{"title":"A hybrid grey wolf-whale optimization algorithm for classification of corona virus genome sequences using deep learning","authors":"M. Muthulakshmi, G. Murugeswari","doi":"10.34028/iajit/20/3/5","DOIUrl":"https://doi.org/10.34028/iajit/20/3/5","url":null,"abstract":"","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":"75050082","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}
Latent fingerprints are adapted as prominent evidence for the identification of crime suspects from ages. The unavailability of complete minutiae information, poor quality of impressions, and overlapping of multi-impressions make the latent fingerprint recognition process a challenging task. Although the contributions in the field are efficient for determining the match, there is a requirement to ameliorate the existing techniques as false identification can put the benign behind bars. This research work has amalgamated the Cuckoo Search (CS) algorithm with Ant Colony Optimization (ACO) for the recognition of latent fingerprints. It reduces the demerits of the individual cuckoo search algorithm, such as the probability of falling into local optima, the inefficient creation of nests at the boundary due to random walk and Levy flight attributes. The positive feedback mechanism of ant colony optimization makes it easy to combine with other techniques, reducing the risk of local failure and evaluating the global best solution. Prior to the evaluation of the proposed amalgamated technique on the latent fingerprint dataset of NIST SD-27, it is tested with the benchmark functions for different shapes and physical attributes. The benchmark testing and latent fingerprint evaluation result in the betterment of the amalgamated technique over the individual cuckoo search algorithm. The state-of-the-art comparison indicates that the amalgamation technique outperformed the other fingerprint matching techniques.
{"title":"Latent Fingerprint Recognition using Hybrid Ant Colony Optimization and Cuckoo Search","authors":"Richa Jindal, Sanjay Singla","doi":"10.34028/iajit/20/1/3","DOIUrl":"https://doi.org/10.34028/iajit/20/1/3","url":null,"abstract":"Latent fingerprints are adapted as prominent evidence for the identification of crime suspects from ages. The unavailability of complete minutiae information, poor quality of impressions, and overlapping of multi-impressions make the latent fingerprint recognition process a challenging task. Although the contributions in the field are efficient for determining the match, there is a requirement to ameliorate the existing techniques as false identification can put the benign behind bars. This research work has amalgamated the Cuckoo Search (CS) algorithm with Ant Colony Optimization (ACO) for the recognition of latent fingerprints. It reduces the demerits of the individual cuckoo search algorithm, such as the probability of falling into local optima, the inefficient creation of nests at the boundary due to random walk and Levy flight attributes. The positive feedback mechanism of ant colony optimization makes it easy to combine with other techniques, reducing the risk of local failure and evaluating the global best solution. Prior to the evaluation of the proposed amalgamated technique on the latent fingerprint dataset of NIST SD-27, it is tested with the benchmark functions for different shapes and physical attributes. The benchmark testing and latent fingerprint evaluation result in the betterment of the amalgamated technique over the individual cuckoo search algorithm. The state-of-the-art comparison indicates that the amalgamation technique outperformed the other fingerprint matching techniques.","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":"89777580","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}
Yousra Odeh, Dina Tbaishat, Faten F. Kharbat, O. Shamieh, M. Odeh
Adherence to the Universal Health Coverage (UHC) principles in relation to palliative care is a key WHO directive to attain as a right for every citizen. However, UHC principles have been observed to be hindered by several barriers. Moreover, the UNSDGs, and in particular the UNSDG 3, demands “Good Health and Well Being” with the two key indicators UNSDG 3.8.1 and 3.8.2 that can be considered as metrics to assess governance conformance to palliative care. This paper reports on addressing the current research gap in linking the UHC principles to UNSDGs and, in particular, UNSDG3 and the WHO identified Palliative Care Barriers (PCB) using the i* framework Strategic Dependency (SD) and Strategic Rationale (SR) models applied to Home Healthcare Care (HHC) of a regional cancer care organisation, namely King Hussain Cancer Center (KHCC). Building on our i* HHC SD and SR developed models, and for HHC being an essential and critical part of palliative care, an integrated framework has been developed that not only links UHC principles and WHO barriers of palliative care to UNSDG 3, but a full network of dependencies that facilitates observing the linkages and impact of the most critical and strategic actors in HHC on