Department of Computer Science and Engineering (CSE) Sun, 11 Apr 2021 04:17:16 GMT 2021-04-11T04:17:16Z Intelligent Automation System for Critical Analysis of Sewing Thread Breakage and Effects to Enhance Productivity Intelligent Automation System for Critical Analysis of Sewing Thread Breakage and Effects to Enhance Productivity Alam, S M Masum Sewing thread Breakage is one the leading cause for damage the garments productivity all over the world. In Bangladesh, various factories face the similar problem. The factors that are affecting the breakage are depended on the properties and quality of the yarn. Except for the thread quality, some other features also involve to breakage the thread. Types of the fabric, the weight of the unit area, the thickness of the fabric, thread count, strength, elongation % of the sewing thread and Needle number, machine speeds, yarn tension, stitch per inch are major factors for thread breakage. An appropriate prediction mechanism technique can apply to significantly reduce this problem. In this work, we have proposed an intelligent system that can make an effective prediction of a possible sewing thread breakage. Eleven (11) features and nine (09) well known supervised machine learning algorithms we have applied to analyze the prediction accuracy percentages. The overall process has been categorized into four stages. Stage 1: we have provided a comprehensive literature review where we summarize various related machine learning algorithms. Stage 2: Thread breakage data have been collected by the survey questionnaires and practical observations from the different apparel industries of Bangladesh. Stage 3: The feature factors data have been filtered and processing. Finally, fed the data to suitable machine learning algorithms to determine the predictive model and accuracy percentage of sewing thread breakage. ANN (artificial neural network) multilayer perceptron scaling accuracy specified for 11 functions, Comparative supervised machine learning, decision tree (J48), random forest, stochastic gradient, and sequential optimization have 100% accuracy, and with other classification algorithms such as Perceptron Multilayer, accuracy (99.80%) and logistic regression (98, 40%). Unlike Naive Bayesian Naive Bayesian (94%) has been updated. In Bangladesh, most companies are unaware of the cost of refining yarn and remanufacturing productivity. The main reasons for thread cutting are the lack of skills associated with production and inexperienced labor This intelligent system can predict the condition of a seamstress by measuring simple information that plant personnel find useful. Finally, it will improve productivity and the economy as a whole. Tue, 02 Mar 2021 00:00:00 GMT 2021-03-02T00:00:00Z BANGLA ASPECT-BASED SENTIMENT ANALYSIS BY SUPERVISED LEARNING BASED ON ASPECT TERM EXTRACTION BANGLA ASPECT-BASED SENTIMENT ANALYSIS BY SUPERVISED LEARNING BASED ON ASPECT TERM EXTRACTION An Naim, Forhad Sentiment Analysis is the process of retrieving human sentiments from the text. Aspect based sentiment analysis goes one step ahead than sentiment analysis by automatically assigning sentiments to specific aspect terms. It is a text analysis technique that extracts and separates each aspect term and identifies the sentiment polarity associated with each aspect term. Bangla is the seventh most native language in the world. Almost 230 million people are spoken in Bangla. So, sentiment analysis in the Bangla language is considered as crucial and well-timed research topics. Recently, aspect-based sentiment analysis is progressing because of identifying fine-grained opinion polarity associated with specific aspect terms. But due to lack of proper resources like annotated dataset, corpora, etc. Aspect-based sentiment analysis is a complicated task. In this thesis, we have used two publicly available datasets named cricket and restaurant. To perform aspect category extraction known as one of ASBS's task, we have conducted our experiments based on two recent studies from 2018 and 2020. In those studies, researchers used some conventional supervised learning algorithms as 2018 [SVM, RF, KNN] and 2020 [SVM, RF, KNN, NB, LR]. In our work, after pre-processing the dataset, we applied a new technique named PSPWA (Priority Sentence Part Weight Assignment) on the dataset. After that, we used a few conventional supervised learning algorithms (SVM, KNN, RF, LR, and NB) to demonstrate results. Whereas our dataset is imbalanced, we considered F1-score as a performance measure factor. Then we compared our results with the previous research works on the same dataset. In the cricket dataset, SVM, KNN, LR, NB performed better than two existing works during the experiment and resulting F1 score of 46%, 31%, 43%, 32%. In the restaurant dataset, SVM, LR, NB performed better than two existing works during the experiment and resulting in an F1 score of 49%, 44%, 34%. For both cricket and restaurant dataset, SVM achieved the best F1 score between all algorithms and scored 46% and 49% respectively. Tue, 20 Oct 2020 00:00:00 GMT 2020-10-20T00:00:00Z BlinkFruity: A Real-Time EEG Based Neurofeedback Game for Brain-Computer Interface BlinkFruity: A Real-Time EEG Based Neurofeedback Game for Brain-Computer Interface Parbez, Rana Md Shahariar The Brain-Computer Interface (BCI) is a communication channel between the brain and the computer. It works by detecting the neural signal of brain activity. Brain signals can be detected with various BCI methods that include observing the changes in magnetic fields due to electric currents, changes associate with blood flow and neural electrical activity. Based on the selection of the required area of brain signal and the BCI method, the signal acquisition process might require from non-invasive method to an invasive (surgical) method. Despite invasive methods require a surgical procedure to implement brain signal recording implants, brain signals recorded via invasive methods offers unparalleled spatial resolution with a very low signal to noise ratio (SNR). On the contrary, non-invasive methods are easy to implement and provides good temporal resolution with high SNR. Among many non-invasive brain signal recording methods, Electroencephalography (EEG) is one of the most popular methods. EEG controlled applications widely range from strictly medical to non-medical applications. Non-medical applications can not only be used for entertainment purposes but also can help a subject to experience BCI application, achieve better control with rehabilitation systems and can be a strong motivation to practise the BCI system. Brain signals recorded via EEG are weak and contain several artefacts like muscle movements, cardiac, eye blink, power source, and amplitude artefacts. Although eye blink is considered as one of the strongest artefacts, but it can be used to drive BCI enabled applications. With this in mind, this study uses eye blink as a control signal to play the BlinkFruity game, where users collect fruits into a basket using eye blink only. To attain this objective, at first, the brain activity was recorded using OpenBCI device and 500ms window data was taken to process in real-time. Then the notch filter was applied to remove powerline noise. The eye blink was detected by using the signal thresholding method reading from EEG data. Blink detection average accuracy of 84.8% was obtained using blink control applying on subjects. The primary objective of this study is to design a simple BCI enabled system for users who are experiencing BCI for the first time and find it interesting. Then evaluate the proposed system and user’s experience. Even though in this experiment, blink has been used for experiment purpose, there are several areas where blink can be used as home automation, rehabilitation and augmented mobile application experience. Moreover, findings from this study can be resourceful and enhance our understanding and capacity for developing BCI application. Mon, 22 Feb 2021 00:00:00 GMT 2021-02-22T00:00:00Z Design and development of a secured electronic voting system Design and development of a secured electronic voting system Masnad, S.M. Mahadi; Mamun, Al; Zahed, Abu This electronic voting system using biometric method is a voting system where there is no chance of vote fraud in case of voting. Through biometric voting, it is possible to take the voting system to an unadulterated, corruption-free level. Voter secrecy will be maintained at every stage in this system. This system is a secret, reliable, and secure voting system. Our biometric voting system consists of a device, server and database. Here the user is verified by matching with the saved data with the fingerprint of the server and then voting can be done only with the verified user. Counting votes in this system is very easy and can be done in the shortest time. Sun, 22 Nov 2020 00:00:00 GMT 2020-11-22T00:00:00Z