2021 2nd International Informatics and Software Engineering Conference (IISEC)最新文献_第5页

Application of Deep Convolution Neural Network in Breast Cancer Prediction using Digital Mammograms 深度卷积神经网络在数字乳房x光片乳腺癌预测中的应用

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672368

Rafsan Al Mamun, Gazi Abu Rafin, Adnan Alam, Md. Al Imran Sefat

{"title":"Application of Deep Convolution Neural Network in Breast Cancer Prediction using Digital Mammograms","authors":"Rafsan Al Mamun, Gazi Abu Rafin, Adnan Alam, Md. Al Imran Sefat","doi":"10.1109/iisec54230.2021.9672368","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672368","url":null,"abstract":"Cancer, a diagnosis so dreaded and scary, that its fear alone can strike even the strongest of souls. The disease is often thought of as untreatable and unbearably painful, with usually, no cure available. Among all the cancers, breast cancer is the second most deadliest, especially among women. What decides the patients' fate is the early diagnosis of the cancer, facilitating subsequent clinical management. Mammography plays a vital role in the screening of breast cancers as it can detect any breast masses or calcifications early. However, the extremely dense breast tissues pose difficulty in the detection of cancer mass, thus, encouraging the use of machine learning (ML) techniques and artificial neural networks (ANN) to assist radiologists in faster cancer diagnosis. This paper explores the MIAS database, containing 332 digital mammograms from women, which were augmented and preprocessed, and fed into a custom and different pre-trained convolutional neural network (CNN) models, with the aim of differentiating healthy tissues from cancerous ones with high accuracy. Although the pre-trained CNN models produced splendid results, the custom CNN model came out on top, achieving test accuracy, AUC, precision, recall and $mathbf{F}_{1}$ scores of 0.9362, 0.9407, 0.9200, 0.8025 and 0.8572 respectively while having minimal to no overfitting. The paper, along with proposing a new custom CNN model for better breast cancer classification using raw mammograms, focuses on the significance of computer-aided detection (CAD) models overall in the early diagnosis of breast cancer. While a diagnosis of breast cancer may still leave patients dreaded, we believe our research can be a symbol of hope for all.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128457908","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

Risks of Digital Transformation: Review of Machine Learning Algorithms in Credit Card Fraud Detection 数字化转型的风险:信用卡欺诈检测中的机器学习算法综述

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672354

Güneş Gürsoy, A. Varol

引用次数: 0

Biletini Devret: A Secure Mobile App for Ticket Sales Biletini Devret:一个安全的移动票务销售应用程序

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672404

Firat Ak, Veli Batuhan Özkan, Gokhan Gonder, Ersun Sumeroglu, Meltem Eryilmaz

引用次数: 0

Deep Learning Approach to Predict Forest Fires Using Meteorological Measurements 利用气象测量预测森林火灾的深度学习方法

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672446

Naaman Omar, Adel Al-zebari, A. Şengur

{"title":"Deep Learning Approach to Predict Forest Fires Using Meteorological Measurements","authors":"Naaman Omar, Adel Al-zebari, A. Şengur","doi":"10.1109/iisec54230.2021.9672446","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672446","url":null,"abstract":"Forest fires are a serious environmental concern that causes economic and ecological harm as well as puts human lives in danger. Controlling such a condition necessitates quick identification. One option is to employ artificial intelligence (AI) techniques based on some measurements, such as those supplied by meteorological stations. Meteorological measurements namely temperature, relative humidity, rain, and wind are known to impact forest fires, and numerous fire indices, such as the Forest Fire Weather Index (FWI), rely on this information. In this paper, a deep learning approach namely the long short-term memory (LSTM) based regression method is used for efficient prediction of the forest fires. The LSTM approach is a recurrent neural network (RNN) that has become popular recently in the field of machine learning. A dataset that contains 12 features and 536 instances is used in the experimental works. The dataset is available in the UCI machine repository. The hold-out cross-validation method is used in the experiments and various metrics are used to evaluate the accuracy of the proposed model achievements. The results show that the proposed method produces reasonable predictions and outperforms traditional machine learning approaches.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129323783","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: 7