the UHC, barriers to palliative care and UNSDG 3. Furthermore, such highly comprehensive UHC-PCB-UNSDG-i* framework network instantiations have led to identifying patterns of categories or groups of associations between UNSDG3 KPIs, UHC principles, WHO palliative care barriers and HHC actors. Hence, this contributes to healthcare policy and decision makers to revisit their policies, plans, budgets, and constraints for the deficiencies in the qualitative satisfaction of the universal health coverage principles and how palliative care barriers can be alleviated in association with the actors in the i* SD and SR models and associated goals, tasks and resources. A further corollary of this research is that change impact analysis can be timely attained to study the impact of a change driven by updating goals, tasks, and resources of the i* model to improve adherence to the UNSDG3 KPIS and UHC principles. Finally, this work has inspired work in progress to develop a data analytics platform from the evolving instances of applying palliative care processes using the resultant UHC-PCB-UNSDG-i* framework
{"title":"Linking palliative homecare to the universal health coverage principles and the united nations sustainability development goals using the i* frameworks strategic and social requirements modelling, applied to a cancer care organisation","authors":"Yousra Odeh, Dina Tbaishat, Faten F. Kharbat, O. Shamieh, M. Odeh","doi":"10.34028/iajit/20/3a/12","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/12","url":null,"abstract":"Adherence to the Universal Health Coverage (UHC) principles in relation to palliative care is a key WHO directive to attain as a right for every citizen. However, UHC principles have been observed to be hindered by several barriers. Moreover, the UNSDGs, and in particular the UNSDG 3, demands “Good Health and Well Being” with the two key indicators UNSDG 3.8.1 and 3.8.2 that can be considered as metrics to assess governance conformance to palliative care. This paper reports on addressing the current research gap in linking the UHC principles to UNSDGs and, in particular, UNSDG3 and the WHO identified Palliative Care Barriers (PCB) using the i* framework Strategic Dependency (SD) and Strategic Rationale (SR) models applied to Home Healthcare Care (HHC) of a regional cancer care organisation, namely King Hussain Cancer Center (KHCC). Building on our i* HHC SD and SR developed models, and for HHC being an essential and critical part of palliative care, an integrated framework has been developed that not only links UHC principles and WHO barriers of palliative care to UNSDG 3, but a full network of dependencies that facilitates observing the linkages and impact of the most critical and strategic actors in HHC on the UHC, barriers to palliative care and UNSDG 3. Furthermore, such highly comprehensive UHC-PCB-UNSDG-i* framework network instantiations have led to identifying patterns of categories or groups of associations between UNSDG3 KPIs, UHC principles, WHO palliative care barriers and HHC actors. Hence, this contributes to healthcare policy and decision makers to revisit their policies, plans, budgets, and constraints for the deficiencies in the qualitative satisfaction of the universal health coverage principles and how palliative care barriers can be alleviated in association with the actors in the i* SD and SR models and associated goals, tasks and resources. A further corollary of this research is that change impact analysis can be timely attained to study the impact of a change driven by updating goals, tasks, and resources of the i* model to improve adherence to the UNSDG3 KPIS and UHC principles. Finally, this work has inspired work in progress to develop a data analytics platform from the evolving instances of applying palliative care processes using the resultant UHC-PCB-UNSDG-i* framework","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":"88813116","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 aim of this research work is to study the issues and challenges that Al al-Bayt University faculty members faced in the transition from face-to-face learning to online learning during the COVID-19 pandemic, and to highlight successful online learning strategies adopted. These issues and challenges are identified using question and solution-based analysis covering several issues that include the procedures and mechanisms adopted for the rapid transition from face-to-face learning to online learning, the online learning environment used, the impact of the transition to online learning on faculty members, courses’ content and students, and the challenges of online learning and the impacts it had on teaching and scientific research. The successful strategies adopted provide many practical methods for faculty members and leaders to follow for future online learning. In addition, the results of this work are expected to provide faculty members with a clear and insightful view on how to successfully integrate online learning and traditional learning into a blended learning approach.
