Search results for: predictive modlleing

Authors: Ali Kazemi

Abstract:

Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Nordila Ahmad, Thamer Mohammad, Bruce W. Melville, Zuliziana Suif

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Current methods for predicting local scour at wide bridge piers, were developed on the basis of laboratory studies and very limited scour prediction were tested with field data. Laboratory wide pier scour equation from previous findings with field data were presented. A wide range of field data were used and it consists of both live-bed and clear-water scour. A method for assessing the quality of the data was developed and applied to the data set. Three other wide pier-scour equations from the literature were used to compare the performance of each predictive method. The best-performing scour equation were analyzed using statistical analysis. Comparisons of computed and observed scour depths indicate that the equation from the previous publication produced the smallest discrepancy ratio and RMSE value when compared with the large amount of laboratory and field data.

Keywords: field data, local scour, scour equation, wide piers

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Authors: Neda Valizadeh, Soudabeh Shafiee Ardestani, Arvin Najafi

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Objectives: The aim of this study is to evaluate the association of mid-regional pro-atrial natriuretic peptide (MRproANP), procalcitonin (PCT), proendothelin-1 (proET-1) levels with sepsis severity in Emergency ward patients. Materials and Methods: We assessed the predictive value of MRproANP, PCT, copeptin, and proET-1 in early sepsis among patients referring to the emergency ward with a suspected sepsis. Results-132 patients were enrolled in this study. 45 (34%) patients had a final diagnosis of sepsis. A higher percentage of patients with definite sepsis had systemic inflammatory response syndrome (SIRS) criteria at initial visit in comparison with no-sepsis patients (P<0.05) and were admitted to the hospital (P<0.05). PCT levels were higher in sepsis patients [P<0.05]. There was no significant differences for MRproANP or proET-1 in sepsis patients (P=0.47). Conclusion: A combination of SIRS criteria and PCT levels is beneficial for the early sepsis diagnosis in emergency ward patients with a suspicious infection disease.

Keywords: emergency, prolactin, sepsis, biomarkers

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Authors: Evisa Mitrou, Nicholas Tsitsianis, Supriya Shinde

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In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affects the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients.

Keywords: BDA-clients, BDA-vendors, big data analytics, financial performance

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Authors: Atefeh Esmailnejad, Gholamreza Nikbakht Brujeni

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Major histocompatibility complex (MHC) is one of the best characterized genetic regions associated with immune responses and controlling disease resistance in chicken. Association of the MHC with a wide range of immune responses makes it a valuable predictive factor for the disease pathogenesis and outcome. In this study, the association of MHC with cell-mediated immune responses was analyzed in commercial broiler chicken. The tandem repeat LEI0258 was applied to investigate the MHC polymorphism. Cell-mediated immune response was evaluated by peripheral blood lymphocyte proliferation assay using MTT method. Association study revealed a significant influence of MHC alleles on cellular immune responses in this population. Alleles 385 and 448 bp were associated with elevated cell-mediated immunity. Haplotypes associated with improved immune responses could be considered as candidate markers for disease resistance and applied to breeding strategies.

Keywords: MHC, cell-mediated immunity, broiler, chicken

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Authors: Natcha Lueangapapong, Chariya Chuthapisith, Lunliya Thampratankul

