Pub Date : 2023-12-01DOI: 10.11591/ijai.v12.i4.pp1854-1863
R. Q. Hassan, Zainab N. Sultani, B. N. Dhannoon
A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.
{"title":"Content-based image retrieval based on corel dataset using deep learning","authors":"R. Q. Hassan, Zainab N. Sultani, B. N. Dhannoon","doi":"10.11591/ijai.v12.i4.pp1854-1863","DOIUrl":"https://doi.org/10.11591/ijai.v12.i4.pp1854-1863","url":null,"abstract":"A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65354814","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}
Pub Date : 2023-12-01DOI: 10.11591/ijai.v12.i4.pp1836-1844
Ikram Ben Abdel Ouahab, Lotfi Elaachak, M. Bouhorma
In recent times, malware visualization has become very popular for malwareclassification in cybersecurity. Existing malware features can easily identifyknown malware that have been already detected, but they cannot identify newand infrequent malwares accurately. Moreover, deep learning algorithmsshow their power in term of malware classification topic. However, we foundthe use of imbalanced data; the Malimg database which contains 25 malwarefamilies don’t have same or near number of images per class. To address theseissues, this paper proposes an effective malware classifier, based on costsensitive deep learning. When performing classification on imbalanced data, some classes get less accuracy than others. Cost-sensitive is meant to solve this issue, however in our case of 25 classes, classical cost-sensitive weights wasn’t effective is giving equal attention to all classes. The proposed approach improves the performance of malware classification, and we demonstrate this improvement using two Convolutional Neural Network models using functional and subclassing programming techniques, based on loss, accuracy, recall and precision.
{"title":"Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset","authors":"Ikram Ben Abdel Ouahab, Lotfi Elaachak, M. Bouhorma","doi":"10.11591/ijai.v12.i4.pp1836-1844","DOIUrl":"https://doi.org/10.11591/ijai.v12.i4.pp1836-1844","url":null,"abstract":"In recent times, malware visualization has become very popular for malwareclassification in cybersecurity. Existing malware features can easily identifyknown malware that have been already detected, but they cannot identify newand infrequent malwares accurately. Moreover, deep learning algorithmsshow their power in term of malware classification topic. However, we foundthe use of imbalanced data; the Malimg database which contains 25 malwarefamilies don’t have same or near number of images per class. To address theseissues, this paper proposes an effective malware classifier, based on costsensitive deep learning. When performing classification on imbalanced data, some classes get less accuracy than others. Cost-sensitive is meant to solve this issue, however in our case of 25 classes, classical cost-sensitive weights wasn’t effective is giving equal attention to all classes. The proposed approach improves the performance of malware classification, and we demonstrate this improvement using two Convolutional Neural Network models using functional and subclassing programming techniques, based on loss, accuracy, recall and precision.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65355077","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}
Pub Date : 2023-12-01DOI: 10.11591/ijai.v12.i4.pp1821-1827
Eman I. Abd El-Latif, Nour Eldeen M. Khalifa
Nowadays, the internet has become a typical medium for sharing digitalimages through web applications or social media and there was a rise inconcerns about digital image privacy. Image editing software’s have preparedit incredibly simple to make changes to an image's content without leavingany visible evidence for images in general and medical images in particular.In this paper, the COVID-19 digital x-rays forgery classification modelutilizing deep learning will be introduced. The proposed system will be ableto identify and classify image forgery (copy-move and splicing) manipulation.Alexnet, Resnet50, and Googlenet are used in this model for feature extractionand classification, respectively. Images have been tampered with in threeclasses (COVID-19, viral pneumonia, and normal). For the classification of(Forgery or no forgery), the model achieves 0.9472 in testing accuracy. Forthe classification of (Copy-move forgery, splicing forgery, and no forgery),the model achieves 0.8066 in testing accuracy. Moreover, the model achieves0.796 and 0.8382 for 6 classes and 9 classes problems respectively.Performance indicators like Recall, Precision, and F1 Score supported theachieved results and proved that the proposed system is efficient for detectingthe manipulation in images.
