Traffic congestion is a major problem in many cities of the Hashemite Kingdom of Jordan as in most countries. The rapidly increase of vehicles and dealing with the fixed infrastructure have caused traffic congestion. One of the main problems is that the current infrastructure cannot be expanded further. Therefore, there is a need to make the system work differently with more sophistication to manage the traffic better, rather than creating a new infrastructure. In this research, a new adaptive traffic lights system is proposed to determine vehicles type, calculate the number of vehicles in a traffic junction using patterns detection methods, and suggest the necessary time for each side of the traffic junction using machine learning tools. In this context, the contributions of this paper are: (a) creating a new image-based dataset for vehicles, (b) proposing a new time management formula for traffic lights, and (c) providing literature of many studies that contributed to the development of the traffic lights system in the past decade. For training the vehicle detector, we have created an image-based dataset related to our work and contains images for traffic. We utilized Region-Based Convolutional Neural Networks (R-CNN), Fast Region-Based Convolutional Neural Networks (Fast R-CNN), Faster Region-Based Convolutional Neural Networks (Faster R-CNN), Single Shot Detector (SSD), and You Only Look Once v4 (YOLO v4) deep learning algorithms to train the model and obtain the suggested mathematical formula to the required process and give the appropriate timeslot for every junction. For evaluation, we used the mean Average Precision (mAP) metric. The obtained results were as follows: 78.2%, 71%, 75.2%, 79.8%, and 86.4% for SSD, R-CNN, Fast R-CNN, Faster R-CNN, and YOLO v4, respectively. Based on our experimental results, it is found that YOLO v4 achieved the highest mAP of the identification of vehicles with (86.4%) mAP. For time division (the junctions timeslot), we proposed a formula that reduces about 10% of the waiting time for vehicles.
与大多数国家一样，交通堵塞是约旦哈希姆王国许多城市的一个主要问题。车辆的快速增加和对固定基础设施的处理造成了交通拥堵。其中一个主要问题是，目前的基础设施无法进一步扩大。因此，有必要让系统以不同的方式工作，更复杂地管理交通，而不是创建一个新的基础设施。在本研究中，提出了一种新的自适应交通灯系统来确定车辆类型，使用模式检测方法计算交通路口的车辆数量，并使用机器学习工具建议交通路口每侧的必要时间。在此背景下，本文的贡献是:(a)为车辆创建了一个新的基于图像的数据集，(b)提出了一个新的交通信号灯时间管理公式，以及(c)提供了过去十年中对交通信号灯系统发展做出贡献的许多研究的文献。为了训练车辆检测器，我们创建了一个与我们的工作相关的基于图像的数据集，其中包含交通图像。我们利用基于区域的卷积神经网络(R-CNN)、快速区域卷积神经网络(Fast R-CNN)、更快区域卷积神经网络(Faster R-CNN)、单镜头检测器(SSD)和You Only Look Once v4 (YOLO v4)深度学习算法对模型进行训练，得到所需过程的建议数学公式，并给出每个连接点的适当时间段。为了进行评估，我们使用了平均精度(mAP)度量。结果表明:SSD、R-CNN、Fast R-CNN、Faster R-CNN、YOLO v4分别为78.2%、71%、75.2%、79.8%、86.4%。根据我们的实验结果，YOLO v4的车辆识别mAP最高，为86.4%。对于时间划分(路口时隙)，我们提出了一个公式，可以减少约10%的车辆等待时间。
{"title":"An adaptive traffic lights system using machine learning","authors":"M. Ottom, A. Al-Omari","doi":"10.34028/iajit/20/3/13","DOIUrl":"https://doi.org/10.34028/iajit/20/3/13","url":null,"abstract":"Traffic congestion is a major problem in many cities of the Hashemite Kingdom of Jordan as in most countries. The rapidly increase of vehicles and dealing with the fixed infrastructure have caused traffic congestion. One of the main problems is that the current infrastructure cannot be expanded further. Therefore, there is a need to make the system work differently with more sophistication to manage the traffic better, rather than creating a new infrastructure. In this research, a new adaptive traffic lights system is proposed to determine vehicles type, calculate the number of vehicles in a traffic junction using patterns detection methods, and suggest the necessary time for each side of the traffic junction using machine learning tools. In this context, the contributions of this paper are: (a) creating a new image-based dataset for vehicles, (b) proposing a new time management formula for traffic lights, and (c) providing literature of many studies that contributed to the development of the traffic lights system in the past decade. For training the vehicle detector, we have created an image-based dataset related to our work and contains images for traffic. We utilized Region-Based Convolutional Neural Networks (R-CNN), Fast Region-Based Convolutional Neural Networks (Fast R-CNN), Faster Region-Based Convolutional Neural Networks (Faster R-CNN), Single Shot Detector (SSD), and You Only Look Once v4 (YOLO v4) deep learning algorithms to train the model and obtain the suggested mathematical formula to the required process and give the appropriate timeslot for every junction. For evaluation, we used the mean Average Precision (mAP) metric. The obtained results were as follows: 78.2%, 71%, 75.2%, 79.8%, and 86.4% for SSD, R-CNN, Fast R-CNN, Faster R-CNN, and YOLO v4, respectively. Based on our experimental results, it is found that YOLO v4 achieved the highest mAP of the identification of vehicles with (86.4%) mAP. For time division (the junctions timeslot), we proposed a formula that reduces about 10% of the waiting time for vehicles.