Scientific Data Research Articles

A Comprehensive Analysis of various Covid-19 Detection and Pandemic Prevention Systems and Methods Using Data Science

Abstract: The paper proposes a technique for early Covid-19 test prediction using AI and data science. The study analyzes various data sources to understand the patterns and trends of the pandemic and the impact of various interventions on the spread of the virus. The proposed system integrates data from multiple sources to identify potential cases and can also track the spread of the virus in real-time. The system has high accuracy in predicting positive cases and has the potential to improve our ability to detect and predict Covid-19 cases, ultimately saving lives and reducing the economic impact of the pandemic. The proposed technique for early Covid-19 test prediction using AI and data science can be a game- changer in our fight against the pandemic. With the integration of multiple data sources, including individual health records, social media, and geographic data, the system can identify potential cases even before symptoms appear. This early detection can help individuals get timely medical attention and prevent the spread of the virus to others.

Safe Driving Analytics

Abstract: Are you aware that violations of safe driving regulations account for over half of all traffic accidents? However, what if we gave drivers who adhere to those guidelines rewards? It might significantly impact the number of accidents brought on by careless driving. To improve our understanding of how people drive, researchers are also examining how data from automobile cameras may be used. However, the amount of data we collect makes it more difficult to use data science and artificial intelligence to examine it all. In this article, we discuss how we're creating tools to support that analysis and how we might use the data to comprehend the factors that affect people's driving behaviors. We are essentially developing a system that receives video from automobile cameras, interprets it, and then assists us in determining which types of driving pose a risk. To be able to identify dangerous drivers could significantly contribute to making our roads safer for everyone, as driving behavior plays a major role in the majority of crashes. Therefore, improving our understanding of driving behavior could have a revolutionary impact on road safety and the reduction of fatalities and accidents.

Data Visualization & Analysis of Disease X [Nipah Virus

Abstract: This article is about performing mathematical analysis and visualization of data that was researched and collected about Nipah Virus; which is treated as Disease X[deadly disease] by World Health Organization. It includes information about the field of Data Analysis and Visualization techniques which associate closely with current era and the project itself. Furthermore, there is description which provides information about the main application developed and implemented. The variety of research papers that were made use of, along-with significantly valuable data are cited too. Data Analyses has very close connection with data science, however, the key difference lies in its actual working, as Data Analysis makes extensive use of historically available data & generates important insights from them, which are very considerable for future operations. Thus, due to such complex level usage, Data Analysis has gained recognition in the recent years, which makes it important for professionals to add it as a part of daily life tasks. When it comes to real time usage, Data Analyses is used for many purposes which includes but is not limited to customer preferences, finance sectors, research in markets and also in assessment of variety of risks. Data Analysis thus, has introduced a massive number of futuristic achievable tasks by taking important notes from the ones that were conducted in the past

Integrating data science and neuroscience in developmental psychopathology: Formative examples and future directions.

This commentary discusses opportunities for advancing the field of developmental psychopathology through the integration of data science and neuroscience approaches. We first review elements of our research program investigating how early life adversity shapes neurodevelopment and may convey risk for psychopathology. We then illustrate three ways that data science techniques (e.g., machine learning) can support developmental psychopathology research, such as by distinguishing between common and diverse developmental outcomes after stress exposure. Finally, we discuss logistical and conceptual refinements that may aid the field moving forward. Throughout the piece, we underscore the profound impact of Dr Dante Cicchetti, reflecting on how his work influenced our own, and gave rise to the field of developmental psychopathology.

