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SBCSim: Classification and Prioritization of Similarities Between Versions SBCSim:版本间相似性的分类和优先级
IF 0.6
International Journal of Software Innovation Pub Date : 2022-01-01 DOI: 10.4018/ijsi.309111
Ritu Garg, R. K. Singh
{"title":"SBCSim: Classification and Prioritization of Similarities Between Versions","authors":"Ritu Garg, R. K. Singh","doi":"10.4018/ijsi.309111","DOIUrl":"https://doi.org/10.4018/ijsi.309111","url":null,"abstract":"","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"10 1","pages":"1-18"},"PeriodicalIF":0.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470986","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 Study on Deep Learning Model Autonomous Driving Based on Big Data 基于大数据的深度学习模型自动驾驶研究
IF 0.6
International Journal of Software Innovation Pub Date : 2021-10-01 DOI: 10.4018/ijsi.289174
{"title":"A Study on Deep Learning Model Autonomous Driving Based on Big Data","authors":"","doi":"10.4018/ijsi.289174","DOIUrl":"https://doi.org/10.4018/ijsi.289174","url":null,"abstract":"Autonomous driving requires a large amount of data to improve performance, and we tried to solve this problem by using CARLA simulation. In order to utilize the actual data, when the sensor installed in the vehicle recognizes the dangerous situation, the embedded device detects and judges the danger 5-10 seconds in advance, and the acquired various dangerous situation data is sent to the iCloud(server) for retraining with new data. Over time, the learning model's performance gets better and more perfect. The deep learning model used for training is a detection model based on a convolution neural network (CNN), and a YOLO model that shows optimal detection performance. We propose a connectivity vehicle technology system solution, which is an important part of autonomous driving, using big data-based deep learning algorithms. In this study, We implement and extensively evaluate the system by auto ware under various settings using a popular end-to-end self-driving software Autoware on NVIDIA Corporation for the development of autonomous vehicles.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44010407","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
Network traffic analysis using Machine Learning Techniques in IoT Network 物联网网络中使用机器学习技术的网络流量分析
IF 0.6
International Journal of Software Innovation Pub Date : 2021-10-01 DOI: 10.4018/ijsi.289172
{"title":"Network traffic analysis using Machine Learning Techniques in IoT Network","authors":"","doi":"10.4018/ijsi.289172","DOIUrl":"https://doi.org/10.4018/ijsi.289172","url":null,"abstract":"Internet of things devices are not very intelligent and resource-constrained; thus, they are vulnerable to cyber threats. Cyber threats would become potentially harmful and lead to infecting the machines, disrupting the network topologies, and denying services to their legitimate users. Artificial intelligence-driven methods and advanced machine learning-based network investigation prevent the network from malicious traffics. In this research, a support vector machine learning technique was used to classify normal and abnormal traffic. Network traffic analysis has been done to detect and prevent the network from malicious traffic. Static and dynamic analysis of malware has been done. Mininet emulator was selected for network design, VMware fusion for creating a virtual environment, hosting OS was Ubuntu Linux, network topology was a tree topology. Wireshark was used to open an existing pcap file that contains network traffic. The support vector machine classifier demonstrated the best performance with 99% accuracy.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49161101","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
KDA based WKNN-SVM Method for Activity Recognition System from Smartphone Data 基于KDA的WKNN-SVM智能手机数据活动识别方法
IF 0.6
International Journal of Software Innovation Pub Date : 2021-10-01 DOI: 10.4018/ijsi.289170
{"title":"KDA based WKNN-SVM Method for Activity Recognition System from Smartphone Data","authors":"","doi":"10.4018/ijsi.289170","DOIUrl":"https://doi.org/10.4018/ijsi.289170","url":null,"abstract":"This article describes a new scheme for a physical activity recognition process based on carried smartphone embedded sensors, such as accelerometer and gyroscope. For this purpose, the WKNN-SVM algorithm has been proposed to predict physical activities such as Walking, Standing or Sitting. It combines Weighted K-Nearest Neighbours (WKNN) and Support Vector Machines (SVM). The signals generated from the sensors are processed and then reduced using the Kernel Discriminant Analysis (KDA) by selecting the best discriminating components of the data. We performed different tests on four public datasets where the participants performed different activities carrying a smartphone. We demonstrated through several experiments that KDA/WKNN-SVM algorithm can improve the overall recognition performances, and has a higher recognition rate than the baseline methods using the machine learning and deep learning algorithms.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47533361","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
On the Accelerated Convergence of Genetic Algorithm Using GPU Parallel Operations 基于GPU并行运算的遗传算法加速收敛
IF 0.6
International Journal of Software Innovation Pub Date : 2015-10-01 DOI: 10.4018/IJSI.2015100101
Cheng-Chieh Li, Jung-Chun Liu, Chu-Hsing Lin, Winston Lo
