MCD64A1 Burnt Area Dataset Assessment using Sentinel-2 and Landsat-8 on Google Earth Engine: A Case Study in Rompin, Pahang in Malaysia

This research paper intends to explore the suitability of adopting the MCD64A1 product to detect burnt areas using Google Earth Engine (GEE) in Peninsular Malaysia. The primary aim of this study is to find out if the MCD64A1 is adequate to identify the small-scale fire in Peninsular Malaysia. To evaluate the MCD64A1, a fire that was instigated in Rompin, a district of Pahang on March 2021 has been chosen as the case study in this work. Although several other burnt area datasets had also been made available in GEE, only MCD64A1 is selected due to its temporal availability. In the absence of validation information associated with the fire from the Malaysian government, public news sources are utilized to retrieve details related to the fire in Rompin. Additionally, the MCD64A1 is also validated with the burnt area observed from the true color imagery produced from the surface reflectance of Sentinel-2 and Landsat-8. From the burnt area assessment, we scrutinize that the MCD64A1 product is practical to be exploited to discover the historical fire in Peninsular Malaysia. However, additional case studies involving other locations in Peninsular Malaysia are advocated to be carried out to substantiate the claims discussed in this work.Comment: 13th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE 2023) - Accepted on 29 March 202

Stacking Ensemble Approach for Churn Prediction: Integrating CNN and Machine Learning Models with CatBoost Meta-Learner

In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors that exhibit nonlinear dependencies. In these complex scenarios, single classifiers may be unable to fully utilize the available information to capture the underlying interactions effectively. In contrast, ensemble learning that combines various base classifiers empowers a more thorough data analysis, leading to improved prediction performance. In this paper, a heterogeneous ensemble model is proposed for churn prediction in the telecom industry. The model involves exploratory data analysis, data pre-processing and data resampling to handle class imbalance. In this proposed model, multiple trained base classifiers with different characteristics are integrated through a stacking ensemble technique. Specifically, convolutional-based neural network, logistic regression, decision tree and Support Vector Machine (SVM) are considered as the base classifiers in this work. The proposed stacking ensemble model utilizes the unique strengths of each base classifier and leverages collective knowledge to improve prediction performance with a meta-learner. The efficacy of the proposed model is assessed on a real-world dataset, i.e., Cell2Cell. The empirical results demonstrate the superiority of the proposed model in churn prediction with 62.4% f1-score and 60.62% recall

Wild bitter gourd improves metabolic syndrome: A preliminary dietary supplementation trial

<p>Abstract</p> <p>Background</p> <p>Bitter gourd (<it>Momordica charantia </it>L.) is a common tropical vegetable that has been used in traditional or folk medicine to treat diabetes. Wild bitter gourd (WBG) ameliorated metabolic syndrome (MetS) in animal models. We aimed to preliminarily evaluate the effect of WBG supplementation on MetS in Taiwanese adults.</p> <p>Methods</p> <p>A preliminary open-label uncontrolled supplementation trial was conducted in eligible fulfilled the diagnosis of MetS from May 2008 to April 2009. A total of 42 eligible (21 men and 21 women) with a mean age of 45.7 ± 11.4 years (23 to 63 years) were supplemented with 4.8 gram lyophilized WBG powder in capsules daily for three months and were checked for MetS at enrollment and follow-up monthly. After supplementation was ceased, the participants were continually checked for MetS monthly over an additional three-month period. MetS incidence rate were analyzed using repeated-measures generalized linear mixed models according to the intention-to-treat principle.</p> <p>Results</p> <p>After adjusting for sex and age, the MetS incidence rate (standard error, <it>p </it>value) decreased by 7.1% (3.7%, 0.920), 9.5% (4.3%, 0.451), 19.0% (5.7%, 0.021), 16.7% (5.4%, 0.047), 11.9% (4.7%, 0.229) and 11.9% (4.7%, 0.229) at visit 2, 3, 4, 5, 6, and 7 compared to that at baseline (visit 1), respectively. The decrease in incidence rate was highest at the end of the three-month supplementation period and it was significantly different from that at baseline (<it>p </it>= 0.021). The difference remained significant at end of the 4th month (one month after the cessation of supplementation) (<it>p </it>= 0.047) but the effect diminished at the 5th and 6th months after baseline. The waist circumference also significantly decreased after the supplementation (<it>p </it>< 0.05). The WBG supplementation was generally well-tolerated.</p> <p>Conclusion</p> <p>This is the first report to show that WBG improved MetS in human which provides a firm base for further randomized controlled trials to evaluate the efficacy of WBG supplementation.</p

