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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 Keywords

AI/ML, Python, Frameworks, etc.

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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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 Keywords

Cnn,lstm, XGboost,EEG

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

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 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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 Keywords

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

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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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SEIR, Jacobian, Goa, Malaria, Infected migrants

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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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 Keywords

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

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 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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 Keywords

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

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 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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 Keywords

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

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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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 Keywords

Hashtag clustering , LSTM, NLP

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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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 Keywords

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

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The major purpose of this article is to examine and analyzes how the psychological profile of investors influences green bonds' demand, price, and performance in terms of sustainable development. A green bond is a specific form of financial instrument meant for financing projects that impact the environment positively, such as renewable energy, clean transport, and resource conservation. Because the global market is rising, it is increasingly becoming relevant for both issuers and investors to understand the psychological forces at work behind the investment decisions.

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Green Bond Investment, Sustainability, Investor Perception, Financial Decision-Making, Environmental Consciousness, Risk and Return Trade-off

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This research paper presents an innovative real-time anti-sleep alert and vehicle control system designed to enhance road safety. The system integrates a drowsiness detection module using an eye-blink sensor, a vibration sensor, and an Arduino microcontroller, along with an automatic engine lock mechanism to prevent accidents caused by driver fatigue. When signs of drowsiness are detected, an immediate alarm is triggered, followed by gradual engine control protocols to halt the vehicle safely if the driver remains unresponsive. Experimental results show high accuracy in drowsiness detection and reliable vehicle immobilization, offering a scalable, low-cost solution for reducing road accidents due to driver fatigue.

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 Keywords

Driver drowsiness detection, Eye blink sensor, Vehicle safety system, Anti-sleep alarm, Engine lock mechanism, Arduino-based system, Road accident prevention.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The reproductive choices of Intellectual and Developmental Disabilities (IDD) are matter of concern, particularly as women with intellectual disability have often not been allowed to take decision regarding their reproduction. However equal accessibility to reproductive rights and related services remains a challenge for them in India. The paper seeks to explore the prevailing stereotypes and societal attitudes surrounding parenting, sexuality, and reproductive autonomy that lead to a higher likelihood of sterilization for women with disabilities compared to their non-disabled counterparts. Further it discusses the rights of disabled women in India with reference to their reproductive rights including the area of mental illness and their consent for termination of pregnancy, highlighting the legal, social and institutional challenges they encounter. Women with intellectual disabilities are more vulnerable and likely to experience infringement of their sexual and reproductive rights including pregnancy due to sexual offences, acquisition of sexual transmitted infections, forced sterilisation and unsafe abortions, poorly managed pregnancy, and unjust termination of parental right. The paper also examines the legal and practical dimensions of consent in reproductive decisions among women with IDD in India, highlighting the urgent need to respect their autonomy. Lastly the research aims to contribute to a policy framework that upholds their rights, dignity, and reproductive independence.

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Reproductive Rights, Disability, Consent; Mental Health, Medical Termination Pregnancy, Intellectual and Developmental Disability.

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 Abstract

Night awakening or Ratrijagaran is a phenomenon that involves extended wakefulness during nighttime hours, a practice influenced by cultural, social and individual factors. This research project delves into the multifaceted effects of Ratrijagaran on human health, behavioural and psychology, integrating insights from modern sleep science and Ayurvedic principles. The study examines the psychological consequences of Ratrijagaran including circadian rhythm disruption, alterations in cortisol levels and the onset of sleep disorders such as insomnia. Parallelly, the Ayurvedic perspective highlights Vataand Pitta imbalances caused by irregular sleep cycles, which are linked to mental fatigue, digestive issues and compromised immunity. To ground these theoretical insights, a comprehensive survey was conducted, encompassing questions about general sleep patterns, the frequency of Night awakening and it's perceived effects on physical and mental health. The data revealed trends in how nocturnal habits influence cognitive performance, emotional stability and overall well-being. This approach ensures a comprehensive understanding of both the short term and long term consequences of this practice. Through this project, we aim to provide insights into the coping mechanisms employed by individuals, ranging from meditation and structured sleep schedules to nutritional adjustments. By harmonizing scientific research and traditional wisdom, this study not only highlights the adverse effects of Ratrijagaran but also provides actionable recommendations for mitigating it's impact. The findings aim to foster awareness and encourage the adoption of balanced practices that support holistic health in a fast paced world.