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Background: There is a need to find an appropriate tool to help healthcare providers determine sleep problems in children for early diagnosis and management. The Japanese Sleep Questionnaire for Preschoolers (JSQ-P) is a parent-reported sleep questionnaire that has good psychometric properties and can be used in the context of Asian culture, which is likely suitable for Thai children. Objectives: This study aimed to translate and validate the Japanese Sleep Questionnaire for Preschoolers (JSQ-P) into a Thai version and to evaluate factors associated with sleep disorders in preschoolers. Methods: After approval by the original developer, the cross-cultural adaptation process of JSQ-P was performed, including forward translation, reconciliation, backward translation, and final approval of the Thai version of JSQ-P (TH-JSQ-P) by the original creator. This study was conducted between March 2021 and February 2022. The TH-JSQ-P was completed by 2,613 guardians whose children were aged 2-6 years twice in 10-14 days to assess its reliability and validity. Content validity was measured by an index of item-objective congruence (IOC) and a content validity index (CVI). Face validity, content validity, structural validity, construct validity (discriminant validity), criterion validity and predictive validity were assessed. The sensitivity and specificity of the TH-JSQ-P were also measured by using a total JSQ-P score cutoff point 84, recommended by the original JSQ-P and each subscale score among the clinical samples of obstructive sleep apnea syndrome. Results: Internal consistency reliability, evaluated by Cronbach’s α coefficient, showed acceptable reliability in all subscales of JSQ-P. It also had good test-retest reliability, as the intraclass correlation coefficient (ICC) for all items ranged between 0.42-0.84. The content validity was acceptable. For structural validity, our results indicated that the final factor solution for the Th-JSQ-P was comparable to the original JSQ-P. For construct validity, age group was one of the clinical parameters associated with some sleep problems. In detail, parasomnias, insomnia, daytime excessive sleepiness and sleep habits significantly decreased when the children got older; on the other hand, insufficient sleep was significantly increased with age. For criterion validity, all subscales showed a correlation with the Epworth Sleepiness Scale (r = -0.049-0.349). In predictive validity, the Epworth Sleepiness Scale was significantly a strong factor that correlated to sleep problems in all subscales of JSQ-P except in the subscale of sleep habit. The sensitivity and specificity of the total JSQ-P score were 0.72 and 0.66, respectively. Conclusion: The Thai version of JSQ-P has good internal consistency reliability and test-retest reliability. It passed 6 validity tests, and this can be used to evaluate sleep problems in preschool children in Thailand. Furthermore, it has satisfactory general psychometric properties and good reliability and validity. The data collected in examining the sensitivity of the Thai version revealed that the JSQ-P could detect differences in sleep problems among children with obstructive sleep apnea syndrome. This confirmed that the measure is sensitive and can be used to discriminate sleep problems among different children.

Keywords: preschooler, questionnaire, validation, Thai version

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Authors: Mark Harmon, Abdolghani Ebrahimi, Patrick Lucey, Diego Klabjan

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In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain-specific knowledge. Although intuitive, recent work in deep learning has shown, this approach is prone to missing important predictive features. To circumvent this issue, we present a convolutional neural network (CNN) approach where we initially represent the multiagent behavior as an image. To encode the adversarial nature of basketball, we use a multichannel image which we then feed into a CNN. Additionally, to capture the temporal aspect of the trajectories, we use “fading.” We find that this approach is superior to a traditional FFN model. By using gradient ascent, we were able to discover what the CNN filters look for during training. Last, we find that a combined FFN+CNN is the best performing network with an error rate of 39%.

Keywords: basketball, computer vision, image processing, convolutional neural network

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Authors: Obe Olumide Olayinka, Victor Balanica, Eugen Neagoe

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The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: neural network, hypertension, data set, training set, supervised learning

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Authors: Amir Taghizadeh Vahed, Adithya Thaduri

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Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

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Authors: Jie Lin, Yiwen Pan, Li Zhang, Zhangyong Xia

