2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)最新文献_第3页

Prediction of Recurrence in Non Small Cell Lung Cancer Patients with Gene Expression Data Using Machine Learning Techniques 利用机器学习技术的基因表达数据预测非小细胞肺癌患者的复发

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085448

Sudipto Bhattacharjee, Banani Saha, S. Saha

{"title":"Prediction of Recurrence in Non Small Cell Lung Cancer Patients with Gene Expression Data Using Machine Learning Techniques","authors":"Sudipto Bhattacharjee, Banani Saha, S. Saha","doi":"10.1109/ICCECE51049.2023.10085448","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10085448","url":null,"abstract":"Lung cancer is the deadliest cancer and the non-small cell lung cancer (NSCLC) contributes to 80-85% of lung cancer cases. Cancer recurrence is defined as the resurgence of cancer despite the surgical resection of the tumor and occurs in more than 30% of NSCLC patients. It occurs due to several genomic factors, incomplete removal of the tumor, resistance to drugs and chemotherapy, and the presence of cancer stem cells. A preoperative assessment of the risk of recurrence can be crucial for clinicians. The aim of this work is to develop machine learning (ML) models to predict recurrence in NSCLC patients with gene expression data. The gene expression data of 130 NSCLC patients were obtained from a public dataset, named NSCLC-Radiogenomics. Monte-Carlo Feature Selection (MCFS), Boruta feature selection and a combination of MCFS and Boruta were used to identify significant genes which are to be used as input features. Supervised ML models were trained with 5-fold cross validation using Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and Random Forest (RF) algorithms. Synthetic Minority Oversampling Technique (SMOTE) was used to handle the class-imbalance in the input data. The models trained on SMOTE-applied data outperformed the models trained on original (imbalanced) data. The optimal performance with 5-fold cross validation was obtained by the SVM model with accuracy of 0.99 and MCC of 0.99. The SVM model also achieved an area under receiver operator characteristics curve of 0.98. The models also achieved good performance while validating on the held-out blind dataset. In summary, the ML-based prediction of recurrence in NSCLC patients can aid clinicians in finalizing postoperative treatment.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133657974","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}

引用次数: 0

Effect of barrier variabilities on the strain propagation and 2DEG profile of GaN/AlGaN HEMT heterostructures 势垒变化对GaN/AlGaN HEMT异质结构应变扩展和2DEG分布的影响

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085316

Priyesh Kumar, J. Saha

引用次数: 0

A Review of Multi-Band Reflectarray Antenna Designs with Mutual Coupling Considerations 考虑互耦合的多波段反射天线设计综述

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085232

Venkatraman S, Komathi B J

引用次数: 0

Optimized Novel DC to DC Converter for PV Fed Grid Tied EV Charging Station 光伏并网电动汽车充电站新型DC - DC变换器的优化设计

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10084962

T. Kumar, C. Rajan

{"title":"Optimized Novel DC to DC Converter for PV Fed Grid Tied EV Charging Station","authors":"T. Kumar, C. Rajan","doi":"10.1109/ICCECE51049.2023.10084962","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10084962","url":null,"abstract":"The use of Electric Vehicles (EVs) has been increasing in a wider range owing to the increase in population and energy demand.Nowadays, the Solar, which is one of the RES (Renewable Energy sources),assists in EV charging and is gaining higher importance. Moreover, the main function involved in an EV system is the battery charging, and many different types of issues like battery life time, interruptible power supply and poor energy management occur in conventional types of charging. Hence, in the proposed paper, a novel EV charging station is introduced and it involves an optimized DC-DC Bi-directional Boost-Zeta converter. In this work, the EV battery attains energy directly from the PV panel and the additional energy produced by PV is transferred to the grid. The proposed converter functions in boost mode and aids in improving the PV output; the resulting DC-link voltage is regulated and maintained constant using an optimization algorithm known as Firefly Algorithm (FFA). Further, through a single phase VSI, the DC link voltage is given to the grid. In order to attain grid synchronization, a PI controller is employed. Whenever, there is less sunshine, the energy from the grid is fed to the EV battery, thereby supplies continuous power to the EV, even in the absence of solar energy and during this condition, the DC-DC converter functions in Zeta mode. Then, for analysing the performance of the proposed work, it is implemented in MATLAB/Simulink and from an analysis, it is identified that the proposed EV charging station possess a less THD of 3.1%, better switching losses and reactive power compensation.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"239 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114282353","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}

