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

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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.
Evaluating Machine Learning Models for Real-Time IoT Intrusion Detection: A Comparative Study with RTSS Analysis Ahmed Alwan; Asadullah Shah; Alwan Abdullah Abdul Rahman Alwan; Shams Ul Arfeen 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-8.2.1

Abstract

With the ever-increasing sophistication and volume of cyber-attacks, there is a critical need for effective intrusion Detection Systems (IDS) to protect computer networks. Machine Learning (ML) offers powerful tools for IDS by automatically identifying patterns of malicious behavior. This research proposal aims to evaluate and compare the performance of several supervised ML algorithms for network threat detection using the CICIDS 2023 dataset. This paper focuses on widely-used classifiers—logistic regression, Support Vector Machine (SVM), Random Forest, eXtreme Gradient Boosting (XGBoost), and k-Nearest Neighbors (KNN) – applied to both binary (benign vs. attack) and multi-class (multiple attack types) classification tasks. This paper outlines a methodology for data preprocessing, model training, and performance evaluation using metrics like accuracy, precision, recall, and F1-score. By leveraging the comprehensive CICIDS 2023 intrusion dataset, which includes 33 modern attack scenarios across seven categories, this paper expects to gain insights into the relative strengths of each ML approach in detecting diverse cyber threats. The anticipated outcome is an identification of which algorithms (or combination thereof) are most promising for intrusion detection in contemporary network environments, guiding future developments of intelligent IDS. This proposal details the problem motivation, related work, planned methodology, and expected results, establishing a foundation for a thorough experimental study.
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.
Evaluating Supervised Machine Learning Algorithms for Cybersecurity Threat Detection Using the CICIDS 2023 Dataset Ahmed Alwan; Asadullah Shah; Alwan Abdullah Abdulrahman Alwan; Shams Ul Arfeen Laghari
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-9.1.1

Abstract

With the increasing volume and sophistication of network threats in IoT environments, real-time intrusion detection has become essential for securing cyber-physical systems. This study investigates the use of supervised machine learning algorithms to detect network intrusions using the CICIDS 2023 dataset. Five classification models—Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and k-Nearest Neighbors—were evaluated for their effectiveness in both binary and multi-class classification tasks. The study incorporates feature selection, dimensionality reduction, and a deployment-oriented performance metric called Real-Time Suitability Score (RTSS) to assess the trade-off between accuracy, inference speed, and model size. The experimental results highlight the potential of lightweight models for deployment in constrained environments and demonstrate the impact of feature importance and classification performance on real-time detection. The findings contribute to the design of efficient and explainable AI-based intrusion detection systems, and recommendations for future work include improving model interpretability and expanding evaluation to more diverse threat categories.
Co-Authors Abdul Salam Shah Adil Maqsood Ahmed Alwan Alwan Abdullah Abdul Rahman Alwan Alwan Abdullah Abdulrahman Alwan Amar Dinesh Asif Ali Laghari Mirza Farooq Muhammad Adnan Kaim Khani Muhammad Usama Shahbaz Ali Khan Shahjahan Samoo Shams Ul Arfeen Laghari Syed Hyder Abbas