In the present times, the healthcare sector has seen an enormous growth in the usage of technology ranging from EHRs (electronic health records) to personal health trackers. Currently, there is a need for managing EHRs effectively with respect to storage, privacy and security measures. State-of-art technologies such as blockchain and artificial intelligence (AI) are applied in the healthcare domain. Innovation in AI is steadily advancing and is finding its place in different industries. The integration of blockchain and AI looks promising as there are several benefits. Blockchain can make the AI more secure and autonomous whereas AI can drive the blockchain with intelligence. The objective of this article is to explore the uses of blockchain as well as AI technology in the field of healthcare. We aim to survey the advantages, issues and challenges of integrating blockchain with AI technology, including future research directions in the healthcare domain. In this study, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) rules and an efficient searching protocol were used to examine several scientific databases to recognize and investigate every important publication. A solid systematic review was carried out on integration of blockchain and AI in the healthcare domain to identify existing challenges and benefits of integrating these two technologies in healthcare. Our study found that the integration of AI and blockchain technology has a potential to provide several benefits in terms of performance and security which conventional EHRs lack. The inherent benefits of blockchain and AI together are limitless, but the bare outcomes based on blockchain powered by AI technology are yet to be obtained. In addition, the outcome of our detailed study may aid researchers to carry out further research.
{"title":"Survey on Electronic Health Record Management Using Amalgamation of Artificial Intelligence and Blockchain Technologies","authors":"K. P. Rao, S. Manvi","doi":"10.18267/j.aip.194","DOIUrl":"https://doi.org/10.18267/j.aip.194","url":null,"abstract":"In the present times, the healthcare sector has seen an enormous growth in the usage of technology ranging from EHRs (electronic health records) to personal health trackers. Currently, there is a need for managing EHRs effectively with respect to storage, privacy and security measures. State-of-art technologies such as blockchain and artificial intelligence (AI) are applied in the healthcare domain. Innovation in AI is steadily advancing and is finding its place in different industries. The integration of blockchain and AI looks promising as there are several benefits. Blockchain can make the AI more secure and autonomous whereas AI can drive the blockchain with intelligence. The objective of this article is to explore the uses of blockchain as well as AI technology in the field of healthcare. We aim to survey the advantages, issues and challenges of integrating blockchain with AI technology, including future research directions in the healthcare domain. In this study, Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) rules and an efficient searching protocol were used to examine several scientific databases to recognize and investigate every important publication. A solid systematic review was carried out on integration of blockchain and AI in the healthcare domain to identify existing challenges and benefits of integrating these two technologies in healthcare. Our study found that the integration of AI and blockchain technology has a potential to provide several benefits in terms of performance and security which conventional EHRs lack. The inherent benefits of blockchain and AI together are limitless, but the bare outcomes based on blockchain powered by AI technology are yet to be obtained. In addition, the outcome of our detailed study may aid researchers to carry out further research.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43358988","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 increase in data diversity and the fact that database design is a difficult process make it practically impossible to design a unique database schema for all datasets encountered. In this paper, we introduce a fully automatic genetic algorithm-based relational database normalization method for revealing the right database schema using a raw dataset and without the need for any prior knowledge. For measuring the performance of the algorithm, we perform a simulation study using 250 datasets produced using 50 well-known databases. A total of 2500 simulations are carried out, ten times for each of five denormalized variations of all database designs containing different synthetic contents. The results of the simulation study show that the proposed algorithm discovers exactly 72% of the unknown database schemas. The performance can be improved by fine-tuning the optimization parameters. The results of the simulation study also show that the devised algorithm can be used in many datasets to reveal structs of databases when only a raw dataset is available at hand.