引用次数: 1

Envy Prediction from Users’ Photos using Convolutional Neural Networks 使用卷积神经网络从用户照片中预测嫉妒

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085092

Mohaimenul Azam Khan Raiaan, Abdullah Al Mamun, Md. Adnanul Islam, Mohammed Eunus Ali, Md. Saddam Hossain Mukta

引用次数: 2

Retinal and Semantic Segmentation of Diabetic Retinopathy Images Using MobileNetV3 基于MobileNetV3的糖尿病视网膜病变图像的视网膜和语义分割

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085191

Manish Prajapati, Santos Kumar Baliarsingh, Jhalak Hota, Prabhu Prasad Dev, Shuvam Das

{"title":"Retinal and Semantic Segmentation of Diabetic Retinopathy Images Using MobileNetV3","authors":"Manish Prajapati, Santos Kumar Baliarsingh, Jhalak Hota, Prabhu Prasad Dev, Shuvam Das","doi":"10.1109/ICCECE51049.2023.10085191","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10085191","url":null,"abstract":"The eye is affected by diabetic retinopathy (DR), a condition caused by diabetes. DR may initially show no symptoms or cause minor vision problems. However, it may lead to blindness if not diagnosed and treated early. The goal of this research is to segment and classify DR into five stages: no DR, mild, moderate, severe, and proliferative retinopathy. In this work, a deep learning-based convolutional neural network (CNN), namely, MobileNetV3 is employed on a set of 5590 images to identify the stages and patterns of DR. For this, accurate retinal vascular analysis is necessary. This can be accomplished by retinal segmentation to provide a precise result. Retinal segmentation is the process of automatically identifying blood vessel borders. It is designed to handle heavy-duty use cases as well as low-resource use cases. Here, retinal segmentation is carried out by U-net architecture. Due to the region merging process, characteristics loss in the segmentation are preserved and passed on to the image classifier, which has an accuracy rate of up to 97%. MobileNetV3-Large and MobileNetV3-Small are both deep neural networks designed for heavy and low-resource uses, respectively. A model with variable parameters is tweaked and used to identify objects and perform semantic segmentation tasks. Implementation results show that MobileNetV3-Large is 3.2% more accurate than MobileNetV2, while the latency has been reduced by 20% compared to MobileNetV2. MobileNetV3-Small is 6.6% more accurate than the MobileNetV2 model with equal latency.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115969689","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}

引用次数: 0

A New hybrid Feature selection-Classification model to Improve Cancer Sample Classification Accuracy in Microarray Gene Expression Data 一种新的混合特征选择-分类模型提高微阵列基因表达数据中癌症样本分类的准确性

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085390

Ritaban Bandyopadhyay, Arijt Das Sharma, Bidya Dasgupta, Ankita Ghosh, C. Das, Shilpi Bose

{"title":"A New hybrid Feature selection-Classification model to Improve Cancer Sample Classification Accuracy in Microarray Gene Expression Data","authors":"Ritaban Bandyopadhyay, Arijt Das Sharma, Bidya Dasgupta, Ankita Ghosh, C. Das, Shilpi Bose","doi":"10.1109/ICCECE51049.2023.10085390","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10085390","url":null,"abstract":"Machine learning techniques are one kind of techniques of Artificial Intelligence that enables systems to learn and improve from data without being explicitly programmed. Machine learning techniques are widely used in medical applications since it has the property to detect inherent patterns from large and complex datasets. Cancer classification based on bio molecular gene expression data is a very crucial topic for medical science as it helps to improve the diagnostic accuracy of cancer samples and is very useful in cancer sample detection and prognosis. But the traditional classifiers performance vitiates due to presence of high feature dimensionality and class imbalance problem present in microarray data. So, in this research work, a new computer aided diagnostic tool is being proposed for cancer sample classification based on bio molecular gene expression data. This tool called MI-TLBO-EB operates in two phases. The first phase selects the best features from the dataset using mutual information and teaching learning based optimization algorithm named MI-TLBO algorithm and the second phase classifies the cancer samples with the help of an extended version of bagging. The proposed model is advantageous in many ways. It helps to curb the curse of higher dimensionality and increases the classification accuracy via handling class imbalance problem with the help of bagging model. The model is applied on different high dimensional microarray gene expression datasets for cancer sample classification and from the experimental results, it has been found that the generalization performance/testing accuracy of the proposed hybrid model is significantly better compared to other well-known existing models.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133666887","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}

引用次数: 0

Employee attrition prediction for imbalanced data using genetic algorithm-based parameter optimization of XGB Classifier 基于遗传算法的XGB分类器参数优化的不平衡数据员工流失预测