{"title":"COVID-19 digital x-rays forgery classification model using deep learning","authors":"Eman I. Abd El-Latif, Nour Eldeen M. Khalifa","doi":"10.11591/ijai.v12.i4.pp1821-1827","DOIUrl":"https://doi.org/10.11591/ijai.v12.i4.pp1821-1827","url":null,"abstract":"Nowadays, the internet has become a typical medium for sharing digitalimages through web applications or social media and there was a rise inconcerns about digital image privacy. Image editing software’s have preparedit incredibly simple to make changes to an image's content without leavingany visible evidence for images in general and medical images in particular.In this paper, the COVID-19 digital x-rays forgery classification modelutilizing deep learning will be introduced. The proposed system will be ableto identify and classify image forgery (copy-move and splicing) manipulation.Alexnet, Resnet50, and Googlenet are used in this model for feature extractionand classification, respectively. Images have been tampered with in threeclasses (COVID-19, viral pneumonia, and normal). For the classification of(Forgery or no forgery), the model achieves 0.9472 in testing accuracy. Forthe classification of (Copy-move forgery, splicing forgery, and no forgery),the model achieves 0.8066 in testing accuracy. Moreover, the model achieves0.796 and 0.8382 for 6 classes and 9 classes problems respectively.Performance indicators like Recall, Precision, and F1 Score supported theachieved results and proved that the proposed system is efficient for detectingthe manipulation in images.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65355165","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}
Pub Date : 2023-09-01DOI: 10.11591/ijai.v12.i3.pp1169-1177
Hatem Fahd Al-Selwi, Nawaid Hassan, Hadhrami Ab Ghani, Nur Asyiqin Binti Amir Hamzah, Azlan Bin Abd Aziz
Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.
{"title":"Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme","authors":"Hatem Fahd Al-Selwi, Nawaid Hassan, Hadhrami Ab Ghani, Nur Asyiqin Binti Amir Hamzah, Azlan Bin Abd Aziz","doi":"10.11591/ijai.v12.i3.pp1169-1177","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp1169-1177","url":null,"abstract":"Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45070794","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}
Pub Date : 2023-09-01DOI: 10.11591/ijai.v12.i3.pp1448-1458
A. Susanto, Ibnu Utomo Wahyu Mulyono, Christy Atika Sari, Eko Hari Rachmawanto, De Rosal Ignatius Moses Setiadi, M. K. Sarker
Although numerous studies have been conducted on handwritten recognition, there is little and non-optimal research on Javanese script recognition due to its limitation to basic characters. Therefore, this research proposes the design of a handwritten Javanese Script recognition method based on twelve layers deep convolutional neural network (DCNN), consisting of four convolutions, two pooling, and five fully connected (FC) layers, with SoftMax classifiers. Five FC layers were proposed in this research to conduct the learning process in stages to achieve better learning outcomes. Due to the limited number of images in the Javanese script dataset, an augmentation process is needed to improve recognition performance. This method obtained 99.65% accuracy using seven types of geometric augmentation and the proposed DCNN model for 120 Javanese script character classes. It consists of 20 basic characters plus 100 others from the compound of basic and vowels characters.
{"title":"Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation","authors":"A. Susanto, Ibnu Utomo Wahyu Mulyono, Christy Atika Sari, Eko Hari Rachmawanto, De Rosal Ignatius Moses Setiadi, M. K. Sarker","doi":"10.11591/ijai.v12.i3.pp1448-1458","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp1448-1458","url":null,"abstract":"Although numerous studies have been conducted on handwritten recognition, there is little and non-optimal research on Javanese script recognition due to its limitation to basic characters. Therefore, this research proposes the design of a handwritten Javanese Script recognition method based on twelve layers deep convolutional neural network (DCNN), consisting of four convolutions, two pooling, and five fully connected (FC) layers, with SoftMax classifiers. Five FC layers were proposed in this research to conduct the learning process in stages to achieve better learning outcomes. Due to the limited number of images in the Javanese script dataset, an augmentation process is needed to improve recognition performance. This method obtained 99.65% accuracy using seven types of geometric augmentation and the proposed DCNN model for 120 Javanese script character classes. It consists of 20 basic characters plus 100 others from the compound of basic and vowels characters.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48386621","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}
Pub Date : 2023-09-01DOI: 10.11591/ijai.v12.i3.pp1468-1475
Youb Ibtissam, Azmani Abdallah, Hamlich Mohamed
The rise of online access panels has profoundly changed the market research landscape. Often presented by their owners as very powerful tools, they nevertheless raise important scientific questions, particularly regarding the representativeness of the samples they produce and, consequently, the validity of the information they provide. In this paper, we present an innovative approach, based on deep learning and sentiment analysis techniques, to assess in real time the representativeness of an online panel sample. The idea is to measure the extent to which the opinions of an online panel converge with opinions on social networks. To validate the proposed method, we conducted a case study on the emerging discussion on Coronavirus disease (COVID-19) vaccination. The results not only proved the representativeness of online panel sample, but also demonstrated the feasibility and effectiveness of our approach.
