Int. Arab J. Inf. Technol.最新文献_第6页

TSO clustered protocol to extend lifetime of IoT based mobile wireless sensor networks TSO集群协议延长基于物联网的移动无线传感器网络的生命周期

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/4/1

Giji Kiruba Dasebenezer, Benita Joselin

{"title":"TSO clustered protocol to extend lifetime of IoT based mobile wireless sensor networks","authors":"Giji Kiruba Dasebenezer, Benita Joselin","doi":"10.34028/iajit/20/4/1","DOIUrl":"https://doi.org/10.34028/iajit/20/4/1","url":null,"abstract":"Mobile Wireless Sensor Networks (MWSNs) energy utilization is the most important trouble in recent years various research works going related to it. Clustering approaches are most proficient methods to accomplish the energy utilization. Cluster Heads (CHs) determination is a significant task in MWSNs as it utilizes huge energy while receiving, broadcasting, capturing the data from IoT nodes and broadcast it to the Basestation (BS). Inappropriate choice of CHs utilizes energy so that diminishes network existence. An energy resourceful network with appropriate optimization methodology is to be espoused to determine the CHs. A clustered methodology is proposed based on Tiger Swarm Optimization (TSO) approach to diminish the energy spending throughout cluster formation and broadcast stage. TSO clustered approach is established to consider parameters as intra cluster remoteness among of sensors to CH and lingering energy of sensors. The approach is experimented broadly on diverse environments, unstable sensors and CHs. The proposed TSO is evaluated with Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) and Multi-objective Hybrid Genetic Algorithm (MHGA) based on data delivery, delay, lingering energy are simulated in ns2.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88272965","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: 2

Machine learning models for statistical analysis 用于统计分析的机器学习模型

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/3a/8

Marko Grebovic, Luka Filipović, Ivana Katnic, M. Vukotić, Tomo Popović

{"title":"Machine learning models for statistical analysis","authors":"Marko Grebovic, Luka Filipović, Ivana Katnic, M. Vukotić, Tomo Popović","doi":"10.34028/iajit/20/3a/8","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/8","url":null,"abstract":"Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one-step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74331188","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

MIRNA: adaptive 3D game to assist children's distance learning difficulties; design and teachers' intention to use MIRNA:自适应3D游戏，辅助儿童远程学习困难;设计与教师使用意图

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/3a/10

Dheya Ghazi Mustafa, I. Mustafa, Samah Zriqat, Q. Althebyan

{"title":"MIRNA: adaptive 3D game to assist children's distance learning difficulties; design and teachers' intention to use","authors":"Dheya Ghazi Mustafa, I. Mustafa, Samah Zriqat, Q. Althebyan","doi":"10.34028/iajit/20/3a/10","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/10","url":null,"abstract":"The global transition from traditional classroom instruction to online learning has been hastened through COVID-19. Notwithstanding its benefits, students were unable to quickly adjust to the difficulties of change. This research offers MIRNA, an assistive 3D instructional tool for slow learners in primary school, mostly for Arabic, English, and Math topics, to address academic issues that develop with online education. The proposed tool can be fully integrated with remedial programs to assist students who struggled to adjust to distant learning during the pandemic, slow learners, or even those who are unable to attend school. This application categorizes students based on academic performance rather than age and automatically adjusts to their limitations. Additionally, MIRNA offers a simple interface that allows teachers to personalize it with their own content and game scenarios. We carried out an empirical investigation to assess teachers' intentions to use MIRNA as an online learning platform in the learning process. The findings of the study show that teachers’ attitude towards the game was positive, and they intended to use the game in the learning process in the future","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81717739","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

Human facial emotion recognition using deep neural networks 基于深度神经网络的人类面部情感识别

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/3/2

S. Benisha, T. MirnalineeT.

引用次数: 0

Coverless data hiding in VoIP based on DNA steganography with authentication 基于DNA隐写和身份验证的VoIP无覆盖数据隐藏

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/2/5

Deepikaa Soundararajan, Saravanan Ramakrishnan

引用次数: 1

A novel codebook generation by smart fruit fly algorithm based on exponential flight 基于指数飞行的智能果蝇编码本生成算法

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/4/4

I. Kilic

引用次数: 0

On Satellite Imagery of Land Cover Classification for Agricultural Development 基于农业开发的土地覆盖分类卫星影像研究

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/1/2

Ali Abdullah M. Alzahrani, Al-Amin Bhuiyan

{"title":"On Satellite Imagery of Land Cover Classification for Agricultural Development","authors":"Ali Abdullah M. Alzahrani, Al-Amin Bhuiyan","doi":"10.34028/iajit/20/1/2","DOIUrl":"https://doi.org/10.34028/iajit/20/1/2","url":null,"abstract":"Distribution of chronological land cover modifications has attained a vibrant concern in contemporary sustainability research. Information delivered by satellite remote sensing imagery plays momentous role in enumerating and discovering the expected land cover for vegetation. Fuzzy clustering has been found successful in implementing a significant number of optimization problems associated with machine learning due to its fractional membership degrees in several neighbouring constellations. This research establishes a framework on land cover classification for agricultural development. The approach is focused on object-oriented classification and is organized with a Fuzzy c-means clustering over segmentation on CIE L*a*b* colour scheme which provides analysis of vegetation coverage and enhances land planning for sustainable developments. This research investigates the land cover variations of the eastern province of Saudi Arabia throughout an elongated span of period from 1984 to 2018 to recognize the possible roles of the land cover alterations on farming. The Landsat satellite imagery and Geographical Information System (GIS), in tandem with Google Earth chronological imagery are employed for land use variation analysis. Experimental results exhibit a reasonable spread in the cultivated zones and reveal that this Colour Segmented Fuzzy Clustering (CSFC) strategy achieves better than the relevant counterpart approaches considering classification accuracy.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79199003","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

