Volume 13 | Issue 3 |

 Abstract

An electric vehicle (EV) is a transportation mode which is driven by one or multiple electric motors. Recently, there has been a rise in both public budget allocations and corporate investments in electric vehicles (EVs) with the aim of attaining a specific goal. Governments worldwide are implementing policies to encourage the adoption of electric vehicles, including financial incentives, regulatory measures, and investments in charging infrastructure. These initiatives aim to address environmental challenges, reduce dependence on traditional fossil fuels, and promote a sustainable and eco-friendly mode of transportation. This study examines the prevalence of e-vehicles over the past five years and forecasts estimated sales for future years, providing valuable insights to companies as they chart their course ahead. A significant upward trajectory has been observed in the sales of 2-wheeler electric vehicles over the past five years. While sales were relatively modest in the 2018-19 period, there were substantial increases noted in 2022 and 2023. Conversely, a decline in the trend was observed for 3-wheeler e-vehicles during the years 2019-20 and 2020-21. However, an increasing trend in sales was noticed in 2021-22 and 2022-23 for 3-wheeler e-vehicles. The sales of 4-wheeler e-vehicles have shown a consistent increase over time. Notably, the growth rate of 2-wheeler and 4-wheeler e-vehicles during the study period exhibited significant increases.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Electric vehicle, Sales, Trend, Forecast

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A Multifunctional Smart Mirror for Home Security and Daily Updates depicts the design and development of a smart mirror that offers an elegant interface for displaying essential information while also functioning as a thief detection system in a home environment. This smart mirror operates as a traditional reflective surface but is enhanced with the capability to display real-time data, including the current date, time, temperature, and weather details. A two-way mirror is used to seamlessly integrate the display and hide the webcam, allowing for a clean and modern look while still capturing footage for home security. The Raspberry Pi connects web usage to an IoT device via a Wi-Fi module, enabling the mirror to receive and display real-time data. Additionally, the webcam and IR sensors are employed for detecting unauthorized entry, ensuring home security when the occupants are away. This innovative solution combines aesthetic appeal with practical functionality, making it an ideal addition to modern smart homes.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart Mirror, Home Security, Real-Time Updates, IoT, Raspberry Pi, Two-Way Mirror, Thief Detection, Web Camera, IR Sensors, Modern Smart Homes

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Automated Attendance Management System using Raspberry Pi leverages facial recognition technology to streamline attendance tracking in educational institutions and workplaces. The system integrates Python, OpenCV, and Haar Cascade with LBPH for detecting and recognizing faces in real time. Attendance data is logged in a CSV file and sent to authorized users at the end of the day. This system provides a reliable, efficient, and error-free alternative to traditional and technology-based attendance tracking methods. It minimizes manual effort and prevents proxy attendance, offering a cost-effective and scalable solution for attendance management.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Raspberry Pi, Facial Recognition, Automated Attendance, Python, Image Processing, Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Text description-to-3D object generation We integrate natural language processing (NLP), long short-term memory (LSTM) networks and generative adversarial networks (GANs) to automate text description-to-accurate and aesthetically appealing three-dimensional (3D) models. NLP methods, including named entity recognition and semantic analysis, process and extract meaningful features from the text. LSTM networks analyze the sequential nature of the text and capture contextual relationships and dependencies. The processed features are used by GANs, where the generator constructs 3D models, and the discriminator ensures their accuracy and realism. This method overcomes the difficulties of manual 3D modelling by providing a scalable and efficient solution. Potential applications include virtual reality, gaming, architecture and cultural heritage preservation. The integration of NLP and Ai driven techniques shows a significant improvement in automating the generation of 3D models from text.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

: Natural Language processing, objects, Generative Adversarial network, model, generative AI,3D features.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Work-life balance for women employees has turned out to be an increasingly important topic in modern workplaces. This study attempts to identify the variables affecting women's work-life balance in the textile industry, which is recognized for its hard working conditions and historically male-dominated workforce. This study also tries to shed light on the particular difficulties experienced by women workers in achieving work-life harmony through a structured questionnaire. To understand their impact on work-life balance outcomes, important aspects such job demands, family responsibilities, organizational support, and individual coping mechanisms will be investigated. By pointing up obstacles and enablers to work-life balance and making practical suggestions, the objective of this research is to advance women employees well-being in the textile industry through organizational interventions.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Work-life balance, Women, Harmony, Textile industry