{"title":"On the Accelerated Convergence of Genetic Algorithm Using GPU Parallel Operations","authors":"Cheng-Chieh Li, Jung-Chun Liu, Chu-Hsing Lin, Winston Lo","doi":"10.4018/IJSI.2015100101","DOIUrl":"https://doi.org/10.4018/IJSI.2015100101","url":null,"abstract":"The genetic algorithm plays a very important role in many areas of applications. In this research, the authors propose to accelerate the evolution speed of the genetic algorithm by parallel computing, and optimize parallel genetic algorithms by methods such as the island model. The authors find that when the amount of population increases, the genetic algorithm tends to converge more rapidly into the global optimal solution; however, it also consumes greater amount of computation resources. To solve this problem, the authors take advantage of the many cores of GPUs to enhance computation efficiency and develop a parallel genetic algorithm for GPUs. Different from the usual genetic algorithm that uses one thread for computation of each chromosome, the parallel genetic algorithm using GPUs evokes large amount of threads simultaneously and allows the population to scale greatly. The large amount of the next generation population of chromosomes can be divided by a block method; and after independently operating in each block for a few generation, selection and crossover operations of chromosomes can be performed among blocks to greatly accelerate the speed to find the global optimal solution. Also, the travelling salesman problem TSP is used as the benchmark for performance comparison of the GPU and CPU; however, the authors did not perform algebraic optimization for TSP.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"8 1","pages":"1-16"},"PeriodicalIF":0.6,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80545652","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: 11
Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering 利用多数排序模糊聚类提高软件故障预测的准确性
IF 0.6
International Journal of Software Innovation Pub Date : 2014-10-01 DOI: 10.4018/ijsi.2014100105
Golnoush Abaei, A. Selamat
{"title":"Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering","authors":"Golnoush Abaei, A. Selamat","doi":"10.4018/ijsi.2014100105","DOIUrl":"https://doi.org/10.4018/ijsi.2014100105","url":null,"abstract":"Despite proposing many software fault prediction models, this area has yet to be explored as still there is a room for stable and consistent model with better performance. In this paper, a new method is proposed to increase the accuracy of fault prediction based on the notion of fuzzy clustering and majority ranking. The authors investigated the effect of irrelevant and inconsistent modules on software fault prediction and tried to decrease it by designing a new framework, in which the entire project modules are clustered. The obtained results showed that fuzzy clustering could decrease the negative effect of irrelevant modules on prediction performance. Eight data sets from NASA and Turkish white-goods software is employed to evaluate our model. Performance evaluation in terms of false positive rate, false negative rate, and overall error showed the superiority of our model compared to other predicting models. The authors proposed majority ranking fuzzy clustering approach showed between 3% to 18% and 1% to 4% improvement in false negative rate and overall error, respectively, compared with other available proposed models (ACF and ACN) in more than half of the testing cases. According to the results, our systems can be used to guide testing effort by identifying fault prone modules to improve the quality of software development and software testing in a limited time and budget.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"25 1","pages":"179-193"},"PeriodicalIF":0.6,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88337706","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: 24
Preliminary Evaluation of a Software Security Learning Environment 软件安全学习环境的初步评价
IF 0.6
International Journal of Software Innovation Pub Date : 2014-07-01 DOI: 10.4018/ijsi.2014070103
A. Hazeyama, Masahito Saito
{"title":"Preliminary Evaluation of a Software Security Learning Environment","authors":"A. Hazeyama, Masahito Saito","doi":"10.4018/ijsi.2014070103","DOIUrl":"https://doi.org/10.4018/ijsi.2014070103","url":null,"abstract":"The importance of software security technologies is increasingly recognized with the increase in services available on the Internet. It is also important to foster human resources with knowledge and skills relevant to software security technologies. This article aims to construct a software security learning environment. It proposes a learning process for software security and constructed a learning environment that supported the learning process. This article describes a preliminary experiment to evaluate the learning process and the learning environment. It confirms usefulness of the learning process. It also identifies some improvements for the knowledge base system and learning environment, such as visualization support and traceability support.","PeriodicalId":55938,"journal":{"name":"International Journal of Software Innovation","volume":"4 1","pages":"113-125"},"PeriodicalIF":0.6,"publicationDate":"2014-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85251121","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: 4
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