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"358 1","pages":"407-418"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84891721","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}
Rapid advancement in internet has made many Social Networking Sites (SNS) popular among a huge population, as various SNS accounts are interlinked with each other, spread of stored susceptible information of an individual is increasing. That has led to various security and privacy issues; one of them is impersonation or identity fraud. Identity fraud is the outcome of illegitimate or secret use of account owner’s identity to invade his/her account to track personal information. There are possibilities that known persons like parents, spouse, close friends, siblings who are interested in knowing what is going on in the account owner’s online life may check their personal SNS accounts. Hence an individual’s private SNS accounts can be invaded by an illegitimate user secretly without the knowledge of the account owner’s which results in compromise of private information. Thus, this paper proposes an in loco identity fraud detection strategy that employs a statistical analysis approach to constantly authenticate the authorized user, which outperforms the previously known technique. This strategy may be used to prevent stalkers from penetrating a person's SNS account in real time. The accuracy attained in this research is greater than 90% after 1 minute and greater than 95% after 5 minutes of observation.
{"title":"In loco identity fraud detection model using statistical analysis for social networking sites: a case study with facebook","authors":"Shalini Hanok, Shankaraiah","doi":"10.34028/iajit/20/2/15","DOIUrl":"https://doi.org/10.34028/iajit/20/2/15","url":null,"abstract":"Rapid advancement in internet has made many Social Networking Sites (SNS) popular among a huge population, as various SNS accounts are interlinked with each other, spread of stored susceptible information of an individual is increasing. That has led to various security and privacy issues; one of them is impersonation or identity fraud. Identity fraud is the outcome of illegitimate or secret use of account owner’s identity to invade his/her account to track personal information. There are possibilities that known persons like parents, spouse, close friends, siblings who are interested in knowing what is going on in the account owner’s online life may check their personal SNS accounts. Hence an individual’s private SNS accounts can be invaded by an illegitimate user secretly without the knowledge of the account owner’s which results in compromise of private information. Thus, this paper proposes an in loco identity fraud detection strategy that employs a statistical analysis approach to constantly authenticate the authorized user, which outperforms the previously known technique. This strategy may be used to prevent stalkers from penetrating a person's SNS account in real time. The accuracy attained in this research is greater than 90% after 1 minute and greater than 95% after 5 minutes of observation.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"14 1","pages":"282-292"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88497903","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}
