Bag-Based Feature-Class Correlation Analysis for Multi-Instance Learning Application

Multi-instance Learning (MIL) is widely applied in image classification. In MIL, an image is presented as a bag. A bag consists of multi-instance which is known as patches. Irrelevant features of the image presented to the classifier affects the classification performance. Feature selection is one of the essential phases to select relevant. However, limited studies discuss the feature selection phase in MIL. Correlation between feature-class (FC) relationship is one important criterion to analyse features’ relevance. However, it cannot be performed directly in MIL. To address this gap, this study proposed the MultiBag-FCCorr feature selection technique. It consists of three steps: transformation, evaluation and fusion. The bags of feature information are acquired from summarization from different statistical central tendency measures of trimmed mean, mode and median. In feature evaluation step, extended point biserial correlation has been used to measure FC correlation and then the FC score has been analysed. The selected features are validated via two prominent classifiers (Support Vector Machine (SVM) and K-Nearest Neighbour (KNN)) on benchmark MI image datasets: UCSB Breast Cancer, Tiger, Elephant and Fox datasets. The selected features of UCSB Breast Cancer dataset were reduced to 92% number of features from the proposed technique giving the best result of average accuracy with 86.8.% using SVM and 84.5% using KNN. The average accuracy improved 6.3% using SVM and 16.4% using KNN compared without implementing the proposed feature selection. The results proved that the selected feature set improved the performance of MI image classification

Breast cancer diagnosis through an optimization-driven multispectral gamma correction (ODMGC)

The Optimization-Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi-step optimisation process that categorises grey-scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub-regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over- and under enhancement, and supports breast tumour diagnosis. The optimised-based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension-reduced significant Grey-Level Co-occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method’s superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%

Future temperature for drought prediction in Bukit Merah, Perak by using SDSM modelling

Climate change is a worldwide phenomenon that can cause many sudden changes, especially to water resources. Malaysia has experienced warming and rainfall abnormalities, especially in the last two decades, and therefore estimates of climate change in Malaysia receive a lot of attention. Global climate research is increasingly focused on severe temperature changes due to severe climatic phenomena like droughts and heat waves globally. This research aims to forecast maximum and minimum temperatures in Bukit Merah, Perak, for the years 2020-2050 and 2050-2080. This project predicted the magnitude of drought over the next 60 years, and the data collected is aid hydrologic modelling in the Bukit Merah, Perak. The findings analysed and addressed to estimate the future drought that may occur in the next 60 years. SDSM has been widely used for downscaling climate variables such as precipitation, rainfall, and temperature among statistical downscaling methods. Statistical downscaling provides local scale statistics, which is useful in climate change analysis. It involves the use of past weather data for a longer time to collect large-scale variables. Therefore, it was necessary to utilize the Root mean Square error (RSME) and the coefficient R2 to evaluate the performance of historical and simulated data from the model during the calibration and validation periods. The coefficient of determination (R2) during calibration and validation for maximum temperature were 0.89 and 0.67, while for minimum temperature, the value for calibration and validation is 0.83 and 0.85. Therefore, the drought forecasting is an early warning system that the most crucial stages for drought management that will arise in the futur

Chemical characterization of asphalt binder containing palm oil mill sludge

Modification of asphalt binder is continuously explored due to its escalating cost and increasing demand for this non-renewable material. As an alternative, the potential of waste materials was assessed for use as a modifier in asphalt binder. This study focuses on investigating the physical properties of unmodified and modified asphalt binders, with a specific emphasis on the chemical properties of palm oil mill sludge (POMS) modified asphalt binder. In this investigation, the control sample employed was PEN 60/70, while the POMS content ranged from 0% to 5% with an increment of 1%. Penetration and softening point tests were conducted on the POMS-modified binder, and Fourier Transform Infrared Spectroscopy (FTIR) tests were conducted to assess the chemical properties of both un-aged and short-term aged asphalt binders. The results revealed that the addition of POMS modified the asphalt binder by inducing a softening effect proportional to the percentage of POMS. The aging process was found to be significantly delayed in the POMS-modified binder with increasing POMS content

Mobile weather station with drones

In contemporary weather monitoring, the fusion of innovative technologies like mobile weather stations and drones offers unprecedented capabilities for real-time data collection and analysis. This project aims to create a user-friendly dashboard for intuitive weather monitoring by combining the agility of drones with ground-based weather stations. The objectives include displaying crucial real-time information such as temperature, humidity, and barometric pressure, while ensuring an accessible interface for efficient data interpretation and analysis. The system utilizes an ESP32 microcontroller, various sensors, and the Blynk IoT platform for seamless connectivity. Through a systematic hardware setup and software integration, the prototype demonstrates the feasibility of a mobile weather station with drone collaboration, showcasing its potential in diverse applications, from agriculture to environmental research. The project's success highlights the promising future of integrated technologies for advanced weather monitoring and analysi

Biological material spider silk by direct incorporation onto fiber ferrule for wavelength tunable Q-switched application

This study presents a novel structure saturable absorber (SSA) based on spider silk for wavelength tunable Q-switched erbium-doped fiber laser (EDFL) operation from S to L bands. The nonlinear optical absorption of spider silk was measured, showing a high modulation depth of 64.92%, a low saturation intensity of 0.03 MW cm−2 , and a non-saturable loss of 24%. By adjusting the polarization controller, a wavelength tunable Q-switched EDFL was achieved, with a tuning range of 64 nm from 1522 nm to 1586 nm. The Q-switched pulses had a repetition rate varying from 20.62 kHz to 6.57 kHz and a pulse width ranging from 14.02 µs to 26.30 µs, corresponding to an output power from −11.31 dBm to −9.02 dBm at the maximum pump power of 151.40 mW. The proposed SSA using spider silk offers a low-cost, eco-friendly, and high-performance solution for wide wavelength tunable Q-switched EDFL applications in optical testing, fiber communication, optical fiber sensing, and ultrafast lasers

