17 research outputs found
On the Reduction of the Driving Force in Shear-driven Flows
In shear-driven flows, an external driving force is needed to maintain the relative movement of horizontal plates. This thesis presents a systematic analysis on using spatially periodic heating and grooved surfaces to control this force. It is found that the use of periodic heating creates a buoyancy-driven effect that always reduces this force. The use of proper heating may even lead to the complete elimination of this force. It is further found that the use of isothermal grooved surfaces always enhances flow resistance, resulting in an increase of this force. When grooves and heating are applied together, their interaction induces a horizontal pressure force that can either increase or decrease the driving force, depending on the relative positions of the groove and heating patterns. Mechanisms leading to such changes of the driving force are discussed
Modeling and Simulation of Solar Photovoltaic Cell for the Generation of Electricity in UAE
This paper proposes the implementation of a circuit based simulation for a
Solar Photovoltaic (PV) cell in order to get the maximum power output. The
model is established based on the mathematical model of the PV module. As the
PV cell is used to determine the physical and electrical behavior of the cell
corresponding to environmental factors such as temperature and solar
irradiance, this paper evaluates thirty years solar irradiation data in United
Arab Emirates (UAE), also analyzes the performance parameters of PV cell for
several locations. Based on the Shockley diode equation, a solar PV module is
presented. However, to analyze the performance parameters, Solarex MSX 120, a
typical 120W module is selected. The mathematical model for the chosen module
is executed in Matlab. The consequence of this paper reflects the effects of
variation of solar irradiation on PV cell within UAE. Conclusively, this paper
determines the convenient places for implementing the large scale solar PV
modules within UAE.Comment: To be published in 5th International Conference on Advances in
Electrical Engineering (ICAEE-2019
Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers
The central goal of this paper is to establish two commonly available
dimensionality reduction (DR) methods i.e. t-distributed Stochastic Neighbor
Embedding (t-SNE) and Multidimensional Scaling (MDS) in Matlab and to observe
their application in several datasets. These DR techniques are applied to nine
different datasets namely CNAE9, Segmentation, Seeds, Pima Indians diabetes,
Parkinsons, Movement Libras, Mammographic Masses, Knowledge, and Ionosphere
acquired from UCI machine learning repository. By applying t-SNE and MDS
algorithms, each dataset is transformed to the half of its original dimension
by eliminating unnecessary features from the datasets. Subsequently, these
datasets with reduced dimensions are fed into three supervised classification
algorithms for classification. These classification algorithms are K Nearest
Neighbors (KNN), Extended Nearest Neighbors (ENN), and Support Vector Machine
(SVM). Again, all these algorithms are implemented in Matlab. The training and
test data ratios are maintained as ninety percent: ten percent for each
dataset. Upon accuracy observation, the efficiency for every dimensionality
technique with availed classification algorithms is analyzed and the
performance of each classifier is evaluated.Comment: 2020 IEEE Region 10 Symposium (TENSYMP), 5-7 June 2020, Dhaka,
Banglades
Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers
The central aim of this paper is to implement Deep Autoencoder and
Neighborhood Components Analysis (NCA) dimensionality reduction methods in
Matlab and to observe the application of these algorithms on nine unlike
datasets from UCI machine learning repository. These datasets are CNAE9,
Movement Libras, Pima Indians diabetes, Parkinsons, Knowledge, Segmentation,
Seeds, Mammographic Masses, and Ionosphere. First of all, the dimension of
these datasets has been reduced to fifty percent of their original dimension by
selecting and extracting the most relevant and appropriate features or
attributes using Deep Autoencoder and NCA dimensionality reduction techniques.
Afterward, each dataset is classified applying K-Nearest Neighbors (KNN),
Extended Nearest Neighbors (ENN) and Support Vector Machine (SVM)
classification algorithms. All classification algorithms are developed in the
Matlab environment. In each classification, the training test data ratio is
always set to ninety percent: ten percent. Upon classification, variation
between accuracies is observed and analyzed to find the degree of compatibility
of each dimensionality reduction technique with each classifier and to evaluate
each classifier performance on each dataset.Comment: 2nd International Conference on Innovation in Engineering and
Technology (ICIET
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
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