High Performance Field Emitters.

The field electron emission performance of bulk, 1D, and 2D nanomaterials is here empirically compared in the largest metal-analysis of its type. No clear trends are noted between the turn-on electric field and maximum current density as a function of emitter work function, while a more pronounced correlation with the emitters dimensionality is noted. The turn-on field is found to be twice as large for bulk materials compared to 1D and 2D materials, empirically confirming the wider communities view that high aspect ratios, and highly perturbed surface morphologies allow for enhanced field electron emitters.M.T.C. thanks the Oppenheimer Trust, Cambridge University, for generous financial support. This work was supported by an EPSRC Impact Acceleration grant and an Innovate UK Advanced Materials Feasibility Study award. CC thanks the EPSRC Centre for Doctoral Training in Ultra Precision.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/advs.20150031

Reissuable Biometrics Through Offline Handwritten Signature Verification

In this thesis, an ffline handwritten verification system is proposed. The system canbe viewed in image acquisition and preprocessing module, enrolment and verification modules. Image acquisition and preprocessing module is responsible for extracting signature image from a scanned document and fterwards preprocessing it for farther feature extraction

State of the art: Signature verification system

This paper presents a review of some online/dynamic and offline/static signature verification system that have been proposed from year 2000 to 2010. There are numerous signature verification systems algorithms and methods been proposed in the last decade. This paper will mainly focus on discussing the signature verification techniques from year 2000 onwards to make a novice summary and conclusion for them

Touch-Stroke Dynamics Authentication Using Temporal Regression Forest

Touch-stroke dynamics is a relatively recent behavioral biometrics. It authenticates an individual by observing his behavior when swiping a “stroke” on a smartphone or tablet. Several studies have attempted to determine the optimum authentication accuracy of classifiers, but none of them has used time series or temporal machine learning techniques. We postulate that when a user performs a series of touch strokes in a continuous manner, it can be perceived as a temporal behavior characteristic of the person. In this letter, we propose the use of a temporal regression forest to unearth this hidden but vital temporal information. By incorporating this temporal information in the authentication process, the proposed model is able to achieve average equal error rates of ~4.0% and ~2.5% on the Serwadda dataset and Frank dataset, respectively

Proposed Integration of Mobile Interactive System in the Classroom

In the twenty-first century, there are many technologies available to assist us in our daily lives. One of the most commonly owned technology is mobile technology. Due to its nature of high mobility and easier to be owned, it has become a preferred education technology. Furthermore, the conventional way of teaching is still the mainstream among other approaches, which is ineffective. Hence, the introduction of the proposed mobile interactive system is aimed to make a change in the classroom environment for altering both the teaching and learning process. Teachers’ capabilities and abilities in facilitating classes using the mobile interactive system are somehow related to their prior technological knowledge, which is also one of the elements in the TPACK framework. Moreover, the TPACK framework is meant to assess a teacher’s level of understanding about technology and how the knowledge is being utilized and applied in their teaching plan and teaching materials. As a solution, this study discovered the mapping of teachers’ efficiency, students’ engagement and students’ performance into the TPACK framework can address the efficiency of the proposed mobile interactive system as an alternative mean for a teacher to deliver and transform knowledge to students. This study aims to integrate the proposed mobile interactive system in the TPACK framework and map with teachers’ efficiency, students’ engagement and students’ performance