Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036594
Mst. Rehena Khatun, Md. Ekramul Hamid, Md. Iqbal Aziz Khan
This paper presents classification of gas bubble in a Doppler ultrasound signal using Synchrosqueezing Transform (SST). The SST decomposes the signal into a number of scales. In this research work, initially two statistical parameters, the peak value and variance are estimated to Figure out the scales that contains gas bubbles. Then the signal is reconstructed from the coefficient values within the selected scale. Some parameters are defined and calculated from the reconstructed signal. These parameters are used to classify gas bubble signal using naïve Bayes classifier. However, two classes “bubble” and “not bubble” are identified by training data sets. Therefore, on the basis of posterior probability, the class of the signal is defined. Finally, performance of gas bubble detection is evaluated in terms of sensitivity and positive predictivity tests. Our proposed method is applied on grade 0, I, II, and III signals. It is observed that, good classification result is achieved in grade I and grade II. In grade 0, no gas bubble is found. In the experiment, 92% gas bubble is classified in grade I, 84% gas bubble is classified in grade II and 80% gas bubble is classified in grade III. Experimental result shows that the proposed method achieves better accuracy than the conventional method in the literature.
{"title":"Classification of Gas Bubble in A Doppler Ultrasound Signal Using Synchrosqueezing Transform","authors":"Mst. Rehena Khatun, Md. Ekramul Hamid, Md. Iqbal Aziz Khan","doi":"10.1109/IC4ME247184.2019.9036594","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036594","url":null,"abstract":"This paper presents classification of gas bubble in a Doppler ultrasound signal using Synchrosqueezing Transform (SST). The SST decomposes the signal into a number of scales. In this research work, initially two statistical parameters, the peak value and variance are estimated to Figure out the scales that contains gas bubbles. Then the signal is reconstructed from the coefficient values within the selected scale. Some parameters are defined and calculated from the reconstructed signal. These parameters are used to classify gas bubble signal using naïve Bayes classifier. However, two classes “bubble” and “not bubble” are identified by training data sets. Therefore, on the basis of posterior probability, the class of the signal is defined. Finally, performance of gas bubble detection is evaluated in terms of sensitivity and positive predictivity tests. Our proposed method is applied on grade 0, I, II, and III signals. It is observed that, good classification result is achieved in grade I and grade II. In grade 0, no gas bubble is found. In the experiment, 92% gas bubble is classified in grade I, 84% gas bubble is classified in grade II and 80% gas bubble is classified in grade III. Experimental result shows that the proposed method achieves better accuracy than the conventional method in the literature.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130825827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036489
Md. Kalim Amzad Chy, Sheikh Arif Ahmed, Ali Haider Doha, Abdul Kadar Muhammad Masum, S. I. Khan
Social media has a significant impact on our daily life, and the popularity is increasing rapidly because of the ability to be attached to people around the world and share feelings, photos, videos, etc. So, it bears a high-security concern. However, most of the social media user does not know the security level of their account, including what features of social media should consider if the account is in a risk situation. The posting, friendship, etc. sometimes brings unfortunate events like identity theft, sexual harassment, cyber-crime, etc. To overcome such kind of unexpected issues, this research proposes a classification via clustering algorithm based predictive model by which one can know his safety level in the social media. A dataset is formed through a closed-ended questionnaire. Essential features are selected via gain ration method as high dimensional data is costly to train a model. An unsupervised algorithm, hierarchical clustering, cluster the users into three groups that are labeled for further analysis. The various classification algorithm is chosen to train the predictive model. From the model evaluation result, “Logistic Regression” predicts the safety level of a social media user with high accuracy. So, this model will bring an extra dimension in social media user account safety.
