: Clear images are a prerequisite of high-level underwater vision tasks, but images captured underwater are often degraded due to absorption and scattering of light. To solve this issue, traditional methods have shown some success, but often generate unwanted artifacts for knowledge priori dependency. In contrast, learning-based approaches can produce more refined results. Most popular methods are based on an encoder-decoder configuration for simply learning the nonlinear transformation of input and output images, so their ability to capture details is limited. In addition, the significant pixel-level features and multi-scale features are often overlooked. Accordingly, we propose a novel and efficient network that incorporates triple attention and a multi-scale pyramid with an encoder-decoder architecture. Specifically, a triple attention module that captures the channel-pixel-spatial features is used as the transformation of the encoder-decoder module to focus on the fog region; then, a multi-scale pyramid module designed for refining the preliminary defog results are used to improve the restoration visibility. Extensive experiments on the EUVP and UFO-120 datasets corroborate that the proposed method outperforms the state-of-the-art methods in quantitative metrics Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Patch-based Contrast Quality Index (PCQI) and visual quality.
{"title":"Incorporating triple attention and multi-scale pyramid network for underwater image enhancement","authors":"Kaichuan Sun, Yubo Tian","doi":"10.34028/iajit/20/3/11","DOIUrl":"https://doi.org/10.34028/iajit/20/3/11","url":null,"abstract":": Clear images are a prerequisite of high-level underwater vision tasks, but images captured underwater are often degraded due to absorption and scattering of light. To solve this issue, traditional methods have shown some success, but often generate unwanted artifacts for knowledge priori dependency. In contrast, learning-based approaches can produce more refined results. Most popular methods are based on an encoder-decoder configuration for simply learning the nonlinear transformation of input and output images, so their ability to capture details is limited. In addition, the significant pixel-level features and multi-scale features are often overlooked. Accordingly, we propose a novel and efficient network that incorporates triple attention and a multi-scale pyramid with an encoder-decoder architecture. Specifically, a triple attention module that captures the channel-pixel-spatial features is used as the transformation of the encoder-decoder module to focus on the fog region; then, a multi-scale pyramid module designed for refining the preliminary defog results are used to improve the restoration visibility. Extensive experiments on the EUVP and UFO-120 datasets corroborate that the proposed method outperforms the state-of-the-art methods in quantitative metrics Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Patch-based Contrast Quality Index (PCQI) and visual quality.","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":"72778634","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}
A huge amount of crucial information is contained in documents. The vast increase in the number of E-documents available for user access makes the utilization of automated text classification essential. Classifying or arranging documents into predefined groups is called Text classification. Feature selection (FS) is needed for minimizing the dimensionality of high-dimensional data and extracting only the features that are most pertinent to a particular task. One of the widely used algorithms for feature selection in text classification is the Evolutionary algorithm. In this paper, the filter method chi-square and the Artificial Bee Colony (ABC) algorithm were both used as FS methods. The chi-square method is a useful technique for reducing the number of features and removing those that are superfluous or redundant. The ABC technique considers the chi-square method's chosen features as viable solutions (food sources). The ABC algorithm searches for the most efficient selection of features that increase classification performance. Support Vector Machine and Naïve Bayes classifiers were used as a fitness function for the ABC algorithm. The experiment results demonstrated that the proposed feature selection method was able of decreasing the number of features by approximately 89.5%, and 94%, respectively when NB and SVM were used as fitness functions in comparison to the original dataset, while also enhancing classification performance
{"title":"Utilizing artificial bee colony algorithm as feature selection method in arabic text classification","authors":"M. Hijazi, A. Zeki, A. Ismail","doi":"10.34028/iajit/20/3a/11","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/11","url":null,"abstract":"A huge amount of crucial information is contained in documents. The vast increase in the number of E-documents available for user access makes the utilization of automated text classification essential. Classifying or arranging documents into predefined groups is called Text classification. Feature selection (FS) is needed for minimizing the dimensionality of high-dimensional data and extracting only the features that are most pertinent to a particular task. One of the widely used algorithms for feature selection in text classification is the Evolutionary algorithm. In this paper, the filter method chi-square and the Artificial Bee Colony (ABC) algorithm were both used as FS methods. The chi-square method is a useful technique for reducing the number of features and removing those that are superfluous or redundant. The ABC technique considers the chi-square method's chosen features as viable solutions (food sources). The ABC algorithm searches for the most efficient selection of features that increase classification performance. Support Vector Machine and Naïve Bayes classifiers were used as a fitness function for the ABC algorithm. The experiment results demonstrated that the proposed feature selection method was able of decreasing the number of features by approximately 89.5%, and 94%, respectively when NB and SVM were used as fitness functions in comparison to the original dataset, while also enhancing classification performance","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":"87405210","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}
