Enhancement Of Pre-Trained Deep Learning Models To Improve Brain Tumor Classification
Published in: Informatica
Oct 01, 2023
The early detection of brain tumors is crucial due to their highly dangerous nature and the potential for life-threatening consequences if left undiagnosed. Brain tumors significantly shorten life expectancy and cause extensive damage. To accurately diagnose brain tumors, medical imaging techniques such as MRI and other diagnostic tests play a vital role in the classification process. Artificial intelligence, specifically deep learning and computer vision, offers valuable techniques fo...
AI-enabled framework for mobile network experimentation leveraging ChatGPT: Case study of channel capacity calculation for η-µ fading and co-channel interference
Published in: Electronics
Sep 29, 2023
Dragana Krstic
Nenad Petrovic
Suad Suljovic
Issam Al-Azzoni
Artificial intelligence has been identified as one of the main driving forces of innovation in state-of-the-art mobile and wireless networks. It has enabled many novel usage scenarios, relying on predictive models for increasing network management efficiency. However, its adoption requires additional efforts, such as mastering the terminology, tools, and newly required steps of data importing and preparation, all of which increase the time required for experimentation. Therefore, we aimed to au...
Machine Learning Model for Measuring Cyclomatic Complexity of Source Code
Published in: International Conference On Intelligent Computing, Communications, Networking And Services (ICCNS2023)
Jun 20, 2023
Software developers strive to build high-performance and quality software with a very high degree of coherence among its components, simplified structure, and reduced complexity. A cyclomatic complexity measure is used to identify areas of code that are difficult to maintain or debug by determining how many independent paths, conditional and iteration statements there are through the source code. Using AI, it is possible to estimate and measure the cyclomatic complexity of source code by analyz...
Using natural language processing for programming language code classification with Multinomial Naive Bayes
Published in: Revue d'Intelligence Artificielle
Jun 01, 2023
Ayman Odeh
Munther Odeh
Hussein Odeh
Nada Odeh
Classifying Programming Languages scripts is very important task for several reasons such as: automated analysis, code maintenance, code search, quality assurance, and code understanding; this process is similar to processing natural languages, especially high-level languages like Python, Java, C#, C, C++, PHP, JavaScript, and others. Leveraging natural language processing concepts, this research explores the application of the Multinomial Naïve Bayes (MNB) algorithm to identify and classify pr...