• Computed Tomography
• Convolutional Neural Networks

• Podcast

• Local News Aggregator

Explore an intelligent and adaptive virtual assistant implemented in Python, revolutionizing human-computer interactions. Leveraging natural language processing (NLP) and machine learning, this system excels in voice commands, chat interactions, and dynamic responses tailored to user preferences. Adapting based on user history, it integrates external APIs for diverse functionalities and ensures scalability for additional features. Evaluation metrics demonstrate its effectiveness in accurate interpretation, relevant information provision, and dynamic conversational adaptability. This research contributes to human-computer interaction, presenting a framework for intelligent virtual assistants using Python. With applications in smart home automation, personal productivity, and accessibility tools, it highlights Python's significance in creating adaptive virtual assistants.

• Human-Computer Interactions
• Natural Language Processing

This Python implementation introduces a car sound-based classification system utilizing Convolutional Neural Networks (CNN), implemented with TensorFlow and Keras. Trained on a diverse dataset, the CNN effectively distinguishes car-related sounds, displaying high accuracy. The system's robustness to environmental variations positions it for applications in automotive diagnostics, smart cities, and intelligent transportation. This research contributes to audio signal processing and machine learning, providing a scalable solution for categorizing car sounds across diverse settings, fostering advancements in automotive technologies.

• Convolutional Neural Networks
• TensorFlow

Exploring the realm of biometric authentication, this study delves into dorsal hand vein patterns as a distinctive and secure means of identity verification. Employing non-intrusive near-infrared sensors, dorsal hand vein images undergo sophisticated processing using advanced machine learning algorithms, including Convolutional Neural Networks (CNNs). The resulting authentication system undergoes rigorous training, validation, and evaluation using key performance metrics. Comparative analyses underscore the unique advantages of dorsal hand vein authentication, while addressing practical considerations such as user acceptability and ethical implications. This non-intrusive and forgery-resistant biometric method contributes significantly to the field of secure identification, finding potential applications in finance, healthcare, and secure facility access.

• Convolutional Neural Networks
• Dorsal Hand Vein

The Electric Vehicle (EV) market is rapidly expanding with the increasing demand for sustainable transportation options. However, one of the major challenges of owning an EV is finding charging stations while on the go. To address this issue, an Electric Vehicle Charging Station Finder application has been developed for EV owners. This application provides users with useful information such as station availability, slot booking, charging speed, and pricing. The app also allows users to reserve charging stations in advance and receive notifications when their vehicle is fully charged. With the Electric Vehicle Charging Station Finder app, EV owners can experience a hassle-free and convenient charging experience, making electric transportation more accessible and sustainable for all. In addition to the Electric Vehicle Charging Station Finder app, we also offers a platform for EV owners to share their charging stations with others.

• App Development
• Electric Vehicle

Mining helmets primarily protect against solid impacts, yet detecting hazardous gases is vital in deep mines. Improved security is imperative due to the severity of underground disasters such as gas explosions and inadequate lighting. Blame for accidents often rests on supervisors, emphasizing the need for better communication among miners, supervisors, and control stations. The proposed system aims to bolster mining safety by integrating a network into helmets. This network senses the miner's environment and transmits real-time data online via IoT. This enables the control station to monitor conditions and provide immediate assistance during emergencies. The system comprises an Arduino microcontroller, LCD, and buzzer for signaling coworkers during crises. Various sensors, including those for Gas, Humidity, Temperature, LDR, and IR, contribute to the system's functionality, with the IR sensor acting as a helmet removal sensor.

• Arduino Microcontroller
• IR sensor

In the battle against COVID-19, social distancing is vital. While we're encouraged to stay home, essential visits may be necessary. However, traditional doorbells can transmit the virus through touch. A solution is to convert them into contactless doorbells using IoT. An Arduino board and an Ultrasonic Sensor HC-SR04 are used to sense a person's presence and activate a Servo motor to press the doorbell switch. This setup is cost-effective and helps prevent virus spread. The Arduino microcontroller plays a central role, enabling a buzzer sound as well. This contactless doorbell minimizes physical contact, enhancing safety during the pandemic.

• Arduino Automation
• IoT Doorbell

Solar panels are increasingly popular for converting solar energy into electricity. They can operate as stand-alone systems or connect to the grid. Earth receives 84 Terawatts of solar power daily, while we consume 12 Terawatts. To maximize solar-to-electrical energy conversion, panels need to be positioned perpendicularly to the sun. This project aims to design an automatic tracking system that locates the sun's position and adjusts the solar panel accordingly. It utilizes photoresistors as sensors and comprises a light-sensing system, an Arduino microcontroller, gear motors, and the solar panel. The system is expected to boost energy output by up to 40% compared to static panels. The key component is the light-dependent resistor (LDR) connected to the Arduino, which continuously tracks the sun. Two servo motors reposition the solar panel in real-time to keep it facing the sun. This innovation offers an efficient and sustainable solution for meeting our increasing energy demands.

• Arduino Automation
• Light-Dependent Resistor (LDR)