{"title":"Online panel data quality: a sentiment analysis based on a deep learning approach","authors":"Youb Ibtissam, Azmani Abdallah, Hamlich Mohamed","doi":"10.11591/ijai.v12.i3.pp1468-1475","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp1468-1475","url":null,"abstract":"The rise of online access panels has profoundly changed the market research landscape. Often presented by their owners as very powerful tools, they nevertheless raise important scientific questions, particularly regarding the representativeness of the samples they produce and, consequently, the validity of the information they provide. In this paper, we present an innovative approach, based on deep learning and sentiment analysis techniques, to assess in real time the representativeness of an online panel sample. The idea is to measure the extent to which the opinions of an online panel converge with opinions on social networks. To validate the proposed method, we conducted a case study on the emerging discussion on Coronavirus disease (COVID-19) vaccination. The results not only proved the representativeness of online panel sample, but also demonstrated the feasibility and effectiveness of our approach.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45845910","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}
Pub Date : 2023-09-01DOI: 10.11591/ijai.v12.i3.pp1360-1369
Noor Maher, Suhad A. Yousif
The coronavirus disease 2019 (COVID-19) epidemic still impacts every facet of life and necessitates a fast and accurate diagnosis. The need for an effective, rapid, and precise way to reduce radiologists' workload in diagnosing suspected cases has emerged. This study used the tree-based pipeline optimization tool (TPOT) and many machine learning (ML) algorithms. TPOT is an open-source genetic programming-based AutoML system that optimizes a set of feature preprocessors and ML models to maximize classification accuracy on a supervised classification problem. A series of trials and comparisons with the results of ML and earlier studies discovered that most of the AutoML beat traditional ML in terms of accuracy. A blood test dataset that has 111 variables and 5644 cases were used. In TPOT, 450 pipelines were used, and the best pipeline selected consisted of radial basis function (RBF) Sampler preprocessing and Gradient boosting classifier as the best algorithm with a 99% accuracy rate.
{"title":"An automated machine learning model for diagnosing coronavirus disease 2019 (COVID-19) infection","authors":"Noor Maher, Suhad A. Yousif","doi":"10.11591/ijai.v12.i3.pp1360-1369","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp1360-1369","url":null,"abstract":"The coronavirus disease 2019 (COVID-19) epidemic still impacts every facet of life and necessitates a fast and accurate diagnosis. The need for an effective, rapid, and precise way to reduce radiologists' workload in diagnosing suspected cases has emerged. This study used the tree-based pipeline optimization tool (TPOT) and many machine learning (ML) algorithms. TPOT is an open-source genetic programming-based AutoML system that optimizes a set of feature preprocessors and ML models to maximize classification accuracy on a supervised classification problem. A series of trials and comparisons with the results of ML and earlier studies discovered that most of the AutoML beat traditional ML in terms of accuracy. A blood test dataset that has 111 variables and 5644 cases were used. In TPOT, 450 pipelines were used, and the best pipeline selected consisted of radial basis function (RBF) Sampler preprocessing and Gradient boosting classifier as the best algorithm with a 99% accuracy rate.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49272833","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}
Pub Date : 2023-09-01DOI: 10.11591/ijai.v12.i3.pp1238-1249
Iman Subhi Mohammed, Maher Khalaf Hussien
Biometric authentication is a technology that has become significant in the high level of personal identity security. This paper provides a signature recognition system. This paper provides a static signature recognition system (SSRS). We have classified the signature in two ways. The first method uses the genetic algorithm (GA), considering that the signature is the chromosome with 35 genes, and each feature is a gene. With applying the processes of the GA between chromosomes and the formation of generations in sequence until we reach the optimal solution by finding the chromosome closest to the chromosome that enters the system. In the second method, we have classified the signature by calculating the Euclidean Distance between the query signature and the signatures stored in the database. The signature closest to a confirmed threshold is considered the desired goal. The database uses 25 handwritten signatures (15 signatures for training and five original signatures, and five fake signatures written by other people for testing), so we have a database of 500 signatures. With a 94% discrimination rate, the genetic recognition system (GRS) was able to access the solutions, and with a (91% rate) the euclidean recognition system (ERS) was done. The application uses MATLAB.
{"title":"Off-line handwritten signature recognition based on genetic algorithm and euclidean distance","authors":"Iman Subhi Mohammed, Maher Khalaf Hussien","doi":"10.11591/ijai.v12.i3.pp1238-1249","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp1238-1249","url":null,"abstract":"Biometric authentication is a technology that has become significant in the high level of personal identity security. This paper provides a signature recognition system. This paper provides a static signature recognition system (SSRS). We have classified the signature in two ways. The first method uses the genetic algorithm (GA), considering that the signature is the chromosome with 35 genes, and each feature is a gene. With applying the processes of the GA between chromosomes and the formation of generations in sequence until we reach the optimal solution by finding the chromosome closest to the chromosome that enters the system. In the second method, we have classified the signature by calculating the Euclidean Distance between the query signature and the signatures stored in the database. The signature closest to a confirmed threshold is considered the desired goal. The database uses 25 handwritten signatures (15 signatures for training and five original signatures, and five fake signatures written by other people for testing), so we have a database of 500 signatures. With a 94% discrimination rate, the genetic recognition system (GRS) was able to access the solutions, and with a (91% rate) the euclidean recognition system (ERS) was done. The application uses MATLAB.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65351122","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}
Pub Date : 2023-06-12DOI: 10.36079/lamintang.ijai-01001.488
N. Azman, S. Subramaniam, M. Esro
Blood is a vital fluid where required for saving human’s life. Blood is stored in a blood bank which is a bank of blood components, gathered as a result of blood donations that are responsible for collecting, storing and preserved for the use of medical purpose. Investigation of the existing blood collection and tracking system is essential to efficiently manage, control and monitor on all aspect of a blood bank. A comprehensive data acquisition system from collection location to a cloud-based system enables a paperless system with minimum human intervention to oversee the entire collection to dispatch process in a blood bank. A research has been made that most blood banks practicing stand-alone which may contribute to wastage of donated blood. For that matter, this collected data system allows connectivity between the blood banks to effectively conduct and systematically manage their daily activities within one integrated system. This application helps blood donation center receives the registered donated blood from any hospitals easily as it records the donated blood information in cloud immediately.
