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    Temporal cross-validation in forecasting: A case study of COVID-19 incidence using wastewater data
    (Quality and Reliability Engineering International, 2024-11) Lai, M.; Wulff, S. S.; Cao, Y.; Robinson, T. J.; Rajapaksha, R.
    Two predominant methodologies in forecasting temporal processes include traditional time series models and machine learning methods. This paper investigates the impact of time series cross-validation (TSCV) on both approaches in the context of a case study predicting the incidence of COVID-19 based on wastewater data. The TSCV framework outlined in the paper begins by engineering interpretable features hypothesized as potential predictors of COVID-19 incidence. Feature selection and hyperparameter tuning are then utilized with TSCV to identify the best features and hyperparameters for optimal model performance given a specific forecast horizon. While evidence supporting the utility of TSCV for auto-regressive integrated moving average model with exogenous variables (TS-ARIMAX) forecasts is lacking in this study, such an approach proves advantageous for gradient boosting machine forecasts (TS-GBM). In Wyoming, for instance, TS-GBM had a 34.9% improvement compared to naïve predictions, whereas GBM without TSCV only had a 15.6% improvement. However, TSCV also enhances interpretability for both TS-ARIMAX and TS-GBM models as this approach selects specific features, such as lagged values of COVID-19 cases, based on forecast performance and forecast length. Future research should work to explore the influence of stationarity and model averaging on the performance of TSCV in forecasting applications.
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    Advancements and Challenges in Real-Time Electronic Vision Technologies for Canned Fish Quality Inspection: A Comprehensive Review
    (2024) Sharmilan, Tharaga; Mahatheesan, Anis Jeluxsha
    The global demand for high-quality canned fish products has driven the adoption of advanced inspection technologies to ensure consistency, safety, and compliance with industry standards. This paper provides a comprehensive review of real-time electronic vision technologies employed in the inspection of canned fish quality. It traces the evolution of the canned fish industry from manual inspection methods to sophisticated automated systems, emphasizing the role of technologies such as hyperspectral imaging, machine learning algorithms, and electronic vision systems. The effectiveness of these technologies in detecting defects, assessing quality parameters, and maintaining product integrity is critically analyzed. Despite their benefits, challenges such as high costs, the need for specialized skills, and integration complexities with existing production processes are significant barriers. This review addresses these challenges and proposes solutions, including cost-reduction strategies, workforce training, and the development of adaptable systems. The paper concludes by outlining future research directions, particularly in validating these technologies in real-world scenarios and enhancing their accessibility to the industry. The findings offer valuable insights for researchers and industry stakeholders aiming to advance the quality control of canned fish products through innovative technological solutions.
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    Advancements and Challenges in Real-Time Electronic Vision Technologies for Canned Fish Quality Inspection: A Comprehensive Review
    (European Modern Studies Journal, 2024-09) Mahatheesan, A. J.; Sharmilan, T.
    The global demand for high-quality canned fish products has driven the adoption of advanced inspection technologies to ensure consistency, safety, and compliance with industry standards. This paper provides a comprehensive review of real-time electronic vision technologies employed in the inspection of canned fish quality. It traces the evolution of the canned fish industry from manual inspection methods to sophisticated automated systems, emphasizing the role of technologies such as hyperspectral imaging, machine learning algorithms, and electronic vision systems. The effectiveness of these technologies in detecting defects, assessing quality parameters, and maintaining product integrity is critically analyzed. Despite their benefits, challenges such as high costs, the need for specialized skills, and integration complexities with existing production processes are significant barriers. This review addresses these challenges and proposes solutions, including cost-reduction strategies, workforce training, and the development of adaptable systems. The paper concludes by outlining future research directions, particularly in validating these technologies in real-world scenarios and enhancing their accessibility to the industry. The findings offer valuable insights for researchers and industry stakeholders aiming to advance the quality control of canned fish products through innovative technological solutions.
