Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the wel

Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the wel

This quasi-experimental study investigated generative AI (GenAI) tools—Copilot for chemistry and GitHub Copilot for mathematics—on academic achievement and sustainable professional development among 160 undergraduates (40 experimental/control per department) at the University of Baghdad’s Ibn Al-Haitham College of Education for Pure Sciences (2024–2025). Non-random assignment controlled for covariates. Pre/post validated tests (α ≥ .85; 15 MCQ + 5 essay items) measured outcomes. ANOVA revealed significant gains for experimental groups (p < .001, η2 = .41, Cohen’s d = 0.72 [95% CI: 0.45–0.98]). Chemistry excelled in affective domains; mathematics in cognitive/skills. Findings affirm GenAI’s domain-specific efficacy, prov

Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s

Sustainability is a concept embodied in traditional architecture around the world since ancient times through spontaneous experimental compatibility with the environment and efficient exploitation of the natural resources of the environment according to a rapid development of trial and error over the years . Architecture was the basic means that man devised to protect him from the harsh conditions of the external environment by exploiting natural energy sources such as the sun, wind, and soil capabilitie, Many of the principles of sustainability overlapped with the social organization and human behavior of the community as it was reflected in the urban production at the level of standards of city planning and urban agglomerations as well

HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

Human relationships are shaped and affected by the place they live in. This article analyzes Lynn Nottage’s Sweat from the perspective of spatial theory, particularly using the theories of Yi-Fu Tuan and Doreen Massey, to explore the complex connection between place and human relationships. The article examines how Nottage presents the reciprocal effect between people and their setting. By examining the city, the factory, and the local community around it, the study shows how place serves as a dynamic force that shapes the characters’ identities and relationships rather than just serving as a background. While Massey’s concept of place as a product of social relations offers a framework to explore the tensions and solidarity that aris

Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s