Browsing by Author "Rezaul, Karim Mohammed"

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A Weighted Scoring Based Rating Scale to Identify the Severity Level of Mathematics Anxiety in Students
(Scopus, 2021) Tamal, Maruf Ahmed; Akter, Rabia; Hossain, Syed Akhter; Rezaul, Karim Mohammed
The purpose of the current study was to develop an effective scale that can be used to assess the severity level of Mathematics Anxiety (MA) among students. Generally, measures for assessing MA adopt primitive questionnaires and unweighted rating-scale based approaches which are predominantly intended for a particular range of students. As a consequence, this type of approach is inherently static and not effective to be used widely. To bridge this gap, considering the view of 839 students, the present study has proposed a Weighted Scoring Based Mathematics Anxiety Rating Scale (WSB-MARS) which represents a more reliable, valid, generalized, and new approach to assess the severity level of mathematics anxiety in students. Besides, the proposed scale can be implemented as a mobile application that is applicable to the research & education field.
Heart Disease Prediction Based on External Factors
(International Journal of Advanced Computer Science and Applications, 2019) Tamal, Maruf Ahmed; Islam, Md Saiful; Ahmmed, Md Jisan; Aziz, Md. Abdul; Miah, Pabel; Rezaul, Karim Mohammed
Technology has immensely changed the world over the last decade. As a consequence, the life of the people is undergoing multiple changes that directly have positive and negative effects on health. Less physical activity and a lot of virtual involvements are pushing people into various health-related issues and heart disease is one of them. Currently, it has gained a great deal of attention among various life-threatening diseases. Heart disease can be detected or diagnosed by different medical tests by considering various internal factors. However, this type of approach is not only time-consuming but also expensive. Concurrently, there are very few studies conducted on heart disease prediction based on external factors. To bridge this gap, we proposed a heart disease prediction model based on the machine learning approach which enables predicting heart disease with 95% accuracy. To acquire the best result, 6 distinct machine learning classifiers (Decision Tree, Random Forest, Naive Bayes, Support Vector Machine, Quadratic Discriminant, and Logistic Regression) were used. At the same time, sklearn.ensemble. Extra Trees Classifier has been used to extract relevant features to improve predictive accuracy and control over-fitting. Findings reveal that Support Vector Machine (SVM) outperforms the others with greater accuracy (95%)
Internet of Things-Driven Precision in Fish Farming: A Deep Dive into Automated Temperature, Oxygen, and pH Regulation
(2024-10-12) Nayoun, Md. Naymul Islam; Hossain, Syed Akhter; Rezaul, Karim Mohammed; Noor e Alam Siddiquee, Kazy; Shabiul Islam, Md.; Jannat, Tajnuva
The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply.