Volume 13 | Issue 4 |

 Abstract

Sewage treatment plants are vital infrastructures that safeguard public health and protect the environment by treating and purifying wastewater. The design of these plants plays a crucial role in ensuring efficient and effective treatment processes. This research paper focuses on the design principles and considerations for sewage treatment plants and explores various methods for improving existing facilities. By implementing innovative design strategies and adopting optimization techniques, wastewater treatment plants can enhance their performance, increase treatment capacity, and achieve higher levels of environmental sustainability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sewage treatment plant, wastewater, design principles, optimization, environmental sustainability.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Mental health disorders, particularly depression, are on the rise due to factors like excessive social media use, stress, and unhealthy lifestyle habits. To tackle this issue, we've developed a groundbreaking system that combines artificial intelligence, natural language processing, and personalized insights to provide real-time mental wellness support. Our Deep Learning-Powered Holistic Mental Health and Wellness Analyzer is designed to assess user mood, detect depression symptoms, and offer tailored recommendations. Using advanced sentiment analysis and emotional intelligence, our system achieves an impressive 98.9% accuracy in depression detection. But that's not all. Our platform also includes a personalized nutrition module that explores the link between diet and mental well-being, as well as Fit-Harmony music therapy, which creates customized playlists to promote relaxation and balance. With accuracy rates of 78.5% in mood-based music recommendations and 82.3% in dietary suggestions, our system provides users with a comprehensive and supportive mental health journey. By harnessing the power of machine learning, predictive analytics, and real-time monitoring, our platform offers an interactive and user-friendly mental health tracking experience.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep Learning, Mental Health Analysis, Depression Detection, Sentiment Analysis, Natural Language Processing (NLP), Recurrent Neural Network (RNN), Two-State LSTM (TS-LSTM), Support Vector Machine (SVM), Emotion Classification, Personalized Mental Health Recommendations, Fit-Harmony Music Therapy, Personalized Nutrition Insights, Machine Learning in Healthcare, Mental Wellness Companion, Healthcare Data Analysis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Low back pain is a significant and common public health issue. Low back pain (LBP) is a public health issue that has reached epidemic proportion. Most of patients suffering from back pain practice yoga. Astang yoga comprised of eight limbs Yama (rule of moral conduct),Niyama(rule of personal conduct),Asana(postures),Breath control(pranayama),Pratyahara(sense withdrawal),Dharma(concentration),Dhyana(meditation)and Samadhi(self-realization).out of many style of yoga, Iyenger Yoga has applied yoga pose emphasis on precise structural alignment and has a good result in low back pain.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Back pain; Yoga; Stiffness and Pain

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Creative Commons Attribution 4.0 and The Open Definition

 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.

Licence: creative commons attribution 4.0

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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.

Licence: creative commons attribution 4.0

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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

Licence: creative commons attribution 4.0

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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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Creative Commons Attribution 4.0 and The Open Definition

 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.

Licence: creative commons attribution 4.0

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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.

Licence: creative commons attribution 4.0

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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.

Licence: creative commons attribution 4.0

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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