Babylonian Journal of Machine Learning <p>The Babylonian Journal of Machine Learning (BJML) (EISSN: 3006-5429) is a specialized publication dedicated to the exploration and integration of modern machine learning methodologies. As a platform for researchers and scholars, the journal focuses on the intersection of cutting-edge advancements in machine learning. Through high-quality articles, it fosters interdisciplinary discussions aimed at propelling forward the field of machine learning research.</p> en-US Wed, 03 Jan 2024 06:18:54 +0000 OJS 60 A Proposed Method of Gesture-controlled presentation software design <p>This paper introduces an innovative method for developing a presentation application that empowers users to seamlessly control slide transitions and other essential actions through intuitive hand gestures. The approach integrates sophisticated computer vision algorithms capable of real-time gesture detection and interpretation from a standard webcam feed. Furthermore, machine learning techniques personalize the system to individual users' unique gestures, enhancing usability and accuracy. The proposed method is a groundbreaking innovation that seamlessly integrates with existing presentation tools. Furthermore, the research delves into cross-device synchronization, enabling a cohesive presentation experience. To ensure optimal usability and performance, we follow established software engineering principles, resulting in a user-friendly interface and an efficiently structured codebase. This paper comprehensively guides the design, implementation, and potential of this gesture-controlled presentation software.</p> Nadia Mahmood Hussien, Yasmin Makki Mohialden , wurood A. jbara Copyright (c) 2024 Nadia Mahmood Hussien, Yasmin Makki Mohialden , wurood A. jbara Mon, 01 Apr 2024 00:00:00 +0000 A Short Review on Supervised Machine Learning and Deep Learning Techniques in Computer Vision <p>In last years, computer vision has shown important advances, mainly using the application of supervised machine learning (ML) and deep learning (DL) techniques. The objective of this review is to show a brief review of the current state of the field of supervised ML and DL techniques, especially on computer vision tasks. This study focuses on the main ideas, advantages, and applications of DL in computer vision and highlights their main concepts and advantages. This study showed the strengths, limitations, and effects of computer vision supervised ML and DL techniques.</p> Ahmed Adil Nafea, Saeed Amer Alameri , Russel R Majeed, Meaad Ali Khalaf , Mohammed M AL-Ani Copyright (c) 2024 Ahmed Adil Nafea, Saeed Amer Alameri , Russel R Majeed, Meaad Ali Khalaf , Mohammed M AL-Ani Sun, 11 Feb 2024 00:00:00 +0000 Green Building Techniques: Under The Umbrella of the Climate Framework Agreement <p>Various green building rating systems have been devised to assess the sustainability levels of buildings, offering a standardized approach to evaluate their environmental impact. However, adapting these existing methods to diverse regions requires addressing additional considerations, such as distinct climatic conditions and regional variations. This study delves into a comprehensive exploration of widely utilized environmental building assessment methodologies, including BREEAM, LEED, SB-Tool, CASBEE, GRIHA, and Eco-housing. A new building environmental assessment scheme tailored to the global landscape is needed due to limitations of existing assessment schemes. A framework based on principal component analysis is introduced to develop this new scheme. PCA applied to a dataset of many responses on building sustainability revealed nine key components, including site selection, environmental impact, building resources and re-use, building services and management, innovative construction techniques, environmental health and safety, mechanical systems, indoor air quality, and economic considerations. A framework for sustainable building development in world is proposed. The study provides insights for designers and developers in developing countries, offering a roadmap for achieving green development. The framework prioritizes key components for a nuanced evaluation of sustainability in building projects, contributing to the global discourse on environmentally responsible construction practices.