Computer Journal最新文献_第6页

Hyper-Hamiltonian Laceability of Cartesian Products of Cycles and Paths 循环与路径的笛卡儿积的超哈密顿可缺性

4区计算机科学

Computer Journal Pub Date : 2023-01-22 DOI: 10.1093/comjnl/bxac196

Yuxing Yang

引用次数: 0

Android Malware Detection in Bytecode Level Using TF-IDF and XGBoost 利用TF-IDF和XGBoost在字节码级别检测Android恶意软件

4区计算机科学

Computer Journal Pub Date : 2023-01-22 DOI: 10.1093/comjnl/bxac198

Gokhan Ozogur, Mehmet Ali Erturk, Zeynep Gurkas Aydin, Muhammed Ali Aydin

{"title":"Android Malware Detection in Bytecode Level Using TF-IDF and XGBoost","authors":"Gokhan Ozogur, Mehmet Ali Erturk, Zeynep Gurkas Aydin, Muhammed Ali Aydin","doi":"10.1093/comjnl/bxac198","DOIUrl":"https://doi.org/10.1093/comjnl/bxac198","url":null,"abstract":"Abstract Android is the dominant operating system in the smartphone market and there exists millions of applications in various application stores. The increase in the number of applications has necessitated the detection of malicious applications in a short time. As opposed to dynamic analysis, it is possible to obtain results in a shorter time in static analysis as there is no need to run the applications. However, obtaining various information from application packages using reverse engineering techniques still requires a substantial amount of processing power. Although some attempts have been made to solve this problem by analyzing binary files without decoding the source code, there is still more work to be done in this area. In this study, we analyzed the applications in bytecode level without decoding the binary source files. We proposed a model using Term Frequency - Inverse Document Frequency (TF-IDF) word representation for feature extraction and Extreme Gradient Boosting (XGBoost) method for classification. The experimental results show that our model classifies a given application package as a malware or benign in 2.75 s with 99.05% F1-score on a balanced dataset, and in 3.30 s with 99.35% F1-score on an imbalanced dataset containing obfuscated malwares.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135047366","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

A Deep Learning Model for Energy-Aware Task Scheduling Algorithm Based on Learning Automata for Fog Computing 基于雾计算学习自动机的能量感知任务调度算法深度学习模型

4区计算机科学

Computer Journal Pub Date : 2023-01-10 DOI: 10.1093/comjnl/bxac192

Reza Ebrahim Pourian, Mehdi Fartash, Javad Akbari Torkestani

{"title":"A Deep Learning Model for Energy-Aware Task Scheduling Algorithm Based on Learning Automata for Fog Computing","authors":"Reza Ebrahim Pourian, Mehdi Fartash, Javad Akbari Torkestani","doi":"10.1093/comjnl/bxac192","DOIUrl":"https://doi.org/10.1093/comjnl/bxac192","url":null,"abstract":"Abstract This paper presents an artificial intelligence deep learning model for an energy-aware task scheduling algorithm based on learning automata (LA) in the Fog Computing (FC) Applications. FC is a distributed computing model that serves as an intermediate layer between the cloud and Internet of Things (IoT) to improve the quality of service. The IoT is the closest model to the wireless sensor network (WSN). One of its important applications is to create a global approach to health care system infrastructure development that reflects recent advances in WSN. The most influential factor in energy consumption is task scheduling. In this paper, the issue of reducing energy consumption is investigated as an important challenge in the fog environment. Also, an algorithm is presented to solve the task scheduling problem based on LA and measure the makespan (MK) and cost parameters. Then, a new artificial neural network deep model is proposed, based on the presented LA task scheduling fog computing algorithm. The proposed neural model can predict the relation among MK, energy and cost parameters versus VM length for the first time. The proposed model results show that all of the desired parameters can be predicted with high precision.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136266660","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

Hitting Times of Random Walks on Edge Corona Product Graphs 边电晕积图上随机行走的命中次数

4区计算机科学

Computer Journal Pub Date : 2023-01-09 DOI: 10.1093/comjnl/bxac189

Mingzhe Zhu, Wanyue Xu, Wei Li, Zhongzhi Zhang, Haibin Kan

引用次数: 0

IoT Data Quality Issues and Potential Solutions: A Literature Review 物联网数据质量问题和潜在解决方案:文献综述

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-10-01 DOI: 10.1093/comjnl/bxab183

Taha Mansouri;Mohammad Reza Sadeghi Moghadam;Fatemeh Monshizadeh;Ahad Zareravasan

引用次数: 1

Reusable Group Fuzzy Extractor and Group-Shared Bitcoin Wallet 可重用组模糊提取器和组共享比特币钱包

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-10-01 DOI: 10.1093/comjnl/bxab185

Jie Ma;Bin Qi;Kewei Lv

引用次数: 0

Particle Rider Mutual Information and Dendritic-Squirrel Search Algorithm With Artificial Immune Classifier for Brain Tumor Classification 基于粒子骑士互信息和树突状松鼠搜索的人工免疫分类器脑肿瘤分类

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-10-01 DOI: 10.1093/comjnl/bxab194