Unraveling Financial Markets: Deep Neural Network-Based Models for Stock Price Prediction

This paper delves into the potential and challenges of leveraging deep neural networks (DNNs) in stock price forecasting. Traditional econometric models often grapple with the complexities of financial time series data, leading to the exploration of DNNs, especially architectures like Long Short-Term Memory (LSTM) networks, to capture intricate patterns in such data. While these networks present promising results, challenges such as model interpretability, non-stationarity of data, overfitting, and computational demands remain. The financial sector's increasing digitization and influx of alternative data offer a unique opportunity for DNNs, emphasizing their capability for automatic feature extraction. However, the integration of DNNs necessitates a multi-disciplinary approach, involving financial experts, data scientists, and computational specialists. The paper concludes with an optimistic outlook for the synergy between deep learning and traditional financial theories, aiming for a harmonious blend of modern computational techniques with foundational financial principles.

Identifying globally relevant learnings from Africa’s challenges and solutions to climate change and air pollution-related health impacts: a data science scoping review protocol

IntroductionLeveraging data science could significantly advance the understanding of the health impacts of climate change and air pollution to meet health systems’ needs and improve public health in Africa. This...

Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy.

NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.

Multi-Scale Habitat Selection by the Wintering Whooper Swan (Cygnus cygnus) in Manas National Wetland Park, Northwestern China

Habitat selection has been a central focus of animal ecology, with research primarily concentrating on habitat choice, utilization, and evaluation. However, studies confined to a single scale often fail to reveal the habitat selection needs of animals fully and accurately. This paper investigates the wintering whooper swan (Cygnus cygnus) in Manas National Wetland Park, Xinjiang, using satellite tracking to determine their locations. The Maximum Entropy model (MaxEnt) was applied to explore the multi-scales habitat selection needs of Manas National Wetland Park’s wintering whooper swans across nighttime, daytime, and landscape scales. This study showed that the habitat selection of the wintering whooper swans varied in different scales. At the landscape scale, wintering whooper swans prefer habitats with average winter precipitations of 6.9 mm and average temperatures of −6 °C, including water bodies and wetlands, indicating that climate (precipitation and temperature) and land type (wetlands and water bodies) influence their winter habitat selection. During daytime, whooper swans prefer areas close to wetlands, water bodies, and bare land, with a more dispersed distribution of water bodies. For nighttime, they tend to choose areas within the wetland park where human disturbance is minimal and safety is higher. This study can provide scientific basis and data support for habitat conservation and management of wintering waterbirds like whooper swans, recommending targeted conservation measures to effectively manage and protect the wintering grounds of whooper swans.

Big data and its impact on the 3Rs: a home cage monitoring oriented review

Undisturbed home cage recording of mouse activity and behavior has received increasing attention in recent years. In parallel, several technologies have been developed in a bid to automate data collection and interpretation. Thanks to these expanding technologies, massive datasets can be recorded and saved in the long term, providing a wealth of information concerning animal wellbeing, clinical status, baseline activity, and subsequent deviations in case of experimental interventions. Such large datasets can also serve as a long-term reservoir of scientific data that can be reanalyzed and repurposed upon need. In this review, we present how the impact of Big Data deriving from home cage monitoring (HCM) data acquisition, particularly through Digital Ventilated Cages (DVCs), can support the application of the 3Rs by enhancing Refinement, Reduction, and even Replacement of research in animals.

Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.

Revolutionizing Cybersecurity with Machine Learning: A Comprehensive Review and Future Directions

In the realm of computing, data science has revolutionized cybersecurity operations and technologies. The key to creating automated and intelligent security systems lies in extracting patterns or insights from cybersecurity data and building data-driven models. Data science, encompassing various scientific approaches, machine learning techniques, processes, and systems, studies real-world occurrences through data analysis. Machine learning techniques, known for their flexibility, scalability, and adaptability to new and unknown challenges, have been applied across many scientific fields. Cybersecurity is rapidly expanding due to significant advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, and more. Various machine learning techniques have effectively addressed a wide range of computer security issues. This article reviews several machine learning applications in cybersecurity, including phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns associated with machine learning techniques themselves. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine learning model using supervised learning algorithms. These models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and employ ensemble methods that combine multiple machine learning models for improved performance