{"title":"Social Media User’s Safety Level Detection through Classification via Clustering Approach","authors":"Md. Kalim Amzad Chy, Sheikh Arif Ahmed, Ali Haider Doha, Abdul Kadar Muhammad Masum, S. I. Khan","doi":"10.1109/IC4ME247184.2019.9036489","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036489","url":null,"abstract":"Social media has a significant impact on our daily life, and the popularity is increasing rapidly because of the ability to be attached to people around the world and share feelings, photos, videos, etc. So, it bears a high-security concern. However, most of the social media user does not know the security level of their account, including what features of social media should consider if the account is in a risk situation. The posting, friendship, etc. sometimes brings unfortunate events like identity theft, sexual harassment, cyber-crime, etc. To overcome such kind of unexpected issues, this research proposes a classification via clustering algorithm based predictive model by which one can know his safety level in the social media. A dataset is formed through a closed-ended questionnaire. Essential features are selected via gain ration method as high dimensional data is costly to train a model. An unsupervised algorithm, hierarchical clustering, cluster the users into three groups that are labeled for further analysis. The various classification algorithm is chosen to train the predictive model. From the model evaluation result, “Logistic Regression” predicts the safety level of a social media user with high accuracy. So, this model will bring an extra dimension in social media user account safety.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132146987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036659
Badal Chandra Mitra, A. Akter, Rahat Hossain Faisal, Md. Mostafijur Rahman
Aerial image classification has become one of the most important topics to the computer vision researchers because of its numerous real world application. A great number of census transform based descriptors have been introduced in recent years to classify the aerial images. But the major drawback of these census transform based techniques is, most of these techniques works only with the center pixel information of an image with respect to their neighboring pixels. Hence, no information about the relationship among the neighboring pixels is obtained. To mitigate this problem, we introduce an Augmented Census Transform Histogram (ACENTRIST) for aerial image classification which encodes both the center pixel information and neighboring pixel information. The proposed technique augments two local binary pattern based descriptor which encodes the center pixel information with respect to the neighboring pixels and information of the angular difference of the neighboring pixels. We have conducted thorough experiments in two of the well-known aerial image dataset, UC Merced Land Use (Land Use 21) and In-House (Banja Luka), and the experimental result shows that the proposed methodology gains considerable higher accuracy over the state of the art methods.
航空图像分类由于其在现实世界中的广泛应用，已成为计算机视觉研究的重要课题之一。近年来，人们引入了大量基于人口普查变换的描述符来对航空图像进行分类。但这些基于人口普查变换的技术的主要缺点是，大多数这些技术只能处理图像的中心像素信息相对于它们的相邻像素。因此，没有关于相邻像素之间关系的信息。为了缓解这一问题，我们引入了一种增强人口普查变换直方图(ACENTRIST)用于航空图像分类，该方法对中心像素信息和相邻像素信息进行编码。该技术增强了两个基于局部二进制模式的描述符，该描述符对中心像素信息和相邻像素的角差信息进行编码。我们在两个著名的航空图像数据集，UC Merced Land Use (Land Use 21)和house (Banja Luka)中进行了彻底的实验，实验结果表明，所提出的方法比最先进的方法获得了更高的精度。
{"title":"ACENTRIST: An Augmented Census Transform Histogram for Aerial Image Classification","authors":"Badal Chandra Mitra, A. Akter, Rahat Hossain Faisal, Md. Mostafijur Rahman","doi":"10.1109/IC4ME247184.2019.9036659","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036659","url":null,"abstract":"Aerial image classification has become one of the most important topics to the computer vision researchers because of its numerous real world application. A great number of census transform based descriptors have been introduced in recent years to classify the aerial images. But the major drawback of these census transform based techniques is, most of these techniques works only with the center pixel information of an image with respect to their neighboring pixels. Hence, no information about the relationship among the neighboring pixels is obtained. To mitigate this problem, we introduce an Augmented Census Transform Histogram (ACENTRIST) for aerial image classification which encodes both the center pixel information and neighboring pixel information. The proposed technique augments two local binary pattern based descriptor which encodes the center pixel information with respect to the neighboring pixels and information of the angular difference of the neighboring pixels. We have conducted thorough experiments in two of the well-known aerial image dataset, UC Merced Land Use (Land Use 21) and In-House (Banja Luka), and the experimental result shows that the proposed methodology gains considerable higher accuracy over the state of the art methods.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122422625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036541
Md. Dulal Haque, N. Kamata, A. T. Touhidul Islam, M. Julkarnain, S. Yagi, H. Yaguchi, Y. Okada
Nonradiative recombination (NRR) centers in GaAs:N $delta$-doped superlattices (SLs) grown by molecular beam epitaxy (MBE) has been investigated by two-wavelength excited photoluminescence (TWEPL) method for conduction band scheme. The PL intensity of $E_{-}$ band of the samples with lower nitrogen (N) concentration (0.317% N) initially increases after addition of below-gap excitation (BGE) light over above-gap excitation (AGE) light and then quenches at higher BGE density and energy, while that of GaAs (e-A°) emission of GaAs layers decreases monotonically. For sample with higher N concentration (1.18% N) both the $E _{-}$ band and GaAs (e-A°) emission decreases monotonically with enhancing BGE density and degree of decreasing of PL intensity is higher compared to the low N concentration sample. The quenching of PL intensity indicates the existence of NRR centers in GaAs layers and GaAs:N $delta$-doped SL region. The recombination models have been proposed for explaining the results from the experiments.