这项研究工作的目的是研究Al Al - bayt大学教师在2019冠状病毒病大流行期间从面对面学习向在线学习过渡所面临的问题和挑战，并重点介绍所采用的成功在线学习策略。这些问题和挑战是通过基于问题和解决方案的分析来确定的，这些分析涵盖了几个问题，包括从面对面学习到在线学习的快速过渡所采用的程序和机制，所使用的在线学习环境，向在线学习过渡对教师、课程内容和学生的影响，以及在线学习的挑战及其对教学和科研的影响。所采用的成功策略为教师和领导者今后的在线学习提供了许多实用的方法。此外，这项工作的结果有望为教师提供关于如何成功地将在线学习和传统学习整合为混合学习方法的清晰而有见地的观点。
{"title":"On rapid transitioning to online learning under COVID-19: challenges and solutions at al al-bayt university","authors":"S. Bani-Mohammad, I. Ababneh","doi":"10.34028/iajit/20/3a/2","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/2","url":null,"abstract":"The aim of this research work is to study the issues and challenges that Al al-Bayt University faculty members faced in the transition from face-to-face learning to online learning during the COVID-19 pandemic, and to highlight successful online learning strategies adopted. These issues and challenges are identified using question and solution-based analysis covering several issues that include the procedures and mechanisms adopted for the rapid transition from face-to-face learning to online learning, the online learning environment used, the impact of the transition to online learning on faculty members, courses’ content and students, and the challenges of online learning and the impacts it had on teaching and scientific research. The successful strategies adopted provide many practical methods for faculty members and leaders to follow for future online learning. In addition, the results of this work are expected to provide faculty members with a clear and insightful view on how to successfully integrate online learning and traditional learning into a blended learning approach.","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":"77184171","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}
Massive Multiple Input Multiple Output (MIMO) is an evolving technology based on the principle of spatial multiplexing winch consisting in using at the same time the same radio frequencies to send different signals. The several transmitting antennas from a base station can transmit different signals and several receiving antennas from a device can receive and divide them simultaneously. Due to the physically difficult of installing antennas close to each other, standard MIMO networks generally limit four antenna-side transmitters and receivers for data transmission while it could be more. The study aims to review the traditional MIMO different types as well as investigates the SNR between Single Input Single Output (SISO) and MIMO to ensure the best wireless connection functionality. In addition to that, a simple comparison to distinguish between SISO, SIMO, MISO, and MIMO in term of capacity and data rate to provide an indication for the quality of the wireless connection. The work's contribution is to illustrate technological benefits like MIMO, which boosts data speeds and increases the reliability of wireless networks. The outcome shows a SISO system would have a lower data rate than other systems because it only has one antenna at the transmitter and receiver, whereas a MISO system would typically have a greater Signal-to-Noise Ratio (SNR) than a SISO or SIMO system because it uses several transmit antennas. MIMO, however, took advantage of all the positive characteristics and emerged as the best solution overall.