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Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipids, immune cells, and extracellular matrix in the arterial walls. This pathological process can lead to the formation of plaques that can obstruct blood flow and trigger various cardiovascular diseases such as heart attack and stroke. The underlying molecular mechanisms still remain unclear, although many studies revealed the dysfunction of endothelial cells, recruitment and activation of monocytes and macrophages, and the production of pro-inflammatory cytokines and chemokines in atherosclerosis. This study aimed to identify hub genes involved in the progression of atherosclerosis and to analyze their biological function in silico, thereby enhancing our understanding of the disease’s molecular mechanisms. Through the analysis of microarray data, we examined the gene expression in media and neo-intima from plaques, as well as distant macroscopically intact tissue, across a cohort of 32 hypertensive patients. Initially, 112 differentially expressed genes (DEGs) were identified. Subsequent immune infiltration analysis indicated a predominant presence of 27 immune cell types in the atherosclerosis group, particularly noting an increase in monocytes and macrophages. In the Weighted gene co-expression network analysis (WGCNA), 10 modules with a minimum of 30 genes were defined as key modules, with blue, dark, Oliver green and sky-blue modules being the most significant. These modules corresponded respectively to monocyte, activated B cell, and activated CD4 T cell gene patterns, revealing a strong morphological-genetic correlation. From these three gene patterns (modules morphology), a total of 2509 key genes (Gene Significance >0.2, module membership>0.8) were extracted. Six hub genes (CD36, DPP4, HMOX1, PLA2G7, PLN2, and ACADL) were then identified by intersecting 2509 key genes, 102 DEGs with lipid-related genes from the Genecard database. The bio-functional analysis of six hub genes was estimated by a robust classifier with an area under the curve (AUC) of 0.873 in the ROC plot, indicating excellent efficacy in differentiating between the disease and control group. Moreover, PCA visualization demonstrated clear separation between the groups based on these six hub genes, suggesting their potential utility as classification features in predictive models. Protein-protein interaction (PPI) analysis highlighted DPP4 as the most interconnected gene. Within the constructed key gene-drug network, 462 drugs were predicted, with ursodeoxycholic acid (UDCA) being identified as a potential therapeutic agent for modulating DPP4 expression. In summary, our study identified critical hub genes implicated in the progression of atherosclerosis through comprehensive bioinformatic analyses. These findings not only advance our understanding of the disease but also pave the way for applying similar analytical frameworks and predictive models to other diseases, thereby broadening the potential for clinical applications and therapeutic discoveries.

Keywords: atherosclerosis, hub genes, drug prediction, bioinformatics

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Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

Procedia PDF Downloads 779

Authors: A. Delimitsou, C. Gouedard, E. Konstanta, A. Koletis, S. Patera, E. Manou, K. Spaho, S. Murray

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Background: There remains a lack of International Standard Control Reference materials for Next Generation Sequencing-based approaches or device calibration. We have designed and validated dsDNA biomimetic reference materials for targeted such approaches incorporating proprietary motifs (patent pending) for device/test calibration. They enable internal single-sample calibration, alleviating sample comparisons to pooled historical population-based data assembly or statistical modelling approaches. We have validated such an approach for BRCA Copy Number Variation analytics using iQRS™-CNVSUITE versus Mixed Ligation-dependent Probe Amplification. Methods: Standard BRCA Copy Number Variation analysis was compared between mixed ligation-dependent probe amplification and next generation sequencing using a cohort of 198 breast/ovarian cancer patients. Next generation sequencing based copy number variation analysis of samples spiked with iQRS™ dsDNA biomimetics were analysed using proprietary CNVSUITE software. Mixed ligation-dependent probe amplification analyses were performed on an ABI-3130 Sequencer and analysed with Coffalyser software. Results: Concordance of BRCA – copy number variation events for mixed ligation-dependent probe amplification and CNVSUITE indicated an overall sensitivity of 99.88% and specificity of 100% for iQRS™-CNVSUITE. The negative predictive value of iQRS-CNVSUITE™ for BRCA was 100%, allowing for accurate exclusion of any event. The positive predictive value was 99.88%, with no discrepancy between mixed ligation-dependent probe amplification and iQRS™-CNVSUITE. For device calibration purposes, precision was 100%, spiking of patient DNA demonstrated linearity to 1% (±2.5%) and range from 100 copies. Traditional training was supplemented by predefining the calibrator to sample cut-off (lock-down) for amplicon gain or loss based upon a relative ratio threshold, following training of iQRS™-CNVSUITE using spiked iQRS™ calibrator and control mocks. BRCA copy number variation analysis using iQRS™-CNVSUITE™ was successfully validated and ISO15189 accredited and now enters CE-IVD performance evaluation. Conclusions: The inclusion of a reference control competitor (iQRS™ dsDNA mimetic) to next generation sequencing-based sequencing offers a more robust sample-independent approach for the assessment of copy number variation events compared to mixed ligation-dependent probe amplification. The approach simplifies data analyses, improves independent sample data analyses, and allows for direct comparison to an internal reference control for sample-specific quantification. Our iQRS™ biomimetic reference materials allow for single sample copy number variation analytics and further decentralisation of diagnostics to single patient sample assessment.

