Computer network assets expose to various cyber threats in today’s digital era. Network Anomaly Detection Systems (NADS) play a vital role in protecting digital assets in the purview of network security. Intrusion detection systems data are imbalanced and high dimensioned, affecting models’ performance in classifying malicious traffic. This paper uses a denoising autoencoder (DAE) for feature selection to reduce data dimension. To balance the data, the authors use a combined approach of oversampling technique, adaptive synthetic (ADASYN) and a cluster-based under-sampling method using a clustering algorithm, Kmeans. Then, a one-dimensional convolutional neural network (1D-CNN) is used to perform classification. The performance of the proposed model is evaluated on UNSW-NB15 and NSL-KDD datasets. The experimental results show that the model produces a detection rate of 98.79% and 97.23% on UNSW-NB15 for binary classification and multiclass classification, respectively. In the evaluation using NSL-KDD, the model yields a detection rate of 98.52% for binary type classification and 98.16% for multiclass type classification.
{"title":"Network Intrusion Detection with 1D Convolutional Neural Networks","authors":"Mohammad Kazim Hooshmand, M. D. Huchaiah","doi":"10.54963/dtra.v1i2.64","DOIUrl":"https://doi.org/10.54963/dtra.v1i2.64","url":null,"abstract":"Computer network assets expose to various cyber threats in today’s digital era. Network Anomaly Detection Systems (NADS) play a vital role in protecting digital assets in the purview of network security. Intrusion detection systems data are imbalanced and high dimensioned, affecting models’ performance in classifying malicious traffic. This paper uses a denoising autoencoder (DAE) for feature selection to reduce data dimension. To balance the data, the authors use a combined approach of oversampling technique, adaptive synthetic (ADASYN) and a cluster-based under-sampling method using a clustering algorithm, Kmeans. Then, a one-dimensional convolutional neural network (1D-CNN) is used to perform classification. The performance of the proposed model is evaluated on UNSW-NB15 and NSL-KDD datasets. The experimental results show that the model produces a detection rate of 98.79% and 97.23% on UNSW-NB15 for binary classification and multiclass classification, respectively. In the evaluation using NSL-KDD, the model yields a detection rate of 98.52% for binary type classification and 98.16% for multiclass type classification.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115617881","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}
Schizophrenia is a common psychotic disorder which affects a substantial amount of the population, where the paranoid variant is viewed as the most common form of the disorder. This form of psychosis has been seen to affect both adults and adolescents; where in the case of adolescents, it is increasingly challenging to diagnose with traditional means involving clinical interviews. The use of electroencephalography (EEG) signals has proven to be an effective means of non-invasively diagnosing brain disorders, alongside having the ability to mitigate any form of subjective bias from the diagnosis process. This paper explores the use of acquired EEG signals, metaheuristics and deep wavelet scattering decomposition, and a combination of supervised and unsupervised learning, for the automated prediction of adolescent schizophrenia. The results showed the best accuracy for the metaheuristic decomposition alongside the candidate learning methods, in the region of 95%+ across the various classification metrics, which showcases an enhanced means of prediction of adolescent schizophrenia. Further work would now explore the use of Long ShortTerm Memory and Convolution Neural Networks to investigate the classification performances.
{"title":"On the Application of Metaheuristics and Deep Wavelet Scattering Decompositions for the Prediction of Adolescent Psychosis Using EEG Brain Wave Signals","authors":"E. Nsugbe","doi":"10.54963/dtra.v1i2.40","DOIUrl":"https://doi.org/10.54963/dtra.v1i2.40","url":null,"abstract":"Schizophrenia is a common psychotic disorder which affects a substantial amount of the population, where the paranoid variant is viewed as the most common form of the disorder. This form of psychosis has been seen to affect both adults and adolescents; where in the case of adolescents, it is increasingly challenging to diagnose with traditional means involving clinical interviews. The use of electroencephalography (EEG) signals has proven to be an effective means of non-invasively diagnosing brain disorders, alongside having the ability to mitigate any form of subjective bias from the diagnosis process. This paper explores the use of acquired EEG signals, metaheuristics and deep wavelet scattering decomposition, and a combination of supervised and unsupervised learning, for the automated prediction of adolescent schizophrenia. The results showed the best accuracy for the metaheuristic decomposition alongside the candidate learning methods, in the region of 95%+ across the various classification metrics, which showcases an enhanced means of prediction of adolescent schizophrenia. Further work would now explore the use of Long ShortTerm Memory and Convolution Neural Networks to investigate the classification performances.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124695703","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}
Business information systems have extensive databases that are mainly managed in relational databases. What is often missing are automated procedures to analyze these inventories without major restructuring. Based on this, we develop the Fuzzy Classification Query Language, FCQL, which enables fuzzy queries to the extended database schema using linguistic variables and converts them into SQL statements to the database. With this, we give the user a data mining tool so that he can start extended queries on his databases based on a pre-defined fuzzy classification and obtain an improved basis for decision making. As a result, the fuzzy classification query language enables marketers to improve customer value, launch useful programs, automate overall customization, and refine business campaigns.