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085402

Karabi Konar, Saptarshi Das, Samiran Das

{"title":"Employee attrition prediction for imbalanced data using genetic algorithm-based parameter optimization of XGB Classifier","authors":"Karabi Konar, Saptarshi Das, Samiran Das","doi":"10.1109/ICCECE51049.2023.10085402","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10085402","url":null,"abstract":"Attrition of employees is vital for any organization as it significantly influences productivity and hampers the long-term growth strategies of the organization. Since employee attrition leads to loss of skills and experiences any organization always try to find a way to retain their employees to reduce training and recruiting cost as well as to achieve their business goal smoothly. Machine learning approaches, which predict the possibility of attrition based on the employee attributes avoid the tedious, and biased manual prediction, and help the organization take preventive measures. This paper presents a framework for attrition prediction that emphasizes imbalance classification and the adoption of genetic algorithms to optimize the model. First, we have adopted different oversampling methods like Synthetic Minority Over-sampling Technique (SMOTE), Adaptive Synthetic (ADASYN), and Borderline Synthetic Minority Over-sampling Technique to balance our data set. We have used XGBoost classifiers for classification with the data that are obtained from different over-sampling techniques. As the XGBoost classifier has many hyperparameter a genetic algorithm is used to optimize our model where the accuracy is chosen as the fitness function. The comparative performance analysis of different over-sampling methods as well as hyper-parameter tuning (Amongst Genetic algorithm, GridSearchCV, and with the default value of different hyper-parameter) on the real dataset suggests that SMOTE for oversampling techniques and genetic algorithm for optimization attains improved performance.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115433635","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}

引用次数: 0

Prediction of Idiopathic Recurrent Spontaneous Miscarriage using Machine Learning 利用机器学习预测特发性复发性自然流产

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085363

Dadoma Sherpa, Rajwade Dhruva Abhijit, I. Mitra, Dhruba Dhar, Sunita Sharma, P. Chakraborty, K. Chaudhury

{"title":"Prediction of Idiopathic Recurrent Spontaneous Miscarriage using Machine Learning","authors":"Dadoma Sherpa, Rajwade Dhruva Abhijit, I. Mitra, Dhruba Dhar, Sunita Sharma, P. Chakraborty, K. Chaudhury","doi":"10.1109/ICCECE51049.2023.10085363","DOIUrl":"https://doi.org/10.1109/ICCECE51049.2023.10085363","url":null,"abstract":"Recurrent spontaneous miscarriage (RSM) is defined as the spontaneous loss of two or more clinically diagnosed pregnancies within 20 weeks of gestation. Despite extensive research, etiology remains undefined in 50% of RSM cases, and are classified as idiopathic. Thus, further study is warranted to understand molecular mechanism associated with the disease pathogenesis. In the present study, we aim to identify Raman fingerprints in endometrial/uterine tissues of women with history of idiopathic recurrent spontaneous miscarriage (IRSM) and controls by performing Raman spectroscopy with chemometric analysis and spectral classification models. Unsupervised analysis such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and supervised analysis such as orthogonal projections to latent structures discriminant analysis (OPLS-DA) showed a distinct separation between IRSM and controls. The principal component loading plots indicated that proteins, amino acids, cholesterol and glutamate were responsible for the separation between the two groups. The pre-processed Raman spectral data were subjected to eight different machine learning (ML) classifiers with hyperparameter optimization to develop prediction models. Comparing the various algorithms, support vector machine (SVM), decision tree (DT), Extreme Gradient Boosting (XGBoost), convolutional neural network (CNN), and artificial neural network (ANN) outperform the other models based on accuracy (< 85%). Next, grid search and Bayesian optimization was used for tuning the hyperparameters of all methods. Further, 10-fold cross-validation was done to validate the model performances.The present findings confirm the feasibility of using Raman spectroscopy combined with ML algorithm may facilitate a better understanding of this pathology.","PeriodicalId":447131,"journal":{"name":"2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134487611","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}

引用次数: 0

Seven Level CHB Multilevel Inverter based STATCOM using Decoupled Control & DC Voltage Balancing 基于解耦控制和直流电压平衡的七电平CHB多电平逆变器

2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) Pub Date : 2023-01-20 DOI: 10.1109/ICCECE51049.2023.10085627

Vineet Bharadwaj, Samudra Panda, Sourabh Kundu, Subrata Banerjee

引用次数: 1