Keywords: validation, diagnostics, oncology, copy number variation, reference material, calibration

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Authors: Chawarat Rotejanaprasert, Andrew Lawson

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Early detection of unusual health events depends on the ability to detect rapidly any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler (SKL) measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate (SCPO) within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Properties of the proposed surveillance techniques including timeliness and detection precision are investigated using a simulation study.

Keywords: Bayesian, spatial, temporal, surveillance, prospective

Procedia PDF Downloads 311

Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Daniel Vélez-Díaz, Edith Olaco García

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In this paper, we present the use of the discriminant analysis to select evolutionary algorithms that better solve instances of the vehicle routing problem with time windows. We use indicators as independent variables to obtain the classification criteria, and the best algorithm from the generic genetic algorithm (GA), random search (RS), steady-state genetic algorithm (SSGA), and sexual genetic algorithm (SXGA) as the dependent variable for the classification. The discriminant classification was trained with classic instances of the vehicle routing problem with time windows obtained from the Solomon benchmark. We obtained a classification of the discriminant analysis of 66.7%.

Keywords: Intelligent Transportation Systems, data-mining techniques, evolutionary algorithms, discriminant analysis, machine learning

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Authors: Prateek Saxena, Sanjeev Singh, Julius Roy

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The security of the power distribution grid remains a paramount to the utility professionals while enhancing and making it more efficient. The most serious threat to the system can be maintaining the transformers, as the load is ever increasing with the addition of elements like electric vehicles. In this paper, intelligent transformer monitoring and grid management has been proposed. The engineering is done to use the evolving data from the smart meter for grid analytics and diagnostics for preventive maintenance. The two-tier architecture for hardware and software integration is coupled to form a robust system for the smart grid. The proposal also presents interoperable meter standards for easy integration. Distribution transformer analytics based on real-time data benefits utilities preventing outages, protects the revenue loss, improves the return on asset and reduces overall maintenance cost by predictive monitoring.

Keywords: electric vehicle charging, transformer monitoring, data analytics, intelligent grid

Procedia PDF Downloads 790

Authors: Vahid Bairami Rad

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Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented.

Keywords: spatial data base, knowledge discovery database, data mining, spatial relationship, predictive data mining

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Authors: Ben Morrow, Patrick O'Hara, Natalie Ban, Tunai Marques, Molly Fraser, Christopher Bone

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Relative to commercial fisheries, recreational fisheries are often poorly understood and pose various challenges for monitoring frameworks. In British Columbia (BC), Canada, Pacific salmon are heavily targeted by recreational fishers while also being a key source of nutrient flow and crucial prey for a variety of marine and terrestrial fauna, including endangered Southern Resident killer whales (Orcinus orca). Although commercial fisheries were historically responsible for the majority of salmon retention, recreational fishing now comprises both greater effort and retention. The current monitoring scheme for recreational salmon fisheries involves aerial-roving creel surveys. However, this method has been identified as costly and having low predictive power as it is often limited to sampling fragments of fluid and temporally dynamic fisheries. This study used imagery from two shore-based autonomous cameras in a highly active recreational fishery around Sooke, BC, and evaluated their efficacy in supplementing existing aerial-roving surveys for monitoring a recreational salmon fishery. This study involved continuous monitoring and high temporal resolution (over one million images analyzed in a single fishing season), using a deep learning-based vessel detection algorithm and a custom image annotation tool to efficiently thin datasets. This allowed for the quantification of peak-season effort from a busy harbour, species-specific retention estimates, high levels of detected fishing events at a nearby popular fishing location, as well as the proportion of the fishery management area represented by cameras. Then, this study demonstrated how it could substantially enhance the temporal resolution of a fishery through diel activity pattern analyses, scaled monthly to visualize clusters of activity. This work also highlighted considerable off-season fishing detection, currently unaccounted for in the existing monitoring framework. These results demonstrate several distinct applications of autonomous cameras for providing enhanced detail currently unavailable in the current monitoring framework, each of which has important considerations for the managerial allocation of resources. Further, the approach and methodology can benefit other studies that apply shore-based camera monitoring, supplement aerial-roving creel surveys to improve fine-scale temporal understanding, inform the optimal timing of creel surveys, and improve the predictive power of recreational stock assessments to preserve important and endangered fish species.