{"title":"A Novel Automatic Relational Database Normalization Method","authors":"Emre Akadal, Mehmet Hakan Satman","doi":"10.18267/j.aip.193","DOIUrl":"https://doi.org/10.18267/j.aip.193","url":null,"abstract":"The increase in data diversity and the fact that database design is a difficult process make it practically impossible to design a unique database schema for all datasets encountered. In this paper, we introduce a fully automatic genetic algorithm-based relational database normalization method for revealing the right database schema using a raw dataset and without the need for any prior knowledge. For measuring the performance of the algorithm, we perform a simulation study using 250 datasets produced using 50 well-known databases. A total of 2500 simulations are carried out, ten times for each of five denormalized variations of all database designs containing different synthetic contents. The results of the simulation study show that the proposed algorithm discovers exactly 72% of the unknown database schemas. The performance can be improved by fine-tuning the optimization parameters. The results of the simulation study also show that the devised algorithm can be used in many datasets to reveal structs of databases when only a raw dataset is available at hand.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46966078","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}
Low qualification of employees newly hired to service desks contributes to the high turnover of service desk agents and consequently to low quality of services delivered. This paper proposes a conceptual artefact comprising two modules for tacit knowledge elicitation and knowledge transfer during the onboarding training process. The design of the artefact follows the design science methodology. Ex-ante evaluation methods are chosen to evaluate the importance of a problem domain and evaluate the artefact feasibility. Expert interviews and focus group discussions with experts from the field were performed to support the evaluation activities. The proposed framework uses eye-tracking technology to complement captured knowledge with tacit knowledge. Next, the proposed model incorporates a simulated environment for enhanced training experience and effective knowledge transfer from expert employees to novice ones. This paper and the proposed artefact aim to improve the training process of service desk employees and to contribute to wider use of tacit knowledge capture and elicitation techniques in IT service management.
{"title":"Service Desk Onboarding Training Environment","authors":"Michal Dostál","doi":"10.18267/j.aip.188","DOIUrl":"https://doi.org/10.18267/j.aip.188","url":null,"abstract":"Low qualification of employees newly hired to service desks contributes to the high turnover of service desk agents and consequently to low quality of services delivered. This paper proposes a conceptual artefact comprising two modules for tacit knowledge elicitation and knowledge transfer during the onboarding training process. The design of the artefact follows the design science methodology. Ex-ante evaluation methods are chosen to evaluate the importance of a problem domain and evaluate the artefact feasibility. Expert interviews and focus group discussions with experts from the field were performed to support the evaluation activities. The proposed framework uses eye-tracking technology to complement captured knowledge with tacit knowledge. Next, the proposed model incorporates a simulated environment for enhanced training experience and effective knowledge transfer from expert employees to novice ones. This paper and the proposed artefact aim to improve the training process of service desk employees and to contribute to wider use of tacit knowledge capture and elicitation techniques in IT service management.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48196616","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 goal of this paper is to examine whether, in Q-system inventory control policy, a combination of the reorder point exceeding order quantity leads to minimal holding and ordering costs when dealing with sporadic demand. For this purpose, a past stock movement simulation is applied to a set of randomly generated data with different numbers of zero demand periods ranging from 10 to 90%. The outputs of the simulation prove that in situations where stock holding costs are too high, the simulation tends to reduce average stock by overcoming periods between two demand peaks with an increase in the numbers of small replenishment orders and reaches lower stock holding and ordering costs. Furthermore, the correlation analysis proves that there is a statistically significant relationship (r = .847, p = .004) between the number of time series that reach minimal holding and ordering costs under the control of reorder point (replenishment order) and the demand standard deviation affected by the evolving sporadicity. These findings can support decision making linked with inventory management of products with sporadic demand and contribute to development of business information systems.
