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All Journal Journal of ICT, Design, Engineering and Technological Science
Adil Maqsood
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Journal : Journal of ICT, Design, Engineering and Technological Science

 1 
Smart Gas Leak Detection And Emergency Response System Using Iot For Homes Abdul Salam Shah; Amar Dinesh; Asadullah Shah; Mirza Farooq; Adil Maqsood; Muhammad Adnan Kaim Khani
Journal of ICT, Design, Engineering and Technological Science Volume 8, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-8.1.3

Abstract

As we know, safety is a massive problem in this world today. We can use technology to combat the issue of safety. One of the safety issues is gas leakage, which caused the accident. In this project, we design and develop a system that is based on IoT and detects and monitors gas leakage in real time in homes and small businesses. The project uses NodeMCU as a microcontroller, gas sensors, and other devices like the Wi-Fi module, servo motor, and exhaust fans. This project shows how to integrate different hardware components and hardware with software. The traditional gas detectors found in the market can only alert the user through audio and visual alerts that are only viable if a person is present to combat the issue; what this project does is not only alerts using audio and video, it also alerts the user and the emergency department using a notification sent to an application in the mobile phone. The integration of the app not only increases user interface experience and responsive time but also allows the user to adjust the system's parameters through the app and gives real-time status to prevent accidents; the project also deploys prevention measures such as opening the window, turning on the exhaust, and shutting off the main gas valve to avoid chances of fireand damage.
Intelligent Vehicle Number Plate Recognition System Using Yolo For Enhanced Security In Smart Buildings Muhammad Adnan Kaim Khani; Muhammad Usama; Abdul Salam Shah; Asadullah Shah; Syed Hyder Abbas; Adil Maqsood; Asif Ali Laghari
Journal of ICT, Design, Engineering and Technological Science Volume 8, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-8.2.3

Abstract

The demand for advanced security solutions has increased with the continuous growth of urban infrastructure; hence, automated surveillance systems are vital across universities, hospitals, and commercial spaces. This project proposes an end-to-end Automatic Number Plate Recognition (ANPR) system to identify vehicle license plates by capturing high-speed images under optimal lighting conditions, isolating and analyzing plate characters, and translating the visual data into machine-readable text. By deploying these models on embedded systems, the system uses Convolutional Neural Networks (CNNs) and YOLO (You Only Look Once) for real-time object detection and recognition. The solution leverages the power of edge computing to achieve high performance and low latency for effective vehicle monitoring, data logging, and enhancing overall security infrastructure in buildings.
Enhancing Residential Safety and Comfort Through Smart Home Security and Automation Technologies Shahbaz Ali Khan; Shahjahan Samoo; Abdul Salam Shah; Adil Maqsood; Muhammad Adnan Kaim Khani; Asadullah Shah
Journal of ICT, Design, Engineering and Technological Science Volume 8, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-8.2.5

Abstract

In the digital era, technology is changing rapidly, and humans are trying to make lives easier, but it brings a new challenge: security. Computer programs or developed hardware can be compromised if not appropriately designed or because of the simple mistakes of an authorized person. The project aims to secure a home using face recognition to unlock the doors and alarm in an emergency. The home security automation technology uses a wireless network to support the alarm and deactivation requirements. The face detection unit uses an internetconnection via an ESP32 CAM; the primary controlled systems are utilized with Wi-Fi technologies. ESP32 manages home electronic appliances and camera devices, featuring a cost-effective structure, easy-to-use interface, and simple deployment. In this project, the system primarily fulfills home security demands using face-detection gadgets, utilizing a controller with a camera. The device can manage a high-power scoring load using security locks.
Utilizing a Hybrid Deep Learning Architecture For Salat Posture Detection Abdul Salam Shah; Farhan Akbar; Muhammad Adnan Kaim Khani; Adil Maqsood; Fahad Shah Bukhari
Journal of ICT, Design, Engineering and Technological Science Volume 9, Issue 1
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-9.1.3

Abstract

A lot of Muslims have trouble getting their daily prayers right. You know, Salat with the movements and the recitations. It disrupts their religious duties. They do not get quick feedback on how their form looks. So we put together this system. It grabs images right as they happen. Then it checks them out using a convolutional neural network. That is CNN for short. It spots and confirms the basic postures in Salat. The thing covers six main positions. Takbir. Qiyam. Ruku. Sujood. Tashahhud. And Salam. Pretty much opens it up for tons of people to use. We tested how well it works. Looked at pose detection accuracy. Response time, too. And what users thought about it. Turns out the system helps a bunch. Folks can improve their Salat quality with it. Shows how computer vision and deep learning fit into something like this. Not your usual setup.
Co-Authors Abdul Salam Shah Amar Dinesh Asadullah Shah Asif Ali Laghari Fahad Shah Bukhari Farhan Akbar Mirza Farooq Muhammad Adnan Kaim Khani Muhammad Usama Shahbaz Ali Khan Shahjahan Samoo Syed Hyder Abbas