Browsing by Author "Shakir, Asif Khan"

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A Comparative Analysis of Four Classification Algorithms for University Students Performance Detection
(Lecture Notes in Electrical Engineering, Springer, 2020-03-24) Das, Dipta; Shakir, Asif Khan; Rabbani, Md. Shah Golam; Rahman, Mostafijur; Shaharum, Syamimi Mardiah; Khatun, Sabira; Fadilah, Norasyikin Binti; Qaiduzzaman, Khandker M.; Islam, Md. Shariful; Arman, Md. Shohel
The student’s performance plays an important role in producing the best quality graduate who will responsible for the country’s economic growth and social development. The labor market also concerns with student’s performance because the fresh graduate students are considered as an employee depends on their academic performance. So, identification of the reason behind student’s performance variation provides valuable information for planning education and policies. Many researchers try to find out the reason with different types of data mining approaches in different countries. However, none of them worked with Bangladeshi students. This paper proposed a model for identifying the key factors of variation Bangladeshi students’ academic performance and predicts their results. This paper proposes a model which able to identify the students who need special attention. Different types of feature selection methods were used such as Co-relation, Chi-Square and Euclidean distance to select valuable features and feature selections result through decision tree, Naive Bayes, K-nearest neighbor and Artificial Neural Network classifiers algorithm were compared. The performance analysis is done by using student SGPA and review on given facilities from a university. From the performance analysis result it is found that, decreasing number of classes in dataset, the Artificial Neural Network (ANN) (93.70%) performs better than Decision Tree (DT) (92.18%), K-Nearest Neighbors (KNN) (77.74%) and Naïve Bayes (NB) (68.33%). However, an increasing number of classes in dataset the DT perform better than ANN, KNN, NB.
Aspect Based Sentiment Analysis in Bangla Dataset Based on Aspect Term Extraction
(Springer, 2020-07-30) Haque, Sabrina; Rahman, Tasnim; Shakir, Asif Khan; Arman, Md. Shohel; Biplob, Khalid Been Badruzzaman; Himu, Farhan Anan; Das, Dipta; Islam, Md Shariful
Recent years have seen rapid growth of research on sentiment analysis. In aspect-based sentiment analysis, the idea is to take sentiment analysis a step further and find out what exactly someone is talking about, and then measuring the sentiment if she or he likes or dislikes it. Sentiment analysis in Bengali language is progressing and is considered as an important research interest. Due to scarcity of resources like proper annotated dataset, corpora, lexicon such as part of speech tagger etc. aspect-based sentiment analysis hardly has been done in Bengali language. In this paper, we have conducted our experiments based on a recent work from 2018 using conventional supervised machine learning algorithms (RF, SVM, KNN) to perform one of the ABSA’s tasks - aspect category extraction. The work is done on two datasets named – Cricket and Restaurant. We then compared our results with the existing work. We used two traditional steps to clean data and found that less preprocessing leads to better F1 Score. For Cricket dataset, SVM and KNN performed better, resulting F1 score of 37% and 27%. For Restaurant dataset, RF and SVM achieved improved score of 35% and 39% respectively. Additionally, we selected two more algorithms LR and NB, LR achieved best F1 score (43%) for Restaurant dataset among all.
Bangladeshi Stock Price Prediction and Analysis with Potent Machine Learning Approaches
(Springer, 2020-07-30) Das, Sajib; Arman, Md. Shohel; Hossain, Syeda Sumbul; Islam, Md. Sanzidul; Himu, Farhan Anan; Shakir, Asif Khan
Stock price forecasting, is one of the most significant financial complexities, since data are not reliable and noisy, impacting many factors. This article offers a machine learning model for the stock price prediction using Support Vector Machine-Regression (SVR) with two different kernels which are Radial Basis Function (RBF) and linear kernel. This study shows the Prediction and accuracy comparison between Support Vector Regression (SVR) and Linear Regression (LR) and also the accuracy comparison for different kernels of Support vector Regression (SVR). The model has used sum squared error (SSE) to determine the accuracy of each algorithm; which has shown significant improvement than the other studies. This analysis is conducted on the price data of about five years of Grameenphone listed on Dhaka Stock Exchange (DSE). The highest accuracy was found with Linear Regression model in every case with the highest accuracy of about 97.07% followed by SVR (Linear) model and SVR (radial basis function) model with the highest accuracy rate of about 97.06% and 96.82%. In some cases the accuracy of SVR (radial basis function) was higher than SVR (linear). But it was the Linear Regression which had the highest accuracy of all in every case.
Detection and Classification of Road Damage Using R-CNN and Faster R-CNN
(Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, Springer, 2020-07-30) Arman, Md. Shohel; Hasan, Md. Mahbub; Sadia, Farzana; Shakir, Asif Khan; Sarker, Kaushik; Himu, Farhan Anan
Road surface monitoring is mostly done manually in cities which is an intensive process of time consuming and labor work. The intention of this paper is to research on road damage detection and classification from road surface images using object detection method. This paper applied multiple convolutional neural network (CNN) algorithm to classify road damage and discovered which algorithm performs better in road damage detection and classification. The damages are classified in three categories pothole, crack and revealing. For this research data was collected from street of Dhaka city using smartphone camera and prepossessed the data like image resize, white balance, contrast transformation, labeling. This study applies R-CNN and faster R-CNN for object detection of road damages and apply Support Vector Machine (SVM) for classification and gets a better result from previous studies. Then losses are calculated using different loss functions. The results demonstrate the highest 98.88% accuracy and the lowest loss is 0.01.
Medicine Prediction Based on Doctor’s Degree
(Daffodil International University, 2022-05-05) Arman, Md Shohel; Sarker, Kaushik; Shakir, Asif Khan; Hossain, Shah Fahad; Hasan, Afia
The effective use of information mining in profoundly unmistakable fields like e - business, promoting and retail has prompted its application in different enterprises. There is an absence of powerful investigation devices to find concealed connections and patterns in information. This examination paper expects to give a review of ebb and flow systems of learning reve lation in databases utilizing information mining strategies that are being used in today’s therapeutic research especially in medicine prediction. Correlation, Chi - square and Euclidean distance feature selections are used to select features and showing the comparison of the result between K - Nearest neighbors, Naïve Bayes, decision tree, artificial neural network . The result uncovers that decision tree beats and sometime Bayesian grouping is having comparative precision as of choice tree. The analysis of per formance can be done in such as doctor’s degrees may vary the diseases medicine.