This paper proposes a parametric study of modified rectangular microstrip antenna in the frequency range between 1.4-2.65 GHz for wireless communication applications, incorporating with optimization methods Particle Swarm Optimization (PSO) and Fruit Fly Optimization (FOA). To design an antenna using optimization methods a fitness function of required parameters is needed. The resonance frequency of Microstrip Patch Antennas (MPAs) depends on various parameters and a standard frequency function does not exist for MPAs. In this study, a rectangular patch antenna is designed for the required resonance frequency and modified with circular quarter slots. The frequency-shift with the change of design variables, which are the substrate thickness and the radius of the slots, is observed. The resonance frequency is obtained as a function of the design variables and it is used in the optimization process to minimize the difference between the target frequency and the calculated one. The original algorithms FOA and PSO have been adapted for its application to a modified rectangular patch antenna design problem: resonance frequency and design of antenna. The design parameter values obtained via optimization and the performances of the optimization methods are presented. The results showed that both PSO and FOA find the dimensions correctly. It is also observed that the sensitivity of the FOA increases with the fruit fly population and the convergence gets faster. The outcomes of this paper show that the PSO algorithm gives better results when compared to the FOA for the proposed antenna.
{"title":"Optimization and comparative analysis of quarter-circular slotted microstrip patch antenna using particle swarm and fruit fly algorithms","authors":"E. Karpat, Fatih Imamoglu","doi":"10.34028/iajit/20/4/9","DOIUrl":"https://doi.org/10.34028/iajit/20/4/9","url":null,"abstract":"This paper proposes a parametric study of modified rectangular microstrip antenna in the frequency range between 1.4-2.65 GHz for wireless communication applications, incorporating with optimization methods Particle Swarm Optimization (PSO) and Fruit Fly Optimization (FOA). To design an antenna using optimization methods a fitness function of required parameters is needed. The resonance frequency of Microstrip Patch Antennas (MPAs) depends on various parameters and a standard frequency function does not exist for MPAs. In this study, a rectangular patch antenna is designed for the required resonance frequency and modified with circular quarter slots. The frequency-shift with the change of design variables, which are the substrate thickness and the radius of the slots, is observed. The resonance frequency is obtained as a function of the design variables and it is used in the optimization process to minimize the difference between the target frequency and the calculated one. The original algorithms FOA and PSO have been adapted for its application to a modified rectangular patch antenna design problem: resonance frequency and design of antenna. The design parameter values obtained via optimization and the performances of the optimization methods are presented. The results showed that both PSO and FOA find the dimensions correctly. It is also observed that the sensitivity of the FOA increases with the fruit fly population and the convergence gets faster. The outcomes of this paper show that the PSO algorithm gives better results when compared to the FOA for the proposed antenna.","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":"85775179","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}
Aseel Alfaidi, H. Alwadei, Areej Alshutayri, Shahd Alahdal
In Natural Language Processing (NLP), Part Of Speech (POS) tagging is an important step; it is a fundamental requirement for many applications, such as information extraction, machine translation, and grammar checking. Successful POS taggers have been developed for many languages, including Arabic. Currently, the spread of social media has increased the diversity of dialects as people use them in their online communications. Therefore, it has become more difficult for researchers to classify some words that are understood by humans but not computers. In addition, most Arabic POS research focuses on Modern Standard Arabic (MSA), while Dialect Arabic (DA) receives less attention. This paper aims to evaluate the performance of two Arabic taggers when used on dialect Arabic tweets and determine which tagger is the appropriate one, which will accordingly help to improve the existent taggers for dialect Arabic tweets. We used the Farasa and CAMeL taggers, which are commonly used to analyze Arabic texts and are considered the best taggers for Arabic. The results indicate that CAMeL tagger performed better than Farasa tagger, with accuracies of 92% and 83% respectively. In other words, a hybrid POS tagger trained with MSA and DA returns better results than the one trained on MSA.