用双波长激发光致发光(TWEPL)方法研究了分子束外延(MBE)生长的GaAs:N $delta$掺杂超晶格(SLs)中的非辐射复合(NRR)中心。低氮(N)浓度(0.317% N)样品的$E_{-}$带的PL强度在加隙下激发(BGE)光后先增加，然后在更高的BGE密度和能量下猝灭，而GaAs层的GaAs (e-A°)发射的PL强度单调降低。对于高N浓度(1.18% N)样品，随着BGE密度的增加，$E _{-}$波段和GaAs (E - a°)发射均单调下降，且PL强度的下降程度高于低N浓度样品。PL强度的猝灭表明在GaAs层和GaAs:N $delta$掺杂的SL区存在NRR中心。本文提出了重组模型来解释实验结果。
{"title":"Study of nonradiative recombination centers in GaAs:N δ-doped superlattices structures revealed by below-gap excitation light","authors":"Md. Dulal Haque, N. Kamata, A. T. Touhidul Islam, M. Julkarnain, S. Yagi, H. Yaguchi, Y. Okada","doi":"10.1109/IC4ME247184.2019.9036541","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036541","url":null,"abstract":"Nonradiative recombination (NRR) centers in GaAs:N $delta$-doped superlattices (SLs) grown by molecular beam epitaxy (MBE) has been investigated by two-wavelength excited photoluminescence (TWEPL) method for conduction band scheme. The PL intensity of $E_{-}$ band of the samples with lower nitrogen (N) concentration (0.317% N) initially increases after addition of below-gap excitation (BGE) light over above-gap excitation (AGE) light and then quenches at higher BGE density and energy, while that of GaAs (e-A°) emission of GaAs layers decreases monotonically. For sample with higher N concentration (1.18% N) both the $E _{-}$ band and GaAs (e-A°) emission decreases monotonically with enhancing BGE density and degree of decreasing of PL intensity is higher compared to the low N concentration sample. The quenching of PL intensity indicates the existence of NRR centers in GaAs layers and GaAs:N $delta$-doped SL region. The recombination models have been proposed for explaining the results from the experiments.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114580055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036582
Munni Rani Banik, Tonmoy Das
Application of Genetic Algorithms (GAs) for allotting sensors in Structural Health Monitoring (SHM) has received wide attention during the last three decades because of their potential as global search technique. However, from computational perspective, sensor allocation is a complex combinatorial optimization problem and can lead to some constraints that reduces the efficiency of simple GAs. To eradicate such dilemma, an Integer Constrained Genetic Algorithm (ICGA) is introduced for finding the optimal placement of sensors. Integer coded string and modal assurance criteria oriented objective function are adopted respectively to represent and measure the utility of a sensor configuration. A benchmark bridge structure is studied to demonstrate the feasibility and effectiveness of ICGA. Later, the simulation results obtained by the ICGA are compared to the conventional GA. The result shows that ICGA can satisfactorily identify the number of sensors along with their locations and enhances the convergence of the algorithm. More apparently, proposed algorithm can reduce the dissipative storage space generated by conventional methods, removes any redundancy of sensor and improves the balance between exploitation and exploration of the search space.