Rahul Ramesh Chakre;Dipak V Patil

{"title":"Particle Rider Mutual Information and Dendritic-Squirrel Search Algorithm With Artificial Immune Classifier for Brain Tumor Classification","authors":"Rahul Ramesh Chakre;Dipak V Patil","doi":"10.1093/comjnl/bxab194","DOIUrl":"https://doi.org/10.1093/comjnl/bxab194","url":null,"abstract":"Magnetic Resonance Images (MRI) is an imperative imaging modality employed in the medical diagnosis tool for detecting brain tumors. However, the major obstacle in MR images classification is the semantic gap between low-level visual information obtained by MRI machines and high-level information alleged by the clinician. Hence, this research article introduces a novel technique, namely Dendritic-Squirrel Search Algorithm-based Artificial immune classifier (Dendritic-SSA-AIC) using MRI for brain tumor classification. Initially the pre-processing is performed followed by segmentation is devised using sparse fuzzy-c-means (Sparse FCM) is employed for segmentation to extract statistical and texture features. Furthermore, the Particle Rider mutual information (PRMI) is employed for feature selection, which is devised by integrating Particle swarm optimization, Rider optimization algorithm and mutual information. AIC is employed to classify the brain tumor, in which the Dendritic-SSA algorithm designed by combining dendritic cell algorithm and Squirrel search algorithm (SSA). The proposed PRMI-Dendritic-SSA-AIC provides superior performance with maximal accuracy of 97.789%, sensitivity of 97.577% and specificity of 98%.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"66 3","pages":"743-762"},"PeriodicalIF":1.4,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49946833","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

Multi-Class Liver Cancer Diseases Classification Using CT Images 基于CT图像的肝癌多类型分类

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-10-01 DOI: 10.1093/comjnl/bxab162

A Krishan;D Mittal

引用次数: 2

PBFL: Communication-Efficient Federated Learning via Parameter Predicting 基于参数预测的高效沟通联邦学习

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-10-01 DOI: 10.1093/comjnl/bxab184

Kaiju Li;Chunhua Xiao

{"title":"PBFL: Communication-Efficient Federated Learning via Parameter Predicting","authors":"Kaiju Li;Chunhua Xiao","doi":"10.1093/comjnl/bxab184","DOIUrl":"https://doi.org/10.1093/comjnl/bxab184","url":null,"abstract":"Federated learning (FL) is an emerging privacy-preserving technology for machine learning, which enables end devices to cooperatively train a global model without uploading their local sensitive data. Because of limited network bandwidth and considerable communication overhead, communication efficiency has become an essential bottleneck for FL. Existing solutions attempt to improve this situation by reducing communication rounds while usually come with more computation resource consumption or model accuracy deterioration. In this paper, we propose a parameter Prediction-Based DL (PBFL). In which an extended Kalman filter-based prediction algorithm, a practical prediction error threshold setting mechanism and an effective global model updating strategy are included. Instead of collecting all updates from participants, PBFL takes advantage of predicting values to aggregate the model, which substantially reduces required communication rounds while guaranteeing model accuracy. Inspired by the idea of prediction, each participant checks whether its prediction value is out of the tolerance threshold limits and only uploads local updates that have an inaccurate prediction value. In this way, no additional local computational resources are required. Experimental results on both multilayer perceptrons and convolutional neural networks show that PBFL outperforms the state-of-the-art methods and improves the communication efficiency by >66% with 1% higher model accuracy.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"66 3","pages":"626-642"},"PeriodicalIF":1.4,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49977706","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: 1

A New Approach for Resource Recommendation in the Fog-Based IoT Using a Hybrid Algorithm 基于雾的物联网资源推荐的混合算法

IF 1.4 4区计算机科学

Computer Journal Pub Date : 2021-10-01 DOI: 10.1093/comjnl/bxab189

Zhiwang Xu;Huibin Qin;Shengying Yang;Seyedeh Maryam Arefzadeh

{"title":"A New Approach for Resource Recommendation in the Fog-Based IoT Using a Hybrid Algorithm","authors":"Zhiwang Xu;Huibin Qin;Shengying Yang;Seyedeh Maryam Arefzadeh","doi":"10.1093/comjnl/bxab189","DOIUrl":"https://doi.org/10.1093/comjnl/bxab189","url":null,"abstract":"Internet of things (IoT) is an architecture of connected physical objects; these objects can communicate with each other and transmit and receive data. Also, fog-based IoT is a distributed platform that provides reliable access to virtualized resources based on various technologies such as high-performance computing and service-oriented design. A fog recommender system is an intelligent engine that suggests suitable services for fog users with less answer time and more accuracy. With the rapid growth of files and information sharing, fog recommender systems’ importance is also increased. Besides, the resource management problem appears challenging in fog-based IoT because of the fog's unpredictable and highly variable environment. However, many current methods suffer from the low accuracy of fog recommendations. Due to this problem's Non-deterministic Polynomial-time (NP)-hard nature, a new approach is presented for resource recommendation in the fog-based IoT using a hybrid optimization algorithm. To simulate the suggested method, the CloudSim simulation environment is used. The experimental results show that the accuracy is optimized by about 1–8% compared with the Cooperative Filtering method utilizing Smoothing and Fusing and Artificial Bee Colony algorithm. The outcomes of the present paper are notable for scholars, and they supply insights into subsequent study domains in this field.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"66 3","pages":"692-710"},"PeriodicalIF":1.4,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49977707","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: 1