{"title":"Exploring the performance of farasa and CAMeL taggers for arabic dialect tweets","authors":"Aseel Alfaidi, H. Alwadei, Areej Alshutayri, Shahd Alahdal","doi":"10.34028/iajit/20/3/7","DOIUrl":"https://doi.org/10.34028/iajit/20/3/7","url":null,"abstract":"In Natural Language Processing (NLP), Part Of Speech (POS) tagging is an important step; it is a fundamental requirement for many applications, such as information extraction, machine translation, and grammar checking. Successful POS taggers have been developed for many languages, including Arabic. Currently, the spread of social media has increased the diversity of dialects as people use them in their online communications. Therefore, it has become more difficult for researchers to classify some words that are understood by humans but not computers. In addition, most Arabic POS research focuses on Modern Standard Arabic (MSA), while Dialect Arabic (DA) receives less attention. This paper aims to evaluate the performance of two Arabic taggers when used on dialect Arabic tweets and determine which tagger is the appropriate one, which will accordingly help to improve the existent taggers for dialect Arabic tweets. We used the Farasa and CAMeL taggers, which are commonly used to analyze Arabic texts and are considered the best taggers for Arabic. The results indicate that CAMeL tagger performed better than Farasa tagger, with accuracies of 92% and 83% respectively. In other words, a hybrid POS tagger trained with MSA and DA returns better results than the one trained on MSA.","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":"83461488","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}
Network demands are expanding dramatically, especially in educational sectors where systems are acting in a non-tradition network environment. Most of the services are published on the cloud so students can access teaching or learning materials directly; such demand is a heavy burden to systems administrators who needs to monitor critical educational services around the clock. However, such solutions need efforts, money, time, and space to be built; in this paper, the Internet of Things (IoT) is proposed as a small and cheap device that can be installed and configured to analyze packets locally for each service while analyzed logs can be synced simultaneously to have a complete view about systems behavior from any location for the education’s system. Based on the results, the proposed approach showed a significant solution for the heavy demands on the educational system. Moreover, the results showed that the presented approach is more efficient when compared to the state of art packet analysis and monitoring approaches.
{"title":"IoT based technique for network packet analyzer","authors":"N. Alhindawi","doi":"10.34028/iajit/20/4/14","DOIUrl":"https://doi.org/10.34028/iajit/20/4/14","url":null,"abstract":"Network demands are expanding dramatically, especially in educational sectors where systems are acting in a non-tradition network environment. Most of the services are published on the cloud so students can access teaching or learning materials directly; such demand is a heavy burden to systems administrators who needs to monitor critical educational services around the clock. However, such solutions need efforts, money, time, and space to be built; in this paper, the Internet of Things (IoT) is proposed as a small and cheap device that can be installed and configured to analyze packets locally for each service while analyzed logs can be synced simultaneously to have a complete view about systems behavior from any location for the education’s system. Based on the results, the proposed approach showed a significant solution for the heavy demands on the educational system. Moreover, the results showed that the presented approach is more efficient when compared to the state of art packet analysis and monitoring approaches.","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":"75571016","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}
Ease of self-driving technological developments revives Vehicular Adhoc Networks (VANETs) and motivates the Intelligent Transportation System (ITS) to develop novel intelligent solutions to amplify the VANET safety and efficiency. Collision warning system plays a significant role in VANET due to the avoidance of fatalities in vehicle crashes. Different kinds of collision warning systems have been designed for diverse VANET scenarios. Among them, reinforcement-based machine learning algorithms receive much attention due to the dispensable of explicit modeling about the environment. However, it is a censorious task to retrieve the Q-learning parameters from the dynamic VANET environment effectively. To handle such issue and safer the VANET driving environment, this paper proposes a cloud aided pliable Q-Learning based Collision Warning Prediction and Safety message Dissemination (QCP-SD). The proposed QCP-SD integrates two mechanisms that are pliable Q-learning based collision prediction and Safety alert Message Dissemination. Firstly, the designing of pliable Q-learning parameters based on dynamic VANET factors with Q-learning enhances collision prediction accuracy. Further, it estimates the novel metric named as Collision Risk Factor (CRF) and minimizes the driving risks due to vehicle crashes. The execution of pliable Q-learning only at RSUs minimizes the vehicle burden and reduces the design complexity. Secondly, the QCP-SD sends alerts to the vehicles in the risky region through highly efficient next-hop disseminators selected based on a multi-attribute cost value. Moreover, the performance of QCP-SD is evaluated through Network Simulator (NS-2). The efficiency is analyzed using the performance metrics that are duplicate packet, latency, packet loss, packet delivery ratio, secondary collision, throughput, and overhead.
