Multistage testing (MST) is a portion of computational adaptive testing that adapts assessment structure at the sublevel rather than the component level. The goal of the MST algorithm is to identify bugs in computer programming, and there is a significant cost to utilising MST due to its decreased versatility during software development and maintenance. The efficiency of most algorithms drastically reduces for adaptive MST with complex feasible regions, while some modern algorithms function well while tackling computerised MST with a basic practicable range. The study offers an automated Adaptive Multistage Testing algorithm based on Adaptive Genetic Algorithm (AMST-AGA) for optimisation and scalability problems, in which constraints are successively introduced and dealt with at various evolutionary phases. In this paper, many test cases will aid in finding bugs and meeting completeness goals. Each time test cases are created, these testing scenarios must continue to pass.
{"title":"Improvement of Computer Adaptive Multistage Testing Algorithm Based on Adaptive Genetic Algorithm","authors":"Zhaoxia Zhang","doi":"10.4018/ijiit.344024","DOIUrl":"https://doi.org/10.4018/ijiit.344024","url":null,"abstract":"Multistage testing (MST) is a portion of computational adaptive testing that adapts assessment structure at the sublevel rather than the component level. The goal of the MST algorithm is to identify bugs in computer programming, and there is a significant cost to utilising MST due to its decreased versatility during software development and maintenance. The efficiency of most algorithms drastically reduces for adaptive MST with complex feasible regions, while some modern algorithms function well while tackling computerised MST with a basic practicable range. The study offers an automated Adaptive Multistage Testing algorithm based on Adaptive Genetic Algorithm (AMST-AGA) for optimisation and scalability problems, in which constraints are successively introduced and dealt with at various evolutionary phases. In this paper, many test cases will aid in finding bugs and meeting completeness goals. Each time test cases are created, these testing scenarios must continue to pass.","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966019","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}
With the rapid development of the aerospace industry, the structure of airborne electronic equipment has become more complex, which to some extent increases the difficulty of fault detection and maintenance of airborne electronic equipment. Traditional manual fault diagnosis methods can no longer fully meet the diagnostic needs of airborne electronic equipment. Therefore, this chapter uses dynamic Bayesian network to diagnose the faults of airborne electronic equipment. The basic idea of using a dynamic Bayesian network-based fault diagnosis method for airborne electronic devices is to mine data based on historical fault data of airborne electronic devices, and obtain fault symptoms and training data of airborne electronic devices. For non-essential fault symptoms, rough set theory was introduced to reduce their attributes and obtain the simplest attribute set, thereby simplifying the network model.
{"title":"Fault Diagnosis of Airborne Electronic Equipment Based on Dynamic Bayesian Networks","authors":"Julan Chen, Wengao Qian","doi":"10.4018/ijiit.335033","DOIUrl":"https://doi.org/10.4018/ijiit.335033","url":null,"abstract":"With the rapid development of the aerospace industry, the structure of airborne electronic equipment has become more complex, which to some extent increases the difficulty of fault detection and maintenance of airborne electronic equipment. Traditional manual fault diagnosis methods can no longer fully meet the diagnostic needs of airborne electronic equipment. Therefore, this chapter uses dynamic Bayesian network to diagnose the faults of airborne electronic equipment. The basic idea of using a dynamic Bayesian network-based fault diagnosis method for airborne electronic devices is to mine data based on historical fault data of airborne electronic devices, and obtain fault symptoms and training data of airborne electronic devices. For non-essential fault symptoms, rough set theory was introduced to reduce their attributes and obtain the simplest attribute set, thereby simplifying the network model.","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997624","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 decision tree classification algorithm is becoming increasingly important in machine learning (ML) technology. It is being used in a variety of fields to solve extremely complicated issues. DTCA is also utilised in medical health data to identify chronic kidney disorders such as cancer and diabetes utilising computer-aided diagnosis. Deep learning is an intelligent area of machine learning in which neural networks are used to learn unsupervised from unstructured or unlabeled data. For CKD, the DL employed the deep stacked auto-encoder and soft-max classifier techniques. Kidney illness is another condition that can lead to a variety of health problems. Random forest, SVM, C5.0, decision tree classification algorithm, C4.5, ANN, neuro-fuzzy systems, classification and clustering, DSAE, DNN, FNC, MLP are used in this study to predict and identify an early diagnosis of CKD patients using various machine and deep learning algorithms using R Studio and Python Colab software. The many stages of chronic kidney disease are identified in this paper.
