Online Data Sources Research Articles

Systematic Review of Machine Learning and Deep Learning Techniques for Spatiotemporal Air Quality Prediction

Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors’ search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O3 concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles.

An automated data collection process for constructing graph data relying on LLMs

This paper introduces a process that is designed to harvest data automatically from a variety of online sources. The core of this process lies in its data-handling techniques, which include drawing, cleaning, deduplicating, extracting, and categorizing of raw data to convert unstructured data into a structured format represented and imported in a graph database. The data extraction step utilizes Large Language Model (LLMs) for Named Entity Recognition (NER). A case study on deploying course data collection illustrates the enhancements brought about by this automation, showcasing improvements in the accuracy, completeness, and timeliness of updates in the course data. An evaluation carried out on the extraction and matching methods shows that the F1-score and precision rates are high. Overall, this study contributes to advancement of the field by providing a methodology for automating the collection and processing of online data sources, significantly improving the quality of data collection from online sources.

Struggling for Islamic Caliphate in a Changing Malay Society

Despite being banned by the government, HTI continues to struggle for Islamic caliphate as a clandestine movement. This study is aimed to explain factors resulting in the relatively successful penetration of caliphate ideology in a changing Malay society, as represented by Bangka Belitung Archipelagic Province. It employed a qualitative approach through the library study of both offline and online data sources in order to gain a holistic comprehension of HTI’s success. The research results elaborate HTI’s mobilization strategies as the internal factor and the context of changing Malay society and the dysfunction of moderate socio-religious organizations as the external factor contribute to HTI’s success in the changing society. Mobilization strategies comprising the intensive implementation of religious learning and preaching, publication, public demonstration, good relation and network with local important figures, and the competent use of communication and information technology become effective in the context of changing Malay society reconstructing identity and seeking solutions to its social and economic problems. In addition, the dysfunction of moderate socio-religious organizations producing a theological vacuum accentuates HTI’s relative victory. Thus, the performance of HTI depends on its internal capabilities and external context and the function of socio-religious organizations in society.

Response Satisficing Across Online Data Sources: Effects of Satisficing on Data Quality and Policy-Relevant Results

The use of crowdsourced data has become extremely popular in marketing and public policy research. However, there are concerns about the validity of studies that source data from crowdsourcing platforms such as Amazon Mechanical Turk (MTurk). Using five different online sample sources, including multiple MTurk samples and professionally managed panels, the authors address issues related to online data quality and its effects on results for a policy-based 2 × 2 between-subjects experiment. They show that survey response satisficing, as well as multitasking, is related to attention check performance measures beyond demographic differences, and there are substantial differences across the five different online data sources. The authors specifically identify segments of high and low response satisficers using a multi-item measure and show that there are critical differences in the policy-relevant results of the experiment for these segments of online respondents. Findings suggest implications for concerns about failures to replicate results in the policy and consumer well-being, business, and social science literatures. The authors offer some suggestions for attempting to reduce problematic effects of response satisficing and data quality that are shown to differ substantially across the sample sources examined.

INTEGRATING DUAL FUNDUS IMAGE DEEP FEATURE POOLING BASED ON CUP AND DISC SEGMENTATION FOR EFFICIENT GLAUCOMA DETECTION FRAMEWORK INDULGING HEURISTIC CONCEPT

The permanent damage occurring to the optical nerve, which transmits the visualized images to the brain leading to permanent vision loss, is called glaucoma. The detection and diagnosis of glaucoma at the beginning stage are essential to prevent permanent damage caused to vision as there are no initial symptoms to detect the onset of the damage, and there is no cure while the damage progresses to its extreme stage. Many models utilizing deep learning technology have been executed to identify the presence of glaucoma using digital Fundus Images (FI). However, these methods need specialized hardware and are computationally complicated as there is not much-labeled data available, leading to generalization issues in these models. Therefore, the development of a deep learning-oriented model for Glaucoma Detection (GD) using the dual feature pooling of cup and disc segmentation features with the aid of the FI is the main motive behind this work. At first, the digital FI is congregated from standardized online data sources. Then the gathered images are considered for the image segmentation procedure. The disc is segmented from the input image with the aid of the Transformer based Adaptive ResUNet++ (TA-ResUNet++) framework. The segmented disc image is now provided to hybrid convolution networks (HCN) for extracting the disc-oriented features. At the same time, the cup is segmented from the gathered input image using the executed TA-ResUnet++. Following this, the cup features are extracted using the same HCN model. The obtained features regarding the disc and the cup are concatenated in order to generate a single pool of glaucoma features. Then these features are inputted into the implemented bidirectional long short-term memory with multi-head attention (Bi-LSTM-MHA) mechanism to detect the presence of glaucoma in people. The segmentation and detection outcomes are made better by optimizing the parameters in the TA-ResUNet++ and Bi-LSTM-MHA models with the aid of hybrid sewing training with the Chameleon swarm optimization algorithm (HST-CSA). The experimental analysis is done to analyze the effective performance of the developed GD model. Jaccard analysis performed for the developed HST-CSOA-TA-ResUNet++-based disc segmentation gained better performance as 18.29% than MobileNet, 14.11% efficient than ResNet, 12.79% superior to Bi-LSTM and 5.43% higher than Bi-LSTM-MHA while considering best as the statistical measure.