{"title":"A VANET Collision Warning System with Cloud Aided Pliable Q-Learning and Safety Message Dissemination","authors":"Nalina Venkatamune, Jayarekha PrabhaShankar","doi":"10.34028/iajit/20/1/12","DOIUrl":"https://doi.org/10.34028/iajit/20/1/12","url":null,"abstract":"Ease of self-driving technological developments revives Vehicular Adhoc Networks (VANETs) and motivates the Intelligent Transportation System (ITS) to develop novel intelligent solutions to amplify the VANET safety and efficiency. Collision warning system plays a significant role in VANET due to the avoidance of fatalities in vehicle crashes. Different kinds of collision warning systems have been designed for diverse VANET scenarios. Among them, reinforcement-based machine learning algorithms receive much attention due to the dispensable of explicit modeling about the environment. However, it is a censorious task to retrieve the Q-learning parameters from the dynamic VANET environment effectively. To handle such issue and safer the VANET driving environment, this paper proposes a cloud aided pliable Q-Learning based Collision Warning Prediction and Safety message Dissemination (QCP-SD). The proposed QCP-SD integrates two mechanisms that are pliable Q-learning based collision prediction and Safety alert Message Dissemination. Firstly, the designing of pliable Q-learning parameters based on dynamic VANET factors with Q-learning enhances collision prediction accuracy. Further, it estimates the novel metric named as Collision Risk Factor (CRF) and minimizes the driving risks due to vehicle crashes. The execution of pliable Q-learning only at RSUs minimizes the vehicle burden and reduces the design complexity. Secondly, the QCP-SD sends alerts to the vehicles in the risky region through highly efficient next-hop disseminators selected based on a multi-attribute cost value. Moreover, the performance of QCP-SD is evaluated through Network Simulator (NS-2). The efficiency is analyzed using the performance metrics that are duplicate packet, latency, packet loss, packet delivery ratio, secondary collision, throughput, and overhead.","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":"80950209","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 amount of spam is increasing rapidly while the popularity of emails is increasing. This situation has led to the need to filter spam emails. To date, many knowledge-based, learning-based, and clustering-based methods have been developed for filtering spam emails. In this study, machine-learning-based spam detection was targeted, and C4.5, ID3, RndTree, C-Support Vector Classification (C-SVC), and Naïve Bayes algorithms were used for email spam detection. In addition, feature selection and data transformation methods were used to increase spam detection success. Experiments were performed on the UC Irvine Machine Learning Repository (UCI) spambase dataset, and the results were compared for accuracy, Receiver Operating Characteristic (ROC) analysis, and classification speed. According to the accuracy comparison, the C-SVC algorithm gave the highest accuracy with 93.13%, followed by the RndTree algorithm. According to the ROC analysis, the RndTree algorithm gave the best Area Under Curve (AUC) value of 0.999, while the C4.5 algorithm gave the second-best result. The most successful methods in terms of classification speed are Naïve Bayes and RndTree algorithms. In the experiments, it was seen that feature selection and data transformation methods increased spam detection success. The binary transformation that increased the classification success the most and the feature selection method was forward selection.