Fatty acids composition, lipids health indices and enzyme activities of longissimus thoracis muscle of six breeds of sheep produced on pasture in Northern region of Uruguay

The determination of fatty acids composition of glycerolipids and glycerophospholipids of meat from longissimus thoracis of six breeds of lamb produced on pasture in Uruguay was undertaken by gas chromatography. Also some lipids health indices and lipids metabolism enzymes were determined. The studied lambs were males aged of 11–12 months of breeds and biotypes Highlander® (H), Merino Dohne (MD), Corriedale (C), Corriedale Pro® (CPRO), a crossing between Corriedale × Australian Merino (C×AM) and Romney Marsh (RM). The animals were reared on pasture in identical conditions without supplementation. The grazing was rotational based on a winter annual crops oats (Avena sativa spp.), cocksfoot, (Dactylis glomerata spp.) and white clover (Trifolium repens spp.). The results of the study did not show substantial differences between breeds regarding the fatty acids composition of meat, except for few relevant fatty acids such as C16:0 (MD>C), C18:3n3 (H<C) and CLA (H<CPRO, CxAM) for glycerolipids. Also C18:1 (H>CPRO, CxAM), C18:2n6 (H<CxAM) and C18:3n3 (H<C) for glycerophospholipids. Likewise, other differences were outlined such as the anteiso monomethyl fatty acid content (MD<RM), the hypocholesterolemic/hypercholesterolemic ratio (MD<C). For lipids metabolism enzymes indices, MD showed a lower Δ–9 desaturase enzyme for C16:0 than C, CPRO and CxAM. Also, H showed a lower Δ–6 desaturase enzyme activity than C, and both MD and CxAM showed a lower elongase enzyme activity than C. The results of the present investigation showed that the meat of the lamb of the different breeds overall present good lipids nutritional indicators, in comparison with the results of other research in lambs. That information could help lamb producers in Uruguay to promote their products based on scientific data.

Data Science's Unnoticed Significance in Finance

The growing influence of artificial intelligence and Data Science on the financial sector, focusing on its role in managing financial products and services, influencing financial markets, and shaping areas like internet finance, credit services, and fraud detection. The study highlights the opportunities and challenges of integrating data science into financial systems, including algorithmic trading of financial services. Despite challenges such as data quality, privacy, regulation, ethics, talent acquisition, and integration with existing systems, the application of data science in finance offers businesses opportunities to develop and broaden their offerings, fostering innovation and improving client experiences. The paper also emphasizes the importance of interpretable and explainable AI models for trust and compliance with regulations.

Decoding E-Commerce: Data Science and Machine Learning Explorations

In the world, the growth of e-commerce is been rapidly increasing day by day. The availability of the products and the demands of customers are highly notable. Here we can use the emerging models of data science and machine learning to build a data driven strategies in developing e-commerce platform. This paper delves into the pivotal role of data science and machine learning in the e-commerce, exploring the applications, advantages, and challenges. Through a better understanding of existing papers, literatures and case studies, it investigates how the data science methodologies and machine learning are utilized for many different applications using different methodologies across the various e-commerce platforms.

Detours in Shared Rides

Detours are considered key for the efficient operation of a shared rides service, but they are also a major pain point for consumers of such services. This paper studies the relationship between the value generated by shared rides and the detours they create for riders. We establish a limit on the sum of value and detour, and we prove that this leads to a tight bound on the Pareto frontier of values and detours in a general setting with an arbitrary number of requests. We explicitly compute the Pareto frontier for one family of city topologies and construct it via simulation for several more networks, including one based on ride-sharing data from commute hours in Manhattan. We find that average detours are usually small, even in low-demand-density settings. We also find that by carefully choosing the match objective, detours can be reduced with a relatively small impact on values and that the density of ride requests is far more important than detours for the effective operations of a shared rides service. In response, we propose that platforms implement a two-product version of shared rides and limit the worst-case detours of its users. This paper was accepted by Hamid Nazerzadeh, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03125 .