{"title":"Intelligent Decision Support for Identifying Chronic Kidney Disease Stages","authors":"V. Shanmugarajeshwari, M. Ilayaraja","doi":"10.4018/ijiit.334557","DOIUrl":"https://doi.org/10.4018/ijiit.334557","url":null,"abstract":"The decision tree classification algorithm is becoming increasingly important in machine learning (ML) technology. It is being used in a variety of fields to solve extremely complicated issues. DTCA is also utilised in medical health data to identify chronic kidney disorders such as cancer and diabetes utilising computer-aided diagnosis. Deep learning is an intelligent area of machine learning in which neural networks are used to learn unsupervised from unstructured or unlabeled data. For CKD, the DL employed the deep stacked auto-encoder and soft-max classifier techniques. Kidney illness is another condition that can lead to a variety of health problems. Random forest, SVM, C5.0, decision tree classification algorithm, C4.5, ANN, neuro-fuzzy systems, classification and clustering, DSAE, DNN, FNC, MLP are used in this study to predict and identify an early diagnosis of CKD patients using various machine and deep learning algorithms using R Studio and Python Colab software. The many stages of chronic kidney disease are identified in this paper.","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624042","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 work aims to review the literature on anomaly detection (AD) in renewable energy. Due to the significance of the RE data quality and sensor performance, it is crucial to ensure that the measurement device works correctly and maintains data accuracy. The review identifies the relevant studies on big data anomaly detection in the energy field and synthesizes the related techniques. Also, the study shows a need for segmentation annotations for solar system electroluminescence imagery complicating the domain development of anomaly segmentation approaches. Consequently, most processes create machine learning (ML) models using semi-supervised techniques. Still, these approaches need more generalization regarding variation in environmental or systematic conditions. Furthermore, the studies discussed here focus on existing algorithms that used big data and AD to propose an improved analysis framework. Finally, the work presents a framework to solve the problem of identifying sensors' issues that will appear in data anomalies.
{"title":"Anomaly Detection in Renewable Energy Big Data Using Deep Learning","authors":"Suzan MohammadAli Katamoura, Mehmet Sabih Aksoy","doi":"10.4018/ijiit.331595","DOIUrl":"https://doi.org/10.4018/ijiit.331595","url":null,"abstract":"This work aims to review the literature on anomaly detection (AD) in renewable energy. Due to the significance of the RE data quality and sensor performance, it is crucial to ensure that the measurement device works correctly and maintains data accuracy. The review identifies the relevant studies on big data anomaly detection in the energy field and synthesizes the related techniques. Also, the study shows a need for segmentation annotations for solar system electroluminescence imagery complicating the domain development of anomaly segmentation approaches. Consequently, most processes create machine learning (ML) models using semi-supervised techniques. Still, these approaches need more generalization regarding variation in environmental or systematic conditions. Furthermore, the studies discussed here focus on existing algorithms that used big data and AD to propose an improved analysis framework. Finally, the work presents a framework to solve the problem of identifying sensors' issues that will appear in data anomalies.","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136353595","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}
Brahami Menaouer, Abdallah El Hadj Mohamed Islem, M. Nada
In the past decade, Android has become a standard smartphone operating system. The mobile devices running on the Android operating system are particularly interesting to malware developers, as the users often keep personal information on their mobile devices. This paper proposes a deep learning model for mobile malware detection and classification. It is based on SAE for reducing the data dimensionality. Then, a CNN is utilized to detect and classify malware apps in Android devices through binary visualization. Tests were carried out with an original Android application (Drebin-215) dataset consisting of 15,036 applications. The conducted experiments prove that the classification performance achieves high accuracy of about 98.50%. Other performance measures used in the study are precision, recall, and F1-score. Finally, the accuracy and results of these techniques are analyzed by comparing the effectiveness with previous works.