{"title":"Investigation and Development of a Data Acquisition System for Blood Bank","authors":"N. Azman, S. Subramaniam, M. Esro","doi":"10.36079/lamintang.ijai-01001.488","DOIUrl":"https://doi.org/10.36079/lamintang.ijai-01001.488","url":null,"abstract":"Blood is a vital fluid where required for saving human’s life. Blood is stored in a blood bank which is a bank of blood components, gathered as a result of blood donations that are responsible for collecting, storing and preserved for the use of medical purpose. Investigation of the existing blood collection and tracking system is essential to efficiently manage, control and monitor on all aspect of a blood bank. A comprehensive data acquisition system from collection location to a cloud-based system enables a paperless system with minimum human intervention to oversee the entire collection to dispatch process in a blood bank. A research has been made that most blood banks practicing stand-alone which may contribute to wastage of donated blood. For that matter, this collected data system allows connectivity between the blood banks to effectively conduct and systematically manage their daily activities within one integrated system. This application helps blood donation center receives the registered donated blood from any hospitals easily as it records the donated blood information in cloud immediately.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84070502","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}
Pub Date : 2023-06-12DOI: 10.36079/lamintang.ijai-01001.482
Rusidah, Risdianti, Jessika Kindly Susanto
A decision support system, also known as a decision support system (DSS), is an interactive information system that offers data, models, and information. DSS is used as a decision aid in semi-structured and unstructured situations where there is no clear decision-making procedure. Determining the preferred major is one of the challenges in universities. The purpose of determining the most popular major is to improve the quality and services provided to students in each department, which is a crucial objective for universities. Currently, Universitas Sari Mulia determines the most popular majors based on qualitative data, which makes the determination of the most popular majors themselves inaccurate; therefore, a method capable of managing data on the selection of the most popular majors is necessary. In this study, the Simple Additive Weighting (SAW) technique will be utilized. This method is used to compare each criterion with one another in order to determine the most popular majors at Sari Mulia University and to evaluate each department.
决策支持系统，也称为决策支持系统(DSS)，是提供数据、模型和信息的交互式信息系统。决策支持系统在没有明确决策程序的半结构化和非结构化情况下用作决策辅助工具。选择自己喜欢的专业是大学生活中的一大挑战。确定最受欢迎的专业的目的是为了提高各院系为学生提供的质量和服务，这是大学的一个重要目标。目前，Universitas Sari Mulia根据定性数据确定最受欢迎的专业，这使得最受欢迎的专业本身的确定不准确;因此，一种能够管理最受欢迎专业选择数据的方法是必要的。在本研究中，简单加性加权(SAW)技术将被使用。这种方法是用来比较每一个标准，以确定最受欢迎的专业在沙里穆里亚大学和评估每个部门。
{"title":"Selecting Favourite Majors at Sari Mulia University Using SAW Method","authors":"Rusidah, Risdianti, Jessika Kindly Susanto","doi":"10.36079/lamintang.ijai-01001.482","DOIUrl":"https://doi.org/10.36079/lamintang.ijai-01001.482","url":null,"abstract":"A decision support system, also known as a decision support system (DSS), is an interactive information system that offers data, models, and information. DSS is used as a decision aid in semi-structured and unstructured situations where there is no clear decision-making procedure. Determining the preferred major is one of the challenges in universities. The purpose of determining the most popular major is to improve the quality and services provided to students in each department, which is a crucial objective for universities. Currently, Universitas Sari Mulia determines the most popular majors based on qualitative data, which makes the determination of the most popular majors themselves inaccurate; therefore, a method capable of managing data on the selection of the most popular majors is necessary. In this study, the Simple Additive Weighting (SAW) technique will be utilized. This method is used to compare each criterion with one another in order to determine the most popular majors at Sari Mulia University and to evaluate each department.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80516983","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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