{"title":"A brief review of massive MIMO technology for the next generation","authors":"I. Elmutasim","doi":"10.34028/iajit/20/2/13","DOIUrl":"https://doi.org/10.34028/iajit/20/2/13","url":null,"abstract":"Massive Multiple Input Multiple Output (MIMO) is an evolving technology based on the principle of spatial multiplexing winch consisting in using at the same time the same radio frequencies to send different signals. The several transmitting antennas from a base station can transmit different signals and several receiving antennas from a device can receive and divide them simultaneously. Due to the physically difficult of installing antennas close to each other, standard MIMO networks generally limit four antenna-side transmitters and receivers for data transmission while it could be more. The study aims to review the traditional MIMO different types as well as investigates the SNR between Single Input Single Output (SISO) and MIMO to ensure the best wireless connection functionality. In addition to that, a simple comparison to distinguish between SISO, SIMO, MISO, and MIMO in term of capacity and data rate to provide an indication for the quality of the wireless connection. The work's contribution is to illustrate technological benefits like MIMO, which boosts data speeds and increases the reliability of wireless networks. The outcome shows a SISO system would have a lower data rate than other systems because it only has one antenna at the transmitter and receiver, whereas a MISO system would typically have a greater Signal-to-Noise Ratio (SNR) than a SISO or SIMO system because it uses several transmit antennas. MIMO, however, took advantage of all the positive characteristics and emerged as the best solution overall.","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":"74916219","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}
Recently, with the development of online transactions, the credit-card transactions begun to be the most prevalent online payment methods. Credit-card fraud refers to the use fake Credit-Cards to purchase goods without paying. With the fast research and development in the area of information technology and data mining methods including the neural networks and decision trees, to advanced machine learning and deep learning methods, researchers have proposed a wide range of antifraud systems. Mainly, the Machine Learning (ML) and Deep Learning (DL) methods are employed to perform the fraud detection task. This paper aims to explore the existing credit-card fraud detection methods, and categorize them into two main categories. In addition, we investigated the deployment of neural network models with credit-card fraud detection problem, since we employed the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). ANN and CNN models are implemented and assessed using a credit-card dataset. The main contribution of this paper focuses on increasing the fraud-detection classification accuracy through developing an efficient deep neural network model.
{"title":"Credit-card fraud detection system using neural networks","authors":"S. A. Balawi, Nojood Aljohani","doi":"10.34028/iajit/20/2/10","DOIUrl":"https://doi.org/10.34028/iajit/20/2/10","url":null,"abstract":"Recently, with the development of online transactions, the credit-card transactions begun to be the most prevalent online payment methods. Credit-card fraud refers to the use fake Credit-Cards to purchase goods without paying. With the fast research and development in the area of information technology and data mining methods including the neural networks and decision trees, to advanced machine learning and deep learning methods, researchers have proposed a wide range of antifraud systems. Mainly, the Machine Learning (ML) and Deep Learning (DL) methods are employed to perform the fraud detection task. This paper aims to explore the existing credit-card fraud detection methods, and categorize them into two main categories. In addition, we investigated the deployment of neural network models with credit-card fraud detection problem, since we employed the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). ANN and CNN models are implemented and assessed using a credit-card dataset. The main contribution of this paper focuses on increasing the fraud-detection classification accuracy through developing an efficient deep neural network model.","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":"82991056","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 quality of generated images is one of the significant criteria for Generative Adversarial Networks (GANs) evaluation in image synthesis research. Previous researches proposed a great many tricks to modify the model structure or loss functions. However, seldom of them consider the effect of combination of data augmentation and multiple penalty areas on image quality improvement. This research introduces a GAN architecture based on data augmentation, in order to make the model fulfill 1-Lipschitz constraints, it proposes to consider these additional data included into the penalty areas which can improve ability of discriminator and generator. With the help of these techniques, compared with previous model Deep Convolutional GAN (DCGAN) and Wasserstein GAN with gradient penalty (WGAN-GP), the model proposed in this research can get lower Frechet Inception Distance score (FID) score 2.973 and 2.941 on celebA and LSUN towers at 64×64 resolution respectively which proves that this model can produce high visual quality results.