{"title":"For Fuzzy Classification of Databases with Fuzzy Classification Query Language","authors":"Seyfali Mahini","doi":"10.54963/dtra.v1i2.34","DOIUrl":"https://doi.org/10.54963/dtra.v1i2.34","url":null,"abstract":"Business information systems have extensive databases that are mainly managed in relational databases. What is often missing are automated procedures to analyze these inventories without major restructuring. Based on this, we develop the Fuzzy Classification Query Language, FCQL, which enables fuzzy queries to the extended database schema using linguistic variables and converts them into SQL statements to the database. With this, we give the user a data mining tool so that he can start extended queries on his databases based on a pre-defined fuzzy classification and obtain an improved basis for decision making. As a result, the fuzzy classification query language enables marketers to improve customer value, launch useful programs, automate overall customization, and refine business campaigns.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, Artificial Intelligence (AI) has emerged as an emerging with advanced methodologies and innovative applications. With the rapid advancement of AI concepts and technologies, there has been a recent trend to add interpretability and explainability to the paradigm. With the increasing complexity of AI applications, their a relationship with data analytics, and the ubiquity of demanding applications in a variety of critical applications such as medicine, defense, justice and autonomous vehicles , there is an increasing need to associate the results with sound explanations to domain experts. All of these elements have contributed to Explainable Artificial Intelligence (XAI).
{"title":"Explainable Artificial Intelligence: A New Era of Artificial Intelligence","authors":"Ashraf Darwish","doi":"10.54963/dtra.v1i1.29","DOIUrl":"https://doi.org/10.54963/dtra.v1i1.29","url":null,"abstract":"Recently, Artificial Intelligence (AI) has emerged as an emerging with advanced methodologies and innovative applications. With the rapid advancement of AI concepts and technologies, there has been a recent trend to add interpretability and explainability to the paradigm. With the increasing complexity of AI applications, their a relationship with data analytics, and the ubiquity of demanding applications in a variety of critical applications such as medicine, defense, justice and autonomous vehicles , there is an increasing need to associate the results with sound explanations to domain experts. All of these elements have contributed to Explainable Artificial Intelligence (XAI).","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126493022","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}
Classification of brain tumor is highly significant in the medical field in real-world to improve the progress of treatments. The seriousness behind the tumors are normally graded based on the size into grade I, grade II, grade III and grade IV. This is where the process of multi-grade brain tumor classification gains attention. Thus, the article focusses on classifying the brain MRI images into four different grades by proposing a novel and a very efficient classification strategy with high accuracy. The acquired images are pre-processed with the help of an Extended Adaptive Wiener Filter (EAWF) and then segmented using the piecewise Fuzzy C- means Clustering (piFCM) technique. Then the most ideal features such as the texture, intensity and shape features that can best explain the growth of tumors are extracted using the Local Binary Pattern (LBP) and the Hybrid Local Directional Pattern with Gabor Filter (HLDP-GF) techniques. After extracting the ideal features, the Manta Ray Foraging Optimization (MRFO) method has been introduced to optimally select the most relevant features. Finally, a Hybrid Deep Neural Network with Adaptive Rain Optimizer Algorithm (HDNN- AROA) is proposed to classify the grades of brain tumors with high accuracy and efficiency. The proposed technique has been compared with the existing state-of-the-art techniques relevant to brain tumor classification in terms of accuracy, precision, recall and dice similarity coefficient to prove the overall efficiency of the system.
{"title":"Hybridized Deep Neural Network Using Adaptive Rain Optimizer Algorithm for Multi-Grade Brain Tumor Classification of MRI Images","authors":"V. Sasank, S. Venkateswarlu","doi":"10.54963/dtra.v1i1.28","DOIUrl":"https://doi.org/10.54963/dtra.v1i1.28","url":null,"abstract":"Classification of brain tumor is highly significant in the medical field in real-world to improve the progress of treatments. The seriousness behind the tumors are normally graded based on the size into grade I, grade II, grade III and grade IV. This is where the process of multi-grade brain tumor classification gains attention. Thus, the article focusses on classifying the brain MRI images into four different grades by proposing a novel and a very efficient classification strategy with high accuracy. The acquired images are pre-processed with the help of an Extended Adaptive Wiener Filter (EAWF) and then segmented using the piecewise Fuzzy C- means Clustering (piFCM) technique. Then the most ideal features such as the texture, intensity and shape features that can best explain the growth of tumors are extracted using the Local Binary Pattern (LBP) and the Hybrid Local Directional Pattern with Gabor Filter (HLDP-GF) techniques. After extracting the ideal features, the Manta Ray Foraging Optimization (MRFO) method has been introduced to optimally select the most relevant features. Finally, a Hybrid Deep Neural Network with Adaptive Rain Optimizer Algorithm (HDNN- AROA) is proposed to classify the grades of brain tumors with high accuracy and efficiency. The proposed technique has been compared with the existing state-of-the-art techniques relevant to brain tumor classification in terms of accuracy, precision, recall and dice similarity coefficient to prove the overall efficiency of the system.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121791064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel method of feature extraction using Fan beam projection-based data. The Fanbeam projection covers the image completely and hence gathers all the important information. Even though the image quality is distorted, this type of feature extraction method helps to gather all the important information as there is a huge volume of projection data. Also, the use of multiple detectors speeds up the entire process. All the projections of the image together form a sinogram image which is unique for each facial expression image. Hence, the sinogram image is divided into grids and the histogram formation results in a feature vector for each image. The classification of these feature vectors using Radial Basis Function-based Extreme learning Machine (RBF-ELM) results in high classification accuracy for all the datasets.
