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

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

A Novel Technology Stack for Automated Road Quality Assessment Framework using Deep Learning Techniques

Road infrastructure plays a pivotal role in supporting societal, economic, and cultural progress. The capacity of a road refers to its ability to handle vehicular volume. Inadequate road capacity and the presence of defects like potholes and cracks result in suboptimal travel conditions and pose significant safety risks for drivers, cyclists, and pedestrians. The regular evaluation of these road quality aspects is essential for effective maintenance. However, current methods for assessing road capacity are time-consuming, subjective, and heavily reliant on manual labor. Moreover, existing deep learning-based approaches for detecting road defects often lack accuracy. To overcome these challenges, a fully automated and accurate system for evaluating road quality is imperative. Thus, the objective of this research work is to propose a novel technology stack for a comprehensive Automated Road Quality Assessment (ARQA) framework designed to assess road quality. The experimental findings demonstrate that the suggested vehicle detection and pothole detection methods work effectively and exhibit enhancements of 18% and 6%, respectively, in comparison to existing approaches

Formulate an Adaptive Technique to Validate Near Field Communication Technology using Attributed Graph Grammar-AGG tool

Recently, developments in such Near field Communication Technology (NFC) and associated integrated devices have pace out an acceleration the demand of transactions in electronic devices system such wearable readers which have short-power consumption and proficiency in high transferring data. NFC has showed the alternative active key solution to existing smart electronic devices that depend on huge batteries to supply the energy in paying bills though such devices. NFC widely utilized via wireless power transfer, where devices share and connect with each other, to increase the efficiency of transferring data, with high superb properties such as permeability. NFC has emerged as a powerful technology and its apparatus devices. The ongoing of NFC growth could be related to its extensibility and more sustainable. NFC can deliver different shapes at providing software application capabilities to connect with each other. NFCs demonstrates an adaptable method to enhance and offer current elements accessible to applications across the Internet.  NFCs are a new fad and modern technology that shares embedded and ubiquitous devices with others, various corporations are finding the advantage by the NFC. This research will propose a formulation technique to explore and adapt an approach of developing and validating the NFC which permits to exchange data via technology wireless (NFC) between the parties. This technique will facilitate the feasibility of having e.g., a system to reduce of losing duration time and cost for users, as well as, an agile technique for validating the suggested approach via Attributed Graph Grammar (AGG) tools

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

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

Multistatic Passive Radar for drone detection based Random Finite State

Considering the implication of radar sensors in our daily life and environment. Localizing and identifying drones are becoming a research with  a greater focus in recent years.  Consequently, when an unmanned aerial vehicle is used with bad intention, this can lead to a serious public safety and privacy probem. This study investigate pratical use of spectrum range for multistatic passive radar (MSPR) signal processing . Firstly, signal processing is performed after MSPR sensing detection range ,this include multipath energy detection, reference signal extraction, and receiving antenna configuration.  Secondly, based on the MSPR nature, the reference signal is extracted and analyzed. In addition,taking into consideration the vulnerability of passive radar when comes to a moving target localization and real time tracking detection ,a novel method for spectrum sensing and detection which relies on Gaussian filter is proposed. The main goal is to optimize the use of the reference signal extracted with minimum interference as the shared reference signal in spectrum sensing. This will improve the system detection capability and spectrum access. Finally, a recursive method based on Bernoulli random filter is proposed, this takes consideration of drone’s present and unknown states based on time. Moreover, a system is developed meticulously to track and enhance detection of the target. A careful result of the experiment demonstrated that spectral detection can be achieved accurately even when the drone is moving while chasing its position. It shows that Cramer Rao lower error bounds remains significantly within 3% range

Development of DOAS System for Hazardous Methane Detection in the Near-Infrared Region