In recent years, digital news has become increasingly prevalent, with many people getting their news and information from online sources rather than traditional print or broadcast media. This shift has been driven, in part, by the convenience and accessibility of digital platforms, as well as the ability to personalize and customize news feeds. Digital news also allows for greater interactivity and engagement with readers and can reach a global audience almost instantly. News articles contain a plethora of hidden spatial information that, when shared with readers, increases comprehension of current events. Only a few news aggregation systems make this information available to users. Many stories, on the other hand, are not clearly geotagged with their spatial information. In this work, we propose the MapNews framework, a novel system that gathers, analyzes, and presents news articles on a map interface, allowing users to take advantage of their underlying spatial information. MapNews pulls content from several different internet news sources and, using a custom-built geotagger, it extracts geographic content from articles. A rapid online clustering method is used to organize articles into story clusters. Panning and zooming MapNews' map interface allows readers to receive news based on geographic location and category importance, and they will view distinct articles depending on their location. MapNews achieved an ARI score of 0.89 for clustering and an accuracy of 95% in usability testing
{"title":"MAPNEWS: a framework for aggregating and organizing online news articles","authors":"Jeelani Ahmed, Muqeem Ahmed","doi":"10.34028/iajit/20/3/10","DOIUrl":"https://doi.org/10.34028/iajit/20/3/10","url":null,"abstract":"In recent years, digital news has become increasingly prevalent, with many people getting their news and information from online sources rather than traditional print or broadcast media. This shift has been driven, in part, by the convenience and accessibility of digital platforms, as well as the ability to personalize and customize news feeds. Digital news also allows for greater interactivity and engagement with readers and can reach a global audience almost instantly. News articles contain a plethora of hidden spatial information that, when shared with readers, increases comprehension of current events. Only a few news aggregation systems make this information available to users. Many stories, on the other hand, are not clearly geotagged with their spatial information. In this work, we propose the MapNews framework, a novel system that gathers, analyzes, and presents news articles on a map interface, allowing users to take advantage of their underlying spatial information. MapNews pulls content from several different internet news sources and, using a custom-built geotagger, it extracts geographic content from articles. A rapid online clustering method is used to organize articles into story clusters. Panning and zooming MapNews' map interface allows readers to receive news based on geographic location and category importance, and they will view distinct articles depending on their location. MapNews achieved an ARI score of 0.89 for clustering and an accuracy of 95% in usability testing","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"26 1","pages":"376-386"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72976373","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}
Motion estimation and motion compensation are the accepted process in H.264 and H.265 video coding standard to reduce temporal redundancy. Several fast block matching algorithms have been developed to reduce the calculation cost in the motion estimation process. But quick block matching algorithms often lead to a local minimum. Several researchers used different population-based nature-inspired algorithms to perform block matching. Algorithms like genetic algorithm, differential evolution, particle swarm optimization were used in numerous motion estimation algorithms. Different algorithms used a fitness approximation strategy to reduce computation cost. Jaya algorithm-based block matching is the most efficient block matching algorithm in the available literature. Jaya algorithm is free from algorithmic specific parameter which speeds up the process. This article proposes a few modifications to the traditional cuckoo search algorithm and then, a block matching algorithm was proposed based on the modified cuckoo search algorithm. Fitness approximation, adaptive termination, and zero motion prejudgment modules were used with the modified cuckoo search algorithm to reduce the number of redundant calculations. The performance of the proposed algorithm was compared with the exhaustive search algorithm and other benchmarking algorithms in terms of Peak Signal to Noise Ratio (PSNR), Structure Similarity Index (SSIM), and average search point required to calculate a motion vector for a block. The proposed algorithm delivers better performance compared to the benchmarking algorithms.