{"title":"Highly Accurate Spam Detection with the Help of Feature Selection and Data Transformation","authors":"Hidayet Takçi, Nusrat Fatema","doi":"10.34028/iajit/20/1/4","DOIUrl":"https://doi.org/10.34028/iajit/20/1/4","url":null,"abstract":"The amount of spam is increasing rapidly while the popularity of emails is increasing. This situation has led to the need to filter spam emails. To date, many knowledge-based, learning-based, and clustering-based methods have been developed for filtering spam emails. In this study, machine-learning-based spam detection was targeted, and C4.5, ID3, RndTree, C-Support Vector Classification (C-SVC), and Naïve Bayes algorithms were used for email spam detection. In addition, feature selection and data transformation methods were used to increase spam detection success. Experiments were performed on the UC Irvine Machine Learning Repository (UCI) spambase dataset, and the results were compared for accuracy, Receiver Operating Characteristic (ROC) analysis, and classification speed. According to the accuracy comparison, the C-SVC algorithm gave the highest accuracy with 93.13%, followed by the RndTree algorithm. According to the ROC analysis, the RndTree algorithm gave the best Area Under Curve (AUC) value of 0.999, while the C4.5 algorithm gave the second-best result. The most successful methods in terms of classification speed are Naïve Bayes and RndTree algorithms. In the experiments, it was seen that feature selection and data transformation methods increased spam detection success. The binary transformation that increased the classification success the most and the feature selection method was forward selection.","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":"81607805","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 accounting profession is undergoing a significant transformation due to the impact of artificial intelligence, robotic process automation, and big data. One of the latest areas of research in this field is exploring the potential use of drones for accounting and auditing tasks. However, this study takes a different approach by proposing a theoretical framework that utilizes drones for cash flow management in long-term construction projects. The proposed framework suggests that drones can be utilized as a supplementary tool to remotely conduct project site inspections and monitor construction progress. The framework addresses the percentage of completion method for recognizing revenues from long-term contracts and highlights the benefits of using drones, such as improved data quality, cost and time efficiency, increased safety during site inspections, and overall effectiveness
{"title":"A proposed model to integrate drone technology in accounting for long term contracts: a cash flow management perspictive","authors":"Amer Qasim, Ghaleb A. El Refae, S. Eletter","doi":"10.34028/iajit/20/3a/5","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/5","url":null,"abstract":"The accounting profession is undergoing a significant transformation due to the impact of artificial intelligence, robotic process automation, and big data. One of the latest areas of research in this field is exploring the potential use of drones for accounting and auditing tasks. However, this study takes a different approach by proposing a theoretical framework that utilizes drones for cash flow management in long-term construction projects. The proposed framework suggests that drones can be utilized as a supplementary tool to remotely conduct project site inspections and monitor construction progress. The framework addresses the percentage of completion method for recognizing revenues from long-term contracts and highlights the benefits of using drones, such as improved data quality, cost and time efficiency, increased safety during site inspections, and overall effectiveness","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":"83093398","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}
M. Zavalsiz, Sleiman Alhajj, Kashfia Sailunaz, Tansel Özyer, Reda Alhajj
Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making the internal structure of the human body visible with various methods. With deep learning models, cancer detection, which is one of the most lethal diseases in the world, can be made possible with high accuracy. Pancreatic Tumor detection, which is one of the cancer types with the highest fatality rate, is one of the main targets of this project, together with the data set of Computed Tomography images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. In the field of image classification, which is a computer vision task, the transfer learning technique, which has gained popularity in recent years, has been applied quite frequently. Using pre-trained models were previously trained on a fairly large dataset and using them on medical images is common nowadays. The main objective of this article is to use this method, which is very popular in the medical imaging field, in the detection of PDAC, one of the deadliest types of pancreatic cancer, and to investigate how it per- forms compared to the custom model created and trained from scratch. The pre-trained models which are used in this project are VGG-16 and ResNet, which are popular Convolutional Neutral Network models, for Pancreatic Tumor Detection task. With the use of these models, early diagnosis of pancreatic cancer, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started, may be possible. Due to the abundance of medical images reviewed by medical professionals, which is one of the main causes for heavy workload of healthcare systems, this application can assist radiologists and other specialists in Pancreatic Tumor detection by providing faster and more accurate method