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Ratrijagarana-Impact & Implication

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With growing concerns over environmental pollution and the depletion of fossil fuel resources, there has been an increasing shift towards alternative fuels. Compressed Natural Gas (CNG) stands as one of the most promising solutions due to its environmental benefits, lower cost, and abundant availability. This paper presents the design and fabrication of a CNG conversion kit for existing gasoline-powered motorcycles. The goal is to provide a cost-effective and sustainable alternative to gasoline-powered motorcycles, reducing both environmental impact and operational costs. The research discusses the key components, design considerations, installation process, and performance evaluation of the CNG kit, followed by an analysis of its impact on fuel efficiency, emissions, and overall functionality.

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CNG Bike, CNG, two wheeler, fuel, Natural gas, Research, CNG as Alternative Fuel.

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Jaypala (Croton tiglium Linn.), a traditional medicinal plant widely used in Ayurveda, holds significant therapeutic potential due to its strong purgative properties. Despite its benefits, the plant is classified as Upavisha (semi-poisonous) because of its inherent toxicity, primarily found in its seeds. This research explores the traditional and scientific approaches to detoxifying Jaypala seeds through Shodhana (purification) methods, which significantly reduce its harmful effects. The word Upavisha means nearer to Visha i.e. drugs which possess the same qualities of visha, but not that much potent. Jayapala (Croton tiglium) is one among the Upavisha and a well-known plant in Indian System of Medicine as certain Number of formulations include this drug as an ingredient after proper purification. Also it is one of the known purgative Drug in Ayurveda with huge therapeutic values. This review article includes overall information about the plant Jayapala, Its botanical description, Toxicological aspect, treatment medico legal aspects in both Ayurveda and Modern toxicology, Its Shodhana (purification) processes.

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Jaypala, Croton tiglium, Purgative, Crotin.

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 Abstract

Ab

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Ab

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Abstract I think therefore, I am, said, Rene Descartes, (1637) a French philosopher. As humans, we are thinking beings or we can consider ourselves thinking animals. Our living state is determined by the quality of our faculty of thinking. Our quality of life depends on our quality of thinking. A critical thinking skill is a skill which generates new methods, concepts, understandings, inventions and works of arts. This paper is to present a Critical Thinking a Key Skill for Higher Secondary School Students of Jharkhand on the basis of Gender, Location, School Board, Medium of Instruction and Type of Family. The population consists of Higher Secondary School Students in Ranchi, district of Jharkhand. The sample consisted of 1000 higher secondary school students. The investigator adapted the survey method. Self-constructed and validated tool to establish the validity and reliability of the tool. Mean, standard deviation and t-test was used to analyses the data to infer findings. The results revealed that there was a significant difference between mean scores of higher secondary school students of Jharkhand in their Critical Thinking Skills on the basis of gender, habitation, type of school whereas there was no significant difference between mean scores of higher secondary school students of Jharkhand in their Critical Thinking Skills on the basis type of board.

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Critical Thinking, Key to Outshine, Higher Secondary School, Jharkhand

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Smart Feast is an innovative AI-powered food waste management system designed to monitor and reduce food waste in real-time. Utilizing a Raspberry Pi as the central processing unit, the system integrates a variety of sensors, including the DHT11 for temperature and humidity monitoring, the MQ3 for detecting bad odors, and the LDR for light condition assessment. The ADC module ensures accurate sensor data collection. In the event of abnormal conditions, the GSM module sends timely alerts, while a buzzer provides an immediate auditory warning. The system employs a Random Forest machine learning algorithm to analyze sensor data and make intelligent decisions regarding the status of food waste.

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 Keywords

Food Quality Detection, Machine Learning (ML), Artificial Intelligence (AI), IoT-enabled Sensors, Predictive Analytics, Food Safety Management, Real-time Monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

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Sign language is the primary mode of communication for speech and hearing-impaired individuals. However, understanding sign language is often challenging for those unfamiliar with it. This project aims to bridge the communication gap between sign language users and non-signers through an automated real-time sign language interpretation system. The proposed system utilizes Python programming with OpenCV for video processing and deep learning algorithms like YOLOv5 and Convolutional Neural Networks (CNN) for gesture recognition. The system captures real-time video input using a webcam, detects hand gestures, classifies them, and converts them into readable text instantly on the screen. This system can recognize various hand gestures and interpret alphabets or words based on the sign shown. The accurate and real-time translation of sign language will make communication easier and more effective for deaf and mute individuals, promoting inclusivity in daily life. Future enhancements may include voice output, support for multiple sign languages, and mobile-based applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sign Language Recognition, Real-Time Interpretation, Speech and Hearing Impaired, Python Programming, OpenCV, YOLOv5, Convolutional Neural Networks (CNN), Gesture Detection, Gesture Classification, Inclusive Communication, Hand Gesture Recognition, Automated Translation System

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