</p> Ali Saleh, Noah Saleh, Obed Ali, Raed Hasan, Omar Ahmed , Azil Alias, Khalil Yassin Copyright (c) 2024 Ali Saleh, Noah Saleh, Obed Ali, Raed Hasan, Omar Ahmed , Azil Alias, Khalil Yassin Wed, 10 Jan 2024 00:00:00 +0000 Artificial Intelligence Predictions in Cyber Security: Analysis and Early Detection of Cyber Attacks <p>&nbsp;</p> <p>The landscape of cyber-attacks has changed due, to the upward push of digitalization and interconnected structures. This necessitates the need for revolutionary techniques to emerge as aware of and mitigate these threats at a degree. This studies delves into the correlation amongst cyber security and artificial intelligence (AI) with a focus on how AI can decorate detection of cyber-attacks via assessment, prediction and different strategies. By harnessing machine mastering, neural networks and records analytics predictive models driven with the useful resource of AI have emerged as an approach to deal with the ever evolving demanding situations posed through cyber threats. The number one goal of this observe is to look at the effectiveness of AI powered prediction fashions, in cyber security. It ambitions to evaluate how nicely those AI based systems carry out as compared to cyber security techniques emphasizing their capability to proactively locate and mitigate cyber threats as a way to minimize their effect. Additionally ability obstacles and ethical issues associated with AI based cyber security answers are also discussed. Also using AI algorithms to Analysis and Early Detection of Cyber Attacks using python programming language. The research's conclusions are extremely important for the field of cyber security since they provide information about how threat mitigation and incident response will develop in the future. This research helps to develop cutting-edge cyber security solutions by addressing the dynamic and constantly-evolving landscape of cyber threats.</p> Meaad Ali Khalaf, Amani Steiti Copyright (c) 2024 Meaad Ali Khalaf, Amani Steiti Thu, 09 May 2024 00:00:00 +0000 Image Enhancement using Convolution Neural Networks <p>The research presents a comprehensive exploration of the topic of image enhancement using convolutional neural networks (CNN).The research goes deeper into the advanced field of image processing based on the use of neural networks to automatically and efficiently improve the quality and detail of images. The thesis shows that convolutional neural networks are one of the types of deep neural networks, which are specially designed to gain knowledge from big data and extract complex features and patterns found in images. The different layers of the grid are discussed in detail, dealing with images incrementally and extracting different attributes in each layer. The research also highlights CNN's ability to detect, learn and improve important details found in images through convolutions, filtering and data aggregation processes. The proposed CNN image enhancement model was developed and tested on both medical and normal images. The images were optimized using the proposed model and compared with other models. Various quality measures were used to evaluate the results. The results showed that the proposed model can significantly improve the quality of images.</p> Hasan Ahmed Salman, Ali Kalakech Copyright (c) 2024 Hasan Ahmed Salman, Ali Kalakech Thu, 25 Jan 2024 00:00:00 +0000 Intrusion Detection System Based on Machine Learning Algorithms:( SVM and Genetic Algorithm) <p>The widespread utilization of the internet and computer systems has resulted in notable security concerns, characterized by a surge in intrusions and vulnerabilities. Malicious users manipulate internal systems, resulting in the exploitation of software flaws and default setups.&nbsp;&nbsp; With the integration of the internet into society, there is an emergence of new risks such as viruses and worms, which highlights the importance of implementing robust security measures.&nbsp;&nbsp; Intrusion detection systems (IDS) are security technologies utilized to monitor and analyze network traffic or system activity with the purpose of identifying hostile behavior.&nbsp;&nbsp; This article presents a proposed method for detecting intrusion in network traffic using a hybrid approach, which combines a genetic algorithm and an SVM algorithm.&nbsp;&nbsp; The model underwent training and testing on the KDDCup99 dataset, with a reduction in features from 42 to 29 using the hybrid approach.&nbsp;&nbsp; The results demonstrated that throughout the system testing, it exhibited a remarkable accuracy of 0.999. Additionally, it achieved a true positive value of 0.9987 and a false negative rate of 0.012.</p> Abdulazeez Alsajri, Amani Steiti Copyright (c) 2023 Abdulazeez Alsajri, Amani Steiti Wed, 18 Jan 2023 00:00:00 +0000