INVESTIGATION OF THE BULK, SURFACE AND TRANSFER PROPERTIES OF CHLORINE BLEACHED DENIM APPAREL AT DIFFERENT CONDITION

Oxidizing bleaching agent (calcium hypochlorite or bleaching powder) is widely used in the apparel washing plant as a color removing and cost effective finishing chemical. As the faded denim or old look denim is preferred by the today’s youth it has become a crucial issue for the technologists to modify denim apparel to fulfill the demand of existing trend. Calcium hypochlorite (Ca(OCl)Cl) fades the denim effectively but a significant changes are happened in the properties of the denim apparel. The main objective of this paper was to investigate the changes of bulk, surface and transfer properties of denim apparel after the chlorine bleach action at varying length of washing time (10,15 and 30 min) with fixed concentration and temperature (50°C). These properties are related to the performance of the end product. 100% cotton indigo dyed 2/1 twill denim apparel was treated with 5gm/l (Ca(OCl)Cl). To determine the end use performance of the modified denim the changes of tensile strength, stiffness, dimensional stability (bulk properties), hand roughness, rubbing fastness (surface properties), air permeability, water repellency (transfer properties) of the modified denim apparel were evaluated. It has been monitored from the experimental data that the bulk properties play down but the surface properties have a noticeable improvement after the chlorine bleach action. It is also noticed that washing time has a significant effect on air permeability of the treated denim apparel

INVESTIGATION OF THE BULK, SURFACE AND TRANSFER PROPERTIES OF CHLORINE BLEACHED DENIM APPAREL AT DIFFERENT CONDITION

Oxidizing bleaching agent (calcium hypochlorite or bleaching powder) is widely used in the apparel washing plant as a color removing and cost effective finishing chemical. As the faded denim or old look denim is preferred by the today’s youth it has become a crucial issue for the technologists to modify denim apparel to fulfill the demand of existing trend. Calcium hypochlorite (Ca(OCl)Cl) fades the denim effectively but a significant changes are happened in the properties of the denim apparel. The main objective of this paper was to investigate the changes of bulk, surface and transfer properties of denim apparel after the chlorine bleach action at varying length of washing time (10,15 and 30 min) with fixed concentration and temperature (50°C). These properties are related to the performance of the end product. 100% cotton indigo dyed 2/1 twill denim apparel was treated with 5gm/l (Ca(OCl)Cl). To determine the end use performance of the modified denim the changes of tensile strength, stiffness, dimensional stability (bulk properties), hand roughness, rubbing fastness (surface properties), air permeability, water repellency (transfer properties) of the modified denim apparel were evaluated. It has been monitored from the experimental data that the bulk properties play down but the surface properties have a noticeable improvement after the chlorine bleach action. It is also noticed that washing time has a significant effect on air permeability of the treated denim apparel

ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Electrocardiogram Heartbeat Classification Using Convolutional Neural Networks for the Detection of Cardiac Arrhythmia

The classification of the electrocardiogram (ECG) signal has a vital impact on identifying heart-related diseases. This can ensure the premature finding of heart disease and the proper selection of the patient's customized treatment. However, the detection of arrhythmia is a challenging task to perform manually. This justifies the necessity of a technique for automatic detection of abnormal heart signals. Therefore, our work is based on the classification of five classes of ECG arrhythmic signals from Physionet's MIT-BIH Arrhythmia Dataset. Artificial Neural Networks (ANN) have demonstrated significant success in ECG signal classification. Our proposed model is a Convolutional Neural Network (CNN) customized to categorize the ECG signals. Our result testifies that the planned CNN model can successfully categorize arrhythmia with an overall accuracy of 95.2%. The average precision and recall of the proposed model are 95.2% and 95.4%, respectively. This model can effectively be used to detect irregularities of heart rhythm at an early stage.Comment: 4th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2020), IEEE, 7-9 October 2020, TamilNadu, INDI

Prediction of Temperature and Rainfall in Bangladesh using Long Short Term Memory Recurrent Neural Networks

Temperature and rainfall have a significant impact on economic growth as well as the outbreak of seasonal diseases in a region. In spite of that inadequate studies have been carried out for analyzing the weather pattern of Bangladesh implementing the artificial neural network. Therefore, in this study, we are implementing a Long Short-term Memory (LSTM) model to forecast the month-wise temperature and rainfall by analyzing 115 years (1901-2015) of weather data of Bangladesh. The LSTM model has shown a mean error of -0.38oC in case of predicting the month-wise temperature for 2 years and -17.64mm in case of predicting the rainfall. This prediction model can help to understand the weather pattern changes as well as studying seasonal diseases of Bangladesh whose outbreaks are dependent on regional temperature and/or rainfall.Comment: 4th International Symposium on Multidisciplinary Studies and Innovative Technologies, IEEE, 22-24 October, 2020, TURKE

Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images

Diagnosing Brain Tumor with the aid of Magnetic Resonance Imaging (MRI) has gained enormous prominence over the years, primarily in the field of medical science. Detection and/or partitioning of brain tumors solely with the aid of MR imaging is achieved at the cost of immense time and effort and demands a lot of expertise from engaged personnel. This substantiates the necessity of fabricating an autonomous model brain tumor diagnosis. Our work involves implementing a deep convolutional neural network (DCNN) for diagnosing brain tumors from MR images. The dataset used in this paper consists of 253 brain MR images where 155 images are reported to have tumors. Our model can single out the MR images with tumors with an overall accuracy of 96%. The model outperformed the existing conventional methods for the diagnosis of brain tumor in the test dataset (Precision = 0.93, Sensitivity = 1.00, and F1-score = 0.97). Moreover, the proposed model's average precision-recall score is 0.93, Cohen's Kappa 0.91, and AUC 0.95. Therefore, the proposed model can help clinical experts verify whether the patient has a brain tumor and, consequently, accelerate the treatment procedure.Comment: 4th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC 2020), IEEE, 7-9 October 2020, TamilNadu, INDI