Journal of Advances in Information Technology最新文献_第10页

Improved Opinion Mining for Unstructured Data Using Machine Learning Enabling Business Intelligence 使用支持商业智能的机器学习改进非结构化数据的意见挖掘

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.4.821-829

Ruchi Sharma, P. Shrinath

{"title":"Improved Opinion Mining for Unstructured Data Using Machine Learning Enabling Business Intelligence","authors":"Ruchi Sharma, P. Shrinath","doi":"10.12720/jait.14.4.821-829","DOIUrl":"https://doi.org/10.12720/jait.14.4.821-829","url":null,"abstract":"—There has been an exponential increase in usage of social informatics in recent years. This makes opinion mining more complex, especially for unstructured data available online. Although a substantial amount of research has been conducted on the COVID pandemic, post-pandemic research is lacking. Our research focuses on design and implementation of opinion mining framework for unstructured data input for business intelligence dealing with post pandemic work environment in industries. In this paper, we implement opinion mining algorithm in combination with machine learning approaches providing a hybrid approach. Transformer architecture Bidirectional Encoder Representations from Transformers language model is implemented to obtain sentence level feature vector of the document corpus and t-distributed stochastic neighbor embedding is implemented for clustering experimental evaluation. In this work, performance evaluation is undertaken using the Intertopic Distance map. By applying a hybrid strategy of natural language processing and machine learning, the results of this study indicate efficient framework development and anticipated to contribute to the improvement of efficacy of opinion mining models compared to existing approaches. This research is significant and will benefit businesses in gaining valuable insights that will lead to improved decision-making and business insights.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66333622","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

MFTs-Net: A Deep Learning Approach for High Similarity Date Fruit Recognition MFTs-Net:一种高度相似枣果识别的深度学习方法

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.6.1151-1158

Abdellah El Zaar, Rachida Assawab, Ayoub Aoulalay, Nabil Benaya, Toufik Bakir, Smain Femmam, Abderrahim El Allati

{"title":"MFTs-Net: A Deep Learning Approach for High Similarity Date Fruit Recognition","authors":"Abdellah El Zaar, Rachida Assawab, Ayoub Aoulalay, Nabil Benaya, Toufik Bakir, Smain Femmam, Abderrahim El Allati","doi":"10.12720/jait.14.6.1151-1158","DOIUrl":"https://doi.org/10.12720/jait.14.6.1151-1158","url":null,"abstract":"—Artificial Intelligence and Deep Learning applications are well-developed as a part of human life. In the field of object recognition, Convolutional Neural Network (CNN) based methods are getting more and more important and challenging. However, existing CNN methods do not perform well on datasets that exhibit high similarities, resulting in confusion between different classes. In this study, we propose a new Deep Learning approach for recognizing date fruit categories based on the Deep Convolutional Neural Network (DCNN). The modified fine-tuning (MFTs-Net) approach can recognize with high accuracy the different date fruit categories. In order to train and to test the robustness of our proposed method, we have collected a dataset that takes into account different date fruit categories. The presented dataset is challenging as it contains classes of a unique object and presents high similarities concerning the shape, color and texture of date fruit. We show that the MFTs-Net CNN we implemented, trained and tested using the collected dataset can recognize with high accuracy the different date categories compared with state-of-the-arts works. The presented methodology works perfectly with very small datasets, which is one of the main strengths of the proposed method. Our MFTs-Net architecture performs perfectly on test data with an accuracy of 98%. 1","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"46 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":"135610240","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 Simple and Effective Evaluation Method for Fault-Tolerant Routing Methods in Network-on-Chips 片上网络容错路由方法的一种简单有效的评估方法

