http://dspace.ewubd.edu:8080/handle/2525/2937 2026-04-05T23:11:14Z 2026-04-05T23:11:14Z Memi, Afsana Azad Sultana, Nasrin Tabassum, Kanij http://dspace.ewubd.edu:8080/handle/2525/3029 2019-04-01T10:29:26Z 2018-09-17T00:00:00Z

Food and Formalin Detector Using Machine Learning Approach Memi, Afsana Azad; Sultana, Nasrin; Tabassum, Kanij Unethical use of formalin, in the preservation of food items posing threat to public health. Without chemical experts accurately Formalin detection is a time consuming and complicated task. Moreover, the presence of naturally occurring formalin in food items may interfere in detecting artificially added formalin. Purpose of the study was to develop a simple cost-effective and reliable detection technique that can detect contaminated food. Therefore, quantifying artificially added formalin and naturally formed extent it is important to dynamically detect food for the comparison. With this view in mind, we have applied different machine learning algorithms like Naïve Bayes, Logistic regression, Support Vector Machine, K-NN Classifier on fruit’s feature dataset to build a predictive model. We found that the K-NN algorithm works best in terms of accuracy. Finally using food conductance to electricity Rules have been developed and uploaded to the microcontroller unit. Combining with Arduino and the VOC HCHO gas sensor our own android application is able to detect 1-50 ppm of formalin. Several Tests are conducted and polynomial regression has been applied to predict the concentration of formalin in a given sample. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh.

2018-09-17T00:00:00Z asnem, Kazi Nuzhat T Ahmed, Tazin http://dspace.ewubd.edu:8080/handle/2525/3028 2019-04-01T10:23:00Z 2018-05-05T00:00:00Z

Facial Expression Recognition Using Subspace Learning On LBP asnem, Kazi Nuzhat T; Ahmed, Tazin There is different types of methods that can recognize the facial expression but none of them were able to generate the accurate result due to the lack of generalizability. This field has a huge possibilities and can open new doors to human machine interaction. As a result the demand of recognizing the human expression correctly is increasing day by day. So there are many ways to recognize the facial expression. Here in this paper, we are trying to analyze the facial expression on different sub space. First we applied a conventional method, LBP. Then we tried to apply Principal Component Analysis (PCA). We tried another subspace algorithm called Kernel Principal Component Analysis. Then we compared the results. We compared the accuracy of recognizing facial expression of these two algorithm using BSVM tool. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh.

2018-05-05T00:00:00Z Tithi, Sharmin Sulatana Sattar Hasan, Md. Mehedy Hoque, Monica http://dspace.ewubd.edu:8080/handle/2525/3027 2019-04-01T10:06:07Z 2018-09-22T00:00:00Z

QoS-aware Channel Allocation in Directional Wireless Sensor Networks Tithi, Sharmin Sulatana Sattar; Hasan, Md. Mehedy; Hoque, Monica In WSNs, majority of the channel allocation mechanisms considered energy efficiency as the many objective and assumed data traffic with similar priority. However, the introduction of image and video sensors demands certain QoS from the channel allocation mechanism and the underlying network. Managing real-time data requires both energy efficacy and QoS assurance. In this paper, we first present a novel QoS aware channel allocation algorithm, QDCA (QoS-aware Channel Allocation in Directional Wireless Sensor Networks), that supports high data rate for real-time traffic. By exploiting node clustering and directional communication, the channel is allocated dynamically by mapping the data priority requirements of the transmitting to the appropriate channel. The proposed mechanism works in distributed manner to ensure bandwidth and end-to-end delay requirements of real-time data. At the same time, the throughput of non-real-time data is also maximized. We have also proposed an improved QDCA mechanism, IQDCA, that enhances data throughput by inducing little computational and communication overhead. Results evaluated in simulation shows that QDCA and IQDCA mechanisms exhibit enhanced performance in terms of average delay, throughput and fairness. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh.

2018-09-22T00:00:00Z Peu, Sharmin Sultana Roy, Rajashree http://dspace.ewubd.edu:8080/handle/2525/3011 2019-03-21T09:41:33Z 2018-04-21T00:00:00Z

Analyzing Protein Structure and Exploring the Sequence of Protein using Machine Learning Approach Peu, Sharmin Sultana; Roy, Rajashree Protein structure and sequence analysis is an important and essential problem. Now machine learning techniques have been widely used in bioinformatics. In this research we analyze the protein of structure and sequence and predict the class of protein sequence. Also find the accuracy of that class for different machine learning algorithm. For the data set we use the exploratory data analysis (EDA) and extracted 278866 protein features from the data set. We classify the features and measure the accuracy level of the three machine learning algorithm: Support Vector Machine (SVM), Naive Bayes and Random Forest (RF) approach for that protein sequence. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh.

2018-04-21T00:00:00Z