{"title":"An Integer-Constrained Genetic Algorithm for Sensor Placement in Structural Health Monitoring","authors":"Munni Rani Banik, Tonmoy Das","doi":"10.1109/IC4ME247184.2019.9036582","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036582","url":null,"abstract":"Application of Genetic Algorithms (GAs) for allotting sensors in Structural Health Monitoring (SHM) has received wide attention during the last three decades because of their potential as global search technique. However, from computational perspective, sensor allocation is a complex combinatorial optimization problem and can lead to some constraints that reduces the efficiency of simple GAs. To eradicate such dilemma, an Integer Constrained Genetic Algorithm (ICGA) is introduced for finding the optimal placement of sensors. Integer coded string and modal assurance criteria oriented objective function are adopted respectively to represent and measure the utility of a sensor configuration. A benchmark bridge structure is studied to demonstrate the feasibility and effectiveness of ICGA. Later, the simulation results obtained by the ICGA are compared to the conventional GA. The result shows that ICGA can satisfactorily identify the number of sensors along with their locations and enhances the convergence of the algorithm. More apparently, proposed algorithm can reduce the dissipative storage space generated by conventional methods, removes any redundancy of sensor and improves the balance between exploitation and exploration of the search space.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125937630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036697
Tanbin Islam Rohan, Awan-Ur-Rahman, Abu Bakar Siddik, Monira Islam, Md. Salah Uddin Yusuf
Due to breast cancer, a number of women die every year. With an early diagnosis, breast cancer can be cured. Prognosis and early detection of cancer types have become a necessity in cancer research. Thus, a reliable and accurate system is required for the classification of benign and malignant tumor types of breast cancer. This paper explores a supervised machine learning model for classification of malignant and benign tumor types from Wisconsin Breast Cancer dataset retrieved from UCI machine learning repository. The dataset has 458 (65.50%) of benign data and 241 (34.50%) of malignant data, the total of 699 instances, 11 features and 10 attributes. Random Forest (RF) ensemble learning method is implemented with AdaBoost algorithm manifest improved metrics of performance in binary classification between tumor classes. For more accurate estimation of model prediction performance, 10-fold cross-validation is applied. The structure provided accuracy of 98.5714% along with sensitivity and specificity of 100% and 96.296% respectively in the testing phase. Matthews Correlation Coefficient is calculated 0.97 which validates of the structure being a pure binary classifier for this work. The proposed structure outperformed conventional RF classifier for classifying tumor types. Additionally, this model enhances the performance of conventional classifiers.
{"title":"A Precise Breast Cancer Detection Approach Using Ensemble of Random Forest with AdaBoost","authors":"Tanbin Islam Rohan, Awan-Ur-Rahman, Abu Bakar Siddik, Monira Islam, Md. Salah Uddin Yusuf","doi":"10.1109/IC4ME247184.2019.9036697","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036697","url":null,"abstract":"Due to breast cancer, a number of women die every year. With an early diagnosis, breast cancer can be cured. Prognosis and early detection of cancer types have become a necessity in cancer research. Thus, a reliable and accurate system is required for the classification of benign and malignant tumor types of breast cancer. This paper explores a supervised machine learning model for classification of malignant and benign tumor types from Wisconsin Breast Cancer dataset retrieved from UCI machine learning repository. The dataset has 458 (65.50%) of benign data and 241 (34.50%) of malignant data, the total of 699 instances, 11 features and 10 attributes. Random Forest (RF) ensemble learning method is implemented with AdaBoost algorithm manifest improved metrics of performance in binary classification between tumor classes. For more accurate estimation of model prediction performance, 10-fold cross-validation is applied. The structure provided accuracy of 98.5714% along with sensitivity and specificity of 100% and 96.296% respectively in the testing phase. Matthews Correlation Coefficient is calculated 0.97 which validates of the structure being a pure binary classifier for this work. The proposed structure outperformed conventional RF classifier for classifying tumor types. Additionally, this model enhances the performance of conventional classifiers.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124855245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036573
A. Kuddus, Md. Ferdous Rahman, S. Tabassum, J. Hossain, A. Ismail