Chronic Renal Disease Prediction using Clinical Data and Different Machine Learning Techniques 使用临床数据和不同的机器学习技术预测慢性肾脏疾病

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672365

M. Raihan, Eshtiak Ahmed, Asif Karim, S. Azam, M. Raihan, L. Akter, M. Hassan

{"title":"Chronic Renal Disease Prediction using Clinical Data and Different Machine Learning Techniques","authors":"M. Raihan, Eshtiak Ahmed, Asif Karim, S. Azam, M. Raihan, L. Akter, M. Hassan","doi":"10.1109/iisec54230.2021.9672365","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672365","url":null,"abstract":"Chronic Renal Disease (CRD) or Chronic Kidney Disease (CKD) is defined as the continuous loss of kidney function. It's a long-term condition in which the kidney or renal doesn't work properly, gets damaged and can't filter blood on a regular basis. Diabetes, high blood pressure, swollen feet, ankles or hands and other disorders can cause chronic renal disease. By gradual progression and lack of treatment, it can lead to kidney failure. A prior prognosis of CKD can nourish the quality of life to a higher range in such circumstances and can enhance the attribute of life to a larger province. Now a days, bioscience is playing a significant role in the aspect of diagnosing and detecting numerous health conditions. Machine Learning (ML) as well as Data Mining (DM) methods are playing the leading role in the realm of biosciences. Our objective is to predict and diagnose (CKD) with some machine learning algorithms. In this study, an attempt to diagnose chronic renal disease has been taken with four ML algorithms named XGBoost, Adaboost, Logistic Regression (LR) as well as Random Forest (RF). By using decision tree-based classifiers and analyzing the dataset with comparing their performance, we attempted to diagnose CKD in this study. The results of the model in this study showed prosperous indications of a better prognosis for the diagnosis of kidney diseases. Considering and contemplating the performance analysis, it is accomplished that Random Forest ensemble learning algorithm provides better classification performance than other classification methods.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131683672","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: 2

Quantum phase estimation based algorithms for machine learning 基于量子相位估计的机器学习算法

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672406

Oumayma Ouedrhiri, Oumayma Banouar, S. E. Hadaj, S. Raghay

{"title":"Quantum phase estimation based algorithms for machine learning","authors":"Oumayma Ouedrhiri, Oumayma Banouar, S. E. Hadaj, S. Raghay","doi":"10.1109/iisec54230.2021.9672406","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672406","url":null,"abstract":"Quantum computing is certainly one of the greatest advances in the computer science field. Thanks to the parallelism and entanglement properties, it has proved to offer several advantages compared to the classical algorithms especially in the great reduction of the processing time. Quantum phase estimation (QPE) is one of the most important algorithms for quantum computing. It is known as the eigenvalue finding module for unitary operators. The Fourier transform is the key to this procedure. It has been researched and used to solve many problems such as the order finding problem, and the factoring problem. It was also applied for quantum sampling algorithms and the calculation of the eigenvalues of unitary matrices. In this paper, we study three important quantum algorithms for machine learning that use the QPE algorithm as a subroutine: the quantum principal components analysis (PCA) for data visualization, the Harrow-Hassidim-Lloyd (HHL) algorithm for solving linear systems, and the quantum singular value thresholding (SVT) for matrix completion in recommender systems. We also discuss the advantages and limits of such algorithms compared to their classical versions. Then we discuss potential ways of amelioration of such algorithms, and end with a proposed approach for further improvement.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116480652","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

“Trust Pass” - Blockchain-Based Trusted Digital Identity Platform Towards Digital Transformation “信任通”——面向数字化转型的区块链可信数字身份平台

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672336

Kalpa Dissanayake, Pavan Somarathne, Ushan Fernando, Devaki Pathmasiri, C. Liyanapathirana, Dr. Lakmal Rupasinghe