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    Electronic Technologies for Quality Control in the Biscuit Manufacturing Process
    (Lomaka & Romina Publisher, 2024) Lakshani, K.W.I.; Tharaga, Sharmilan
    By 2030, the biscuit industry may go global due to advancements in electronic tools like eNose, eTongue, and eVision. This shift is governed by precision, productiveness, and regulatory compliance. Ultimately, the automation increase is driven by this consequence. This article will critically look at the issues and benefits arising within the biscuit production field after the shift towards the use of electronic control systems. It analyses the present situation and figures out the ineffectiveness on the part of conventional tools in solving the problem as it currently exists and shows how electronic instruments can be better in aiding visual and sensory inspections. While there have been remarkable achievements, these are persisting, of course, and they include high investment costs, specific skills requirements, and less flexibility when adapting to different production conditions. Without thorough research and development, the challenges in the production of the electronic control systems will still stand and no technology will be created to resolve the problems of the system. This study further reaffirms the need for the invention of modern and improved quality control processes for biscuit manufacturing plants. Through identifying previous methods and approaches and, the advantageous features of each, as well as highlighting shortcomings of current quality control strategies, this paper serves as an effective driving force for the future evolution and further improvement of quality control practices during biscuit production. Comprehensive product evaluation is attended to by employed approaches that analyse future benefits and opportunities as well as drawbacks and risks of the application of electronic quality control systems in the biscuit industry.
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    Microbial Remediation Technologies for Mining Waste Management
    (Springer, Singapore, 2024) Samarasekere, P.W.
    Mining activities have significantly contributed to pollution and environmental degradation, generating vast amounts of waste that pose substantial risks to ecosystems. Conventional remediation methods often fail to address the complex nature of pollutants in mining wastes. Alternative approaches, such as microbial remediation, have emerged as promising solutions for sustainable remediation of contaminated sites. This chapter provides a detailed overview of microbial remediation technologies specifically tailored to mining and industrial waste. It explores the diversity of microorganisms capable of degrading various pollutants commonly found in these waste, including heavy metals, organic pollutants, and toxic chemicals. Additionally, it examines factors that affect microbial activity and the optimization of remediation processes. Furthermore, it highlights the advantages, limitations, and applicability of microbial remediation techniques for different types of mining and industrial waste. The chapter also discusses the challenges and considerations regarding the real-world implementation of microbial remediation. Additionally, it reviews the synergistic effects of combining different antimicrobial approaches to enhance overall efficacy and efficiency. Overall, this chapter presents a valuable resource for interested parties seeking to understand and apply microbial remediation technologies for mining and industrial waste. By harnessing the power of microbes, these techniques offer promising prospects for restoring contaminated sites, reducing environmental impacts, and promoting sustainable development.
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    Proposed hybrid approach for three-dimensional subsurface simulation to improve boundary determination and design of optimum site investigation plan for pile foundations
    (Soils and Foundations, 2023) Oluwatuyi, O. E.; Rajapakshage, R.; Wulff, S. S.; Ng, K.
    Geological uncertainty refers to the changeability of a geomaterial category embedded in another. It arises from predicting a geomaterial category at unobserved locations using categorical data from a site investigation (SI). In the design of bridge foundations, geological uncertainty is often not considered because of the difficulties of assessing it using sparse borehole data, validating the quality of predictions, and incorporating such uncertainties into pile foundation design. To overcome these problems, this study utilizes sparse borehole data and proposes a hybrid approach of various spatial Markov Chain (spMC) models and Monte Carlo simulation to predict three-dimensional (3D) geomaterial categories and assess geological uncertainties. The 3D analysis gives realistic and comprehensive information about the site. Characteristics of the proposed hybrid approach include the estimation of transition rates, prediction of 3D geomaterial categories, and simulation of multiple realizations to propagate the uncertainties quantified by information entropy. This proposed hybrid approach leads to specific novelties that include the development of optimal SI plans to reduce geological uncertainty and the determination of geomaterial layer boundaries according to the quantified geological uncertainty. Reducing the geological uncertainties and accurately determining spatial geomaterial boundaries will improve the design reliability and safety of bridge foundations. The hybrid approach is applied to the Lodgepole Creek Bridge project site in Wyoming to demonstrate the application of the hybrid approach and the associated novelties. Outcomes are cross-validated to evaluate the geomaterial prediction accuracy of the hybrid approach.
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    Optimal Site Investigation Through Combined Geological and Property Uncertainties Analysis
    (Geotechnical and Geological Engineering, 2023) Oluwatuyi, O. E.; Ng, K.; Wulff, S. S.; Rajapakshage, R.
