EMITTER International Journal of Engineering Technology

Lite-FBCN: Lightweight Fast Bilinear Convolutional Network for Brain Disease Classification from MRI Image

Fri, 20 Dec 2024 12:41:15 +0000

Achieving high accuracy with computational efficiency in brain disease classification from Magnetic Resonance Imaging (MRI) scans is challenging, particularly when both coarse and fine-grained distinctions are crucial. Current deep learning methods often struggle to balance accuracy with computational demands. We propose Lite-FBCN, a novel Lightweight Fast Bilinear Convolutional Network designed to address this issue. Unlike traditional dual-network bilinear models, Lite-FBCN utilizes a single-network architecture, significantly reducing computational load. Lite-FBCN leverages lightweight, pre-trained CNNs fine-tuned to extract relevant features and incorporates a channel reducer layer before bilinear pooling, minimizing feature map dimensionality and resulting in a compact bilinear vector. Extensive evaluations on cross-validation and hold-out data demonstrate that Lite-FBCN not only surpasses baseline CNNs but also outperforms existing bilinear models. Lite-FBCN with MobileNetV1 attains 98.10% accuracy in cross-validation and 69.37% on hold-out data (a 3% improvement over the baseline). UMAP visualizations further confirm its effectiveness in distinguishing closely related brain disease classes. Moreover, its optimal trade-off between performance and computational efficiency positions Lite-FBCN as a promising solution for enhancing diagnostic capabilities in resource-constrained and or real-time clinical environments.

From Waste to Power: Fly Ash-Based Silicone Anode Lithium-Ion Batteries Enhancing PV Systems

Kania Yusriani Amalia, Tresna Dewi, Rusdianasari Rusdianasari — Fri, 20 Dec 2024 13:22:43 +0000

Indonesia's high solar irradiance, averaging 4.8 kWh/m²/day, presents a significant opportunity to harness solar power to meet growing energy demands. Fly ash, abundant in Indonesia and rich in silicon dioxide (40-60% SiO₂), can be repurposed into high-value silicon anodes. The successful extraction of silicon from fly ash, increasing SiO₂ content from 49.21% to 93.52%, demonstrates the potential for converting industrial waste into valuable battery components. Combining these advanced batteries with PV systems improves overall efficiency and reliability. Energy charge and discharge experiments reveal high energy efficiency for silicon-anode batteries, peaking at 80.53% and declining to 67.67% after ten cycles. Impedance spectroscopy tests indicate that the S120 sample, with the lowest impedance values, is most suitable for high-efficiency applications. Photovoltaic (PV) system integration experiments show that while increased irradiance generally boosts power output, other factors like PV cell characteristics and load conditions also play crucial roles. In summary, leveraging Indonesia's solar potential with fly ash-based silicon anode batteries and advanced predictive analytics addresses energy and environmental challenges. This innovative approach enhances battery performance and promotes the circular economy by converting waste into high-value products, paving the way for a sustainable and efficient energy future.

Impact of Principal Component Analysis on the Performance of Machine Learning Models for the Prediction of Length of Stay of Patients

Jagriti Jagriti, Naresh Sharma, Sandeep Aggarwal — Fri, 20 Dec 2024 13:26:38 +0000

Patient inflow, limited resources, criticality of diseases and service quality factors have made it essential for the hospital administration to predict the length of stay (LOS) for inpatients as well as outpatients. An efficient and effective LOS prediction tool can improve the patient care and minimize the cost of service by increasing the efficiency of the system through optimal allocation of available resources in the hospital. For predicting patient’s LOS, machine learning (ML) models can have encouraging results. In this paper, five ML algorithms, namely linear regression, k- nearest neighbours, decision trees, random forest, and gradient boosting regression, have been used to predict the LOS for the patients admitted to the hospital with some medical history, laboratory measurements, and vital signs collected before admission. Additionally, the impact of principal component analysis (PCA) has been analyzed on the predictive performance of all ML algorithms. A five-fold cross-validation technique has been used to validate the results of proposed ML model. The results concluded that the RF and GB model performs better with score of 0.856 and 0.855 respectively among all the ML models without using PCA. However, the accuracy of all the models increased with the PCA except KNN and LR. The GB model when used with principal components has score and MSE approximate to 0.908 and 0.49 respectively compared to the model that incorporates with the original data. Additionally, PCA has an advantageous effect on the DT, RF and GB models. Therefore, LOS for new patients can be predicted effectively using the proposed tree-based RF and GB model with using PCA.

Early Detection of Ball Bearing Faults Using the Decision Tree Method

Fri, 20 Dec 2024 13:35:09 +0000

Bearings are one of the important components in the machine that functions as a holder and positions the shaft alignment radially when rotating. Statistics show that about 50% of failures in electric motors are related to bearings. Therefore, monitoring bearing performance and efficiency before damage occurs is necessary to avoid more serious damage and save repair costs. This research aims to build a classification model that can identify bearings in normal condition and 6 types of damage (inner crack, outer crack, ball crack, and a combination of both) using the HUST dataset. The model building process begins with collecting datasets, processing and extracting dataset features, building classification models and evaluating the models that have been made. A decision tree is a type of supervised machine learning used to categorize or make predictions based on how a previous set of questions were answered. The model is a form of supervised learning, meaning that the model is trained and tested on a set of data that contains the desired categorization. The results of the decision tree model that has been built are able to identify bearing damage with an accuracy of 94.47%.