Study on the performance of zinc oxide (ZnO) and copper oxide (CuO) based heterojunction solar cell for simulation and home fabrication has been presented in this report. SCAPS-1D simulator has been used for simulation and these simulation results are used to fabricate ZnO/CuO heterojunction solar cell (HJSC) experimentally using very low cost and simple spin coating technique. Simulation has been done at wide range of thickness of window n-ZnO layer, absorber p-CuO layer and concentration of photoactive materials. Employing these results, FTO/ZnO/CuO/Al HJSC were fabricated experimentally using sol-gel spin coating technique. Thereafter, photovoltaic characteristics of the cells were investigated under 1.5AM illumination that demonstrates an open-circuit voltage $(mathrm{V}_{oc})$ of 1.37 V, short circuit current $(mathrm{I}_{sc})$ of 2.86 mA, fill factor (FF) of 55% and photoconversion efficiency (PCE) exceeding 1.15 % those were 0.82 V, 14.96 mA/cm2, 52%, and 4.20 % respectively for simulation in where improvement of Voc and FF are noticeable. There is promising performance of home-made spin coated ZnO/CuO HJSC without any selective layer (hole transport and electron transport) and anti-reflection coating in open air. Above results indicates that sol-gel spin coating technique may be an effective and efficient way for fabricating thin film heterojunction solar cell by utilizing the opportunities for improving the cell performance.
{"title":"Study on the Performance of ZnO/CuO Heterojunction Solar Cell Simulation and Experimental","authors":"A. Kuddus, Md. Ferdous Rahman, S. Tabassum, J. Hossain, A. Ismail","doi":"10.1109/IC4ME247184.2019.9036573","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036573","url":null,"abstract":"Study on the performance of zinc oxide (ZnO) and copper oxide (CuO) based heterojunction solar cell for simulation and home fabrication has been presented in this report. SCAPS-1D simulator has been used for simulation and these simulation results are used to fabricate ZnO/CuO heterojunction solar cell (HJSC) experimentally using very low cost and simple spin coating technique. Simulation has been done at wide range of thickness of window n-ZnO layer, absorber p-CuO layer and concentration of photoactive materials. Employing these results, FTO/ZnO/CuO/Al HJSC were fabricated experimentally using sol-gel spin coating technique. Thereafter, photovoltaic characteristics of the cells were investigated under 1.5AM illumination that demonstrates an open-circuit voltage $(mathrm{V}_{oc})$ of 1.37 V, short circuit current $(mathrm{I}_{sc})$ of 2.86 mA, fill factor (FF) of 55% and photoconversion efficiency (PCE) exceeding 1.15 % those were 0.82 V, 14.96 mA/cm2, 52%, and 4.20 % respectively for simulation in where improvement of Voc and FF are noticeable. There is promising performance of home-made spin coated ZnO/CuO HJSC without any selective layer (hole transport and electron transport) and anti-reflection coating in open air. Above results indicates that sol-gel spin coating technique may be an effective and efficient way for fabricating thin film heterojunction solar cell by utilizing the opportunities for improving the cell performance.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"122 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124867597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036525
Utpala Nanda Chowdhury, Md. Al Mehedi Hasan, Shamim Ahmad, M. Islam, Julian M. W. Quinn, M. Moni
Type 2 diabetes (T2D) is a chronic metabolic dysfunction characterized by resistance to insulin. T2D can cause acute and chronic damage to the vascular and immune systems which can increase the risk or severity of other diseases. A welldocumented group of diseases affected by T2D incidence are the neurodegenerative diseases (NDD). However, the interaction or influence of T2D on NDD is still poorly understood because the clinical complexity of NDD and T2D make conventional endocrinological methodologies render this very difficult. As an alternative approach, we used a strategy to discover cellular pathways common to NDD and T2D employing transcriptional analysis of affected tissues. We examined microarray transcript datasets from studies comparing control individuals with T2D patients, and likewise control and NDD sufferers. The latter included Alzheimers disease (AD), Parkinsons disease (PD), Huntingtons disease (HD), multiple sclerosis disease (MSD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FD), spinal muscular atrophy (SMA), Lewy body dementia (LBD) and epilepsy disorders (ED). Differentially expressed genes (DEG) for each selected pathologies were first identified then pairwise overlapping DEG between T2D and each NDD were identified by cross comparison. Gene set enrichment analysis (GSEA) was then undertaken for those common DEG using molecular pathway as well as gene ontology (GO) analysis. We thus uncovered new putative connections between pathological processes in T2D and NDD by identifying cell pathway commonalities. The findings were validated using Online Mendelian Inheritance in Man (OMIM) and dbGaP (gene SNP-disease association) databases for gold-standard benchmarking of their involvement in disease process. This methodology enables data-driven approaches to identify novel mechanisms affecting disease progressions and may enable prediction of disease co-morbidity development in a quantitative way.
