Volume 13 | Issue 4 |

 Abstract

Agriculture is the backbone of many economies, providing food and raw materials essential for human survival. However, plant diseases significantly impact crop productivity, leading to financial losses and food shortages. Early detection and appropriate treatment of plant diseases are critical to maintaining agricultural sustainability. This project, "Plant Disease Detection and Fertilizer Suggestion," presents an AI-powered system that identifies plant diseases using deep learning techniques and suggests suitable fertilizers based on the detected diseases. Traditional plant disease detection methods rely on manual inspection, which is time consuming, prone to human error, and requires expert knowledge. With advancements in computer vision and artificial intelligence, automated systems can now accurately classify plant diseases and provide recommendations. Our system utilizes Convolutional Neural Networks (CNNs) to analyze plant leaf images and detect diseases with high accuracy. By leveraging deep learning, the model can differentiate between multiple plant diseases, ensuring precise diagnosis.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI/ML, Python, Frameworks, etc.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project is to develop an automated system designed to detect and categorize stress levels based on brainwave patterns observed through electroencephalogram (EEG) recordings. EEG is a non-invasive technique that captures electrical activity in the brain, providing valuable insights into neural dynamics that reflect emotional and cognitive states, including stress. Stress, a significant factor impacting health, productivity, and overall well-being, induces a complex physiological response characterized by alterations in brainwave activity. Research has shown that elevated stress levels are often associated with an increase in beta wave activity and a decrease in alpha wave activity, indicating a state of heightened alertness, cognitive arousal, and sometimes anxiety. By implementing advanced signal processing techniques, such as wavelet transforms and feature extraction, the system will pre-process EEG data to isolate relevant brainwave characteristics linked to stress responses. The development of a data-driven, scalable solution for stress monitoring reflects the growing need for innovations that support mental health, and this system stands to make a meaningful contribution in both research and real-world application.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cnn,lstm, XGboost,EEG

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In recent years, significant advancements have been made in the construction sector, with even more expected in the future. However, these developments have also contributed to various global challenges, such as pollution, global warming, and the increasing frequency of natural disasters, including earthquakes. These issues are causing notable changes in the integrity and safety of structures. One critical concern is the development of cracks in pavement structures, which can compromise safety and durability. This paper discusses the various types of crack formation in pavement structures, their detrimental impacts, and preventive measures to mitigate these issue

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Rigid pavement failure, Flexible pavement failure, shear failure, thermal failure, structural cracks

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In recent years technological development has created epochal change. Digitalisation of banking services has given fuel to adopt more cashless services. Now, the concept of branchless banking is emerging at a swift pace. Neo banking is the replacement of traditional banking systems. This study aims to understand the concept of neo banking and how the neobanks are different from digital banks. Furthermore, this study reviews the works of various researchers and tries to find major factors that have influenced the customer adoption of neo banking using the TAM model. This paper identifies various variables, such as social influence (SI) and awareness of the service (AS), that have an impact on perceived usefulness. Computer Self-efficacy (CSE-PEOU) & Quality of Internet connection (QI- PEOU) have an impact on perceived ease to use, attitude, and trust.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Neo-banks, Digitalisation, Customer adoption, TAM model, Perceived usefulness, trust.

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 Abstract

Malaria continues to pose a serious threat to public health challenge, especially in tropical and subtropical areas. The paper deals with understanding Malaria transmission dynamics and evaluating interventional strategies using SEIR (Susceptible-Exposed-Infected-Recovered) model incorporating infected migrants with an infectivity factor ? which is well-suited for studying infectious diseases with latent periods, such as Malaria. The study area is the state of Goa, a popular tourist destination, where domestic travelers act as potential carriers of Malaria, influencing transmission dynamics and disease control efforts. A system of non-linear Ordinary Differential Equations is used, and stability is analyzed using the Jacobian matrix. It is seen that increasing the treatment rate and reducing transmission can significantly mitigate the disease burden. The data is then validated by using graphical method.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

SEIR, Jacobian, Goa, Malaria, Infected migrants

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The integration of e-commerce platforms with accounting systems has become essential for modern businesses seeking to optimize operational efficiency and maintain accurate financial records. This paper presents the design and implementation of a seamless integration framework between Shopify and QuickBooks, aimed at automating the synchronization of product information and invoice data. By utilizing the official APIs of both platforms, the proposed system enables real-time, bidirectional data exchange, ensuring consistent updates to product catalogues, customer profiles, and transactional records. The framework automates the propagation of product changes from Shopify to QuickBooks and generates corresponding invoices in QuickBooks based on real-time order data received from Shopify. Furthermore, the integration leverages Shopify webhooks to detect and respond to events such as order status changes and inventory modifications, allowing for immediate reflection of these changes in the accounting system. This approach minimizes manual data entry, reduces the incidence of human error, and improves the accuracy and timeliness of both inventory and financial data. The synchronization of customer records across platforms further contributes to coherent data management, facilitating streamlined financial reporting and operational transparency. The framework is designed to be robust, scalable, and efficient, incorporating comprehensive error handling, data validation mechanisms, and system logging to support maintainability and reliability. The resulting solution offers a practical contribution to enterprise systems integration, with the potential to enhance data-driven decision-making through real-time business insights.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

QuickBooks, Shopify, Integration Framework, Invoice Automation, Product Synchronization, API, E-commerce, Accounting