Tree-based multicast routing and channel assignment for enhanced throughout in emerging cognitive radio networks 新兴认知无线网络中基于树的多播路由和信道分配

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/3a/1

H. Salameh, Mustafa Ali

{"title":"Tree-based multicast routing and channel assignment for enhanced throughout in emerging cognitive radio networks","authors":"H. Salameh, Mustafa Ali","doi":"10.34028/iajit/20/3a/1","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/1","url":null,"abstract":"In future multi-hop wireless networks like 5G and B5G, efficient large-scale video sharing and data dissemination are expected to rely heavily on multicast routing and Cognitive Radio (CR) technology. While multicast routing is efficient when the network always has access to the spectrum, the dynamic nature of Primary User (PU) activities, heterogeneous spectrum across the CR Network (CRN), and PU access priority make it challenging to implement efficient multicast routing protocols in CRNs. This paper proposes a hierarchical multicast routing mechanism for multi-hop CRNs that exploits the Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) concepts. The proposed multicast routing mechanism consists of tree construction and channel assignment algorithms. The tree-construction algorithm models the network topology as a multicast tree rooted at the CR source and spanning all the CR nodes. Based on the constructed tree, the channel assignment algorithm employs the Probability Of Success (POS) metric to assign channels to the various layers defined by the constructed SPT or MST, ensuring that the most reliable channel is used for the multi-hop multicast transmissions. Simulation experiments are conducted to evaluate the mechanism’s effectiveness, revealing significant improvements in throughput and Packet Delivery Rate (PDR) compared to state-of-the-art protocols under different network conditions. The simulations also show that the SPT-based mechanism outperforms the MST-based mechanism in terms of throughput but has a higher tree construction complexity.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82444204","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

New model of feature selection based chaotic firefly algorithm for arabic text categorization 基于特征选择的混沌萤火虫算法在阿拉伯文本分类中的新模型

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/3a/3

M. Hadni, Hjiaj Hassane

{"title":"New model of feature selection based chaotic firefly algorithm for arabic text categorization","authors":"M. Hadni, Hjiaj Hassane","doi":"10.34028/iajit/20/3a/3","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/3","url":null,"abstract":"The dimensionality reduction is a type of problem that appear in the most classification processes. It contains a large number of features; these features may contain unreliable data which may lead the categorization process to unwanted results. Feature selection can be used for reducing dimensionality of datasets and find interesting relevant information. In Arabic language, the number of works applies a meta-heuristic algorithm for feature selection is still limited due to the complex nature of Arabic inflectional and derivational rules as well as its intricate grammatical rules and its rich morphology. This paper proposes a new model for Arabic Feature Selection that combines the chaotic method in the Firefly Algorithm (CFA). The Chaotic Algorithm replaces the attractiveness coefficient in firefly algorithm by the outputs of chaotic application. The enhancement of the new approach involves introducing a novel search strategy which is able to obtain a good ratio between exploitation and exploration abilities of the algorithm. In terms In terms of performance, the experiments of the proposed method are tested using classifiers, namely Naive Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) and three evaluation measures, including precision, recall, and F-measure. The experimental findings show that the combining of CFA and SVM classifiers outperforms other combinations in terms of precision.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88206908","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 bayesian network-based uncertainty modeling (BNUM) to analyze and predict next optimal moves in given game scenario 一种基于贝叶斯网络的不确定性模型(BNUM)来分析和预测给定博弈场景下的下一步最优走法

Int. Arab J. Inf. Technol. Pub Date : 2023-01-01 DOI: 10.34028/iajit/20/2/6

V. Jagtap, P. Kulkarni

{"title":"A bayesian network-based uncertainty modeling (BNUM) to analyze and predict next optimal moves in given game scenario","authors":"V. Jagtap, P. Kulkarni","doi":"10.34028/iajit/20/2/6","DOIUrl":"https://doi.org/10.34028/iajit/20/2/6","url":null,"abstract":"As machine learning emerged, it is being used in a variety of applications like speech recognition, image recognition, sequence modeling, etc., Sequence modeling is one type of application where resultant sequences are generated based on historical data inputs provided. These sequences are fairly work in an uncertain environment like games or sports. In the case of a game or a sport, there is a sequence of moves selected by multiple players. There is a statistical uncertainty observed for simple to more complex games. For example, while playing chess, a simple statistical modeled uncertainty would be enough to choose the next possible. This move selection is dependent on available free spaces of pieces or pawns. The sports like tennis, cricket, and other games need a more complex design for uncertainty modeling for next move selection. A Bayesian Network model will work if there is fairly less uncertainty in the selection of the next move. A Bayesian Network-based model will be best fitted if all possible moves are included before training any machine learning or deep learning model. This will be achieved with the usage of the Context-Li model. The proposed Bayesian Network-based Uncertainty Modeling (BNUM) is used to incorporate uncertainty, for next move selection. BNUM is a multi-variable, multi-level association to incubate uncertainty in learning. It helps to predict the next move in an uncertain gaming environment. Different case studies are incorporated to verify the hypothesis and the results are a sequence of moves represented in the context graph.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77461059","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