Social and Web Data Framework for Understanding Loneliness.

Loneliness can be studied through social media and web data to gain insights into the dynamic phenomenon. In this paper, we present our proposed framework through a case study on how to deploy various social media and online data sources to study loneliness comprehensively. The framework is important to understand loneliness from data perspective available online and to complement the theoretical and psychosocial understanding of loneliness. The data on loneliness is gathered through surveys and self-reporting mechanisms. This requires complementing the dynamic and vast data available on the web and through social media. Our results found that tools like Google Trend and News Analysis can give the starting point to explore loneliness in a particular region. Tools like X (formerly Twitter), Reddit, and other social media can give behavioral data on loneliness which can be analyzed through sentiment analysis and other social intelligence analysis. The framework's utility lies in its potential to inform policies, interventions, and initiatives addressing loneliness through data. However, it is crucial to acknowledge limitations, such as data availability, biases in user-generated content, and ethical considerations.

Analisis Pelaporan dan Pertanggungjawaban Bantuan Sosial pada Laporan Keuangan Pemerintah Kota Bekasi Tahun 2019 - 2022

The significant increase in the allocation of social assistance funds is a problem in Bekasi City. As the amount of funds allocated increases, the risk of misuse or waste may also increase. The purpose of this study is to analyze the policy on the budget used for social assistance and find out the regulatory basis. As well as knowing the reporting and accountability system of the realization or implementation of the social assistance budget at the Bekasi City Government in 2019 - 2022. This research uses a descriptive qualitative research design. The data used in this study are secondary data. The sample used in this study is the Bekasi City Regional Revenue and Expenditure Budget Realization Report 2019 - 2022. Analysis of documents used in this study such as: Budget Realization Report (LRA), documentation related to social assistance, and online data sources. The results showed that the policies and systems for reporting and accountability of Social Assistance (Bansos) in Bekasi City still have several weaknesses and obstacles that need to be strengthened.

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results.

Exploring the potential for online data sources to enhance species threat mapping through the case study of global bat exploitation.

Expanding digital data sources, including social media and online news, provide a low-cost way to examine human-nature interactions, such as wildlife exploitation. However, the extent to which using such data sources can expand or bias understanding of the distribution and intensity of threats has not been comprehensively assessed. To address this gap, we quantified the geographical and temporal distribution of online sources documenting the hunting and trapping, consumption, or trade of bats (Chiroptera) and compared these with the distribution of studies obtained from a systematic literature search and species listed as threatened by exploitation on the International Union for Conservation of Nature Red List. Online records were collected using automated searches of Facebook, Twitter, Google, and Bing and were filtered using machine classification. This yielded 953 relevant social media posts and web pages, encompassing 1099 unique records of bat exploitation from 84 countries. Although the number of records per country was significantly predicted by the number of academic studies per country, online records provided additional locations and more recent records of bat exploitation, including 22 countries not present in academic literature. This demonstrates the value of online resources in providing more complete geographical representation. However, confounding variables can bias the analysis of spatiotemporal trends. Online bat exploitation records showed peaks in 2020 and 2014, after accounting for increases in internet users through time. The second of these peaks could be attributed to the COVID-19 outbreak, and speculation about the role of bats in its epidemiology, rather than to true changes in exploitation. Overall, our results showed that data from online sources provide additional knowledge on the global extent of wildlife exploitation, which could be used to identify early warnings of emerging threats and pinpoint locations for further research.