{"title":"“Trust Pass” - Blockchain-Based Trusted Digital Identity Platform Towards Digital Transformation","authors":"Kalpa Dissanayake, Pavan Somarathne, Ushan Fernando, Devaki Pathmasiri, C. Liyanapathirana, Dr. Lakmal Rupasinghe","doi":"10.1109/iisec54230.2021.9672336","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672336","url":null,"abstract":"According to the United States Census Bureau, by June 2019 world population on earth was 7.5 billion, which exceeds the world population of 7.2 billion as of 2015. Each of these citizens needs to prove their identity to fulfil their day-to-day routine. In this current digital revolution whole world is transforming to digitalization. Therefore, proving someone's identity in the digital space is a must. Being able to track a person digitally can eliminate identity theft and most incidents related to online harassment. With the focus on data privacy and security of citizens, we have proposed “Trust Pass”: Cyber Security Intelligence-based trusted digital identity platform capable of registering and verifying service providers based on document validation neural network model (95.4% accuracy) and allowing citizens to authenticate themselves to service providers with three-factor biometrics authentication with liveness detection neural network model (99.8% accuracy). The requests of the whole system are secured with Cyber Security Threat Intelligence System, and unusual activities of users are monitored through Informative Data Analytics Engine. All the sensitive user data is saved using a blockchain to ensure user privacy while reducing the system's vulnerability.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114170727","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: 3

Meticulous Use of Turkish Informatics Terms to Deter Defective Turkish 细致地使用土耳其信息学术语来阻止有缺陷的土耳其语

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672408

İ. Tabak, A. Pekel, Koray Özer, Eymen Görgülü, Tuncer ÖOren

引用次数: 0

Driving Through a Bend: Detection of Unsafe Driving Patterns and Prevention of Heavy Good Vehicle Rollovers 驾车通过弯道:检测不安全驾驶模式和预防重型车辆侧翻

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672345

E.M.A.K. Siriwardana, S. K. Amila, S.G.L.D.H. Kaushalya, S. Chandrasiri, Vijani S. Piyawardana

{"title":"Driving Through a Bend: Detection of Unsafe Driving Patterns and Prevention of Heavy Good Vehicle Rollovers","authors":"E.M.A.K. Siriwardana, S. K. Amila, S.G.L.D.H. Kaushalya, S. Chandrasiri, Vijani S. Piyawardana","doi":"10.1109/iisec54230.2021.9672345","DOIUrl":"https://doi.org/10.1109/iisec54230.2021.9672345","url":null,"abstract":"Road Traffic Crashes are simply ordinary within the present world. However, heavy goods vehicles (HGV) rollover has become a significant problem worldwide. Depending on the data collected, the sources used, and several key factors contribute to HGV overturning. Accidents overturn due to longer reaction time, shriveled driving performance, lack of driving experience, and driver carelessness. In further consideration, over-steering to turning over, not steering enough to stay in lane, over speed, high located center of gravity, weather condition, road condition, and the road's curves are the most contributing reasons to the overturning of a long vehicle. Thus, this paper proposes machine learning processes to overcome these problems and reduce the HGV rollovers. The proposed system includes a vehicle-equipped system and a ground-based operational surveillance camera. The Vehicle-equipped system can determine the safe speed at which the vehicle should travel according to the type of vehicle and curvature of the road and can detect road cracks and notify the driver by sending the notification to the vehicle dashboard screen. The ground-based driver support system can detect safe speed for HGVs and determine various other traffic parameters which can affect the HGV rollover accidents.","PeriodicalId":344273,"journal":{"name":"2021 2nd International Informatics and Software Engineering Conference (IISEC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115362198","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

Evaluation of algebraic graph clustering algorithms for complex networks 复杂网络代数图聚类算法的评价

2021 2nd International Informatics and Software Engineering Conference (IISEC) Pub Date : 2021-12-16 DOI: 10.1109/iisec54230.2021.9672390

K. Erciyes

引用次数: 0