{"title":"Delineating Common Cell Pathways that Influence Type 2 Diabetes and Neurodegenerative Diseases using a Network-based Approach","authors":"Utpala Nanda Chowdhury, Md. Al Mehedi Hasan, Shamim Ahmad, M. Islam, Julian M. W. Quinn, M. Moni","doi":"10.1109/IC4ME247184.2019.9036525","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036525","url":null,"abstract":"Type 2 diabetes (T2D) is a chronic metabolic dysfunction characterized by resistance to insulin. T2D can cause acute and chronic damage to the vascular and immune systems which can increase the risk or severity of other diseases. A welldocumented group of diseases affected by T2D incidence are the neurodegenerative diseases (NDD). However, the interaction or influence of T2D on NDD is still poorly understood because the clinical complexity of NDD and T2D make conventional endocrinological methodologies render this very difficult. As an alternative approach, we used a strategy to discover cellular pathways common to NDD and T2D employing transcriptional analysis of affected tissues. We examined microarray transcript datasets from studies comparing control individuals with T2D patients, and likewise control and NDD sufferers. The latter included Alzheimers disease (AD), Parkinsons disease (PD), Huntingtons disease (HD), multiple sclerosis disease (MSD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FD), spinal muscular atrophy (SMA), Lewy body dementia (LBD) and epilepsy disorders (ED). Differentially expressed genes (DEG) for each selected pathologies were first identified then pairwise overlapping DEG between T2D and each NDD were identified by cross comparison. Gene set enrichment analysis (GSEA) was then undertaken for those common DEG using molecular pathway as well as gene ontology (GO) analysis. We thus uncovered new putative connections between pathological processes in T2D and NDD by identifying cell pathway commonalities. The findings were validated using Online Mendelian Inheritance in Man (OMIM) and dbGaP (gene SNP-disease association) databases for gold-standard benchmarking of their involvement in disease process. This methodology enables data-driven approaches to identify novel mechanisms affecting disease progressions and may enable prediction of disease co-morbidity development in a quantitative way.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123598311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036535
Najmus Sakib, Utpala Nanda Chowdhury, M. Islam, F. Huq, Julian M. W. Quinn, M. Moni
The processes that underlie Parkinsons disease (PD) are still unclear, but improved comprehension of genetic and environmental influences on PD, and how these influences interact will help find new approaches to reducing PD progression. We thus employed quantitative framework analysis to reveal some of the complex relationship of various genetic factors affecting PD. In this study, we analyzed gene expression microarray data from cells and tissues affected by PD, ageing (AG), type II diabetes (T2D), high body fat (HBF) and control datasets. We determined genetic associations of PD and these risk factors based on neighborhood-based benchmarking and multilayer network topology. We first identified 1343 significantly dysregulated genes in the PD patient tissues compared to healthy control, including we have 779 genes with down regulated expression and 544 genes up regulated. 45 genes were highly expressed in both for the PD and ageing; the number of shared genes for the PD and the type II diabetes is 51. Ontological and pathway analyses then identified significant gene ontology and molecular pathways that enhance our understanding of the fundamental molecular procedure of the PD progression. Therapeutic targets of the PD could be developed using these identified target genes, ontologies and pathways.