Resilient skies: advancing climate-resilient UAVs for energy-efficient B5G communication in challenging environments

Due to severe climatic circumstances exacerbated by climate change, deploying Beyond Fifth Generation (B5G) networks is critical. Unmanned Aerial Vehicles (UAVs) have become indispensable for B5G connectivity in inclement weather conditions such as snow, fog, and rain. This paper investigates energy-efficient B5G connectivity and climate-resilient UAVs. We evaluate the performance of UAV coverage and energy efficiency at different elevation angles under various weather conditions, including snow, fog, and rain. Additionally, we discuss the challenges environments and propose solutions to improve climateresilient and energy-efficient B5G communication. Emphasizing the adverse effects of climate change on communication networks, The paper’s findings highlight the significant impact of weather conditions on UAV coverage, B5G communication networks, and energy efficiency. This research paves the way for a more resilient and sustainable future

Multidisciplinary Optimization of Axial Turbine Blade Based on CFD Modelling and FEA Analysis

The turbine blade is designed to achieve expansion at high efficiency levels. For improving the turbine efficiency, different aerodynamic design optimisations are performed. On the other hand, the aerodynamic design must be enhanced to match the mechanical design. This research proposes a novel design optimisation method for both aerodynamic and mechanical requirements. A multidisciplinary optimisation approach is used to improve the reliability of the turbine design, which included the use of Computational Fluid Dynamics (CFD) models and Finite Element Analysis (FEA). The primary objective is to guarantee that the aerodynamically optimised blade profile could efficiently withstand mechanical stress. The multidisciplinary optimisation approach is successful in reducing total equivalent pressures from 49.72 MPa to 41.73 MPa while keeping the turbine's overall efficiency at an impressive level of 80.95%. These Results highlight the effectiveness of using a multidisciplinary optimization method to successfully improve the efficiency of a turbine blade profile while simultaneously ensuring its ability to withstand the needed mechanical loads. Using a multidisciplinary optimisation method, the turbine maintains an impressively high efficiency of approximately 83%, with only a marginal reduction of 1.8% compared to the efficiency achieved solely through aerodynamic blade optimisation

Comparative Analysis of ML-Based Outlier Detection Techniques for IoT-Based Smart Energy Management Systems

With the development and advancement of ICST, data-driven technology such as the Internet of Things (IoT) and Smart Technology including Smart Energy Management Systems (SEMS) has become a trend in many regions and around the globe. There is no doubt that data quality and data quality problems are among the most vital topics to be addressed for a successful application of IoT-based SEMS. Poor data in such major yet delicate systems will affect the quality of life (QoL) of millions, and even cause destruction and disruption to a country. This paper aims to tackle this problem by searching for suitable outlier detection techniques from the many developed ML-based outlier detection methods. Three methods are chosen and analyzed for their performances, namely the K-Nearest Neighbour (KNN)+ Mahalanobis Distance (MD), Minimum Covariance Determinant (MCD), and Local Outlier Factor (LOF) models. Three sensor-collected datasets that are related to SEMS and with different data types are used in this research, they are pre-processed and split into training and testing datasets with manually injected outliers. The training datasets are then used for searching the patterns of the datasets through training of the models, and the trained models are then tested with the testing datasets, using the found patterns to identify and label the outliers in the datasets. All the models can accurately identify the outliers, with their average accuracies scoring over 95%. However, the average execution time used for each model varies, where the KNN+MD model has the longest average execution time at 12.99 seconds, MCD achieving 3.98 seconds for execution time, and the LOF model at 0.60 seconds, the shortest among the three

Morphological, TGA, and FTIR on Rigid Polyurethane Composite Laminated with Untreated and Treated Bamboo Fiber Roof Insulation

The performance of roof insulation such as polyurethane decreased due to problems such as insufficient absorption and poor thermal insulation performance, especially during rainstorms. The aims of this study are to investigate the physical property and its potential reinforced material such as rigid polyurethane doped with treated and untreated bamboo fiber composite (RPU-BF) at different ratios of 0, 25, 30, 35, and 40% of bamboo fibers as an insulation material for roof applications. The bamboo fibers were treated by using silane coupling agent treatment. The rigid polyurethane composite samples were prepared and then laminated bamboo fiber to overcome the sound problem in roofs. The physical characterization was investigated by Water Contact Angle (WCA), the morphological by Scanning Electron Microscopy (SEM), Thermo-gravimetric Analysis (TGA), and Fourier Transform Infrared Spectroscopy (FTIR) Analysis. The results showed that the treated bamboo fiber had a 192.5° water contact angle as a super hydrophobic property due to the presence of the chemical bonds Si-O-Si and Si-O-C in the silane coupling agent treatment. The morphology showed that 30% ratios of RPU-BF-T30 give the smallest pore diameter size. The peak of thermal degradation temperature of untreated and treated bamboo fiber was increased from 320°C to 350 °C with a weight loss of 80% to 50%. The treated bamboo fiber exhibited peaks at 3010–3040 cm-1 were associated with stronger Si-O-Si bonding, indicating the formation of new chemical bonds between bamboo fiber and silane coupling agent due to the ester bond from the cellulose, lignin, and hemicellulose. Thus, there was a similar trend peak in the functional chemical group in the FTIR spectrum of the RPU-BF composite. This result shows that RPU-BF composite had the potential of the optimum ratio of bamboo fiber as an insulation material for local communities and beneficial to the bamboo industry