Creaky voice across language and gender: A study of Canadian English-French bilingual speech

This study addresses how non-contrastive creaky voice varies among languages and across speakers (as a function of gender). Spontaneous speech from 9 English-French bilingual speakers born and raised in Ontario/Québec was collected from publicly available online data sources, amounting to roughly 5 min of speech per speaker-language pair and 13 992 vowels total. This corpus will reach 40 speakers by the conference. Acoustic analysis consisted of pitch tracking in Praat, providing a proportion of unreliable f0 tracks for each vowel as well as one spectral slope measure (H1*-H2*) and two Harmonics-to-Noise Ratios (CPP and HNR05) as acoustic correlates of creaky voice. Statistical significance was tested using mixed-effects regression models, with fixed effects of language, gender, and utterance position, and maximal by-word and by-speaker random effects. The main results for gender show that men's vowels have more unreliable f0 tracks, lower H1*-H2*, lower HNR05 and somewhat lower CPP, suggesting that male speakers are creakier overall. Regarding language, English displays more unreliable pitch tracking compared to French, providing some evidence for language-dependent vocal settings. Other acoustic correlates of creak, however, do not show consistent cross-linguistic differences.

Enhancing Health Care Accessibility and Equity Through a Geoprocessing Toolbox for Spatial Accessibility Analysis: Development and Case Study.

Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities. In this study, we developed a geoprocessing toolbox including Python script tools for the ArcGIS Pro environment to measure the spatial accessibility of health services using both classic and enhanced versions of the 2-step floating catchment area method. Each of our tools incorporated both distance buffers and travel time catchments to calculate accessibility scores based on users' choices. Additionally, we developed a separate tool to create travel time catchments that is compatible with both locally available network data sets and ArcGIS Online data sources. We conducted a case study focusing on the accessibility of hemodialysis services in the state of Tennessee using the 4 versions of the accessibility tools. Notably, the calculation of the target population considered age as a significant nonspatial factor influencing hemodialysis service accessibility. Weighted populations were calculated using end-stage renal disease incidence rates in different age groups. The implemented tools are made accessible through ArcGIS Online for free use by the research community. The case study revealed disparities in the accessibility of hemodialysis services, with urban areas demonstrating higher scores compared to rural and suburban regions. These geoprocessing tools can serve as valuable decision-support resources for health care providers, organizations, and policy makers to improve equitable access to health care services. This comprehensive approach to measuring spatial accessibility can empower health care stakeholders to address health care distribution challenges effectively.

A Taxonomic Review of South African Indigenous Meliaceae Using Molecular Systematics and Anatomical Data

The Meliaceae are broadly distributed worldwide, with about 50 genera and over 1400 species. There are 11 genera in South Africa, with 13 indigenous and three naturalized species. Considering the diversity of the indigenous species of this family in South Africa and the lack of recent studies encompassing these species, a taxonomic revision of the South African indigenous species of Meliaceae is presented here. Phylogenetic analysis, anatomical data, herbarium collections, and online data sources were used in this study. The results confirm the monophyly of Melioideae and Swietenioideae. The incongruence of Turraea previously reported was resolved in this study. Most representative genera of South African Meliaceae were recovered monophyletic with strong support. However, multiple samplings of species and including more markers could provide a better understanding of the relationships among South African species of Meliaceae. The review of the taxonomy of the South African Meliaceae, and especially the study of diagnostic characters and hitherto recorded natural distributions, have value in providing an up-to-date inventory of the indigenous genera and species and an easy means of identifying the taxa. Anatomical characters may be of systematic value to explore higher-level relationships in the family. This study is a contribution to tropical botany and to a more comprehensive database for the Meliaceae.

Development of Honey Badger-Cat Swarm Optimisation-Based Parallel Cascaded Deep Network for Software Bug Prediction Framework