在图像合成研究中，生成图像的质量是评价生成对抗网络(GANs)的重要标准之一。以往的研究提出了许多修改模型结构或损失函数的技巧。但是，很少考虑数据增强和多惩罚区域相结合对图像质量提高的影响。本研究引入了一种基于数据增强的GAN结构，为了使模型满足1-Lipschitz约束，提出将这些附加数据纳入罚域，以提高鉴别器和生成器的能力。在这些技术的帮助下，与之前的模型Deep Convolutional GAN (DCGAN)和Wasserstein GAN With gradient penalty (WGAN-GP)相比，本研究提出的模型在celebA和LSUN塔上分别获得了较低的Frechet Inception Distance score (FID)，分别为2.973和2.941，分辨率分别为64×64，证明了该模型可以产生较高的视觉质量结果。
{"title":"Generative adversarial networks with data augmentation and multiple penalty areas for image synthesis","authors":"Li Chen, H. Chan","doi":"10.34028/iajit/20/3/15","DOIUrl":"https://doi.org/10.34028/iajit/20/3/15","url":null,"abstract":"The quality of generated images is one of the significant criteria for Generative Adversarial Networks (GANs) evaluation in image synthesis research. Previous researches proposed a great many tricks to modify the model structure or loss functions. However, seldom of them consider the effect of combination of data augmentation and multiple penalty areas on image quality improvement. This research introduces a GAN architecture based on data augmentation, in order to make the model fulfill 1-Lipschitz constraints, it proposes to consider these additional data included into the penalty areas which can improve ability of discriminator and generator. With the help of these techniques, compared with previous model Deep Convolutional GAN (DCGAN) and Wasserstein GAN with gradient penalty (WGAN-GP), the model proposed in this research can get lower Frechet Inception Distance score (FID) score 2.973 and 2.941 on celebA and LSUN towers at 64×64 resolution respectively which proves that this model can produce high visual quality 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":"72842478","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}
Spam messages have increased dramatically in recent years even as the number of email clients has grown. Email has already become a valuable way of communicating because it saves time and effort. However, numerous emails contain unwelcome content known as spam as a result of social platforms and advertisements. Despite the fact that many techniques have already been created for spam mails categorization, none of them achieves 100 percent efficiency in analyzing spam messages. So, in this research, we propose a novel Gradient Fuzzy Guideline-based Spam Classifier (GFGSC) for classifying the spam e-mails as spam or non-spam. This research uses four types of datasets and these datasets are pre-processed using normalization. Then the set of data can be extracted using Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) techniques. The aspects are selected using Information Gain (IG) and Chi-Square (ChS) techniques. And the GFGSC classifier can be used for classifying the data as spam or non-spam with better effectiveness. Finally, the performances are examined and these metrics are matched with the existing approaches. The results are obtained using the MATLAB tool.
{"title":"A novel spam classification system for e-mail using a gradient fuzzy guideline-based spam classifier (GFGSC)","authors":"Vinoth Narayanan Arumugam Subramaniam, Rajesh Annamalai","doi":"10.34028/iajit/20/3/12","DOIUrl":"https://doi.org/10.34028/iajit/20/3/12","url":null,"abstract":"Spam messages have increased dramatically in recent years even as the number of email clients has grown. Email has already become a valuable way of communicating because it saves time and effort. However, numerous emails contain unwelcome content known as spam as a result of social platforms and advertisements. Despite the fact that many techniques have already been created for spam mails categorization, none of them achieves 100 percent efficiency in analyzing spam messages. So, in this research, we propose a novel Gradient Fuzzy Guideline-based Spam Classifier (GFGSC) for classifying the spam e-mails as spam or non-spam. This research uses four types of datasets and these datasets are pre-processed using normalization. Then the set of data can be extracted using Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) techniques. The aspects are selected using Information Gain (IG) and Chi-Square (ChS) techniques. And the GFGSC classifier can be used for classifying the data as spam or non-spam with better effectiveness. Finally, the performances are examined and these metrics are matched with the existing approaches. The results are obtained using the MATLAB tool.","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":"74056101","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}
Datasets of text images are important for optical text recognition systems. Such datasets can be used to enhance performance and recognition rates. In this research work, we present a bilingual dataset consists of Arabic/English text images to address the lack of availability of bilingual text databases. The presented dataset consists of 97812 text images, which are categorized into two groups; Scanned page and digitized line images. Images of the two forms are written with 10 fonts and four sizes, and prepared/scanned with four dpi resolutions. The dataset preparation process includes text collection, text editing, image construction, and image processing. The dataset can be used in optical text recognition, optical font recognition, language identification, and segmentation. Different text recognition and language identification experiments have been conducted using images of the dataset and Hidden Markov Model (HMM) classifier. For the digitized images recognition experiments, the best-achieved recognition correctness is 99.01% and the best accuracy is 99.01%. The font that has the highest recognition rates was Tahoma. For the scanned images recognition experiments, Tahoma has also shown the highest performance with 97.86% for correctness and 97.73% for accuracy. For the language identification experiments, Tahoma has shown the performance with 99.98% for word-language identification rate.
