Browsing by Author "Sharmin, Farah"

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Humidity Based Automated Room Temperature Controller Using IOT
(Proceedings of the 3rd International Conference on I-SMAC IoT in Social, Mobile, Analytics and Cloud, IEEE, 2019-12-14) Sharmin, Farah; Moon, Nazmun Nessa; Hasan, Mohd. Saifuzzaman Abir; -Bin-Al-Beruni, Shakib; Hossain, Mohammad Alam; Nur, Fernaz Narin
This research proposed a peerless methodology and implementation for an automatic switching speed electric heater, and control room temperature. Before the use of recent intelligent technologies for achieving smart room heater and automation system, different kinds of relay depending analog circuitries were used. These circuits were mainly dependent on temperature and humidity sensors which provided those circuits the major functionalities. In this research, automation is achieved through using a microcontroller which facilitated auto room temperature controlling and toggle switching. The electric fan adjusts the speed dynamically depending on the variations in the temperature of the environment. This electrical hardware fan system includes a combination of sensor, controller, driver and motor with the incorporation of embedded guided programming. The system takes the temperature sensor data, passes it to the microcontroller and controls the AC heater in the output and displays the output status in the LCD screen. By automating all these processes, it is possible to control room temperature and humidity in according to the user's necessity.
IOT-NFC Controlled Remote Access Security and an Exploration through Machine Learning
(2020 18th International Conference on ICT and Knowledge Engineering (ICT&KE), IEEE, 2020-12-25) Khan, Md. Abbas Ali; Hanif Ali, Mohammad; Haque, A.K.M Fazlul; Sharmin, Farah; Jabiullah, Md. Ismail
Internet of Things (IOT) is a system that allows to connect the computing devices without the help of human-to-human or human-to-computer interaction. This paper proposes an app-based remote access control door lock security system (RACDLS) along with a short-range wireless communication naming Near Field Communication (NFC). The RACDLS system generates two sides' authentication systems rather than one side authentication like conventional systems. In the conventional system, users which are registered, can enter the premises only. In the proposed RACDLS system, users require permission from the room owner either they are registered or unregistered. Moreover, for maintaining the integrity and the confidentiality of data, a cryptographic technique (e.g.) we consider computational 512 bits hash function. In contrast, apply AES-192 for encrypting the hashed data. In addition, machine learning (ML) shows the performance of the employee activities including prediction with model accuracy. A definitive objective of this paper is to ensure the security of remote access control as well as allow notification of both ends, accessibility, usability, and permissibility of a personnel to enter the premises.
Prediction of Addiction to Drugs and Alcohol Using Machine Learning
(International Journal of Electrical and Computer Engineering, 2021) Arif, Md. Ariful Islam; Sany, Saiful Islam; Sharmin, Farah; Rahman, Md. Sadekur; Habib, Md. Tarek
Nowadays addiction to drugs and alcohol has become a significant threat to the youth of the society as Bangladesh’s population. So, being a conscientious member of society, we must go ahead to prevent these young minds from life-threatening addiction. In this paper, we approach a machine learning-based way to forecast the risk of becoming addicted to drugs using machine-learning algorithms. First, we find some significant factors for addiction by talking to doctors, drug-addicted people, and read relevant articles and write-ups. Then we collect data from both addicted and no addicted people. After preprocessing the data set, we apply nine conspicuous machine learning algorithms, namely k-nearest neighbors, logistic regression, SVM, naïve Bayes, classification, and regression trees, random forest, multilayer perception, adaptive boosting, and gradient boosting machine on our processed data set and measure the performances of each of these classifiers in terms of some prominent performance metrics. Logistic regression is found outperforming all other classifiers in terms of all metrics used by attaining an accuracy approaching 97.91%. On the contrary, CART shows poor results of an accuracy approaching 59.37% after applying principal component analysis.