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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Suliman Mohamed Fati
Prince Sultan University
Computer Science & IT Electrical & Electronics Engineering Engineering

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A smart fire detection system using iot technology with automatic water sprinkler Hamood Alqourabah; Amgad Muneer; Suliman Mohamed Fati
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp2994-3002

Abstract

House combustion is one of the main concerns for builders, designers, and property residents. Singular sensors were used for a long time in the event of detection of a fire, but these sensors can not measure the amount of fire to alert the emergency response units. To address this problem, this study aims to implement a smart fire detection system that would not only detect the fire using integrated sensors but also alert property owners, emergency services, and local police stations to protect lives and valuable assets simultaneously. The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the system. To get real-life data without putting human lives in danger, an IoT technology has been implemented to provide the fire department with the necessary data. Finally, the main feature of the proposed system is to minimize false alarms, which, in turn, makes this system more reliable. The experimental results showed the superiority of our model in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable.
Smart security door system using SMS based energy harvest Abdullah Hamas; Amgad Muneer; Suliman Mohamed Fati
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3410-3423

Abstract

Over the last decade, different studies have been conducted to increase security to identify sensor technology and provide alternative energy with other energy harvest techniques such as vibration energy harvester and sun energy harvester. There is no combinational approach to utilize the door to create energy and use it for security measures in the literature, making our system different and unique. This proposed system comprises the security and the energy harvest; the security section utilizes a motion detector sensor to detect intruders. For instance, the magnetic door lock type firmly locks the door, which can only open with a generated password. On the other side, the energy harvest section utilizes the door motion to generate electricity for the system, which solves power shortage and limited battery life issues. Moreover, this study includes a GSM module that allows authorized owners to receive a generated password as a security enhancement. This design mainly focuses on improving or optimizing the conventional security doors' overall performance as sliding door, panel door, or revolving door. The experimental results show the system efficiency in terms of power generation and the time needed to authenticate the property owner. Notably, the power generator can generate electricity more rapidly, while the needed time to receive the mobile device's security code is around 3.6 seconds.
Smart health monitoring system using IoT based smart fitness mirror Amgad Muneer; Suliman Mohamed Fati; Saddam Fuddah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.12434

Abstract

The smart fitness mirror proposed in this researchaims to provide the users with a platform to monitor their health and fitness status on a daily basis. The system employs a number of sensors to monitor the body mass index (BMI) and amount of body fat present in the user’s body. A weight scale consisting of four load sensors has been implemented to obtain the weight of the user whereas an ultrasonic sensor has been used to measure the height of the user. In addition, four electrode plates have been implemented on the foot weight scale to infuse a small amount of electric current (1mA) for BIA (bioelectrical impedance analysis) to estimate the amount of body fat percentage, lean body mass and total body water. An IR temperature sensor has been implemented in the research to measure the temperature of the user’s body from the forehead. Tests conducted on the system illustrate that it is able to accurately compute the body mass index and perform a bioelectrical impedance analysis on the user. The system is able to achieve a 92.5 % and 93.7 % accuracy in determining the body mass index and body fat percentage respectively. An accuracy of 95.3 % was observed in the determination of the body temperature.
BMSP-ML: big mart sales prediction using different machine learning techniques Rao Faizan Ali; Amgad Muneer; Ahmed Almaghthawi; Amal Alghamdi; Suliman Mohamed Fati; Ebrahim Abdulwasea Abdullah Ghaleb
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp874-883

Abstract

Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an essential element is to clear the missing data and perform proper feature engineering to better understand them before applying them. The experimental results show that the random forest predictor has outperformed ridge regression, linear regression, and decision tree models among the four machine learning techniques implemented in this study. The performance of the proposed models has been evaluated using root mean square error (RMSE).
Co-Authors Abdullah Hamas Ahmed Almaghthawi Amal Alghamdi Amgad Muneer Amgad Muneer Ebrahim Abdulwasea Abdullah Ghaleb Hamood Alqourabah Rao Faizan Ali Saddam Fuddah