Data Plants: Growing Insights in Agriculture through Data Science

Data Science blends different scientific methods to extract valuable insights from large sets of data. Agriculture is crucial for India's economy because more than half of its population relies on it as their main source of income. Research and Extension systems are key in developing and spreading agricultural technologies to boost farmers' earnings. Recently, the use of data has become a game-changer in farming, transforming traditional practices and promoting sustainability efforts. This paper investigates how data-driven methods like machine learning, remote sensing, and predictive analytics are reshaping farming operations and decision-making processes. It also discusses the challenges hindering the widespread adoption of data-driven approaches in farming, such as data security issues and lack of infrastructure. Despite these challenges, there are many opportunities for further exploration and innovation, emphasizing the importance of collaboration across disciplines and policy interventions to maximize the benefits of data-driven approaches for sustainable food production. In summary, this study highlights the significant role of data in farming and its importance in addressing global food security and environmental sustainability challenges.

Data science for measuring regional competitiveness

Data science for measuring regional competitiveness

Predictive Modelling on Competitor Analysis Performance by using Generalised Linear Models and Machine Learning Approach

Competitive analysis in digital and technology is trending in the business field. However, the field of digital and technology in the business world is vast and challenging to analyse. The purpose of this research is first to identify the success factor which represent the company performance. Second, is to identify the significant services provided by the company to their business user. Then, based on the first and second objectives, a predictive modelling is developed to produce the best solution to their business user. The research is implementing a case study from Telecommunication Company and using data science life cycle methodology. The statistical modelling that is used to develop the competitor’s analysis model is generalised linear model (GLM) which integrated with machine learning approach. Furthermore, the synthetic data set is created by using Gamma Distribution, Gaussian Distribution and Poisson Distribution due to some data from the case study is confidential. The synthetic data set is based on existing real data which are from Telecommunication Company sentiment analysis data, were used to investigate the performance of the proposed model. The machine learning technique is used to get the accuracy of the significant GLM which has been developed. The accuracy is tested by using the error rates of the machine learning technique which are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared. This research discovered that the business solution to the significant service for the business user and discovered the best statistical model to be used for the business solution. The results show that the Gumbel distribution is the best fit model for the synthetic dataset where the values of RMSE is 1.0574, MAE is 0.9168 and R-squared is 0.3994, and the significant success factor that has been identified by using the GLM is advertising success factor. The model developed can be improved with another type of data set and different sizes of data. Hence, further studies and real-world data are required for better validation.

A Bibliometric Analysis of Publications on Cadavers: A Study Based on Web of Science Data From 1978 to 2023.

This study aimed to investigate the significance of publications examining the effectiveness of cadaver studies in the field of medicine with the method of bibliometric analysis, which emerged in the 1950s, offers the opportunity to conduct a detailed analysis of a specific subject, just like a systematic literature review or meta-analysis. Also, it aimed to enlighten the content of cadaver studies in the last half-century and to present a perspective for the future. In the study, an advanced search was conducted on the Web of Science Core Collection database on August 1, 2023, using the keywords "cadaver," "cadaver study," "cadaveric dissection." Review articles were excluded from the study. There were determined 34554 documents. The documents were transferred to the VOSviewer software program. In this way, the authors made detailed analyses of authors, keywords, journals, organizations, institutions, and countries and created scientific maps. The United States was one of the most important countries in terms of research. Generally, the use of cadaver terms in documents belongs to surgery, anatomy, and transplantation journals. Anatomy, cadaver, biomechanics, ultrasound, and computed tomography were the top 5 most frequently used keywords with cadaver term. The findings of our study showed that cadaver studies are used in many fields of medical science. However, clinical studies, including advanced imaging techniques, draw attention to the developing technology. Along with the powerful institutions of the United States, its great contributions to publications stand out.

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China.

Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup. For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.