{"title":"A Systems Biology Approach to Identifying Genetic Markers that Link Progression of Parkinson’s Disease to Risk Factors related to Ageing, Lifestyle and Type 2 Diabetes","authors":"Najmus Sakib, Utpala Nanda Chowdhury, M. Islam, F. Huq, Julian M. W. Quinn, M. Moni","doi":"10.1109/IC4ME247184.2019.9036535","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036535","url":null,"abstract":"The processes that underlie Parkinsons disease (PD) are still unclear, but improved comprehension of genetic and environmental influences on PD, and how these influences interact will help find new approaches to reducing PD progression. We thus employed quantitative framework analysis to reveal some of the complex relationship of various genetic factors affecting PD. In this study, we analyzed gene expression microarray data from cells and tissues affected by PD, ageing (AG), type II diabetes (T2D), high body fat (HBF) and control datasets. We determined genetic associations of PD and these risk factors based on neighborhood-based benchmarking and multilayer network topology. We first identified 1343 significantly dysregulated genes in the PD patient tissues compared to healthy control, including we have 779 genes with down regulated expression and 544 genes up regulated. 45 genes were highly expressed in both for the PD and ageing; the number of shared genes for the PD and the type II diabetes is 51. Ontological and pathway analyses then identified significant gene ontology and molecular pathways that enhance our understanding of the fundamental molecular procedure of the PD progression. Therapeutic targets of the PD could be developed using these identified target genes, ontologies and pathways.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123668291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036505
M. Mitul, M. Mowla
In spite of having great potential supremacy, device-to-device (D2D) communications are facing lack of implementation in large scale. Insufficient bandwidth with high interference in the micro wave ($mu$Wave) band is the crucial complication behind it. Enabling D2D communications in millimeter wave (mmWave) band is considered as an alternative solution to these problems. However, line-of-sight (LOS) is a prerequisite for D2D devices to enable links in mmWave bands. In this paper, a distributed method is considered by which D2D devices can detect the existing LOS link for mmWave communication and further perform beam alignment. If there is scarcity of LOS link, this method permits the D2D devices to switch to $mu$Wave band. Stochastic geometry is considered for system modeling and analysis of this method. Various network criteria such as reference distance, blockage density, frequency, gain of antenna, beam width, density of D2D transmitters, power for D2D and path loss exponents for both LOS and NLOS links are taken into account for analyzing D2D network SINR (signal-to-interference-plus-noise-ratio) coverage probability against SINR threshold. Simulation results reveal that this distributed method has the better coverage probability compared to independently implied mmWave and $mu$Wave communication.
{"title":"A Distributed Method Analysis for Enabling Device-to-Device Communications in Millimeter Wave Bands","authors":"M. Mitul, M. Mowla","doi":"10.1109/IC4ME247184.2019.9036505","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036505","url":null,"abstract":"In spite of having great potential supremacy, device-to-device (D2D) communications are facing lack of implementation in large scale. Insufficient bandwidth with high interference in the micro wave ($mu$Wave) band is the crucial complication behind it. Enabling D2D communications in millimeter wave (mmWave) band is considered as an alternative solution to these problems. However, line-of-sight (LOS) is a prerequisite for D2D devices to enable links in mmWave bands. In this paper, a distributed method is considered by which D2D devices can detect the existing LOS link for mmWave communication and further perform beam alignment. If there is scarcity of LOS link, this method permits the D2D devices to switch to $mu$Wave band. Stochastic geometry is considered for system modeling and analysis of this method. Various network criteria such as reference distance, blockage density, frequency, gain of antenna, beam width, density of D2D transmitters, power for D2D and path loss exponents for both LOS and NLOS links are taken into account for analyzing D2D network SINR (signal-to-interference-plus-noise-ratio) coverage probability against SINR threshold. Simulation results reveal that this distributed method has the better coverage probability compared to independently implied mmWave and $mu$Wave communication.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131278506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：