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.5.876-882

Yota Kurokawa, Masaru Fukushi

{"title":"A Simple and Effective Evaluation Method for Fault-Tolerant Routing Methods in Network-on-Chips","authors":"Yota Kurokawa, Masaru Fukushi","doi":"10.12720/jait.14.5.876-882","DOIUrl":"https://doi.org/10.12720/jait.14.5.876-882","url":null,"abstract":"—This paper proposes a simple and effective evaluation method for fault-tolerant routing methods developed for Network-on-Chip (NoC)-based many-core processors. To cope with faults which significantly degrade the reliability of communication among cores, a variety of fault-tolerant routing methods have been studied. Those methods have been mainly evaluated in terms of communication performance such as latency and throughput by computer simulations of packet routing. However, such evaluations are not practical in that they cannot reveal the performance difference in executing parallel applications with the fault-tolerant routing methods. The proposed method obtains the information of the target parallel application such as task execution time, communication pattern, and communication amount and incorporates it in the conventional packet routing simulations. With the proposed evaluation method, computer simulations have been conducted to evaluate the performance of four famous fault-tolerant routing methods, i.e., Fcube4, Position Route, Passage-Y, and Passage-XY, using NAS Parallel Benchmarks and the performance difference is revealed in executing parallel programs named Integer Sort (IS) and Fast Fourier Transform (FFT). The results show that, Passage-XY outperforms other methods in both IS and FT, and for the case of IS, Passage-XY can reduce the program execution time by up to about 39%, 56%, and 26% compared with Fcube4, Position Route, and Passage-Y, respectively.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"3 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":"135649055","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

An Automated Deep Learning Framework for Human Identity and Gender Detection 一个用于人类身份和性别检测的自动深度学习框架

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.1.94-101

Afaf Tareef, Hayat Al-Dmour, Afnan Al-Sarayreh

{"title":"An Automated Deep Learning Framework for Human Identity and Gender Detection","authors":"Afaf Tareef, Hayat Al-Dmour, Afnan Al-Sarayreh","doi":"10.12720/jait.14.1.94-101","DOIUrl":"https://doi.org/10.12720/jait.14.1.94-101","url":null,"abstract":"Automated detection of human identity and gender offers several industrial applications in near future, such as monitoring, surveillance, commercial profiling and human computer interaction. In this paper, deep learning techniques have been used to investigate the problem of human identity and gender classification using hand images. First, pre-processing techniques have been applied to enhance the appearance of the hand images. The pre-processed image is passed through the convolution neural network to determine the gander. For identity detection, the network has been trained on the images for the determined gender for better recognition. To further enhance the result, the framework has been implemented using different optimizers and k fold cross-validation. Experimental results have shown that highly effective performance is achieved in both the human identification and gender classification objectives. High average accuracy of 97.75% using the dorsal hand side for human identification and 96.79% has been obtained for gender classification using the palm hand side. Conclusively, the proposed method has achieved more accuracy than the previous methods both for identification and gender classification.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66329821","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

The New Collective Signature Schemes Based on Two Hard Problems Using Schnorr's Signature Standard 基于Schnorr签名标准的两个难题的新集体签名方案

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.1.77-84

Tuan Nguyen Kim, Duy Ho Ngoc, Nin Ho Le Viet, N. Moldovyan

引用次数: 0

Comparison of Machine Learning Algorithms for Spam Detection 垃圾邮件检测的机器学习算法比较

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.2.178-184

Azeema Sadia, Fatima Bashir, Reema Qaiser Khan, Ammarah Khalid

{"title":"Comparison of Machine Learning Algorithms for Spam Detection","authors":"Azeema Sadia, Fatima Bashir, Reema Qaiser Khan, Ammarah Khalid","doi":"10.12720/jait.14.2.178-184","DOIUrl":"https://doi.org/10.12720/jait.14.2.178-184","url":null,"abstract":"—The Internet is used as a tool to offer people with endless knowledge. It is a global platform which is used for connectivity, communication, and sharing. At almost no cost, an individual can use the Internet to send email messages, update tweets, and Facebook messages to a vast number of people. These messages can also contain unsolicited advertisement which is identified as a spam. The company Twitter too is massively affected by spamming and it is an alarming issue for them. Twitter considers spam as actions that are unsolicited and repeated. These include tweet repetition, and the URLs that lead users to completely unrelated websites. The authors’ have worked with twitter’s dataset focusing on tweets about “iPhone”. It was collected by using an API which was further pre-processed. In this paper, content-based features have been selected that recognize the spamming tweet by using R. Multiple machine learning algorithms were applied to detect spamming tweets: Naive Bayes, Logistic Regression, KNN, Decision Tree, and Support Vector Machine. It was observed that the best performance was achieved by Naive Bayes Algorithm giving an accuracy of 89%.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66330518","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

