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The accumulation and analysis of large-scale patient data have led to breakthrough discoveries in potential flags for diseases based on pattern recognition, highlight medication efficacy, and local population health trends that would be impossible with traditional paper-based records. However, these benefits come with unique challenges posed by the application of data sharing for research and…

Neural Networks derives intelligent systems and modern autonomous applications. With the complexity introduced in today’s systems, designing architectures remains open research problem in all fields. Despite many efforts to generalize, the resources required by neural architectures remain challenge. Robots design, Smart cities, IoTs …etc. become the leading industry and driving the fourth industrial revolutions. All…

This paper presents the design and fabrication of an electromagnetically actuated PCB-based resonating scanning micro-mirror for LiDAR applications, with optimization targeted towards low-cost fabrication and a high scanning angle. Traditional silicon MEMS-based micro-mirrors, while offering high precision and compatibility with CMOS processing, are limited by fragility at low scanning frequencies and costly fabrication processes. To…

The rapid proliferation of drones across various industries has introduced significant challenges related to privacy, security, and noise pollution. Current drone detection systems, primarily based on visual and radar technologies, face limitations under certain conditions, highlighting the need for effective acoustic-based detection methods. This paper presents a unique and comprehensive dataset of drone acoustic signatures,…

Complex networked systems provide a cornucopia of network statistics, many of which relate to the temporal behaviour of subsystems, devices, or even individual protocol layers. Different and flexible visualizations can play a crucial role in discovering and making patterns, relations, and trends tangible. We developed PerfVis, a tool that visualizes timestamp data to aid in…

Deep neural networks (DNNs) have demonstrated remarkable success across a wide range of machine learning tasks. However, determining an effective network architecture, particularly the sizes of the hidden layers, remains a significant challenge and often relies on inefficient trial-and-error experimentation. In this paper, we propose an automated architecture design approach based on structurally adaptive DNNs,…

We address irregular polygon nesting on sheet materials with a lightweight evolutionary framework that operates directly in the layout space. The method formalizes multi-term fitness combining utilization, overlap penalties, spacing regularity, and local alignment, with all components normalized before aggregation. Feasibility is enforced by an AABB– SAT pipeline and validated via analytic ground-truth cases, degenerate…

Investment decision-making plays a pivotal role in shaping both individual and institutional economic outcomes. Given the increasing complexity and uncertainty in global markets, optimizing investment decisions has become essential for maximizing returns while managing risks. This work explores modern optimization approaches in investment decision-making, focusing on mathematical modeling techniques such as linear programming (LP), mixed-integer…

While prior research has focused on siloed approaches to equipment replacement, this study introduces an integrated decision-making framework that synergizes predictive maintenance (IoT/M), dynamic multi-criteria analysis (MCDM), and sustainability-driven material selection. By validating this model through cross-sector case studies and strategic operational planning across various industrial sectors. We demonstrate a 30% improvement in replacement timing…

As models based in artificial intelligence increase in sophistication, there is a higher demand for the integration of hardware components to heighten real-world implementations. Both facial feature tracking and shape-from-focus are known techniques in computer vision. However, the combination of these two elements, particularly in a cost-effective configuration, has not been extensively explored. In this…

Establishing an environment that allows for the quantitative evaluation of the ability of autonomous driving systems to respond to real-world adverse conditions is crucial to ensuring their safety and reliability. This study proposes a dynamic scenario-based simulation framework that simulates complex and sequential hazardous scenarios frequently encountered in actual road environments. The proposed scenarios are…

Ensuring the quality of higher education in Brazil presents a complex challenge, intensified by factors that directly affect students’ academic performance. The pervasive influence of social media and the overconsumption of superficial digital content undermine students’ ability to engage in deep comprehension, critical thinking, and the practical application of knowledge. Furthermore, inadequate preparation during the…

Understanding and responding to customer feedback is critical for business success. Customer response data offers valuable insights into preferences, behaviours, and sentiment. By analysing this data, businesses can optimize strategies, enhance customer experiences, and drive growth. Many analysis have been conducted in this field, while the review covers a broad range of AI and ML…

Persistent anomalies in modern photovoltaic (PV) systems present a formidable challenge, impeding optimal power output and system resilience. Artificial Intelligence (AI) has surfaced as a game-changing solution, yet existing research has merely scratched the surface of solar panel prognosis, leaving a critical void in leveraging AI’s explainable nature and active learning capabilities. This pioneering study…

This study presents a novel alternative to traditional Net Energy Metering (NEM) by proposing a set of innovative pricing schemes for solar customers participating in utility-led grid service programs through the aggregation of Distributed Energy Resources (DERs). Grounded in cooperative game theory, the proposed framework facilitates equitable and efficient value allocation among key stakeholders, namely…

Artificial Intelligence (AI) is reshaping the global shipbuilding sector, yet existing maturity models fail to capture the domain-specific complexities of this capital-intensive industry. This study reviews over 50 AI maturity models and introduces a specialized framework tailored for shipbuilding. The proposed model outlines four progressive stages—Beginner, Innovation, Integration, and Expert—across eight key dimensions: culture, resilience,…

This study aims to examine the impact of Gen AI utilization on students’ visual literacy skills using a quantitative approach and data instruments in the form of post-test scores of the control class and experimental class which are analyzed to measure the effectiveness of GEN AI in improving students’ visual literacy skills at four universities.…

Natural language processing (NLP) techniques have transformed a number of tasks in the modern age of information explosion where millions of gigabytes of data are generated every day. Despite achieving state-of-the-art performance in high-resource languages, current techniques struggle with processing under-resourced languages which are characterized by data scarcity, linguistic diversity, computational limitations, ambiguity of language…

Providing timely and meaningful feedback in photography education is challenging, particularly in large classes where manual assessment can delay skill development. This paper presents M-Stock, an AI-based automated photo evaluation system that uses Convolutional Neural Networks (CNNs) to assess student photography assignments on web browser. M-Stock evaluates both technical aspects (such as lighting, composition, and…

With the spread of inverters, the attention of designers naturally turned to 5-phase motors, due to their advantageous properties. In this regard, perhaps the most important issue in the design of such machines is the selection of the correct rotor slot number. Many articles have been published on multiphase machines, however, only few of them…

Text line segmentation from document images is a significant challenge in the field of document image analysis. It involves extracting individual text lines from Myanmar handwritten document images to enable text recognition. This task becomes particularly challenging in Myanmar handwritten documents, especially those with irregular or cursive writing styles, due to variations in line spacing,…

Despite the development of the Internet and wired networks such as fiber optics, mobile networks remain the most used thanks to the mobility they offer to the user. However, data protection in these networks is more complex because of the radio channels they use for transmission. Hence,there is a need to find more sophisticated data…

Skewing of the rotor slot of a squirrel-cage induction machine has been commonly used since the beginning. However, little guidance is given regarding the principle of the working effect of the method, but even in such rare cases, the explanation does not cover the true physical reality. Consequently no formula exists for calculation of the…

The rapid expansion of digital universities across Africa addresses the need for scalable higher education solutions, but challenges such as limited physical infrastructure and high dropout rates persist. In digital learning environments, effective interaction with educational content is crucial for student success. This article explores the transformative role of chatbots integrated into digital university platforms,…

A lightning detection system based on a large-diameter Rogowski coil and an analog integrator was developed for wind turbine applications and is presented in this paper. To accurately detect lightning current, the Rogowski coil was designed with a lower cutoff frequency of 0.1 Hz. The analog integrator, comprising an inverting active integrator, and an amplifier,…