Volume 1 Issue 2 (2022)

Article

For Fuzzy Classification of Databases with Fuzzy Classification Query Language

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.

Read more

Article

On the Application of Metaheuristics and Deep Wavelet Scattering Decompositions for the Prediction of Adolescent Psychosis Using EEG Brain Wave Signals

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.

Read more

Article

Network Intrusion Detection with 1D Convolutional Neural Networks

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.

Read more

Article

Artificial Intelligence-Assisted Care for Human Newborns with Neurological Impairments

Seizures are a widespread condition affecting 50~65 million people in the world, and newborns are also susceptible to them. EEG is used to monitor the brain activity of newborns with suspected brain injuries, followed by a qualitative waveform interpretation by a group of clinical experts, where the means towards detection of seizures include a set of distinct characteristics in the waveform. This means of seizure detection has been critiqued, particularly due to subjectivity where, at times, waveform reviewing clinicians fail to reach a consensus on the presence of seizure activity in the brain of a newborn. As a means towards dealing with this problem, the author investigated the use of Artificial Intelligence-driven prediction machines capable of an automated diagnosis of seizure, based on a newborn’s EEG waveform. This approach used a reduced selection of EEG electrodes, the Linear Series Decomposition Learner (LSDL), an ensemble of a group of features, and performance comparison across multiple classification models. Secondary work was also carried out, which leveraged the patient information available alongside the EEG dataset. This involved the use of EEG towards predicting the level of asphyxia within the neonatal brain. The results from the seizure prediction exercise showed an increment in prediction performance of the seizures when preprocessed with the LSDL. The results spanned a range of figures (depending on the classification model), with the highest accuracy of 88.1%, while a probabilistic approach towards predicting the extent of seizures provided a maximum accuracy of 93.5%. The results from the secondary analysis showed a maximum accuracy for asphyxia prediction of 89.1%. The obtained results have helped to demonstrate that a reduced selection of electrode segments, alongside the selected algorithms, can serve towards the prediction of seizures for newborns within a neonatal intensive care unit.

Read more

Article

Programming Techniques for Considering m Desired Conditions from n Possible Conditions

The performance of computer programs (or the hardware that can be programmed such as IoTs, embedded computers, and PLCs) is generally based on the complexity of the particular program development technique. High complexity often uses more execution times and system resources. For this reason, the computer program is less computational complexity to be desired. The conditional statements tell computers what certain information is a major cause of computer program complexities, e.g., considering m desired conditions from n possible conditions. To achieve this aim in computer programs, the data combination is often utilized. However, it is high complexity. Moreover, they cannot give that one condition takes precedence over others. To rid these vulnerabilities of combined conditions, a simple programming technique for considering m desired conditions from n possible conditions is proposed in this work, which is based on the summation of the condition weights. It only has the complexity of search spaces and data constructions to be O(n) and each condition can be set to be different precedence from the others. Furthermore, the proposed technique is evaluated by extensive experiments. From the experimental results, they indicate that the proposed technique is more effective and efficient than the comparative technique.

Read more