Intelligent Fault Detection Based on Reinforcement Learning Technique on Distribution Networks 基于强化学习技术的配电网智能故障检测

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.463-471

T. S. Hlalele, Yanxia Sun, Zenghui Wang

{"title":"Intelligent Fault Detection Based on Reinforcement Learning Technique on Distribution Networks","authors":"T. S. Hlalele, Yanxia Sun, Zenghui Wang","doi":"10.12720/jait.14.3.463-471","DOIUrl":"https://doi.org/10.12720/jait.14.3.463-471","url":null,"abstract":"— The incorporation of distributed energy resources in the distribution networks changes the fault current level and makes the fault detection be more complex. There are several challenges brought by these heterogenous energy systems including power quality, voltage stability, reliability and protection. In this paper, a fault detection based on reinforcement learning approach is proposed. The heart of this approach is a Q learning approach which uses a non-adaptive multi-agent reinforcement learning algorithm to detect and identify nonlinear system faults, and the algorithm learns the policy by telling an agent what actions to take under what circumstances. Moreover, the Discrete Wavelet Transform (DWT) is utilized to extract coefficient values from the captured one-fourth cycle of the three-phase current signal during fault which occurs during the transient stage. The simulations and signal analysis for different faults are used to validate the proposed fault detection method in MATLAB environment. The simulation results show that different types of faults such as CA, AB, ABC and ABCG can be detected and the best correlation coefficient achieved is 0.87851.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"39 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331130","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 Survey on DDoS Detection and Prevention Mechanism DDoS检测与防御机制综述

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.444-453

Foram Suthar, Nimisha Patel

{"title":"A Survey on DDoS Detection and Prevention Mechanism","authors":"Foram Suthar, Nimisha Patel","doi":"10.12720/jait.14.3.444-453","DOIUrl":"https://doi.org/10.12720/jait.14.3.444-453","url":null,"abstract":"—The internet is an obvious target for a cyberattack nowadays. The population on the internet globally is increasing from 3 billion in 2014 to 4.5 billion in 2020, resulting into nearly 59% of the total world population. The attacker is always looking for loopholes and vulnerabilities of internet-connected devices. It has been noticed from the last decade, there are more Denial-of-Service Attack (DoS) or DoS attacks and their variant Distributed Denial-of-Service (DDoS) or DDoS attacks performed by the attacker. This creates a serious problem for the network administrator to secure the infrastructure. The attacker mainly targets reputed organization/ industries and try to violate the major parameter of cyber security— Availability. The most commonly performed attack by the attacker is a Transmission Control Protocol (TCP) Synonym (SYN) DDoS attack, caused due to the design issue of the TCP algorithm. The attacker floods the packets in the network causing the server to crash. Hence, it is important to understand the source of the DDoS attack. Therefore, a real-life and accurate TCP SYN detection mechanism is required. Numerous techniques have been used for preventing and detecting various DDoS flooding attacks, some of which are covered in the literature review. The paper highlights the strengths and weaknesses of the available defense mechanism. To understand the performance status of the system we have implemented a DoS by the hping3 tool. This gives us better clarity in shortlisting and analyzing the parameters for the detection of DDoS attacks. Also, we try to analyze the impact of TCP SYN attack on the network in DDoS attacks.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331376","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