Keywords: cameras, monitoring, recreational fishing, stock assessment

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Authors: Brianna Z. Ross, Jonathan N. Livingston

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Given that discussions of racism and racial tensions have become more salient, there is a need to evaluate the impacts of racialization among Black individuals. Racial discrimination has become one of the most common experiences within the Black American population. Likewise, Black individuals have indicated a need to address their racial identities at an earlier age than their non-Black peers. Further, Black individuals have been found at the forefront of multiple social and political movements, including but not limited to the Civil Rights Movement, Black Lives Matter, MeToo, and Say Her Name. Moreover, the present study sought to explore the predictive relationships that exist between racial discrimination, racial identity, and activism in the Black community. The results of standard and hierarchical regression analyses revealed that racial discrimination and racial identity significantly predict each other, but only racial discrimination is a significant predictor for the relationship to activism. Nonetheless, the results from this study will provide a basis for social scientists to better understand the impacts of racialization on the Black American population.

Keywords: activism, racialization, racial discrimination, racial identity

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Authors: Alina Trifan, António J. R. Neves

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Although most digital cameras acquire images in a raw format, based on a Color Filter Array that arranges RGB color filters on a square grid of photosensors, most image compression techniques do not use the raw data; instead, they use the rgb result of an interpolation algorithm of the raw data. This approach is inefficient and by performing a lossless compression of the raw data, followed by pixel interpolation, digital cameras could be more power efficient and provide images with increased resolution given that the interpolation step could be shifted to an external processing unit. In this paper, we conduct a survey on the use of lossless compression algorithms with raw Bayer images. Moreover, in order to reduce the effect of the transition between colors that increase the entropy of the raw Bayer image, we split the image into three new images corresponding to each channel (red, green and blue) and we study the same compression algorithms applied to each one individually. This simple pre-processing stage allows an improvement of more than 15% in predictive based methods.

Keywords: bayer image, CFA, lossless compression, image coding standards

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

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Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

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Authors: Ivana Lolic, Petar Soric, Mirjana Cizmesija

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Based on Business and Consumer Survey (BCS) data, the European Commission (EC) regularly publishes the monthly Economic Sentiment Indicator (ESI) for each EU member state. ESI is conceptualized as a leading indicator, aimed ad tracking the overall economic activity. In calculating ESI, the EC employs arbitrarily chosen weights on 15 BCS response balances. This paper raises the predictive quality of ESI by applying nonlinear programming to find such weights that maximize the correlation coefficient of ESI and year-on-year GDP growth. The obtained results show that the highest weights are assigned to the response balances of industrial sector questions, followed by questions from the retail trade sector. This comes as no surprise since the existing literature shows that the industrial production is a plausible proxy for the overall Croatian economic activity and since Croatian GDP is largely influenced by the aggregate personal consumption.

Keywords: business and consumer survey, economic sentiment indicator, leading indicator, nonlinear optimization with constraints

