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Conference Site: CS & AI Workshop (i-COSTE 2024)

Power System Faults Analysis, Detection, and Localization in Underground Distribution and Transmission Networks by Deploying AI-based Matlab Model/Simulink

The study integrates advanced AI algorithms into MATLAB/Simulink to enhance the accuracy and efficiency of fault detection and localization. It specifically addresses the unique challenges of underground distribution and transmission networks, which are often harder to monitor and diagnose than overhead systems. The research utilizes MATLAB/Simulink for detailed simulations, enabling robust testing and validation of fault detection and localization methods under various conditions.

Elevating 5G Applications Performance: Harnessing Beamforming with MIMO Antennas

This paper centers on the significance of using beamforming over Multiple Input Multiple Output (MIMO) at the physical layer level within a Radio Access Network (RAN) for 5G network performance.

Unveiling the Dynamics of Customer Shopping Trends Using Machine Learning Algorithm: A Comprehensive Analysis of Demographics, Purchase Behavior, and Payment Preferences

The present paper deeply investigates consumer shopping behaviour guided by the methods of machine learning. Having used the assessment of demographic parameters, purchase behaviour, and payment tendencies, this research comes up with deep-rooted consumer behaviour trends diversified across the various categories of consumer segments. The research includes an extensive database where advanced Artificial Intelligence technologies like clustering, classification, and forecasting are used to extract invaluable intelligence. The results underline certain differences in consumption patterns of clients belonging to the same demographic group which include people of different ages, genders, income levels, and geographical regions. The inclusion of machine learning technologies allows for gaining a supply of information about consumer behaviour that helps to make decisions wisely having sustainable development in the complex and competitive retail environment.

Feature enhancement and matching algorithms for material ablation measurement in high temperature wind tunnels

Aimed at the special requirements for dynamic measurement of material ablation inside high-temperature wind tunnels, a binocular stereo vision system based on straight slider rail laser projection and high-speed camera capture is designed. A feature enhancement method for ablation measurement objects in high-temperature and high-enthalpy environments is proposed, and a mathematical expression formula based on multi-line laser feature enhancement description and extraction of the light strip centerline is derived for adaptive rapid feature matching. This formula takes into account the grayscale centroid, camera frame rate, and the correlation between line laser scanning ranges, effectively reducing search complexity and dependence on high-frame-rate cameras. Experimental results show that the system can complete a 200mm scan within 1 second at a distance of 1350mm. Experiments on planar objects and spherical convex surface platform under various conditions demonstrate that the system can control the total error within 0.5mm at the normal distribution confidence levels of 1σ, 2σ, and 3σ which proving the efficiency, accuracy, and high dynamic characteristics of this method for non-contact measurement of material erosion in high-temperature wind tunnel environments.

Predicting Electricity Market Price Using Machine Learning and Quantifying Dependency Beyond Renewable Energy

We offer (i) a detailed analysis on the impact of variables beyond renewable energy sources on electricity price, and (ii) a unified machine learning-based platform that integrates other diverse factors beyond renewable energy to improve electricity price forecasting.
Our machine learning models predict electricity price by quantifying the dependency on renewable energy and other important diverse factors under unified settings.

A Comparative Analysis of Deep Learning Architectures for Efficient Brain Tumor Detection

This article studies the effectiveness of deep learning (DL) algorithms in detecting brain tumors, focusing on disorders such as “Glioma-Tumor,” “Meningioma-Tumor,” “Pituitary-Tumor,” and “No-Tumor.” Magnetic Resonance Imaging (MRI) is the primary tool for identifying brain tumors, and the paper proposes a convolutional neural network (CNN) architecture for efficient tumor detection. The study explores various CNN models, including DenseNet121, ResNet50V2, DenseNet201, EfficientNetB2, VGG16, and MobileNet, which enhance classification accuracy. The models demonstrate high precision, recall, F1-score, sensitivity, and specificity in predicting brain tumor conditions.