Crowdsensing: Assessment of Cognitive Fitness Using Machine Learning 群体感知:使用机器学习评估认知适应性

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.559-570

Samin Ahsan Tausif, Aysha Gazi Mouri, Ishfaq Rahman, Nilufar Hossain, H. M. Z. Haque

{"title":"Crowdsensing: Assessment of Cognitive Fitness Using Machine Learning","authors":"Samin Ahsan Tausif, Aysha Gazi Mouri, Ishfaq Rahman, Nilufar Hossain, H. M. Z. Haque","doi":"10.12720/jait.14.3.559-570","DOIUrl":"https://doi.org/10.12720/jait.14.3.559-570","url":null,"abstract":"—The expanded use of smartphones and the Internet of Things have enabled the usage of mobile crowdsensing technologies to improve public health care in clinical sciences. Mobile crowdsensing enlightens a new sensing pattern that can reliably differentiate individuals based on their cognitive fitness. In previous studies on this domain, the visual correlation has not been illustrated between physiological functions and the mental fitness of human beings. Therefore, there exists potential gaps in providing mathematical evidence of correlation between physical activities & cognitive health. Moreover, empirical analysis of autonomous smartphone sensing to assess mental health is yet to be researched on a large scale, showing the correspondence between ubiquitous mobile sensors data and Patient Health Questionnaire-9 (PHQ-9) depression scales. This research systematically collects mobile sensors’ data along with standard PHQ-9 questionnaire data and utilizes traditional machine learning techniques (Supervised and Unsupervised) for performing necessary analysis. Moreover, we have conducted statistical t-tests to find similarities or to differentiate between people of distinct cognitive fitness levels. This research has successfully demonstrated the numerical evidence of correlations between physiological activities and the cognitive fitness of human beings. The Fine-tuned regression models built for the purpose of predicting users’ cognitive fitness score, perform accurately to a certain extent. In this analysis, crowdsensing is perceived to differentiate several people’s cognitive fitness levels comprehensively. Furthermore, our study has addressed a significant insights to assessing people’s mental fitness by relying upon their smartphone usage.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66331727","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

Fake News Detection in Social Media: Hybrid Deep Learning Approaches 社交媒体中的假新闻检测:混合深度学习方法

IF 1

Journal of Advances in Information Technology Pub Date : 2023-01-01 DOI: 10.12720/jait.14.3.606-615

Fatoumata Wongbé Rosalie Tokpa, B. H. Kamagaté, Vincent Monsan, S. Oumtanaga

{"title":"Fake News Detection in Social Media: Hybrid Deep Learning Approaches","authors":"Fatoumata Wongbé Rosalie Tokpa, B. H. Kamagaté, Vincent Monsan, S. Oumtanaga","doi":"10.12720/jait.14.3.606-615","DOIUrl":"https://doi.org/10.12720/jait.14.3.606-615","url":null,"abstract":"— Social media refers to communication channels on Internet that enable the creation and publication of content generated by the user and interaction between users. Given the accessibility to these means of communication and their rapidity, people resort more to them comparatively to the traditional media including radio, television and newspapers. However, dubious pieces of information such as fake news are often disseminated for malicious purposes. The proliferation of fake news has a strong negative impact on a society such as damage to the reputation of a personality, an organization or the aggravation of conflicts between its members. Due to the proliferation of fake news on these websites, the notion of veracity of information becomes a crucial issue. Research based on machine learning is promising. However, one of the main limitations is the efficiency of predictions. As a solution to detect fake news, we have proposed two models based on hybrid deep learning and evaluated our models on the two real datasets, namely ISOT and FA-KES. An experience of the proposed models to detect fake news, allowed to obtain on ISOT an accuracy of 99% for both models and on FA-KES, we obtain an accuracy of 68% for one the models and an accuracy of 63% for other. Other experiments in generalizing models on these data sets have proposed. The results obtained are better than other machine learning models.","PeriodicalId":36452,"journal":{"name":"Journal of Advances in Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66332265","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