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Authors: Andrea Ghermandi

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Natural treatment systems such as constructed wetlands and waste stabilization ponds are increasingly used to treat water and wastewater from a variety of sources, including stormwater and polluted surface water. The provision of ancillary benefits in the form of cultural ecosystem services makes these systems unique among water and wastewater treatment technologies and greatly contributes to determine their potential role in promoting sustainable water management practices. A quantitative analysis of these benefits, however, has been lacking in the literature. Here, a critical assessment of the recreational and educational benefits in natural treatment systems is provided, which combines observed public use from a survey of managers and operators with estimated public use as obtained using geotagged photos from social media as a proxy for visitation rates. Geographic Information Systems (GIS) are used to characterize the spatial boundaries of 273 natural treatment systems worldwide. Such boundaries are used as input for the Application Program Interfaces (APIs) of two popular photo-sharing websites (Flickr and Panoramio) in order to derive the number of photo-user-days, i.e., the number of yearly visits by individual photo users in each site. The adequateness and predictive power of four univariate calibration models using the crowdsourced data as a proxy for visitation are evaluated. A high correlation is found between photo-user-days and observed annual visitors (Pearson's r = 0.811; p-value < 0.001; N = 62). Standardized Major Axis (SMA) regression is found to outperform Ordinary Least Squares regression and count data models in terms of predictive power insofar as standard verification statistics – such as the root mean square error of prediction (RMSEP), the mean absolute error of prediction (MAEP), the reduction of error (RE), and the coefficient of efficiency (CE) – are concerned. The SMA regression model is used to estimate the intensity of public use in all 273 natural treatment systems. System type, influent water quality, and area are found to statistically affect public use, consistently with a priori expectations. Publicly available information regarding the home location of the sampled visitors is derived from their social media profiles and used to infer the distance they are willing to travel to visit the natural treatment systems in the database. Such information is analyzed using the travel cost method to derive monetary estimates of the recreational benefits of the investigated natural treatment systems. Overall, the findings confirm the opportunities arising from an integrated design and management of natural treatment systems, which combines the objectives of water quality enhancement and provision of cultural ecosystem services through public use in a multi-functional approach and compatibly with the need to protect public health.

Keywords: constructed wetlands, cultural ecosystem services, ecological engineering, waste stabilization ponds

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Authors: Naveed Ul Hassan, Bilal Aziz, Maryam Mushtaq, Imran Ameen Khan

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Equity risk premium (ERP) is the stock return in excess of risk free return. Even though it is an essential topic of finance but still there is no common consensus upon its forecasting. For forecasting ERP, apart from the macroeconomic variables attention is devoted to technical indicators as well. For this purpose, set of 14 technical and 14 macro-economic variables is selected and all forecasts are generated based on a standard predictive regression framework, where ERP is regressed on a constant and a lag of a macroeconomic variable or technical indicator. The comparative results showed that technical indicators provide better indications about ERP estimates as compared to macro-economic variables. The relative strength of ERP predictability is also investigated by using National Bureau of Economic Research (NBER) data of business cycle expansion and recessions and found that ERP predictability is more than twice for recessions as compared to expansions.

Keywords: equity risk premium, forecasting, macroeconomic indicators, technical indicators

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Authors: R. R. Govind

Abstract:

In the digital age, artificial intelligence (AI) is having a significant impact on the rapid changes that cities are experiencing. This study explores the profound impact of AI on urban morphology, especially with regard to promoting friendly design choices. It addresses a significant research gap by examining the real-world effects of integrating AI into urban design and management. The main objective is to outline a framework for integrating AI to transform urban settings. The study employs an urban design framework to effectively navigate complicated urban environments, emphasize the need for urban management, and provide efficient planning and design strategies. Taking Gangtok's informal settlements as a focal point, the study employs AI methodologies such as machine learning, predictive analytics, and generative AI to tackle issues of 'urban informality'. The insights garnered not only offer valuable perspectives but also unveil AI's transformative potential in addressing contemporary urban challenges.

Keywords: urban design, artificial intelligence, urban challenges, machine learning, urban informality

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Authors: James A. Adeyanju, John O. Olajide, Akinbode A. Adedeji

Abstract:

A predictive mathematical model based on the fundamental principles of mass transfer was developed to simulate the moisture content and oil content during Deep-Fat Frying (DFF) process of dodo. The resulting governing equation, that is, partial differential equation that describes rate of moisture loss and oil uptake was solved numerically using explicit Finite Difference Technique (FDT). Computer codes were written in MATLAB environment for the implementation of FDT at different frying conditions and moisture loss as well as oil uptake simulation during DFF of dodo. Plantain samples were sliced into 5 mm thickness and fried at different frying oil temperatures (150, 160 and 170 ⁰C) for periods varying from 2 to 4 min. The comparison between the predicted results and experimental data for the validation of the model showed reasonable agreement. The correlation coefficients between the predicted and experimental values of moisture and oil transfer models ranging from 0.912 to 0.947 and 0.895 to 0.957, respectively. The predicted results could be further used for the design, control and optimization of deep-fat frying process.