{"title":"A comparative study of different pre-trained deep learning models and custom CNN for pancreatic tumor detection","authors":"M. Zavalsiz, Sleiman Alhajj, Kashfia Sailunaz, Tansel Özyer, Reda Alhajj","doi":"10.34028/iajit/20/3a/9","DOIUrl":"https://doi.org/10.34028/iajit/20/3a/9","url":null,"abstract":"Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making the internal structure of the human body visible with various methods. With deep learning models, cancer detection, which is one of the most lethal diseases in the world, can be made possible with high accuracy. Pancreatic Tumor detection, which is one of the cancer types with the highest fatality rate, is one of the main targets of this project, together with the data set of Computed Tomography images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. In the field of image classification, which is a computer vision task, the transfer learning technique, which has gained popularity in recent years, has been applied quite frequently. Using pre-trained models were previously trained on a fairly large dataset and using them on medical images is common nowadays. The main objective of this article is to use this method, which is very popular in the medical imaging field, in the detection of PDAC, one of the deadliest types of pancreatic cancer, and to investigate how it per- forms compared to the custom model created and trained from scratch. The pre-trained models which are used in this project are VGG-16 and ResNet, which are popular Convolutional Neutral Network models, for Pancreatic Tumor Detection task. With the use of these models, early diagnosis of pancreatic cancer, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started, may be possible. Due to the abundance of medical images reviewed by medical professionals, which is one of the main causes for heavy workload of healthcare systems, this application can assist radiologists and other specialists in Pancreatic Tumor detection by providing faster and more accurate method","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":"87118679","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}
Many countries have strived to popularise electronic voting (e-voting), but owing to various security concerns, large-scale elections are still invariably held using paper ballots. Electronic voting systems must find solutions to various issues with authentication, data privacy and integrity, transparency, and verifiability. On the other hand, Blockchain technology offers an innovative solution to many of these problems. In this study, we constructed a private blockchain network with a large number of nodes, which is only accessible to the relevant voters. Because of its decentralised design, the system is robust against attacks by malicious actors. The security of the system was enhanced using an elliptic curve discrete logarithm problem-based blind multi-document signcryption mechanism. As this mechanism can be used to blindly sign and encrypt multiple voting documents in a single pass, it will minimise redundant signing processes and thus improve efficiency. Furthermore, a self-certification mechanism was used in lieu of centralised certificate servers, so that the voters can participate in the computation of public and private keys. In summary, we designed an electronic voting mechanism that is sufficiently secure for practical purposes, which will improve trust in e-voting, and reduce the costs associated with vote checking.
{"title":"Secure blockchain-based electronic voting mechanism","authors":"Pin-Chang Su, Tai-Chang Su","doi":"10.34028/iajit/20/2/12","DOIUrl":"https://doi.org/10.34028/iajit/20/2/12","url":null,"abstract":"Many countries have strived to popularise electronic voting (e-voting), but owing to various security concerns, large-scale elections are still invariably held using paper ballots. Electronic voting systems must find solutions to various issues with authentication, data privacy and integrity, transparency, and verifiability. On the other hand, Blockchain technology offers an innovative solution to many of these problems. In this study, we constructed a private blockchain network with a large number of nodes, which is only accessible to the relevant voters. Because of its decentralised design, the system is robust against attacks by malicious actors. The security of the system was enhanced using an elliptic curve discrete logarithm problem-based blind multi-document signcryption mechanism. As this mechanism can be used to blindly sign and encrypt multiple voting documents in a single pass, it will minimise redundant signing processes and thus improve efficiency. Furthermore, a self-certification mechanism was used in lieu of centralised certificate servers, so that the voters can participate in the computation of public and private keys. In summary, we designed an electronic voting mechanism that is sufficiently secure for practical purposes, which will improve trust in e-voting, and reduce the costs associated with vote checking.","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":"80086038","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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