{"title":"Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural Networks","authors":"Brahami Menaouer, Abdallah El Hadj Mohamed Islem, M. Nada","doi":"10.4018/ijiit.329956","DOIUrl":"https://doi.org/10.4018/ijiit.329956","url":null,"abstract":"In the past decade, Android has become a standard smartphone operating system. The mobile devices running on the Android operating system are particularly interesting to malware developers, as the users often keep personal information on their mobile devices. This paper proposes a deep learning model for mobile malware detection and classification. It is based on SAE for reducing the data dimensionality. Then, a CNN is utilized to detect and classify malware apps in Android devices through binary visualization. Tests were carried out with an original Android application (Drebin-215) dataset consisting of 15,036 applications. The conducted experiments prove that the classification performance achieves high accuracy of about 98.50%. Other performance measures used in the study are precision, recall, and F1-score. Finally, the accuracy and results of these techniques are analyzed by comparing the effectiveness with previous works.","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41552817","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}
Face recognition is an emerging field of research in recent days. With the rise of deep learning, face recognition has become efficient and precise, creating new milestones. The performance, accuracy, and computational time of the existing schemes can be enhanced by devising a new scheme. In this context, a multiclass classification framework for face recognition using residual network (ResNet) and principal component analysis (PCA) schemes of deep learning with Dlib library is proposed in this paper. The proposed framework produces face recognition accuracy of 99.6% and a reduction of computational time with 68.03% using principal component analysis.
{"title":"ResNet and PCA-Based Deep Learning Scheme for Efficient Face Recognition","authors":"Rajendra Kumar Dwivedi, Devesh Kumar","doi":"10.4018/ijiit.329957","DOIUrl":"https://doi.org/10.4018/ijiit.329957","url":null,"abstract":"Face recognition is an emerging field of research in recent days. With the rise of deep learning, face recognition has become efficient and precise, creating new milestones. The performance, accuracy, and computational time of the existing schemes can be enhanced by devising a new scheme. In this context, a multiclass classification framework for face recognition using residual network (ResNet) and principal component analysis (PCA) schemes of deep learning with Dlib library is proposed in this paper. The proposed framework produces face recognition accuracy of 99.6% and a reduction of computational time with 68.03% using principal component analysis.","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42335463","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}
Diabetic retinopathy is one of the leading causes of visual loss and with timely diagnosis, this condition can be prevented. This research proposes a transfer learning-based model that is trained using retinal fundus images of patients whose severity is graded by trained ophthalmologists into five different classifications. The research uses transfer learning based on a pre-trained model that is ResNet 50, thus it is possible to train the model with the limited amount of labeled training data. The model has been trained and its accuracy has been analyzed using different metrics namely accuracy score, loss graph and confusion matrix. Such deep learning models need to be transparent for approval by the regulatory authorities for clinical use. The clinical practitioner also needs to have information about the working of the classification method to make sure that he/she understands the decision making process of the model.
{"title":"Diabetic Retinopathy Severity Prediction Using Deep Learning Techniques","authors":"Victer Paul, Bivek Benoy Paul, R. Raju","doi":"10.4018/ijiit.329929","DOIUrl":"https://doi.org/10.4018/ijiit.329929","url":null,"abstract":"Diabetic retinopathy is one of the leading causes of visual loss and with timely diagnosis, this condition can be prevented. This research proposes a transfer learning-based model that is trained using retinal fundus images of patients whose severity is graded by trained ophthalmologists into five different classifications. The research uses transfer learning based on a pre-trained model that is ResNet 50, thus it is possible to train the model with the limited amount of labeled training data. The model has been trained and its accuracy has been analyzed using different metrics namely accuracy score, loss graph and confusion matrix. Such deep learning models need to be transparent for approval by the regulatory authorities for clinical use. The clinical practitioner also needs to have information about the working of the classification method to make sure that he/she understands the decision making process of the model.","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70458251","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}
Sara Marques, Rui Gonçalves, Renato Lopes da Costa, L. Pereira, Á. Dias
In today's competitive and changing business environment, the concern about technologies and intelligent systems has gained more notoriety. However, companies still have many tasks performed by humans; in the medium-term, intelligent systems will become more present in companies and will perform tasks that are currently done by humans much more efficiently. There is a need for companies to adapt and to start thinking about combining human and intelligent systems capabilities. This research was focused specifically in the management accounting profession, as these professionals spend a lot of time collecting and organizing data, doing repetitive tasks that can be easily and quickly accomplished by intelligent systems. This research studied the impact that artificial intelligence, big data, and internet of things can have in this profession.