Methane (CH4) is a powerful greenhouse gas that greatly contributes to global warming. It is also very combustible, which means it has a large danger of causing explosions. It is crucial to tackle methane emissions, especially those arising from oil and gas extraction processes like transit pipes. An area of great potential is the advancement of dependable sensors for the detection and reduction of methane leaks, with the aim of averting dangerous consequences. An open-path differential optical absorption spectroscopy (DOAS) system was described in this paper for the purpose of detecting CH4 gas emission at a moderate temperature. An in-depth examination of the absorption lines was conducted to determine the optimal wavelength for measurement. The Near Infrared (NIR) region was identified as the most suitable wavelength for detecting methane. Multiple measurements were conducted at different integration times (1 second, 2 seconds, and 3 seconds) to ensure reliability and determine the optimal integration time for the CH4 detection system. The DOAS system has the capability of precisely detecting methane concentrations at 1M ppm in the NIR region with a quick integration time of 2 seconds. &nbsp

Implementation of Portable Ultrasound for Heart Disease Detection Using Cloud Computing-Based Machine Learning

Heart disease remains one of the leading causes of death globally, including in Indonesia. Cardiovascular disease is the leading cause of death worldwide, resulting in a significant number of fatalities. In Indonesia, access to specialized heart examination services is limited, requiring patients to visit large hospitals equipped with specialized facilities. Echocardiographic examinations using ultrasound can measure various heart parameters, such as hemodynamics, heart mass, and myocardial deformation. Portable ultrasound devices have emerged, enabling flexible and effective heart examinations. These devices capture video data of the patient's heart condition. The data undergoes image preprocessing involving median filtering, high-boost filtering, morphological operations, thresholding, and Canny filtering. Segmentation is performed using region filters, collinear filters, and triangle equations. Tracking utilizes the Optical Flow Lucas-Kanade method, and feature extraction employs Euclidean distance and trigonometric equations. The classification stage uses Support Vector Machine (SVM). Video data is transmitted via a mobile application to the cloud, where all stages from preprocessing to classification are conducted on cloud servers. The classification results are then sent back to the mobile application. The proposed model achieved an accuracy rate of 86% with a standard deviation of 0.09, indicating that the detection system performs effectively

Evaluating YOLOv5s and YOLOv8s for Kitchen Fire Detection: A Comparative Analysis

Accurate and timely detection of kitchen fires is crucial for enhancing safety and reducing potential damage. This paper discusses comparative analysis of two cutting-edge object detection models, YOLOv5s and YOLOv8s, focusing on each performance in the critical application of kitchen fire detection. The performance of these models is evaluated using five main key metrics including precision, F1 score, recall, mean Average Precision across various thresholds (mAP50-95) and mean Average Precision at 50 percent threshold (mAP50). Results indicate that YOLOv8s significantly outperforms YOLOv5s in several metrics. YOLOv8s achieves a recall of 0.814 and an mAP50 of 0.897, compared to YOLOv5s' recall of 0.704 and mAP50 of 0.783. Additionally, YOLOv8s attains an F1 score of 0.861 and an mAP50-95 of 0.465, whereas YOLOv5s records an F1 score of 0.826 and mAP50-95 of 0.342. However, YOLOv5s shows a higher precision of 0.952 compared to YOLOv8s' 0.914. This detailed evaluation underscores YOLOv8s as a more effective model for precise fire detection in kitchen settings, highlighting its potential for enhancing real-time fire safety systems. Additionally, by offering the future work of integration of sensors with latest YOLO involvement can further optimize efficiency and fast detection rate

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

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

Performance Evaluation of mm-Wave Based System in GFDM- 5G and Beyond Channel Model with Dust Storm Scenario

Telecommunication has made tremendous improvements in terms of bandwidth, requiring good frequency location, high data rates, and wideband spectrum availability. One solution to these requirements is the millimeter wave frequency band of 30 GHz. However, communication in this band is facing new challenges due to climate effects such as humidity, dust storms, and temperature. For fifth-generation (5G) mobile networks and beyond, Generalized Frequency Division Multiplexing (GFDM) has been proposed as a compelling candidate to substitute Orthogonal Frequency Division Multiplexing (OFDM). The GFDM's ability to adapt the block size and type of pulse shaping filters enables it to meet various crucial requirements, including low latency, low Out-Of-Band(OOB) radiation, and high data rates. This paper evaluated the overall GFDM performance and investigated the Bit Error Rate (BER) across a Rayleigh channel under various weather conditions. The simulation results show that GFDM outperforms the current OFDM candidate system. Also, GFDM offers better resistance to the Rayleigh channel with moderate and heavy dust storms in terms of BER