Keywords: frying, moisture loss, modelling, oil uptake

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Authors: Kay Thi Yar, Khin Mar Lar Tun

Abstract:

Nowadays, social networks are so popular and widely used in all over the world. In addition, searching personal information of each person and searching connection between them (peoples’ relation in real world) becomes interesting issue in our society. In this paper, we propose a framework with three portions for exploring peoples’ relations from their connected information. The first portion focuses on the Graph database structure to store the connected data of peoples’ information. The second one proposes the graph database searching algorithm, the Modified-SoS-ACO (Sense of Smell-Ant Colony Optimization). The last portion proposes the Deductive Reasoning Algorithm to define two persons’ relationship. This study reveals the proper storage structure for connected information, graph searching algorithm and deductive reasoning algorithm to predict and analyze the personnel relationship from peoples’ relation in their connected information.

Keywords: personnel information, graph storage structure, graph searching algorithm, deductive reasoning algorithm

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Authors: Surapol Raadnui

Abstract:

This paper describes an experimental investigation on a pair of gears in which wear and pitting were intentionally allowed to occur, namely, moisture corrosion pitting, acid-induced corrosion pitting, hard contaminant-related pitting and mechanical induced wear. A back-to-back spur gear test rig was used. The test samples of wear debris were collected and assessed through the utilization of an optical microscope in order to correlate and compare the debris morphology to pitting and wear degradation of the worn gears. In addition, weight loss from all test gear pairs was assessed with the utilization of the statistical design of the experiment. It can be deduced that wear debris characteristics exhibited a direct relationship with different pitting and wear modes. Thus, it should be possible to detect and diagnose gear pitting and wear utilization of worn surfaces, generated wear debris and quantitative measurement such as weight loss.

Keywords: tribology, spur gear wear, predictive maintenance, wear particle analysis

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Authors: Maria Giovanna Pacifico

Abstract:

Maintenance, in Italy, is not yet a consolidated practice despite the benefits that could come from. Among the main reasons, there are the lack of financial resources and personnel in the public administration and a general lack of knowledge about how to activate and to manage a prevented and programmed maintenance. The experimentation suggests that users and tourists could be involved in the maintenance process from the knowledge phase to the monitoring ones by using mobile devices. The goal is to increase the quality of Facility Management for cultural heritage, prioritizing usage needs, and limiting interference between the key stakeholders. The method simplifies the consolidated procedures for the Information Systems, avoiding a loss in terms of quality and amount of information by focusing on the users' requirements: management economy, user safety, accessibility, and by receiving feedback information to define a framework that will lead to predictive maintenance. This proposal was designed to be tested in the Archaeological Park of Pompeii on the state property asset.

Keywords: asset maintenance, key stakeholders, Pompeii, user requirement

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Authors: Samina Khalid, Shamila Nasreen

Abstract:

Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented.

Keywords: age related macular degeneration, feature selection feature subset selection feature extraction/transformation, FSA’s, relief, correlation based method, PCA, ICA

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Authors: Sarwat Sultan, Shahzad Gul

Abstract:

This study was carried out to see the mediating role of problem solving styles (sensing, intuitive, feeling, and thinking) in the predictive relationship of gratitude with subjective well-being. A sample of 454 college students aged 20-26 years old participated in this study and provided data on the measures of gratitude, problem solving styles, and subjective well-being. Results indicated the significant relationships of gratitude with subjective well-being and problem solving styles of intuitive and thinking. Results further indicated the positive link of intuitive and thinking styles with subjective well-being. Findings also provided the evidence for the significant mediating role of problem solving styles in the relationship of gratitude with subjective well-being. The implication for this study is likely to enhance the medium to long term effects of gratitude on subjective well-being among students and as well as assessing its value in promoting psychological health and problem solving strategies among students.

Keywords: gratitude, subjective well-being, problem solving styles, college students

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