{"title":"The Impact of Intelligent Systems on Management Accounting","authors":"Sara Marques, Rui Gonçalves, Renato Lopes da Costa, L. Pereira, Á. Dias","doi":"10.4018/ijiit.324601","DOIUrl":"https://doi.org/10.4018/ijiit.324601","url":null,"abstract":"In today's competitive and changing business environment, the concern about technologies and intelligent systems has gained more notoriety. However, companies still have many tasks performed by humans; in the medium-term, intelligent systems will become more present in companies and will perform tasks that are currently done by humans much more efficiently. There is a need for companies to adapt and to start thinking about combining human and intelligent systems capabilities. This research was focused specifically in the management accounting profession, as these professionals spend a lot of time collecting and organizing data, doing repetitive tasks that can be easily and quickly accomplished by intelligent systems. This research studied the impact that artificial intelligence, big data, and internet of things can have in this profession.","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41508788","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 coronavirus pandemic has led to a dramatic increase in depression cases worldwide. Several people are utilizing social media to share their depression or suicidal thoughts. Thus, the major goal of the proposed study is to examine Twitter posts by users and identify features that may indicate depressed symptoms among online users. A numerical metric for each user is proposed based on the sentiment value of their tweets, and it is demonstrated that this feature can detect depression with good accuracy by using several machine learning classifiers. The paper proposes a novel method for measuring the mental health index of an individual by combining the sentiment score with multimodal features extracted from his online activities. A real-time curve is generated using this index that can monitor a person's mental health in real time and offer real-time information about his state. The proposed model shows an accuracy of 89% using SVM, and proper feature selection is very essential for obtaining good performance.
{"title":"Generating a Mental Health Curve for Monitoring Depression in Real Time by Incorporating Multimodal Feature Analysis Through Social Media Interactions","authors":"Moumita Chatterjee, Piyush Kumar, Dhrubasish Sarkar","doi":"10.4018/ijiit.324600","DOIUrl":"https://doi.org/10.4018/ijiit.324600","url":null,"abstract":"The coronavirus pandemic has led to a dramatic increase in depression cases worldwide. Several people are utilizing social media to share their depression or suicidal thoughts. Thus, the major goal of the proposed study is to examine Twitter posts by users and identify features that may indicate depressed symptoms among online users. A numerical metric for each user is proposed based on the sentiment value of their tweets, and it is demonstrated that this feature can detect depression with good accuracy by using several machine learning classifiers. The paper proposes a novel method for measuring the mental health index of an individual by combining the sentiment score with multimodal features extracted from his online activities. A real-time curve is generated using this index that can monitor a person's mental health in real time and offer real-time information about his state. The proposed model shows an accuracy of 89% using SVM, and proper feature selection is very essential for obtaining good performance.","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47948854","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":"TS2LBDP: Design of an Improved Task-Side SLA Model for Efficient Task Scheduling via Bioinspired Deadline-Aware Pattern Analysis","authors":"P. Shelke, Rekha Shahapurkar","doi":"10.4018/ijiit.309586","DOIUrl":"https://doi.org/10.4018/ijiit.309586","url":null,"abstract":"","PeriodicalId":43967,"journal":{"name":"International Journal of Intelligent Information Technologies","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70458202","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：