{"title":"Modified cuckoo search algorithm for motion vector estimation","authors":"S. Acharjee, S. S. Chaudhuri","doi":"10.34028/iajit/20/3/6","DOIUrl":"https://doi.org/10.34028/iajit/20/3/6","url":null,"abstract":"Motion estimation and motion compensation are the accepted process in H.264 and H.265 video coding standard to reduce temporal redundancy. Several fast block matching algorithms have been developed to reduce the calculation cost in the motion estimation process. But quick block matching algorithms often lead to a local minimum. Several researchers used different population-based nature-inspired algorithms to perform block matching. Algorithms like genetic algorithm, differential evolution, particle swarm optimization were used in numerous motion estimation algorithms. Different algorithms used a fitness approximation strategy to reduce computation cost. Jaya algorithm-based block matching is the most efficient block matching algorithm in the available literature. Jaya algorithm is free from algorithmic specific parameter which speeds up the process. This article proposes a few modifications to the traditional cuckoo search algorithm and then, a block matching algorithm was proposed based on the modified cuckoo search algorithm. Fitness approximation, adaptive termination, and zero motion prejudgment modules were used with the modified cuckoo search algorithm to reduce the number of redundant calculations. The performance of the proposed algorithm was compared with the exhaustive search algorithm and other benchmarking algorithms in terms of Peak Signal to Noise Ratio (PSNR), Structure Similarity Index (SSIM), and average search point required to calculate a motion vector for a block. The proposed algorithm delivers better performance compared to the benchmarking algorithms.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"13 1","pages":"340-348"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91247933","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}
In context of high speed broadband communication, 5G optical communication has become a most stimulating and motivating domain in research. Recent cellular network LTE (4G) will not be adequate and effective to fulfill the needs of higher data rate, higher bandwidth, low latency and Quality of service.To achieve the above requirements and to address the challenges 5G optical communication have major considerations, the incorporation of Dense Wavelength Division Multiplexing (DWDM), Millimeter wave (mm-wave) communication, eMBB to make efficient decisions.This article summarize the empowering technology for DWDM, mm-wave signal communication and enhanced Mobile Broadband uses 5G optical communication as an evolution from 4G-LTE communication amenities, with fast communications, good throughput and high capacity, it is a successful aspirant for the increasing broadband communications. Such empowering technology, focused mainly on improving the network structure and efficiency, involves producing the mm-wave broadband signal with easy and cost adequate systems. This paper gives a thorough review on developing and empowering advancements identified with optical communication framework that can meet the huge data rate request of broadband. The article further addresses the research gaps and outlines the important future research direction for enhancing communication using DWDM.
{"title":"Intensification and interpretation of performance in 5G adopting millimeter wave: a survey and future research direction","authors":"Nivethitha Vijayaraj, A. Sivasubramanian","doi":"10.34028/iajit/20/4/6","DOIUrl":"https://doi.org/10.34028/iajit/20/4/6","url":null,"abstract":"In context of high speed broadband communication, 5G optical communication has become a most stimulating and motivating domain in research. Recent cellular network LTE (4G) will not be adequate and effective to fulfill the needs of higher data rate, higher bandwidth, low latency and Quality of service.To achieve the above requirements and to address the challenges 5G optical communication have major considerations, the incorporation of Dense Wavelength Division Multiplexing (DWDM), Millimeter wave (mm-wave) communication, eMBB to make efficient decisions.This article summarize the empowering technology for DWDM, mm-wave signal communication and enhanced Mobile Broadband uses 5G optical communication as an evolution from 4G-LTE communication amenities, with fast communications, good throughput and high capacity, it is a successful aspirant for the increasing broadband communications. Such empowering technology, focused mainly on improving the network structure and efficiency, involves producing the mm-wave broadband signal with easy and cost adequate systems. This paper gives a thorough review on developing and empowering advancements identified with optical communication framework that can meet the huge data rate request of broadband. The article further addresses the research gaps and outlines the important future research direction for enhancing communication using DWDM.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"10 1","pages":"600-608"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84315059","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}
