Data Analysis Applications Research Articles

Vulnerability and Malware Detection

The increasingly linked digital world oftoday has made cybersecurity a top priority. The rise in cyber dangers, notably vulnerabilities and malware, poses major risks to individuals, organizations, and governments. This review article offers a thorough analysis of the approachesand instruments currently in use for malware and vulnerability detection. It examines and contrasts several detection methods, such as machine learning techniques, static and dynamic analysis, and signature-based detection. The study also examines new developments in the sector, such as the application of big data analytics and artificial intelligence (AI) to the detection of complex threats. It also covers the need for real-time detection methods, changing threat environments, and the difficulties posed by false positives.

Optimizing Communication for a Humanoid Subs-Talker Robot Using RSSI Analysis on ROS System

This study aims to optimize communication for the humanoid "Subs-Talker" robot using Receive Signal Strength Indicator (RSSI) analysis within the Robot Operating System (ROS). The background of this research highlights the importance of stable and strong inter-robot communication in the Indonesian Humanoid Robot Soccer Contest (KRSBI-H), where robots need to function in synergy. The method involves measuring signal strength at specific distances, both with and without the use of an extender device, utilizing the InSSIDer application for RSSI data recording and analysis. The results show that using an extender improves communication stability, especially at longer distances (7.2 and 9.0 meters), although the impact is minimal at closer distances. The discussion suggests that an extender can be an effective solution for enhancing inter-robot communication quality in competitions. In conclusion, the extender significantly improves the communication range and signal quality among robots, supporting optimal performance in collaborative robot interactions within the competition arena.

Insights into Target Gas-Oxygen Interactions in Highly Sensitive Gas Sensors Using Data-Driven Methods.

In the gas-sensing mechanism of a metal-oxide-semiconductor (n-type) gas sensor, oxygen adsorption or desorption on the oxide surface leads to an increase or decrease in the resistance of the gas sensor system. Additionally, oxygen can be adsorbed again at the location where initially adsorbed oxygen reacted with the target gas. Thus, the adsorption-desorption equilibrium of the reducing gas on the oxide surface is a significant factor in determining the sensitivity and reaction rate. In particular, for ultralow-concentration gas measurements, the relative concentration of oxygen was very high. To design an ultrasensitive gas sensor, not only the reaction of the target gas but also the competing reaction between the target gas and oxygen must be considered. Although qualitative investigations of these complex relationships have been performed according to the gas concentration and flow rate, reliable quantitative results are limited. In this study, a quantitative approach was used to understand the correlation between oxygen and a target gas by applying data analysis methods. We investigated the behavior of oxygen and the target molecules depending on the gas concentration and flow rate using the parts per billion level of the acetone gas sensor. Initial response data according to various detection conditions were processed using principal component analysis and K-means clustering; as a result, four types of reaction behaviors were inferred for 15 types of reaction conditions. Furthermore, the response time, depending on the detection conditions, can be distinguished using the suggested categorization. Our investigation suggests a possibility beyond simple optimization through the data analysis of the gas-sensing results.

Sustainable Manufacturing Engineering: Enhancing Product Quality through Green Process Innovations

The imperative for sustainable practices within manufacturing has become increasingly critical, driven by environmental concerns, regulatory pressures, and the evolving expectations of eco-conscious consumers. This paper investigates the role of sustainable manufacturing engineering in enhancing product quality through the implementation of green process innovations. Specifically, the study explores how sustainable manufacturing methods, including energy-efficient systems, additive manufacturing, zero-waste production, and renewable energy integration, can drive quality improvements while reducing the environmental impact of production processes. The research is grounded in a comprehensive analysis of current green innovations in the manufacturing sector, drawing on case studies of companies that have successfully balanced quality and sustainability. The findings indicate that companies adopting sustainable practices not only reduce waste and lower emissions but also experience notable gains in product durability, customer satisfaction, and brand reputation. For instance, additive manufacturing is shown to improve precision and material efficiency, while zero-waste initiatives lead to a more efficient resource cycle, which in turn supports consistent quality standards. This paper further delves into the challenges of implementing green manufacturing processes without compromising product quality, examining factors such as initial costs, technological readiness, and market adaptation. Visual data in the form of tables and graphs are presented to compare different green process innovations and their respective impacts on quality and sustainability metrics. In addition, case study summaries provide real-world examples of how leading companies have enhanced product quality through sustainable engineering approaches. This study underscores the value of sustainable manufacturing engineering as a dual-benefit strategy that can drive environmental stewardship alongside quality enhancement. The paper recommends that future research focus on the development of new, cost-effective green technologies and the application of advanced data analytics for improved process control in sustainable manufacturing. Through these efforts, the manufacturing sector can continue advancing toward a more sustainable, high-quality production paradigm.

