Applied Computer Science最新文献_第7页

CYBER SECURITY IN INDUSTRIAL CONTROL SYSTEMS (ICS): A SURVEY OF ROWHAMMER VULNERABILITY 工业控制系统中的网络安全：ROWHAMMER漏洞调查

Applied Computer Science Pub Date : 2022-06-30 DOI: 10.35784/acs-2022-15

Hakan Aydin, A. Sertbas

引用次数: 4

TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING 基于DENSENET和迁移学习的番茄病害检测模型

Applied Computer Science Pub Date : 2022-06-30 DOI: 10.35784/acs-2022-13

Mahmoud Bakr, Sayed Abdel-Gaber, M. Nasr, M. Hazman

引用次数: 1

A DISTRIBUTED ALGORITHM FOR PROTEIN IDENTIFICATION FROM TANDEM MASS SPECTROMETRY DATA 从串联质谱数据中进行蛋白质鉴定的分布式算法

Applied Computer Science Pub Date : 2022-06-30 DOI: 10.35784/acs-2022-10

Katarzyna Orzechowska, T. Rubel, R. Kurjata, Krzysztof Zaremba

{"title":"A DISTRIBUTED ALGORITHM FOR PROTEIN IDENTIFICATION FROM TANDEM MASS SPECTROMETRY DATA","authors":"Katarzyna Orzechowska, T. Rubel, R. Kurjata, Krzysztof Zaremba","doi":"10.35784/acs-2022-10","DOIUrl":"https://doi.org/10.35784/acs-2022-10","url":null,"abstract":"Tandem mass spectrometry is an analytical technique widely used in proteomics for the high-throughput characterization of proteins in biological samples. Modern in-depth proteomic studies require the collection of even millions of mass spectra representing short protein fragments (peptides). In order to identify the peptides, the measured spectra are most often scored against a database of amino acid sequences of known proteins. Due to the volume of input data and the sizes of proteomic databases, this is a resource-intensive task, which requires an efficient and scalable computational strategy. Here, we present SparkMS, an algorithm for peptide and protein identification from mass spectrometry data explicitly designed to work in a distributed computational environment. To achieve the required performance and scalability, we use Apache Spark, a modern framework that is becoming increasingly popular not only in the field of “big data” analysis but also in bioinformatics. This paper describes the algorithm in detail and demonstrates its performance on a large proteomic dataset. Experimental results indicate that SparkMS scales with the number of worker nodes and the increasing complexity of the search task. Furthermore, it exhibits a protein identification efficiency comparable to X!Tandem, a widely-used proteomic search engine.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43968129","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

APPLICATION OF FINITE DIFFERENCE METHOD FOR MEASUREMENT SIMULATION IN ULTRASOUND TRANSMISSION TOMOGRAPHY 有限差分法在超声透射层析成像测量仿真中的应用

Applied Computer Science Pub Date : 2022-06-30 DOI: 10.35784/acs-2022-16

Konrad Kania, Mariusz Mazurek, T. Rymarczyk

引用次数: 0

USE OF SERIOUS GAMES FOR THE ASSESSMENT OF MILD COGNITIVE IMPAIRMENT IN THE ELDERLY 使用严肃游戏评估老年人轻度认知障碍

Applied Computer Science Pub Date : 2022-03-31 DOI: 10.35784/acs-2022-9

Moon-gee Choi

引用次数: 0

HISTOPATHOLOGY IMAGE CLASSIFICATION USING HYBRID PARALLEL STRUCTURED DEEP-CNN MODELS 基于混合并行结构深度cnn模型的组织病理学图像分类

Applied Computer Science Pub Date : 2022-03-30 DOI: 10.35784/acs-2022-2

K. Dsouza, Z. Ansari

{"title":"HISTOPATHOLOGY IMAGE CLASSIFICATION USING HYBRID PARALLEL STRUCTURED DEEP-CNN MODELS","authors":"K. Dsouza, Z. Ansari","doi":"10.35784/acs-2022-2","DOIUrl":"https://doi.org/10.35784/acs-2022-2","url":null,"abstract":"The healthcare industry is one of the many out there that could majorly benefit from advancement in the technology it utilizes. Artificial intelligence (AI) technologies are especially integral and specifically deep learning (DL); a highly useful data-driven technology. It is applied in a variety of different methods but it mainly depends on the structure of the available data. However, with varying applications, this technology produces data in different contexts with particular connotations. Reports which are the images of scans play a great role in identifying the existence of the disease in a patient. Further, the automation in processing these images using technology like CNN-based models makes it highly efficient in reducing human errors otherwise resulting in large data. Hence this study presents a hybrid deep learning architecture to classify the histopathology images to identify the presence of cancer in a patient. Further, the proposed models are parallelized using the TensorFlow-GPU framework to accelerate the training of these deep CNN (Convolution Neural Networks) architectures. This study uses the transfer learning technique during training and early stopping criteria are used to avoid overfitting during the training phase. these models use LSTM parallel layer imposed in the model to experiment with four considered architectures such as MobileNet, VGG16, and ResNet with 101 and 152 layers. The experimental results produced by these hybrid models show that the capability of Hybrid ResNet101 and Hybrid ResNet152 architectures are highly suitable with an accuracy of 90% and 92%. Finally, this study concludes that the proposed Hybrid ResNet-152 architecture is highly efficient in classifying the histopathology images. The proposed study has conducted a well-focused and detailed experimental study which will further help researchers to understand the deep CNN architectures to be applied in application development.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41613980","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