    Site investigation is crucial in character- izing the geomaterial profile for the design of bridge pile foundations. A site investigation plan should be conducted to maximize geomaterial information and minimize uncertainty. Thus, both geological and property uncertainties should be explicitly incorpo- rated into a site investigation plan. This leads to the question of how to choose the corresponding optimal number and location of boreholes in a multiphase site investigation plan in order to reduce these uncer- tainties. This study addresses these problems using multinomial categorical prediction and universal kriging on a random field with multiple simulations. Site investigation data for this study are taken from a bridge project in Iowa, USA, which consists of four boreholes, each within the proximity of the pile foundation location. Subsequent numbers of recom- mended boreholes and their associated locations are determined to minimize the combined uncertain- ties. The effectiveness of this combined analysis for determining an optimal site investigation plan (OSIP) is validated and compared to an analysis done solely on property uncertainty. The proposed OSIP yields a lower prediction error, improves the prediction of geomaterial type and property, and reduces the sub- surface uncertainties. The incorporation of OSIP invariably improves the design efficiency and perfor- mance of bridge pile foundations
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    Anti-Counterfeit Method for Computer Hardware using Blockchain
    (International Journal of Computer Applications, 2022) Britto, C.D.; Dias, N.G.J.
    Counterfeited computer hardware are products designed looks exactly the same as their genuine products. Most of the people are tricked by the counterfeiters using online markets. This influences the need for a secure and efficient mechanism to identify fake/counterfeited products. The proposed method is implemented using the Blockchain technology. Each Block represents a product and the hash key of that product, calculated using the specified Block attributes. The buyer details were updated by a verified retailer. Thereafter any user can check the validity of the product using the hash key and retailer name. Tampered Block is notified to the customer and then the product is invalid. This system can be upgraded by hosting the application on a web server for distribution and separating the application functions according to the user levels (Manufacturer, retailer, and buyer). Therefore, the proposed method provides a more secure and reliable way to handle computer hardware counterfeits.
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    Inherent variability assessment from sparse property data of overburden soils and intermediate geomaterials using random field approaches
    (Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2022) Oluwatuyi, O. E.; Holt, R.; Rajapakshage, R.; Wulff, S. S.; Ng, K.
    This study assesses the inherent variability in the geomaterial parameter by quantifying the parameter uncertainty and develops a site investigation plan with a low degree of uncertainty. A key research motivation was using sparse borehole data to predict a site geomaterial configuration in order to determine the design of a site investigation plan. This study develops a systematic methodology for carrying out a study of inherent variability in light of the limitations posed by borehole data. The data in this study was provided by the Iowa Department of Transportation which consisted of eight boreholes from which 92 associated SPT N-values was considered as the geomaterial parameter of interest. The systematic methodology then involved the following steps. A general linear model was employed to fit and compare various spatial covariance models with and without a nugget. These spatial covariance models were also evaluated with variograms. Predicted SPT N-values were generated using universal kriging. Simulations were performed conditionally and unconditionally to identify optimal site investigation plans. The results identified site investigation plans with reduced parameter uncertainty. The proposed approach can produce site investigation plans that target any or all geomaterial layers to reduce uncertainty with respect to any geomaterial parameter of interest.
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    Grammatical Structure Oriented Automated Approach for Surface Knowledge Extraction from Open Domain Unstructured Text
    (Journal of Information and Communication Convergence Engineering, 2022) Tissera, M.; Weerasinghe, R.
    News in the form of web data generates increasingly large amounts of information as unstructured text. The capability of understanding the meaning of news is limited to humans; thus, it causes information overload. This hinders the effective use of embedded knowledge in such texts. Therefore, Automatic Knowledge Extraction (AKE) has now become an integral part of Semantic web and Natural Language Processing (NLP). Although recent literature shows that AKE has progressed, the results are still behind the expectations. This study proposes a method to auto-extract surface knowledge from English news into a machine-interpretable semantic format (triple). The proposed technique was designed using the grammatical structure of the sentence, and 11 original rules were discovered. The initial experiment extracted triples from the Sri Lankan news corpus, of which 83.5% were meaningful. The experiment was extended to the British Broadcasting Corporation (BBC) news dataset to prove its generic nature. This demonstrated a higher meaningful triple extraction rate of 92.6%. These results were validated using the inter-rater agreement method, which guaranteed the high reliability.