Software bug prediction is mainly used for testing and code inspection. So, software bug prediction is carried out by network measures over the decades. But, the classical fault prediction method failed to obtain the semantic difference among various programs. Thus, it degrades the working of the prediction model, which is designed using these aspects. It is necessary to obtain the semantic difference for designing the prediction model accurately and effectively. In a software defect prediction system, it faces many difficulties in identifying the defect modules like correlation, irrelevance aspects, data redundancy, and missing samples or values. Consequently, many researchers are designed to detect software bug prediction that categorises faulty as well as non-faulty modules with software matrices. But, there are only a few works focussed to mitigate the class imbalance problem in bug prediction. In order to overcome the problem, it is required to develop an efficient software bug prediction method with the enhanced classifier. For this experimentation, the input data are taken from the standard online data sources. Initially, the input data undergo pre-processing phase and then, the pre-processed data are provided as input to the feature extraction by utilising the Auto-Encoder. These obtained features are utilised in getting the optimal fused features with the help of a new Hybrid Honey Badger Cat Swarm Algorithm (HHBCSA). Finally, these features are fed as input to the Optimised Parallel Cascaded Deep Network (OPCDP), where the “Extreme Learning Machine (ELM) and Deep Belief Network (DBN)” are used for the prediction of software bugs, in which the parameters from both classifiers are optimised by proposed HHBCSA algorithm. From the investigations, the recommended software bug prediction method offers a quicker bug prediction result, which helps to detect and remove the software bug easily and accurately.

A substantial eastern disjunction of Douglas’ Knotweed (<i>Polygonum douglasii</i> Greene, Polygonaceae) in New Brunswick, Canada

We report an isolated population of the native annual Douglas’ Knotweed (Polygonum douglasii Greene) from a dry, south-facing outcrop of conglomerate and sandstone at Big Bluff, near Sussex Corner, New Brunswick, Canada, consisting of about 2500 plants in 2018 and 2022. This occurrence is disjunct by ~450 km from the eastern limit of the known range of Douglas’ Knotweed in southwestern Maine, USA. The nearest known occurrence in Canada is in southern Quebec, ~660 km from Big Bluff. Several lines of evidence indicate that the population in New Brunswick is native. New state records for Wisconsin and Alaska found in online data sources are also verified.

A Multiscale Atrous Convolution-based Adaptive ResUNet3 + with Attention-based ensemble convolution networks for brain tumour segmentation and classification using heuristic improvement

A brain tumor is a tissue group formed by the addition of unusual cells in the brain, and it’s significant to identify brain tumor through Magnetic Resonance Imaging (MRI) for treatment. Because of the different tumor types, better segmentation of brain tumor is a tricky problem in MRI. To conquer the existing issues, the latest scheme is proposed for brain tumor classification and segmentation by deep structured architectures. In the first stage, the source images are garnered from online data sources. The garnered data is then subjected to a new segmentation model which is termed as Multiscale Atrous Convolution-based Adaptive ResUNet3+ (MAC-ARUNet3 + ). Here the parameters of deep learning approaches are optimally tuned by Improved Math Optimizer Accelerated-based Arithmetic Optimization Algorithm (IMOA-AOA). Finally, the tumor classification is carried out by Attention-based Ensemble Convolution Networks (AECN), in which it is encompassed with ResNet, Inception, and MobileNet. Thus, the final results are determined by the high-ranking-based estimation. The performance and comparative analysis are done using diverse measures and distinct algorithms. Hence, the recommended model outperforms with the high segmentation and classification accuracy that aids the practitioner in diagnosing the disease rapidly.

The plastic homes of hermit crabs in the Anthropocene

Plastic is the most pervasive element of marine waste, with harmful impact on wildlife. By using iEcology (i.e., internet Ecology, use of online data sources as a new tool in ecological research), we report on the emergence of a novel behaviour in hermit crabs related to the use of plastic or other anthropogenic materials as protective shells. We analysed images posted on social media to identify 386 individuals with artificial shells — mainly plastic caps (85 %). We report that 10 of the world's 16 terrestrial hermit crabs use artificial shells, a behaviour observed on all of the Earth's tropical coasts. Four non-exclusive mechanisms may drive individual choice for artificial shells: sexual signaling, lightness of artificial shells, odour cues, and camouflage in a polluted environment. Further research is needed to determine the impact of this behaviour on hermit crab evolutionary trajectories.

Automation process in data collection for representing façades in building models as part of the renovation process

A key barrier in building-facade renovation processes is that, contrary to new designs, an initial building model where the design process is based rarely exists, and the technologies usually employed to create it (e.g., based on point cloud scanning) are costly or require modeling skills. This situation is a clear limitation, especially in early decision stages, where the level of detail required is not very high, and the analysis and studies to consider the renovation plan (e.g., simplified energy simulations and renovation potential, or estimation of the number, types, and dimensions of the prefabricated modules incorporating solar panels) highly depend on such digital models. This paper introduces a process that, based on freely available data such as open GIS sources (local Cadasters, OpenStreetMap…) and façade images, can semi-automatically generate the 3D building model of the existing conditions, and in a second step also suggests the prefabricated facades module layout for building upgrades. Additionally, no onsite visit is needed. When the upgrade is focused on the façade, a big opportunity is identified for generating the building model and a realistic representation of its envelope, only using online data sources as input. The process developed consists of a set of easy-to-use software tools that can be used independently or combined in a workflow, depending on the available data and starting conditions. Time saving is very clear and costs can be reduced.

