Comput. Sci. J. Moldova最新文献_第2页

A new type of digital signature algorithms with a hidden group 一种新型的隐群数字签名算法

Comput. Sci. J. Moldova Pub Date : 2023-04-01 DOI: 10.56415/csjm.v31.06

D. Moldovyan

引用次数: 2

Image Description Generator using Residual Neural Network and Long Short-Term Memory 基于残差神经网络和长短期记忆的图像描述生成器

Comput. Sci. J. Moldova Pub Date : 2023-04-01 DOI: 10.56415/csjm.v31.01

Mahesh Kumar Morampudi, Nagamani Gonthina, Nuthanakanti Bhaskar, V. Reddy

{"title":"Image Description Generator using Residual Neural Network and Long Short-Term Memory","authors":"Mahesh Kumar Morampudi, Nagamani Gonthina, Nuthanakanti Bhaskar, V. Reddy","doi":"10.56415/csjm.v31.01","DOIUrl":"https://doi.org/10.56415/csjm.v31.01","url":null,"abstract":"Human beings can describe scenarios and objects in a picture through vision easily whereas performing the same task with a computer is a complicated one. Generating captions for the objects of an image helps everyone to understand the scenario of the image in a better way. Instinctively describing the content of an image requires the apprehension of computer vision as well as natural language processing. This task has gained huge popularity in the field of technology and there is a lot of research work being carried out. Recent works have been successful in identifying objects in the image but are facing many challenges in generating captions to the given image accurately by understanding the scenario. To address this challenge, we propose a model to generate the caption for an image. Residual Neural Network (ResNet) is used to extract the features from an image. These features are converted into a vector of size 2048. The caption generation for the image is obtained with Long Short-Term Memory (LSTM). The proposed model is experimented on the Flickr8K dataset and obtained an accuracy of 88.4%. The experimental results indicate that our model produces appropriate captions compared to the state of art models.\u0000","PeriodicalId":262087,"journal":{"name":"Comput. Sci. J. Moldova","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128062382","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

Performance comparison of CPU and GPGPU calculations using three simple case studies CPU和GPGPU计算的性能比较使用三个简单的案例研究

Comput. Sci. J. Moldova Pub Date : 2023-04-01 DOI: 10.56415/csjm.v31.07

Branislav Lipovský, Slavomír Simonák

引用次数: 0

A Neural Attention-Based Encoder-Decoder Approach for English to Bangla Translation 基于神经注意的英语到孟加拉语翻译编码器-解码器方法

Comput. Sci. J. Moldova Pub Date : 2023-04-01 DOI: 10.56415/csjm.v31.04

Abdullah Al Shiam, S. M. Redwan, Humaun Kabir, Jungpil Shin

引用次数: 0

Graph-based decision making for varying complexity multicriteria problems 基于图的复杂多准则问题决策

Comput. Sci. J. Moldova Pub Date : 2022-12-01 DOI: 10.56415/csjm.v30.21

O. Nesterenko, I. Netesin, V. Polischuk, Yurij Selin

引用次数: 0

An Intelligent Detection of Malicious Intrusions in IoT Based on Machine Learning and Deep Learning Techniques 基于机器学习和深度学习技术的物联网恶意入侵智能检测

Comput. Sci. J. Moldova Pub Date : 2022-12-01 DOI: 10.56415/csjm.v30.16

Saman Iftikhar, Danish Khan, Daniah Al-Madani, K. Alheeti, Kiran Fatima

{"title":"An Intelligent Detection of Malicious Intrusions in IoT Based on Machine Learning and Deep Learning Techniques","authors":"Saman Iftikhar, Danish Khan, Daniah Al-Madani, K. Alheeti, Kiran Fatima","doi":"10.56415/csjm.v30.16","DOIUrl":"https://doi.org/10.56415/csjm.v30.16","url":null,"abstract":"The devices of the Internet of Things (IoT) are facing various types of attacks, and IoT applications present unique and new protection challenges. These security challenges in IoT must be addressed to avoid any potential attacks. Malicious intrusions in IoT devices are considered one of the most aspects required for IoT users in modern applications. Machine learning techniques are widely used for intelligent detection of malicious intrusions in IoT. This paper proposes an intelligent detection method of malicious intrusions in IoT systems that leverages effective classification of benign and malicious attacks. An ensemble approach combined with various machine learning algorithms and a deep learning technique, is used to detect anomalies and other malicious activities in IoT. For the consideration of the detection of malicious intrusions and anomalies in IoT devices, UNSW-NB15 dataset is used as one of the latest IoT datasets. In this research, malicious and normal intrusions in IoT devices are classified with the use of various models. %Moreover, improved results are provided and compared with CorrAuc [1] for training accuracies, cross-validation accuracies, execution time, precision, recall and F1 score.","PeriodicalId":262087,"journal":{"name":"Comput. Sci. J. Moldova","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131507169","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

Privacy and Reader-first Authentication in Vaudenay's RFID Model with Temporary State Disclosure Vaudenay具有临时状态披露的RFID模型中的隐私和读者优先认证

Comput. Sci. J. Moldova Pub Date : 2022-12-01 DOI: 10.56415/csjm.v30.18

F. Ţiplea, Cristian Hristea

引用次数: 4

Wiener Index of Some Brooms 一些扫帚的维纳指数

Comput. Sci. J. Moldova Pub Date : 2022-12-01 DOI: 10.56415/csjm.v30.20

Julian D. Allagan

引用次数: 0

Kurtosis-Based Feature Selection Method using Symmetric Uncertainty to Predict the Air Quality Index 基于峰度的对称不确定性特征选择方法预测空气质量指数

Comput. Sci. J. Moldova Pub Date : 2022-12-01 DOI: 10.56415/csjm.v30.19

Usharani Bhimavarapu, M. Sreedevi

{"title":"Kurtosis-Based Feature Selection Method using Symmetric Uncertainty to Predict the Air Quality Index","authors":"Usharani Bhimavarapu, M. Sreedevi","doi":"10.56415/csjm.v30.19","DOIUrl":"https://doi.org/10.56415/csjm.v30.19","url":null,"abstract":"Feature selection is vital in data pre-processing in machine learning, and it is prominent in datasets with many features. Feature selection analyses the relevant, irrelevant, and redundant features in the dataset. Feature selection removes the irrelevant features, which improves both the accuracy and prediction performance. The significant advantages of reducing the number of features from the dataset are reducing the training time, reducing overfitting, decreasing the curse of dimensionality, and simplifying the prediction model. The filter feature selection techniques can handle the issues with the high number of features, and this paper uses the symmetric uncertainty coefficient to verify the relevance of the independent features. In this paper, a new feature selection method named as kurtosis-based feature selection has been proposed to select the relevant features which affect the air pollution. Kurtosis-based feature selection is compared with seven filter feature selection techniques on air pollution dataset and validated the performance of the proposed algorithm. It has been observed that the kurtosis-based feature selection extracts only PM2.5 as the key feature and has been compared to the accuracy of the five existing methods. The experimental results illustrate that the kurtosis-based feature selection algorithm reduces the original feature set up to 91.66%, but the existing filter feature selection techniques reduce the feature set to only 50%.","PeriodicalId":262087,"journal":{"name":"Comput. Sci. J. Moldova","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117262366","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

Residual Neural Network in Genomics 基因组学中的残差神经网络

Comput. Sci. J. Moldova Pub Date : 2022-12-01 DOI: 10.56415/csjm.v30.17

S. Sabba, M. Smara, Mehdi Benhacine, Loubna Terra, Zine Eddine Terra

引用次数: 0