{"title":"Using MCDM and FaaS in automating the eligibility of business rules in the decision-making process","authors":"Riadh Ghlala, Z. Aouina Kodia, L. B. Said","doi":"10.34028/iajit/20/2/9","DOIUrl":"https://doi.org/10.34028/iajit/20/2/9","url":null,"abstract":"","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"36 1","pages":"224-233"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83955991","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}
Dhivya Samraj, Kuppuchamy Ramasamy, M. Karuppusamy
In this research, Particle Swarm Optimization (PSO) based image equalization is projected to enhance the contrast of different breast cancer images. Breast cancer is the highest and another important root of tumor disease in females worldwide. Mass and microcalcification clusters are a significant early signs of breast cancer. The mortality rate can effectively be decreased by early diagnosis and treatment. Most practical approach for the early detection and identification of breast cancer diseases is mammography. Mammographic images contaminated by noise usually involve image enhancement techniques to aid interpretation. Contrast enhancement is divided into two categories: development of direct contrast and enhancement of indirect contrast. Indirect contrast improvement is used in the image histogram update. Histogram Equalization (HE) is the modest enhancement of the indirect contrast approach usually used for contrast enhancement. The proposed method's average entropy is 5.3251 with the highest structural similarity index 0.99725. The best contrast improvement of this method is 1.0404 and PSNR is 46.3803. The MSE value is 2157.08. This paper recommends an innovative method of enhancing digital mammogram image contrast based on different histogram equalization approaches. The performance of the projected method has been related to other prevailing techniques using the parameters, namely, discrete entropy, contrast improvement index, structural similarity index measure, mean square error, and peak signal-to-noise ratio. Investigational findings indicate that the projected strategy is efficient and robust and shows better results than others.
{"title":"Evolutionary computing model for finding breast cancer masses using image enhancement procedures with artificial intelligent algorithms","authors":"Dhivya Samraj, Kuppuchamy Ramasamy, M. Karuppusamy","doi":"10.34028/iajit/20/4/10","DOIUrl":"https://doi.org/10.34028/iajit/20/4/10","url":null,"abstract":"In this research, Particle Swarm Optimization (PSO) based image equalization is projected to enhance the contrast of different breast cancer images. Breast cancer is the highest and another important root of tumor disease in females worldwide. Mass and microcalcification clusters are a significant early signs of breast cancer. The mortality rate can effectively be decreased by early diagnosis and treatment. Most practical approach for the early detection and identification of breast cancer diseases is mammography. Mammographic images contaminated by noise usually involve image enhancement techniques to aid interpretation. Contrast enhancement is divided into two categories: development of direct contrast and enhancement of indirect contrast. Indirect contrast improvement is used in the image histogram update. Histogram Equalization (HE) is the modest enhancement of the indirect contrast approach usually used for contrast enhancement. The proposed method's average entropy is 5.3251 with the highest structural similarity index 0.99725. The best contrast improvement of this method is 1.0404 and PSNR is 46.3803. The MSE value is 2157.08. This paper recommends an innovative method of enhancing digital mammogram image contrast based on different histogram equalization approaches. The performance of the projected method has been related to other prevailing techniques using the parameters, namely, discrete entropy, contrast improvement index, structural similarity index measure, mean square error, and peak signal-to-noise ratio. Investigational findings indicate that the projected strategy is efficient and robust and shows better results than others.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"31 1","pages":"632-643"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89416417","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}
The main purpose of the classification of fingerprints is to devise a formula by which a given collection of fingerprints can be tracked and registered. To accelerate the system for classifying fingerprints, it is necessary to utilize fingerprint image characteristics and avoid the different fingerprint forms arising from fingerprint rotation. This paper presents a simple, new approach to the extraction of characteristics from fingerprint images. The proposed method demonstrates that, for a given image, the features remain constant even after being subjected to a wide range of rotations; thus, it creates an array of characteristics which can be used to identify a person from their fingerprint. To achieve this goal, a basic hit-and-miss operation with different structural components is used to detect and count various features in the fingerprint picture; these features are directly identified based on the texture of the fingerprint. The chosen features are used to index the finger image by generating a frequency of occurrences for each one, such that every fingerprint is represented as a vector of these features. The application of the proposed method shows efficient utilization of execution time and memory usage.
