation using Gaussian Mixture Models (GMMs), a probabilistic clustering technique. GMMS offer the advantage of capturing complex data patterns and handling both unimodal and multimodal distributions. We demonstrate their application by conducting customer segmentation on a real-world dataset, showcasing the effectiveness of GMMs in identifying distinct customer groups. The results indicate that GMM-based segmentation enhances marketing precision, leading to improved customer engagement and satisfaction. This study underscores the significance of GMMs as a valuable tool in data-driven marketing strategies and customer-centric business practices

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 Keywords

Customer Segmentation, Gaussian Mixture Model, Silhouette Coefficient, K-means

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The Goods and Services Tax (GST) is the largest and most significant overhaul to India's indirect tax system. It is proposed to take effect on July 1, 2017. The current indirect consumption taxes will be replaced by the GST, which will be imposed on both goods and services. Whereas it will be based on consumption for services, it will be based on destination for commodities. Despite the long-term advantages, there's a possibility it might have a detrimental effect on stability and business. Finding out how fast-moving consumer goods (FMCG) wholesalers and retailers feel about the Goods and Services Tax (GST) and how it affects their business is the aim of the research. Both primary and secondary research was conducted to find out what merchants thought about the GST and associated matters. Growing awareness, easier access, and changing lifestyles have been the sector's key development factors. 120 retailers and wholesalers were chosen at random from all throughout Bengaluru, and they were questioned using a variety of multiple-choice questions on a questionnaire. Every answer was gathered and examined. An investigation of the hypothesis was carried out in order to have a better understanding.

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FMCG, GST,WHOLESALERS,RETAILERS

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The digital platform for event management serves as a centralized solution catering to the diverse spectrum of events in our rapidly evolving world. From community gatherings to global-scale events, this platform aims to simplify organizational processes for event planners and enhance accessibility to event details for participants. In the dynamic landscape of today's fast- paced environment, a streamlined and efficient system becomes essential, facilitating seamless event coordination and information dissemination. Moreover, in the era of rapid information flow and global connectivity, the platform serves as a bridge between orga- nizers and participants. It seeks to provide a seamless encounter ensuring that individuals can effortlessly access comprehensive event information, fostering engagement and participation. Since happenings tend to get crucial in various spheres, the event management website stands as a digital cornerstone, adapting to the fast-paced nature of our world and offering a streamlined approach to the intricate art of event execution.

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 Keywords

Event Management System (EMS), UI/UX, Re- sponsive Web Design, Java, Spring Boot, MySQL, CRUD, MVC Architecture, Cloud Computing, Scalability, Cloud-driven EMS

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 Abstract

Our ancient texts from Veda to Ayurveda classics opens the door for Ayurveda doctors how to understand and get knowledge of the importance of the herbs (dravya) via nomenclature, basonyms and synonyms. Also, through the study of pharmacognosy and ayurvedic pharmacology. Our rishis/munis inheritance wisdom about dravya is so rich that during studying of namarupavigyan, a herb speaks through synonyms its own language e.g gandhapatra, sadaphal, manglaya synonyms are of Bilva. Namarupavigyan of a dravya unveils today's modern morphology, organoleptic, pharmacological, therapeutic approach as well as ancient mythological, spiritual, ecological, ethnobotanical approach.

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bilva, dravya, namrupavigyan, nomenclature, synonym.

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Vehicle counting, classification, and detection have become essential tasks in modern transportation systems and surveillance applications. This abstract presents a comprehensive overview of these critical functions using OpenCV, a popular computer vision library. Vehicle counting involves tracking and tallying the number of vehicles passing through a specific region. OpenCV provides tools for frame differencing, contour detection, and object tracking. Classification goes a step further by categorizing vehicles into different classes, such as cars, trucks, or motorcycles.

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object detection, YOLO, OpenCV, CNN ,

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 Abstract

Hunger: A Memoir of (My) Body' by Roxane Gay and 'Dietland' by Sarai Walker are two significant works that present a feminist perspective on the intricate field of body image and beauty standards. The texts are powerful examples of how social pressures and normative factors shape women's perception of their bodies, providing insight into the extent to which these norms can have a significant impact on an individual' life. Additionally, The analysis presented here aims to unravel the multifaceted layers of these stories and explore how they challenge and undermine traditional notions of beauty and body image. Roxane Gay's "Hunger: (My) Body Memory" comes across as truly relatable and introspective. . a story in which the author bravely confronts her own struggles with body image in a thin-worshipping society. Gay's narrative voice is raw and unfiltered, offering readers an intimate look into the complexity of her experiences as a remarkable woman. In her memoir, Gay challenges the reader to rethink preconceptions about beauty and worth, highlighting the resilience and strength that can come from accepting one's true self. However, 'Dietland' by Sarai Walker is a more comical work. critiquing beauty standards with humor and wit to expose the beauty industry's relentless pursuit of perfection. Walker's story follows Plum Kettle as she begins a quest to discover her own beauty and embrace societal boundaries. By using Plum's example, Walker motivates readers to question the notion of socializing their appearance in favor of accepting beauty without any explanation. This study uses qualitative comparative analysis to unpack the themes of body image and beauty standards in Hunger: A Memoir (Of Me) . ) Body" and "'Dietland'". By closely examining these texts, this study aims to explain how these authors challenge and undermine traditional ideals of beauty by offering alternative narratives that celebrate diversity and self-acceptance. In addition, this study is based on feminist theory. create a strong theoretical framework to understand the nuances of these texts and their impact on the wider discourse of feminism and body image. This examination reveals a profound disapproval of social conventions and their influence on the existence of women. 'Hunger: A Memoir of (My) Body'" and "'Dietland'" are powerful reminders to embrace your own unique beauty and resist the pressure to conform to unrealistic ideals. The stories of Roxane Gay and Sarai Walker are a powerful catalyst for change, inspiring readers to question the status quo and redefine beauty in their own terms. The analysis presented in this study emphasizes the importance of body image and beauty standards in shaping women's experiences and identities. In addition to their critical reflections on social norms, these two works, "'Hunger: A Memoir of (My) Body'" by Roxane Gay and "'Dietland'" written by Sarai Walker, also offer a means for developing one's own self-acceptance and empowerment. In the narratives, Gay and Walker encourage readers to explore their own identity by embracing them as self-discoverers who are eager to embrace their bodies and identities with pride of mind.