{"title":"Fan-beam Projection-based Feature Extraction for Facial Expression Recognition","authors":"A. Alphonse","doi":"10.54963/dtra.v1i1.31","DOIUrl":"https://doi.org/10.54963/dtra.v1i1.31","url":null,"abstract":"This paper presents a novel method of feature extraction using Fan beam projection-based data. The Fanbeam projection covers the image completely and hence gathers all the important information. Even though the image quality is distorted, this type of feature extraction method helps to gather all the important information as there is a huge volume of projection data. Also, the use of multiple detectors speeds up the entire process. All the projections of the image together form a sinogram image which is unique for each facial expression image. Hence, the sinogram image is divided into grids and the histogram formation results in a feature vector for each image. The classification of these feature vectors using Radial Basis Function-based Extreme learning Machine (RBF-ELM) results in high classification accuracy for all the datasets.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128731228","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 research aims to identify the social and psychological origins of needs, which may result in the need to obtain certain gratifications from social networking sites, different patterns of social networking sites usage, or cause social networking sites addiction, and the possible consequences they may have on millennials social capital and attitudes toward social networking sites advertising. The study adopts the Uses and Gratifications theory and employs a quantitative research method. The sample of the study consisted of 385 millennials, aged from 21-37 years old, who all used Facebook, Instagram, and YouTube platforms. Data were analyzed using the Structural Equation Modeling. The findings of the study provide useful insights regarding millennials behavior on social networking sites, as well as their attitudes towards social networking sites advertising. The findings suggest several implications and recommendations for marketers, which can help in increasing the effectiveness of advertisements directed to millennials.
{"title":"A Sociopsychological Approach to Millennials Attitudes on Social Networking Sites","authors":"Dina El- Shihy, T. Awad","doi":"10.54963/dtra.v1i1.30","DOIUrl":"https://doi.org/10.54963/dtra.v1i1.30","url":null,"abstract":"This research aims to identify the social and psychological origins of needs, which may result in the need to obtain certain gratifications from social networking sites, different patterns of social networking sites usage, or cause social networking sites addiction, and the possible consequences they may have on millennials social capital and attitudes toward social networking sites advertising. The study adopts the Uses and Gratifications theory and employs a quantitative research method. The sample of the study consisted of 385 millennials, aged from 21-37 years old, who all used Facebook, Instagram, and YouTube platforms. Data were analyzed using the Structural Equation Modeling. The findings of the study provide useful insights regarding millennials behavior on social networking sites, as well as their attitudes towards social networking sites advertising. The findings suggest several implications and recommendations for marketers, which can help in increasing the effectiveness of advertisements directed to millennials.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123333058","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}
Stock market plays a vital role in industrial development of a country. People invest money to make profit from market. Inexperience investors cannot yield profit due to their weak predictions. This research tries to understand the nature of those investors and their demands. Most investors first analyze the prospect of companies based on rate of up-down in prices of share, given bonus, companies’ goodwill, temptation by others, etc.. This research presents a good prediction methodology for the stock market investors and thus, will help them to achieve a profit. It will improve the stability of a market.
{"title":"A Predictive Investment Scheme for Dhaka Stock Exchange","authors":"A. Kamal, Md. Kaysar Abdullah","doi":"10.54963/dtra.v1i1.26","DOIUrl":"https://doi.org/10.54963/dtra.v1i1.26","url":null,"abstract":"Stock market plays a vital role in industrial development of a country. People invest money to make profit from market. Inexperience investors cannot yield profit due to their weak predictions. This research tries to understand the nature of those investors and their demands. Most investors first analyze the prospect of companies based on rate of up-down in prices of share, given bonus, companies’ goodwill, temptation by others, etc.. This research presents a good prediction methodology for the stock market investors and thus, will help them to achieve a profit. It will improve the stability of a market.","PeriodicalId":209676,"journal":{"name":"Digital Technologies Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123095071","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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