Journal of Communications Software and Systems最新文献

Assessment of Transmitted Power Density in the Planar Multilayer Tissue Model due to Radiation from Dipole Antenna 偶极子天线辐射下平面多层组织模型发射功率密度的评估

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Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2022-0050

D. Poljak, A. Šušnjara, A. Fišić

引用次数: 2

IOT Bracelets for Guiding Blind People in an Indoor Environment 用于在室内环境中引导盲人的物联网手环

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Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2022-0160

A. Abusukhon

引用次数: 0

Signature-based Tree for Finding Frequent Itemsets 用于查找频繁项集的基于签名的树

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Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2022-0065

Mohamed El Hadi Benelhadj, Mohamed Mahmoud Deye, Y. Slimani

引用次数: 0

Comparison of Similarity Measures for Trajectory Clustering - Aviation Use Case 轨迹聚类相似度量的比较——航空用例

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Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2022-0116

Marija Todorić, Toni Mastelić

引用次数: 0

An Automated Approach to Horn Antenna Impedance Matching and Manufacturing Using 3D Printing 喇叭天线阻抗匹配与3D打印制造的自动化方法

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Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2023-0100

Dorijan Sablic-Nemec, M. Joler

引用次数: 0

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment 可持续环境中使用机器学习的端到端5G间/内切片性能管理和评估

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Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2022-0163

Noor Mohammedali, T. Kanakis, Ali Al-Sherbaz, Michael Opoku Agyeman

引用次数: 1

Data Analysis of the Web News Headlines based on Natural Language Processing 基于自然语言处理的网络新闻标题数据分析

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Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2023-0047

Hrvoje Karna, Maja Braović, L. Vicković, D. Krstinić

{"title":"Data Analysis of the Web News Headlines based on Natural Language Processing","authors":"Hrvoje Karna, Maja Braović, L. Vicković, D. Krstinić","doi":"10.24138/jcomss-2023-0047","DOIUrl":"https://doi.org/10.24138/jcomss-2023-0047","url":null,"abstract":"— This paper explores the problem of media content data analysis with the focus on the phenomenon of vaccination, closely related to the COVID-19 pandemic. The presented research is an extension of the previous work, but it differs in two main areas. Firstly, the text corpus submitted to the analysis has been considerably increased. Secondly, the previous data analysis was performed on the body part of the posts, while now it is focused on the most prominent part of the news posts, their headlines. This change from body to headline analysis was provoked by significant differences in their characteristics and the fact that most people read only headlines. Described data acquisition uses an advanced content collection approach followed by the modeling process, during which a set of natural language processing algorithms were applied. To enable the comparison, the model uses the same set of algorithms in the modeling phase like in previous work. The main contributions of the work are manifested in: i) approaching the problem from a new perspective, ii) applying more efficient method of data collection, and crucially iii) enabling the comparison of analysis results for individual parts of the content, which ensured a comprehensive insight into the characteristics of news posts.","PeriodicalId":38910,"journal":{"name":"Journal of Communications Software and Systems","volume":"4 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69100806","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

Frequency Synchronization in Frequency Domain OFDM-IM based WLAN Systems 基于频域OFDM-IM的WLAN系统的频率同步

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Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2023-0052

Lalitha H, Navin Kumar

引用次数: 0

An Exploratory Analysis of Feature Selection for Malware Detection with Simple Machine Learning Algorithms 基于简单机器学习算法的恶意软件检测特征选择探索性分析

Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2023-0091

Md Ashikur Rahman, Syful Islam, Yusuf Sulistyo Nugroho, Fatah Yasin Al Irsyadi, Md Javed Hossain

{"title":"An Exploratory Analysis of Feature Selection for Malware Detection with Simple Machine Learning Algorithms","authors":"Md Ashikur Rahman, Syful Islam, Yusuf Sulistyo Nugroho, Fatah Yasin Al Irsyadi, Md Javed Hossain","doi":"10.24138/jcomss-2023-0091","DOIUrl":"https://doi.org/10.24138/jcomss-2023-0091","url":null,"abstract":"Computers have become increasingly vulnerable to malicious attacks with an increase in popularity and the proliferation of open system architectures. There are numerous malware detection technologies available to protect the computer operating system from such attacks. This type of malware detector targets programs based on patterns detected in the properties of computer applications. As the amount of analytical data increases, the computer defense system is adversely affected. The performance of the detection mechanism has been hindered due to the presence of numerous irrelevant characteristics. The goal of this study is to provide a feature selection approach that will help malware detection systems be more accurate by detecting pertinent and significant traits. Furthermore, by selecting the most important features, it is possible to maintain an acceptable level of accuracy in the detection of malware while significantly lowering the computational cost. The proposed method displays the most important features (MIFs) obtained from each machine learning method, including data cleaning and feature selection. Furthermore, the method applies six machine learning classification techniques to the selected feature set. Several classifiers were evaluated based on several characteristics for malware detection, including Support Vector Machines (SVM), Logistic Regression (LR), K-nearest neighbor (K-NN), Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF). Our suggested model was tested on two malware datasets to determine its effectiveness. In terms of accuracy, precision, F1 scores, and recall, the experimental findings show that RF and DT classifiers beat other techniques.","PeriodicalId":38910,"journal":{"name":"Journal of Communications Software and Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135440321","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

Upgrading SIM–OFDM Using a Threshold for Correct Operation with Analytical Proofs 利用正确操作阈值升级SIM-OFDM并给出分析证明

Journal of Communications Software and Systems Pub Date : 2023-01-01 DOI: 10.24138/jcomss-2023-0131

Ahmed N. Jabbar, Samir J. Almuraab, Abdulkareem A. Kadhim

{"title":"Upgrading SIM–OFDM Using a Threshold for Correct Operation with Analytical Proofs","authors":"Ahmed N. Jabbar, Samir J. Almuraab, Abdulkareem A. Kadhim","doi":"10.24138/jcomss-2023-0131","DOIUrl":"https://doi.org/10.24138/jcomss-2023-0131","url":null,"abstract":"A new upgrade to the SIMâOFDM is suggested to solve a critical problem that crashes the system even over noiseless channel. This problem is the interference of the zeros at the IFFT output with the BOOK's zeros that confuses the receiver during demodulation which leads to BER accumulation. The suggested solution is to use a threshold to differentiate the data carried by the BOOK from the IFFT's symbols. The new system is called Threshold SIMâOFDM (TSIMâOFDM). The mathematical analysis of TSIMâOFDM proves it operates normally and meets the theoretical bounds. The TSIMâOFDM preserves the probability of 1 equal to 1/2. This preservation comes from the direct connection of the ON/OFF switching bits to the subcarrier which overrides the majority condition. This new switching technique simplifies the system operation resulting in higher transmission speed and increased spectral and power efficiency. A simple approach to derive the BER for the SIMâOFDM is presented which proves that the SIMâOFDM will never reach zero BER level unlike the TSIMâOFDM. The simulation results show that the TSIMâOFDM BER reaches zero level and the output power is almost half of the OFDM. Adding the threshold will increase the transmitted power slightly and tends to decrease with the increase of IFFT length.","PeriodicalId":38910,"journal":{"name":"Journal of Communications Software and Systems","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135659127","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