STRENGTH ANALYSIS OF A PROTOTYPE COMPOSITE HELICOPTER ROTOR BLADE SPAR 某型复合材料直升机旋翼桨叶强度分析

Applied Computer Science Pub Date : 2022-03-30 DOI: 10.35784/acs-2022-1

R. Kliza, Karol Ścisłowski, K. Siadkowska, Jacek Padyjasek, M. Wendeker

引用次数: 1

DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE LANDSAT-8光学卫星图像红、近红外波段植被区的检测与分类

Applied Computer Science Pub Date : 2022-03-30 DOI: 10.35784/acs-2022-4

Anusha Nallapareddy

{"title":"DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE","authors":"Anusha Nallapareddy","doi":"10.35784/acs-2022-4","DOIUrl":"https://doi.org/10.35784/acs-2022-4","url":null,"abstract":"Detection and classification of vegetation is a crucial technical task in the management of natural resources since vegetation serves as a foundation for all living things and has a significant impact on climate change such as impacting terrestrial carbon dioxide (CO2). Traditional approaches for acquiring vegetation covers such as field surveys, map interpretation, collateral and data analysis are ineffective as they are time consuming and expensive. In this paper vegetation regions are automatically detected by applying simple but effective vegetation indices Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) on red(R) and near infrared (NIR) bands of Landsat-8 satellite image. Remote sensing technology makes it possible to analyze vegetation cover across wide areas in a cost-effective manner. Using remotely sensed images, the mapping of vegetation requires a number of factors, techniques, and methodologies. The rapid improvement of remote sensing technologies broadens possibilities for image sources making remotely sensed images more accessible. The dataset used in this paper is the R and NIR bands of Level-1 Tier 1 Landsat-8 optical remote sensing image acquired on 6th September 2013, is processed and made available to users on 2nd May 2017. The pre-processing involving sub-setting operation is performed using the ERDAS Imagine tool on R and NIR bands of Landsat-8 image. The NDVI and SAVI are utilized to extract vegetation features automatically by using python language. Finally by establishing a threshold, vegetation cover of the research area is detected and then classified.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49162887","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

DETECTION OF SOURCE CODE IN INTERNET TEXTS USING AUTOMATICALLY GENERATED MACHINE LEARNING MODELS 使用自动生成的机器学习模型检测互联网文本中的源代码

Applied Computer Science Pub Date : 2022-03-30 DOI: 10.35784/acs-2022-7

M. Badurowicz

引用次数: 1

IMPROVING CORONARY HEART DISEASE PREDICTION BY OUTLIER ELIMINATION 应用异常值消除改进冠心病预测

Applied Computer Science Pub Date : 2022-03-30 DOI: 10.35784/acs-2022-6

Lubna Riyaz, M. A. Butt, Majid Zaman

{"title":"IMPROVING CORONARY HEART DISEASE PREDICTION BY OUTLIER ELIMINATION","authors":"Lubna Riyaz, M. A. Butt, Majid Zaman","doi":"10.35784/acs-2022-6","DOIUrl":"https://doi.org/10.35784/acs-2022-6","url":null,"abstract":"Nowadays, heart disease is the major cause of deaths globally. According to a survey conducted by the World Health Organization, almost 18 million people die of heart diseases (or cardiovascular diseases) every day. So, there should be a system for early detection and prevention of heart disease. Detection of heart disease mostly depends on the huge pathological and clinical data that is quite complex. So, researchers and other medical professionals are showing keen interest in accurate prediction of heart disease. Heart disease is a general term for a large number of medical conditions related to heart and one of them is the coronary heart disease (CHD). Coronary heart disease is caused by the amassing of plaque on the artery walls. In this paper, various machine learning base and ensemble classifiers have been applied on heart disease dataset for efficient prediction of coronary heart disease. Various machine learning classifiers that have been employed include k-nearest neighbor, multilayer perceptron, multinomial naïve bayes, logistic regression, decision tree, random forest and support vector machine classifiers. Ensemble classifiers that have been used include majority voting, weighted average, bagging and boosting classifiers. The dataset used in this study is obtained from the Framingham Heart Study which is a long-term, ongoing cardiovascular study of people from the Framingham city in Massachusetts, USA. To evaluate the performance of the classifiers, various evaluation metrics including accuracy, precision, recall and f1 score have been used. According to our results, the best accuracy was achieved by logistic regression, random forest, majority voting, weighted average and bagging classifiers but the highest accuracy among these was achieved using weighted average ensemble classifier. ","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42392134","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