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Digital payments have been transformed by the quick expansion of Unified Payment Interface (UPI) transactions, but this has also made them more susceptible to fraud. Due to the dataset's extreme imbalance and the fact that fraudulent transactions make up a very small percentage of all transactions, fraud detection in UPI transactions poses difficulties. This study suggests a strong framework for detecting fraud that makes use of ensemble learning techniques, such as XGBoost, LightGBM, and neural networks, to increase prediction accuracy and manage data imbalance. The use of sophisticated ensemble techniques to handle class imbalances such as oversampling, under sampling, and synthetic data generation--as well as the integration of feature engineering to detect anomalous patterns are among the major advances. Numerous tests show that the suggested models can minimize false positives while achieving high detection rates. The findings imply that ensemble learning considerably improves UPI fraud detection systems' effectiveness, offering a scalable and practical solution for practical uses.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine learning; UPI; Fraud detection, Ensemble learning; boosting; XGboost; LightGBM; neural network

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research analyses the evolving role of literature in instilling environmental awareness, discussing Rachel Carson's Silent Spring (1962) and Claire Vaye Watkins' Gold Fame Citrus (2015) based on ecocriticism and the United Nations Sustainable Development Goals (SDGs). Based on qualitative textual analysis and comparative literary approach, this research investigates the ways in which these works describe environmental deterioration, human responsibility, and sustainability themes. Employing crucial environmental theory--deep ecology and posthumanism--the research investigates the ideological and thematic grounds for both novels. Carson's Silent Spring, widely recognized for initiating the contemporary environmental movement, critiques the rampant application of pesticides like DDT and contests human-centered views that favor economic development over ecological health. Her application of scientific data and convincing writing supports biodiversity preservation and public health, reflecting the fundamental ecological principle of nature's inherent worth. The research places Carson's work in the ecocritical framework, emphasizing literature's function as a means for environmental advocacy and public awareness. By contrast, Watkins' Gold Fame Citrus imagines a desolate future of climactic catastrophe and water scarcity in the American Southwest. Through the use of speculative fiction, Watkins critiques passivity in society and ecological disregard, exploring how climate change affects identity, agency, and social structures. The novel is centered on women's lives and vulnerabilities in a resource-scarce world and is in tune with ecofeminist concerns, while its post-humanist stance challenges traditional human-nature hierarchies and envisions different modes of ecological consciousness. The comparative analysis reveals that while Carson uses a scientific and factual approach to generate urgency and influence policy, Watkins uses narrative creativity to accentuate the long-term consequences of environmental neglect. Together, these works illustrate literature's capacity to educate and encourage action, linking scientific discourse, community engagement, and advocacy for policy reform. This study discovers that ecocriticism is critical in shaping ecological ethics, promoting interdisciplinarity, and driving sustainable approaches. In the face of mounting climate crises, literary narratives offer robust channels for driving environmental consciousness and inspiring profound change.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ecocriticism, Posthumanism, Anthropocentricism, Deep ecology, Environmental Literature, Sustainable Developmental Goals

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The exponential increase of social media has caused a full-size quantity of user generated content often prepared by using hashtags powerful evaluation of hashtags can display trends person sentiment and different treasured insights however figuring out related hashtags and grouping them meaningfully gives a unique task this task introduces a hashtag clustering system using herbal language processing nlp strategies with long quick-term memory lstm neural networks at its core to analyze and cluster similar hashtags based on semantic similarity lstm a recurrent neural network version is nicely ideal for this project because of its capacity to seize contextual statistics inside sequences of text making it perfect for knowledge and classifying hashtags in social media contexts the gadget approaches a fixed of hashtags makes use of lstm to extract contextual and semantic relationships and applies clustering algorithms to group hashtags with comparable meanings or contexts to make the answer handy and consumer-pleasant an internet interface is evolved using flask a lightweight python net framework this interface allows customers to input a set of hashtags view clustered outcomes and explore the underlying semantic institutions the aggregate of lstm and flask enables an green interactive and scalable device for hashtag clustering with packages in fashion analysis advertising strategies and social media tracking

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hashtag clustering , LSTM, NLP

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Green Grocery is a MERN stack-based digital platform developed to overcome key challenges in Indian agriculture, including middlemen exploitation, limited market access, and low digital literacy among farmers [5][7]. This study investigates the effectiveness of integrating modern web technologies to create an inclusive and accessible agri-commerce system. The platform facilitates direct farmer-to-vendor interactions through product listings, real-time chat, and secure payment integration using Razorpay [2][3]. It supports multilingual communication and voice-to-text input via Google APIs to accommodate users with low literacy levels [5][8]. A responsive React.js frontend and a secure Node.js backend ensures a smooth user experience and efficient data flow [7]. Additional features such as customizable delivery options, vendor feedback mechanisms, and an admin dashboard further enhance its functionality and manageability [3][4][6]. Analysis of the platform's impact demonstrates improved market accessibility, reduced dependency on intermediaries, and increased digital engagement among rural users [3][7][9]. The research concludes that Green Grocery provides a scalable and sustainable solution for bridging the rural-urban divide and fostering equitable growth in the agricultural sector [1][7].

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Agriculture, Digital Platform, MERN Stack, E-Commerce, Rural Development, Accessibility.

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Creative Commons Attribution 4.0 and The Open Definition