{"title":"BPTI: bilingual printed text images dataset for recognition purposes","authors":"M. Yahia, H. Al-Muhtaseb","doi":"10.34028/iajit/20/4/12","DOIUrl":"https://doi.org/10.34028/iajit/20/4/12","url":null,"abstract":"Datasets of text images are important for optical text recognition systems. Such datasets can be used to enhance performance and recognition rates. In this research work, we present a bilingual dataset consists of Arabic/English text images to address the lack of availability of bilingual text databases. The presented dataset consists of 97812 text images, which are categorized into two groups; Scanned page and digitized line images. Images of the two forms are written with 10 fonts and four sizes, and prepared/scanned with four dpi resolutions. The dataset preparation process includes text collection, text editing, image construction, and image processing. The dataset can be used in optical text recognition, optical font recognition, language identification, and segmentation. Different text recognition and language identification experiments have been conducted using images of the dataset and Hidden Markov Model (HMM) classifier. For the digitized images recognition experiments, the best-achieved recognition correctness is 99.01% and the best accuracy is 99.01%. The font that has the highest recognition rates was Tahoma. For the scanned images recognition experiments, Tahoma has also shown the highest performance with 97.86% for correctness and 97.73% for accuracy. For the language identification experiments, Tahoma has shown the performance with 99.98% for word-language identification rate.","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":"80713231","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}
Malaria is a deadly syndrome formed by the Plasmodium parasite that spreads through the bite of infected Anopheles mosquitoes. There are several drugs to cure malaria but it is difficult to detect due to inadequate equipment and technology. Microscopic check-ups of blood smear images by experts help to detect malaria-infected parasites accurately. However, manual analysis is tedious and time-consuming as the experts have to deal with many cases. This paper presents computer assisted malaria parasite detection model by classifying the blood smear image with hybrid deep learning methods that have high accuracy for classification. In the proposed approach the blood smear images are pre-processed using bilateral filtering technique in which features are extracted with the convolutional neural network. These features are selected by the improved grey-wolf optimization, and image classification is performed with the support vector machine. To evaluate the efficiency of the proposed technique, the NIH malaria dataset is utilized and the results are compared with existing approaches in terms of accuracy, F-Measure, recall, precision, and specificity. The outcome reveals that the proposed scheme is accurate and can be more helpful to pathologists for reliable parasite detection.
{"title":"Malaria parasite detection on microscopic blood smear images with integrated deep learning algorithms","authors":"C. B. Jones, C. Murugamani","doi":"10.34028/iajit/20/2/3","DOIUrl":"https://doi.org/10.34028/iajit/20/2/3","url":null,"abstract":"Malaria is a deadly syndrome formed by the Plasmodium parasite that spreads through the bite of infected Anopheles mosquitoes. There are several drugs to cure malaria but it is difficult to detect due to inadequate equipment and technology. Microscopic check-ups of blood smear images by experts help to detect malaria-infected parasites accurately. However, manual analysis is tedious and time-consuming as the experts have to deal with many cases. This paper presents computer assisted malaria parasite detection model by classifying the blood smear image with hybrid deep learning methods that have high accuracy for classification. In the proposed approach the blood smear images are pre-processed using bilateral filtering technique in which features are extracted with the convolutional neural network. These features are selected by the improved grey-wolf optimization, and image classification is performed with the support vector machine. To evaluate the efficiency of the proposed technique, the NIH malaria dataset is utilized and the results are compared with existing approaches in terms of accuracy, F-Measure, recall, precision, and specificity. The outcome reveals that the proposed scheme is accurate and can be more helpful to pathologists for reliable parasite detection.","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":"80274868","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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