本文的目的是检验在q系统库存控制策略中，当处理零星需求时，再订货点超过订单数量的组合是否会导致最小的持有和订购成本。为此，将过去的股票运动模拟应用于一组随机生成的数据，这些数据具有从10%到90%不等的不同数量的零需求期。仿真结果证明，在库存成本过高的情况下，通过克服两个需求高峰之间的时间间隔，增加小补货订单数量，仿真结果倾向于降低平均库存，达到较低的库存和订购成本。进一步，相关分析证明，在再订货点(补货顺序)控制下达到最小持有量的时间序列数与订货成本与受偶发性影响的需求标准差之间存在显著的相关关系(r = 0.847, p = 0.004)。这些发现可以支持与零星需求产品的库存管理有关的决策，并有助于商业信息系统的发展。
{"title":"Increasing Efficiency in Inventory Control of Products with Sporadic Demand Using Simulation","authors":"K. Hušková, J. Dyntar","doi":"10.18267/j.aip.184","DOIUrl":"https://doi.org/10.18267/j.aip.184","url":null,"abstract":"The goal of this paper is to examine whether, in Q-system inventory control policy, a combination of the reorder point exceeding order quantity leads to minimal holding and ordering costs when dealing with sporadic demand. For this purpose, a past stock movement simulation is applied to a set of randomly generated data with different numbers of zero demand periods ranging from 10 to 90%. The outputs of the simulation prove that in situations where stock holding costs are too high, the simulation tends to reduce average stock by overcoming periods between two demand peaks with an increase in the numbers of small replenishment orders and reaches lower stock holding and ordering costs. Furthermore, the correlation analysis proves that there is a statistically significant relationship (r = .847, p = .004) between the number of time series that reach minimal holding and ordering costs under the control of reorder point (replenishment order) and the demand standard deviation affected by the evolving sporadicity. These findings can support decision making linked with inventory management of products with sporadic demand and contribute to development of business information systems.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43622899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Current Status and Plans for Further Development of Acta Informatica Pragensia","authors":"Zdenek Smutný, S. Mildeová","doi":"10.18267/j.aip.191","DOIUrl":"https://doi.org/10.18267/j.aip.191","url":null,"abstract":"","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47561022","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}
Cemal Aktürk, Emrah Aydemir, Yasr Mahdi Hama Rashid
Machine learning methods are used for purposes such as learning and estimating a feature or parameter sought from a dataset by training the dataset to solve a particular problem. The transfer learning approach, aimed at transferring the ability of people to continue learning from their past knowledge and experiences to computer systems, is the transfer of the learning obtained in the solution of a particular problem so that it can be used in solving a new problem. Transferring the learning obtained in transfer learning provides some advantages over traditional machine learning methods, and these advantages are effective in the preference of transfer learning. In this study, a total of 1980 eye contour images of 96 different people were collected in order to solve the problem of recognizing people from their eye images. These collected data were classified in terms of person, age and gender. In the classification made for eye recognition, feature extraction was performed with 32 different transfer learning algorithms in the Python program and classified using the RandomForest algorithm for person estimation. According to the results of the research, 30 different classification algorithms were used, with the ResNet50 algorithm being the most successful, and the data were also classified in terms of age and gender. Thus, the highest success rates of 83.52%, 96.41% and 77.56% were obtained in person, age and gender classification, respectively. The study shows that people can be identified only by eye images obtained from a smartphone without using any special equipment, and even the characteristics of people such as age and gender can be determined. In addition, it has been concluded that eye images can be used in a more efficient and practical biometric recognition system than iris recognition.