The buzz around biodiversity decline: Detecting pollinator shifts using a systematic review

Climate and land use change are two of the largest drivers of worldwide biodiversity loss, but detecting drivers of insect decline is more complex. Online data sources can elucidate such responses while identifying systematic data gaps. Using a systematic review, we found 119 studies that document bumble bee and butterfly responses to climate change. While bee literature was limited, there is high confidence that species are emerging earlier (∼17days), mismatching with floral resources (100% of studies), and changing range distributions (-25%). More butterfly literature was available but did not yield consistent responses. Evidence shows earlier emergences (∼5days), decreasing range distributions (-19%), and population shifts amongst generalist (87% increase) versus specialist (65% decrease) groups. We argue that the effect of changing climates on floral emergence, abundance, and distribution may be more significant than the impact of climate change on biodiversity; however, further research is required, particularly within the Southern Hemisphere.

Seasonal seed scenario planning: co-design of a generic framework for matching seed supply and demand using seasonal climate forecasts

To cope with interannual climate variability, many farmers in tropical and sub-tropical regions choose crop varieties that fit seasonal climate conditions. Therefore, seed demand for different varieties, such as early- or late-maturing cultivars, varies between years. Resulting mismatches between relatively constant supply and variable demand create losses for both seed suppliers and farmers. Because demand for seed of different varieties is influenced by seasonal climate, however, probabilistic seasonal rainfall forecasts could help seed suppliers better anticipate upcoming seed demand. To explore this idea, we engaged decision-makers from seed supply organizations in Zimbabwe and Ethiopia. Through a participatory design process, we identified opportunities and challenges for using seasonal rainfall forecasts to inform seed supply decisions. In a case study of maize seed sales in Zimbabwe, we tested our assumptions and iteratively devised a systematic procedure for forecast-based planning in seed supply, relying on free online data sources and expert deliberations. We found that currently accessible rainfall forecasts could indeed be useful for prioritizing likely high-demand varieties during the stages of seed treatment, packaging, and logistics. In practice, though, more flexible and adaptive management of seed supply pipelines might be required to make use of seed demand forecasts. In the future, targeting farmers with climate forecasts along with recommended variety portfolios may strengthen the association between seasonal climate and farmers’ variety demand, increasing the accuracy of demand anticipation. This study highlights opportunities for increased case-specific collaboration between climate scientists and the seed sector to make seasonal forecast information operational.

SELF-TALK ON SPORT PERFORMANCE AND SELECTED PSYCHOLOGICAL VARIABLES: A SYSTEMATIC REVIEW

Self-talk is a psychological skill training that improves motor performance and sports skills among athletes. Previous research has indicated differences between motivational self-talk and instructional self-talk in sports performance. However, there is a lack of attention paid to the effectiveness of self-talk toward psychological barriers. Therefore, the primary aim of this study was to examine past studies on the effect of self-talk on sports performance and to select psychological variables among athletes. Through a systematic review, we analysed the scientific production concerning the sports psychology of self-talk toward athletes. The process review was conducted by the PRISMA protocol and a search performed by using online data sources (Scopus, Web of Science, and Semantic Scholar), as well as, conference proceedings of possibly related papers. The search was limited to articles published within ten years and written in English. A total of 38 studies were analysed. Referring to a total of 38 studies (nine articles focused on the self-talk questionnaire, six articles on motivational self-talk, six articles on instructional studies, and 17 articles focused on both motivational self-talk). Based on this, only 9 studies satisfied the eligibility criteria related to sports performance and psychological effects. As a result, a sample of 9 studies was subjected to systematic quality analysis. The analysis results showed that negative self-talk decreases athletes’ performance. In addition, there is inconsistent evidence on the effect of motivational and instructional self-talk based on specific sports. Based on this review, instructional self-talk is beneficial for fine skills (free-throw basketball and penalty shoot-out), and motivational self-talk is effective for gross skill (running). Thus, it can be concluded that both motivational and instructional self-talk play an important role in enhancing motor performance.