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The increasing reliance on digital learning platforms and online education has brought attention to the need for prediction models that assess student progress using metrics other than traditional academic ones. This work intends to fill gaps in the literature by creating a data-driven model to predict student achievement based on behavioral patterns, ignoring elements linked to psychology and engagement Social connections, attendance, study habits, and internet participation are among the behavioral data about students that were collected from surveys and learning management systems. The suggested method combines XGBoost for high-performance feature selection, SHapley Additive exPlanations (SHAP) for interpretability, and Long Short-Term Memory (LSTM) networks for capturing temporal relationships in student behavior. Results show that student achievement is substantially predicted by time management, social interaction, and study consistency. Whereas behavioral knowledge is integrated into forecasting models, performance accuracy is found to be much enhanced by the study. Practical applications encompass approaches like evidence-based education policy, learning intervention based on learning tailored to each student, and early warning for children at risk. Student-centered instructional practices are supported in this study through a link with academic achievement as provided by behavioral analytics.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Student Performance Prediction, Behavioral Patterns, Educational Data Mining, LSTM, XGBoost, SHAP, Learning Analytics, Online Education, Predictive Modeling, Student Engagement.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

ABSTRACT: The creation of potent antimicrobial drugs has become necessary due to the rising incidence of skin infections. The purpose of this study was to assess the antibacterial activity of herbal soap formulations that included Cardiospermum halicacabum and Cassia alata. The plant extracts were used in varying amounts to create three soap compositions (F1, F2, and F3). Physical properties, antibacterial activity, and risk for skin irritation were assessed for the formulations. The findings demonstrated that F3, which had the highest concentration of extracts from Cassia alata and Cardiospermum halicacabum, had the best antibacterial action against Candida albicans, Staphylococcus aureus, and Escherichia coli. Additionally, F3 showed good physical properties, such as pH, consistency, and foam retention. Skin irritation was not observed in any of the formulations. According to the study's findings, the herbal soap formulation F3 may be a useful antibacterial agent for shielding the skin from germs.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords:Herbal soap, Cassia alata, Cardiospermum halicacabum, antimicrobial activity, skin infections.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research explores how the emergence of self-flying agricultural drones has revolutionized precision farming, enhancing both efficiency and environmental sustainability. It presents a newly developed smart pesticide-spraying drone that integrates artificial intelligence, GPS, IoT, and ArduPilot navigation to apply chemicals precisely where they are needed. The drone autonomously manages all tasks--navigating, mapping farmland, and optimizing spray patterns to minimize pesticide waste and ensure safety for people. With a user-friendly interface, even those with minimal technical skills can operate it effortlessly. The system is equipped with a Pixhawk PX4 2.4.8 flight controller, brushless motors, a LiPo battery, and a high-quality spraying mechanism, all designed to effectively address real agricultural challenges. Field trials demonstrated that it provides more accurate spraying, reduces costs, and enhances crop health. This innovative solution offers a practical and scalable approach to automating farming, paving the way for more sustainable and eco-friendly practices.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

esp32, sensors, greenlight, IoT

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

"Farm Expert" is a platform designed to assist farmers in crop management and agricultural productivity optimization. Using machine learning algorithms like Random Forest, SVM, and deep learning, it provides real-time recommendations for crop selection, fertilizer application, and early disease detection. The platform integrates soil quality data, crop images, and environmental conditions to offer personalized insights tailored to individual farms. Addressing key challenges like inefficient crop monitoring, delayed disease detection, and improper fertilizer use, "Farm Expert" helps farmers make data-driven decisions to improve yields and sustainability. The system's web-based interface ensures ease of access and scalability for different agricultural environments. By analyzing data patterns, it predicts crop diseases, recommends optimal fertilizer usage, and forecasts yield potential.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine learning, Real-time recommendations, Disease detection, Sustainability

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Eye Movement-Based Robot Controlling System is designed to assist individuals with physical disabilities by enabling hands-free robot control through eye movements. This system utilizes a USB camera connected to a Raspberry Pi, which captures and processes real-time eye movements using Python libraries such as OpenCV and Dlib. The system detects directional cues left, right, up, and down and maps them to corresponding robotic actions, such as moving forward, backward, or turning. These commands are then transmitted to a motor driver module to control the robot's movement. The project integrates computer vision, IoT, and robotics to provide an accessible and cost-effective solution for individuals with limited motor abilities. The methodology involves image processing for pupil detection and gaze tracking, signal interpretation, and motor control. Testing demonstrated the system's accuracy in detecting eye movements and translating them into seamless robotic navigation. Results highlight its potential in assistive technology, offering a practical alternative for people with disabilities to interact with their environment. The system emphasizes advancements in human-computer interaction ,presenting a reliable and efficient approach to robotic control using gaze-based inputs. Future improvements could include enhanced tracking precision and integration with machine learning for adaptive responsiveness.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Eye Movement Control, Assistive Robotics, Human-Computer Interaction, IoT-Based Robot Control, Raspberry Pi, OpenCV, Dlib, Computer Vision, Gaze Tracking, Pupil Detection, Hands-Free Navigation

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Creative Commons Attribution 4.0 and The Open Definition