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Feminism, beauty standards

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 Abstract

During the course of our investigation, we reviewed into the following Play Store apps (android apps with different names on different platforms): "APOLLO24/7,1MG, MYFORTIS, PRACTO, ZOCDOC, DOCTOLIB." Certain programs have bugs, while others have been customized for particular places; regardless, only two or three hospitals from any random place are provided after the entire state has been named. Since they essentially serve as consultants for the internet-based platforms, it would be beneficial if they could provide hospitals from all districts, including local ones.

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 Keywords

Play Store, Hospitals, Consultants, Bugs, Programs.

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 Abstract

Cloud computing stands as a pivotal component within the IT industry, promising substantial cost reductions in hardware and software resources. It facilitates seamless data sharing among corporate employees, predominantly leveraging cloud storage. Despite the convenience of storing data as plain text with access controls, relying solely on the cloud's trustworthiness, given its third-party ownership, proves impractical. Encryption thus becomes imperative, mandating data to be stored as cipher text with stringent access controls. However, the presence of malicious insiders who may adhere to sharing policies yet create vulnerable cipher texts poses a formidable challenge. Existing literature predominantly concentrates on ensuring legitimate recipients can decrypt data stored in the cloud, neglecting issues stemming from malicious data publishers. These individuals comply with sharing policies but craft cipher texts susceptible to unauthorized decryption, posing a significant threat to corporate intellectual property. To bridge this gap, we introduce the concept of a Sanitizable Access Control System (SACS) aimed at fortifying cloud storage against malicious data publishers. SACS represents a pioneering approach to access control management, effectively mitigating risks associated with malevolent actors. This research direction offers a practical solution for safeguarding data integrity and confidentiality in cloud environments, underscoring the criticality of addressing emergent threats within cloud computing security. In essence, SACS serves as a robust mechanism to thwart potential breaches stemming from malicious insiders, thereby bolstering the security posture of cloud storage systems and ensuring the protection of sensitive corporate data.

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 Keywords

Cloud Computing, Encryption, Access Control, Receivers, Ciphers, Servers, Security, Secure Cloud Storage, Access Control, Sanitizable, Malicious Data Publisher

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In order to improve picture resolution for a variety of applications, this research examines how well Super-Resolution Generative Adversarial Networks (SRGANs) operate. To increase visual quality and detail preservation in the produced high-resolution (HR) pictures, we present a modified SRGAN architecture that incorporates perceptual loss and residual blocks. By comparing the suggested method's performance to a benchmark picture dataset, quantitative measurements such as Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) show that it can outperform conventional interpolation techniques. These outcomes show how SRGANs may be used to further picture enhancement and restoration in a variety of fields. We also discuss ways this promising technology may go in the future and possible enhancements.

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Generative adversarial networks, Blurred image, Restoration, Enhancing, GAN, SRGAN

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Social media marketing is a process which is used by different brands to create content for different social media platforms and promote the products or services they offer to the consumers. This research paper has two main objectives that are to know the impact of luxury brands social media marketing efforts on consumer behaviour and the other is to find its impact on brand purchase intention. The other objective was to find the impact of luxury brands social media marketing efforts on brand awareness. This research used six dimensions of social media marketing efforts that are trendiness, interaction, entertainment, word of mouth, customization and social media marketing efforts. The dependent variables were consumer behaviour and brand purchase intention. Consumer behaviour included brand preference, customer loyalty and willingness to pay premium price. Brand awareness was also used. The sample size was 160 and a questionnaire was formed on 5-point Likert scale. Google forms was used and non-probability sampling was used. The data analysis was done through descriptive and PLS-SEM method was used in SMART PLS. The results showed that luxury brands social media marketing efforts has a positive impact on both consumer behaviour and brand purchase intention. The results also showed the positive relationship between the social media marketing efforts of luxury brands and the brand awareness. Entertainment does not impact consumer behaviour positively. Maximum of the customers agrees that exclusive offers and promotions shared on social media influence their decision to purchase from a luxury brand. Though the luxury brands need to work on interacting with customers as it does not impact consumer behaviour as well as brand purchase intention.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Consumer Behaviour, Brand Purchase Intention, Social Media Marketing Efforts, Luxury Brands, Brand Awareness

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Crowdfunding represents a modern financing approach that distinguishes itself from traditional borrowing. It champions inclusivity, welcoming both individuals and organizations as participants. In this model, a collection of contributors, known as a crowd, offers small financial contributions to support projects, products, or ideas. These initiatives are initiated by fundraisers, such as entrepreneurs or individuals, who seek capital to materialize their endeavors. Fundraisers connect with investors either directly or through dedicated digital platforms acting as intermediaries. Crowdfunding, now rooted in the web 2.0 era, has gained increasing academic attention. Blockchain technology is revolutionizing traditional crowdfunding techniques in the field of digital finance. The decentralized application (DApp), CryptoBacker builds confidence between project creators and backers by utilizing the immutable ledger of blockchain technology to facilitate safe and transparent transactions. It automates deals using smart contracts, doing away with middlemen and cutting expenses.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain Technology, Crowdfunding, Decentralized Applications

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Creative Commons Attribution 4.0 and The Open Definition