{"title":"A simple and stable method of creating fingerprint features with image rotation","authors":"Mohamad M. Al-Laham, M. Al-Ma’aitah, Z. Alqadi","doi":"10.34028/iajit/20/4/15","DOIUrl":"https://doi.org/10.34028/iajit/20/4/15","url":null,"abstract":"The main purpose of the classification of fingerprints is to devise a formula by which a given collection of fingerprints can be tracked and registered. To accelerate the system for classifying fingerprints, it is necessary to utilize fingerprint image characteristics and avoid the different fingerprint forms arising from fingerprint rotation. This paper presents a simple, new approach to the extraction of characteristics from fingerprint images. The proposed method demonstrates that, for a given image, the features remain constant even after being subjected to a wide range of rotations; thus, it creates an array of characteristics which can be used to identify a person from their fingerprint. To achieve this goal, a basic hit-and-miss operation with different structural components is used to detect and count various features in the fingerprint picture; these features are directly identified based on the texture of the fingerprint. The chosen features are used to index the finger image by generating a frequency of occurrences for each one, such that every fingerprint is represented as a vector of these features. The application of the proposed method shows efficient utilization of execution time and memory usage.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"13 1","pages":"686-692"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86236963","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}
The Software industry’s rapid growth contributes to the need for new technologies. PRICE software system uses Predictive Object Point (POP) as a size measure to estimate Effort and cost. A refined POP metric value for object-oriented software written in Java can be calculated using the Automated POP Analysis tool. This research used 25 open-source Java projects. The refined POP metric improves the drawbacks of the PRICE system and gives a more accurate size measure of software. This paper uses refined POP metrics with curve-fitting neural networks and multi-layer perceptron neural network-based deep learning to estimate the software development effort. Results show that this approach gives an effort estimate closer to the actual Effort obtained through Constructive Cost Estimation Model (COCOMO) estimation models and thus validates refined POP as a better size measure of object-oriented software than POP. Therefore we consider the MLP approach to help construct the metric for the scale of the Object-Oriented (OO) model system.
{"title":"Estimation model for enhanced predictive object point metric in OO software size estimation using deep learning","authors":"Vijay Yadav, Raghuraj Singh, Vibhash Yadav","doi":"10.34028/iajit/20/3/1","DOIUrl":"https://doi.org/10.34028/iajit/20/3/1","url":null,"abstract":"The Software industry’s rapid growth contributes to the need for new technologies. PRICE software system uses Predictive Object Point (POP) as a size measure to estimate Effort and cost. A refined POP metric value for object-oriented software written in Java can be calculated using the Automated POP Analysis tool. This research used 25 open-source Java projects. The refined POP metric improves the drawbacks of the PRICE system and gives a more accurate size measure of software. This paper uses refined POP metrics with curve-fitting neural networks and multi-layer perceptron neural network-based deep learning to estimate the software development effort. Results show that this approach gives an effort estimate closer to the actual Effort obtained through Constructive Cost Estimation Model (COCOMO) estimation models and thus validates refined POP as a better size measure of object-oriented software than POP. Therefore we consider the MLP approach to help construct the metric for the scale of the Object-Oriented (OO) model system.","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"14 1","pages":"293-302"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74849673","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}
{"title":"A Rule-Induction Approach for Building an Arabic Language Interfaces to Databases","authors":"Hanane Bais, M. Machkour","doi":"10.34028/iajit/20/1/6","DOIUrl":"https://doi.org/10.34028/iajit/20/1/6","url":null,"abstract":"","PeriodicalId":13624,"journal":{"name":"Int. Arab J. Inf. Technol.","volume":"19 1","pages":"49-56"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80773866","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}
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