{"title":"Classification of Eye Images by Personal Details With Transfer Learning Algorithms","authors":"Cemal Aktürk, Emrah Aydemir, Yasr Mahdi Hama Rashid","doi":"10.18267/j.aip.190","DOIUrl":"https://doi.org/10.18267/j.aip.190","url":null,"abstract":"Machine learning methods are used for purposes such as learning and estimating a feature or parameter sought from a dataset by training the dataset to solve a particular problem. The transfer learning approach, aimed at transferring the ability of people to continue learning from their past knowledge and experiences to computer systems, is the transfer of the learning obtained in the solution of a particular problem so that it can be used in solving a new problem. Transferring the learning obtained in transfer learning provides some advantages over traditional machine learning methods, and these advantages are effective in the preference of transfer learning. In this study, a total of 1980 eye contour images of 96 different people were collected in order to solve the problem of recognizing people from their eye images. These collected data were classified in terms of person, age and gender. In the classification made for eye recognition, feature extraction was performed with 32 different transfer learning algorithms in the Python program and classified using the RandomForest algorithm for person estimation. According to the results of the research, 30 different classification algorithms were used, with the ResNet50 algorithm being the most successful, and the data were also classified in terms of age and gender. Thus, the highest success rates of 83.52%, 96.41% and 77.56% were obtained in person, age and gender classification, respectively. The study shows that people can be identified only by eye images obtained from a smartphone without using any special equipment, and even the characteristics of people such as age and gender can be determined. In addition, it has been concluded that eye images can be used in a more efficient and practical biometric recognition system than iris recognition.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46250008","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}
J. M. Al-Tuwaijari, Naeem Th. Yousir, Nafea Ali Majeed Alhammad, S. Mostafa
Skin cancer is undoubtedly one of the deadliest diseases, and early detection of this disease can save lives. The usefulness and capabilities of deep learning in detecting and categorizing skin cancer based on images have been investigated in many studies. However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. In this paper, we employed three different pre-trained state-of-the-art deep learning models: DenseNet121, VGG19 and an improved ResNet152, in classifying a skin image dataset. The dataset has a total of 3297 dermatoscopy images and two diagnostic categories: benign and malignant. The three models are supported by transfer learning and have been tested and evaluated based on the criteria of accuracy, loss, precision, recall, f1 score and ROC. Subsequently, the results show that the improved ResNet152 model significantly outperformed the other models and achieved an accuracy score of 92% and an ROC score of 91%. The DenseNet121 and VGG19 models achieve accuracy scores of 90% and 79% and ROC scores of 88% and 75%, respectively. Subsequently, a deep residual learning skin cancer recognition (ResNetScr) system has been implemented based on the ResNet152 model, and it has the capacity to help dermatologists in diagnosing skin cancer.
{"title":"Deep Residual Learning Image Recognition Model for Skin Cancer Disease Detection and Classification","authors":"J. M. Al-Tuwaijari, Naeem Th. Yousir, Nafea Ali Majeed Alhammad, S. Mostafa","doi":"10.18267/j.aip.189","DOIUrl":"https://doi.org/10.18267/j.aip.189","url":null,"abstract":"Skin cancer is undoubtedly one of the deadliest diseases, and early detection of this disease can save lives. The usefulness and capabilities of deep learning in detecting and categorizing skin cancer based on images have been investigated in many studies. However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. In this paper, we employed three different pre-trained state-of-the-art deep learning models: DenseNet121, VGG19 and an improved ResNet152, in classifying a skin image dataset. The dataset has a total of 3297 dermatoscopy images and two diagnostic categories: benign and malignant. The three models are supported by transfer learning and have been tested and evaluated based on the criteria of accuracy, loss, precision, recall, f1 score and ROC. Subsequently, the results show that the improved ResNet152 model significantly outperformed the other models and achieved an accuracy score of 92% and an ROC score of 91%. The DenseNet121 and VGG19 models achieve accuracy scores of 90% and 79% and ROC scores of 88% and 75%, respectively. Subsequently, a deep residual learning skin cancer recognition (ResNetScr) system has been implemented based on the ResNet152 model, and it has the capacity to help dermatologists in diagnosing skin cancer.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43661381","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}
Blockchain is regarded as a significant innovation and shows a set of promising features that can certainly address existing issues in real time applications. Decentralization, greater transparency, improved traceability and secure architecture can revolutionize healthcare systems. With the help of advancement in computer technologies, most healthcare institutions try to store patient data digitally rather than on paper. Electronic health records are regarded as some of the most important assets in healthcare system and are required to be shared among different hospitals and other organizations to improve diagnosis efficiency. While sharing patients’ details, certain basic standards such as integrity and confidentiality of the information need to be considered. Blockchain technology provides the above standards with features of immutability and granting access to stored information only to authorized users. The examination approach depends on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (or PRISMA) rules and an efficient planned search convention is utilized to look through multiple scientific databases to recognize, investigate and separate every important publication. In this paper, we present a solid systematic review on the blockchain and healthcare domain to identify the existing challenges and benefits of applying blockchain technology in healthcare systems. More than 150 scientific papers published in the last ten years are surveyed, resulting in the identifications and summarization of observations made on the different privacy-preserving approaches and also assessment of their performances. We also present a significant architectural solutions of blockchain to achieve interoperability. Thereby, we attempt to analyse the ideas of blockchain in the medical domain, by assessing the advantages and limitations, subsequently giving guidance to other researchers in the area.
