Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I–II-III–IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II, n = 77; Grade III, n = 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification.

Alzheimer’s disease (AD), the most common form of dementia and neurological disorder, affects a significant number of elderly people worldwide. The main objective of this study was to develop an effective method for quickly diagnosing healthy individuals (CN) before they progress to mild cognitive impairment (MCI). Moreover, this study presents a unique approach to decomposing AD into stages using machine-learning architectures with the help of tensor-based morphometric image analysis. The proposed model, which uses a neural network built on the Xception architecture, was thoroughly assessed by comparing it with the most recent convolutional neural network (CNN) models described in the literature. The proposed method outperformed the other models in terms of performance, achieving an impressive average classification accuracy of 95.81% using the dataset. It also had very high sensitivity, specificity, accuracy, and F1 scores, with average scores of 95.41%, 97.92%, 95.01%, and 95.21%, respectively. In addition, it showed a superior classification ability compared to alternative methods, especially for MCI estimation, as evidenced by a mean area under the ROC curve (AUC) of 0.97. Our study demonstrated the effectiveness of deep-learning-based morphometric analysis using brain images for early AD diagnosis.

Glioma is one of the most common brain tumours among the diagnoses of existing brain tumours. Glioma grades are important factors that should be known in the treatment of brain tumours. In this study, the radiomic features of gliomas were analysed and glioma grades were classified by Gaussian Naive Bayes algorithm. Glioma tumours of 121 patients of Grade II and Grade III were examined. The glioma tumours were segmented with the Grow Cut Algorithm and the 3D feature of tumour magnetic resonance imaging images were obtained with the 3D Slicer programme. The obtained quantitative values were statistically analysed with Spearman and Mann–Whitney U tests and 21 features with statistically significant properties were selected from 107 features. The results showed that the best performing among the algorithms was Gaussian Naive Bayes algorithm with 80% accuracy. Machine learning and feature selection techniques can be used in the analysis of gliomas as well as pathological evaluations in glioma grading processes. Keywords: Radiomics, glioma, naive bayes.

En tr With the development of cloud computing technology, learning management systems (LMSs) have gained new features and service options. Consequently, the process of choosing among increasing product alternatives has become difficult. Choosing the most appropriate cloud-based LMS with certain criteria has been an important decision-making problem. In this study, a model has been developed by using Fuzzy Analytic Hierarchy Process (FAHP) to facilitate selection of the most suitable one from a group of cloud-based LMSs according to certain criteria. In this model, content support, interaction and collaboration, measurement and evaluation, course structure, interface, productivity tools, platform flexibility, scalability, security, support and licensing criteria that can be effective in cloud-based LMS selection are examined based on literature and expert opinions, and presented in a hierarchical structure. In the study, both the criteria and six alternatives in the context of a case study were evaluated by seven decision-makers specialized in the field of online education. Based on the determined criteria, the most appropriate cloud-based LMS was chosen as TalentLMS as a result of fuzzy pairwise comparisons between the selected alternatives. Bulut bilişim teknolojisinin gelişmesiyle öğrenme yönetim sistemleri (ÖYS’ler) yeni özellikler ve servis seçenekleri kazanmıştır. Buna bağlı olarak artan ürün alternatifleri arasından seçim yapma süreci zorlaşmıştır. Belli kriterlere bağlı en uygun bulut tabanlı ÖYS’yi seçmek kurumlar için önemli bir karar verme sorunu olmuştur. Bu çalışmada, kurumların bir grup bulut tabanlı ÖYS arasından belli kriterlere uygun seçim yapabilmesini kolaylaştıracak Bulanık Analitik Hiyerarşi Süreci (BAHS) tabanlı bir model geliştirilmiştir. Bu modelde, bulut tabanlı ÖYS seçiminde etkili olabilecek içerik desteği, etkileşim ve iş birliği, ölçme ve değerlendirme, ders yapısı, arayüz, verimlilik araçları, platform esnekliği, ölçeklenebilirlik, güvenlik, destek ve lisanslama kriterleri literatüre ve uzman görüşlerine dayalı incelenmiş ve oluşturulan bir hiyerarşik yapı ile sunulmuştur. Çalışmada hem kriterler hem de durum çalışması kapsamında ele alınan altı alternatif, çevrim-içi eğitim alanında uzman yedi karar verici tarafından değerlendirilmiştir. Belirlenen kriterlere bağlı olarak, seçilen alternatifler arasında yapılan bulanık ikili karşılaştırmalar sonucu en uygun bulut tabanlı ÖYS, TalentLMS olarak belirlenmiştir.

Today, because organisations are getting more targeted by complex cyber attacks that can cost a company millions of dollars in losses as well as damage point of sale, operational disruption etc. While traditional security measures can hardly cope with the volume, velocity and complexity of modern attack landscapes. Artificial Intelligence (AI) and Machine Learning (ML) integration is no longer something that should only be done for securing purposes an long way down on the to-do list per top cybersecurity solutions. Armed with a strategic deployment of AI-driven systems as well as the corresponding difficulties, organizations can greatly improve their security architecture and make themselves substantially more secure against the modern cyber threats in this digital era. This book proposal chapter offers a broad explanation of AI-driven threat detection and response systems, emphasizing their fundamentals; pointing to capabilities; while highlighting the inherent challenges; and proposing potential future directions.