Computer Systems Science and Engineering最新文献_第9页

Implementation of Hybrid Deep Reinforcement Learning Technique for Speech Signal Classification 语音信号分类中混合深度强化学习技术的实现

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.032491

R. Gayathri, K. Rani

引用次数: 0

Detecting Ethereum Ponzi Schemes Through Opcode Context Analysis and Oversampling-Based AdaBoost Algorithm 基于操作码上下文分析和过采样AdaBoost算法的以太坊庞氏骗局检测

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.039569

Mengxiao Wang, Jing Huang

{"title":"Detecting Ethereum Ponzi Schemes Through Opcode Context Analysis and Oversampling-Based AdaBoost Algorithm","authors":"Mengxiao Wang, Jing Huang","doi":"10.32604/csse.2023.039569","DOIUrl":"https://doi.org/10.32604/csse.2023.039569","url":null,"abstract":": Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting (AdaBoost) algorithm. Firstly, this paper uses the n-gram algorithm to extract more comprehensive contract opcode features and combine them with contract account features, which helps to improve the feature extraction effect. Meanwhile, adaptive synthetic sampling (ADASYN) is introduced to deal with class imbalanced data, and integrated with the Adaboost classifier. Finally, this paper uses the improved AdaBoost classifier for the identification of Ponzi scheme contracts. Experimentally, this paper tests our model in real-world smart contracts and compares it with representative methods in the aspect of F1-score and precision. Moreover, this article compares and discusses the state of art methods with our method in four aspects: data acquisition, data preprocessing, feature extraction, and classifier design. Both experiment and discussion validate the effectiveness of our model.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74362350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications 基于SFLA算法的D2D组播通信资源分配

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.030069

Wisam Hayder Mahdi, N. Taspinar

引用次数: 1

Real Time Automation and Ratio Control Using PLC & SCADA in Industry 4.0 工业4.0中PLC和SCADA的实时自动化和比例控制

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.030635

Basant Tomar, Narendra Kumar, M. Sreejeth

引用次数: 4

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model 一种基于深度集合模型的高效乳腺癌自动分类技术

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.035382

M. F. U. Rehman, Jawad Ahmad, E. S. Jaha, Abdullah Marish Ali, M. Alzain, Faisal Saeed

引用次数: 0

Behavioral Intention to Continue Using a Library Mobile App 继续使用图书馆移动应用程序的行为意向

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.033251

X. Zhang, H. Liu, Z. Liu, J. R. Ming, Y. Zhou

引用次数: 0

Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques 利用深度学习技术的包装方法学自动诊断多囊卵巢综合征

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.037812

M. Abouhawwash, S. Sridevi, Suma Christal Mary Sundararajan, Rohit Pachlor, F. Karim, D. S. Khafaga

{"title":"Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques","authors":"M. Abouhawwash, S. Sridevi, Suma Christal Mary Sundararajan, Rohit Pachlor, F. Karim, D. S. Khafaga","doi":"10.32604/csse.2023.037812","DOIUrl":"https://doi.org/10.32604/csse.2023.037812","url":null,"abstract":"One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers. In this research, the tri-stage wrapper method is used because it reduces the computation time. The proposed study for the Automatic diagnosis of PCOS contains preprocessing, data normalization, feature selection, and classification. A dataset with 39 characteristics, including metabolism, neuroimaging, hormones, and biochemical information for 541 subjects, was employed in this scenario. To start, this research pre-processed the information. Next for feature selection, a tri-stage wrapper method such as Mutual Information, ReliefF, Chi-Square, and Xvariance is used. Then, various classification methods are tested and trained. Deep learning techniques including convolutional neural network (CNN), multi-layer perceptron (MLP), Recurrent neural network (RNN), and Bi long short-term memory (Bi-LSTM) are utilized for categorization. The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method + CNN delivers the highest precision (97%), high accuracy (98.67%), and recall (89%) when compared with other machine learning algorithms. 240 CSSE, 2023, vol.47, no.1","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82405671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples 增强自适应脑机接口方法在残疾人智能援助中的应用

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.034682

Ali Usman Gondal, Javed Ferzund, Ahmad Shaf, M. Aamir, Samar M. Alqhtani, K. Mehdar, H. Halawani, Hassan A Alshamrani, Abdullah A. Asiri, Muhammad Irfan

引用次数: 0

Enhanced Best Fit Algorithm for Merging Small Files 小文件合并的增强最佳拟合算法

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.036400

Adnan Ali, N. Mirza, M. Ishak

引用次数: 0

Chest Radiographs Based Pneumothorax Detection Using Federated Learning 基于联邦学习的胸片气胸检测

IF 2.2 4区计算机科学

Computer Systems Science and Engineering Pub Date : 2023-01-01 DOI: 10.32604/csse.2023.039007

Ahmad S. Almadhor, A. Khan, Chitapong Wechtaisong, Iqra Yousaf, N. Kryvinska, U. Tariq, Haithem Ben Chikha

{"title":"Chest Radiographs Based Pneumothorax Detection Using Federated Learning","authors":"Ahmad S. Almadhor, A. Khan, Chitapong Wechtaisong, Iqra Yousaf, N. Kryvinska, U. Tariq, Haithem Ben Chikha","doi":"10.32604/csse.2023.039007","DOIUrl":"https://doi.org/10.32604/csse.2023.039007","url":null,"abstract":": Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse, causing air to enter the pleural cavity, the area close to the lungs and chest wall. The most persistent disease, as well as one that necessitates particular patient care and the privacy of their health records. The radiologists find it challenging to diagnose pneumothorax due to the variations in images. Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems. However, it is challenging to employ it in the medical field due to privacy issues and a lack of data. To address this issue, a federated learning framework based on an Xception neural network model is proposed in this research. The pneumothorax medical image dataset is obtained from the Kaggle repository. Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s performance. Min-max normalization technique is used to normalize the data, and the features are extracted from chest X-ray images. Then dataset converts into two windows to make two clients for local model training. Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side. To decrease the over-fitting effect, every client analyses the results three times. Client 1 performed better in round 2 with a 79.0% accuracy, and client 2 performed better in round 2 with a 77.0% accuracy. The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network, reaching a 79.28% accuracy while also providing privacy to the patient’s data.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76373991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0