{"title":"Survey on Security and Interoperability of Electronic Health Record Sharing Using Blockchain Technology","authors":"R. P. Puneeth, Govindaswamy Parthasarathy","doi":"10.18267/j.aip.187","DOIUrl":"https://doi.org/10.18267/j.aip.187","url":null,"abstract":"Blockchain is regarded as a significant innovation and shows a set of promising features that can certainly address existing issues in real time applications. Decentralization, greater transparency, improved traceability and secure architecture can revolutionize healthcare systems. With the help of advancement in computer technologies, most healthcare institutions try to store patient data digitally rather than on paper. Electronic health records are regarded as some of the most important assets in healthcare system and are required to be shared among different hospitals and other organizations to improve diagnosis efficiency. While sharing patients’ details, certain basic standards such as integrity and confidentiality of the information need to be considered. Blockchain technology provides the above standards with features of immutability and granting access to stored information only to authorized users. The examination approach depends on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (or PRISMA) rules and an efficient planned search convention is utilized to look through multiple scientific databases to recognize, investigate and separate every important publication. In this paper, we present a solid systematic review on the blockchain and healthcare domain to identify the existing challenges and benefits of applying blockchain technology in healthcare systems. More than 150 scientific papers published in the last ten years are surveyed, resulting in the identifications and summarization of observations made on the different privacy-preserving approaches and also assessment of their performances. We also present a significant architectural solutions of blockchain to achieve interoperability. Thereby, we attempt to analyse the ideas of blockchain in the medical domain, by assessing the advantages and limitations, subsequently giving guidance to other researchers in the area.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49595492","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. Balogun, Latifat Adeola Odeniyi, Elijah Olusola Omidiora, S. Olabiyisi, A. Falohun
Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system. O.S.O.:
{"title":"Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System","authors":"M. Balogun, Latifat Adeola Odeniyi, Elijah Olusola Omidiora, S. Olabiyisi, A. Falohun","doi":"10.18267/j.aip.186","DOIUrl":"https://doi.org/10.18267/j.aip.186","url":null,"abstract":"Classification is a crucial stage in identification systems, most specifically in biometric identification systems. A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions. Decision making in biometric systems is done at the classification stage. Due to the importance of this stage, many classifiers have been developed and modified by researchers. However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem). The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems. However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process. Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems. The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems. The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%. The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively. Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively. Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems. The developed biometric system can be adapted by any organization that requires an ultra-secure identification system. O.S.O.:","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48920806","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 text is devoted to a consideration of the “information explosion” phenomenon. The exponential growth of publications is compared to the (similarly exponential) growth of population, especially in the countries where most of the publications are created. The increasing tertiary education gross enrolment ratio (naturally associated with involvement in the publication process) is also taken into account. The text comes to a conclusion that either the exponential growth of publications must decrease its base value in our future, or we are heading towards a time point where an increasing number of publications find no readers (if that point is not yet behind us).
{"title":"What is the Real Threat of Information Explosion?","authors":"Petr Strossa","doi":"10.18267/j.aip.185","DOIUrl":"https://doi.org/10.18267/j.aip.185","url":null,"abstract":"The text is devoted to a consideration of the “information explosion” phenomenon. The exponential growth of publications is compared to the (similarly exponential) growth of population, especially in the countries where most of the publications are created. The increasing tertiary education gross enrolment ratio (naturally associated with involvement in the publication process) is also taken into account. The text comes to a conclusion that either the exponential growth of publications must decrease its base value in our future, or we are heading towards a time point where an increasing number of publications find no readers (if that point is not yet behind us).","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44155628","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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