Leaf disease detection and classification with a CNN-LSTM hybrid model

Abstract. Smart agriculture refers to the use of modern information technology, Internet of Things technology, and artificial intelligence to achieve accurate management, efficient operation and sustainable development of the entire process of agricultural production. Smart agriculture mainly includes the application of data collection and analysis, intelligent agricultural machinery, precise fertilization, disease and pest detection and agricultural products traceability. Leaf disease detection and classification are considered as challenging yet important tasks in smart agriculture. Deep leaning methods have been proven effective for these image-based recognition tasks. In this study, two advanced deep learning methods, namely, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined together to achieve a further improvement. Numerical results demonstrate that the proposed method outperforms both CNN and LSTM variants.

Research on the Legal Risks of Digital Currency in the Big Data Environment

With the widespread application and growth of blockchain technology, cloud computing, and big data analytics in the financial industry, digital currencies have emerged. However, relevant regulations specifically targeting digital currencies have not been officially promulgated yet. In the process of promoting digital currencies, legal risks such as unclear boundaries of compensation liability, unclear ownership, potential privacy breaches, and financial regulatory challenges still need to be overcome. Therefore, it is essential to prioritize the prevention and management of legal risks associated with digital currencies in the big data environment, ensuring the smooth circulation and use of digital legal currencies across regions and promoting the internationalization and legalization of China’s digital legal currencies.

A panel data analysis application to determine the factors affecting the return on equity capital of development and investment banks during times of inflation

A panel data analysis application to determine the factors affecting the return on equity capital of development and investment banks during times of inflation

Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

Purpose This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers. Design/methodology/approach The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes. Findings The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates. Research limitations/implications Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance. Originality/value This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions

Low-Code Development Platforms (LCDPs) empower users to create and deploy custom software with little to no programming. These platforms streamline development, offering benefits like faster time-to-market, reduced technical barriers, and broader participation in software creation, even for those without traditional coding skills. This study explores the application of LCDPs in Precision Agriculture (PA) through a systematic literature review (SLR). By analyzing the general characteristics and challenges of LCDPs, alongside insights from existing PA research, we assess their feasibility and potential impact in agricultural contexts. Our findings suggest that LCDPs can enable farmers and agricultural professionals to create tailored applications for real-time monitoring, data analysis, and automation, enhancing farming efficiency. However, challenges such as scalability, extensibility, data security, and integration with complex IoT systems must be addressed to fully realize the benefits of LCDPs in PA. This study contributes to the growing knowledge base in agricultural technology, offering valuable insights for researchers, practitioners, and policymakers looking to leverage LCDPs for sustainable and efficient farming practices.

Big Data Insights into Coastal Tourism: Analyzing Customer Satisfaction at Egyptian Red Sea Dive Resorts

This research aims to explore the relationship between customer satisfaction and various extracted factors at dive resorts in the Red Sea, Egypt, utilizing a hybrid methodology of descriptive and diagnostic analytics applied to online review data. Employing techniques such as KH coder for text analysis, exploratory factor analysis (EFA), and linear regression, this study systematically identifies key elements that influence customer satisfaction. Findings reveal that activities related to diving and marine life markedly enhance guest satisfaction, underscoring the critical role these aspects play in the overall appeal of Egyptian coastal tourism. Conversely, areas such as dining and amenities were identified as needing improvement. The originality of this study lies in its application of big data analytics to dissect and understand customer feedback in a sector-specific context, providing strategic insights for the sustainable advancement of coastal tourism in Egypt. By focusing on dive resorts, this research highlights their integral role in coastal tourism and offers a model for leveraging online customer reviews to enhance service quality and promote sustainable practices within the tourism industry, contributing to the overall growth and sustainability of coastal tourism.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Experience in applying large language models to analyse sociological data obtained as a result of interviews on students’ perception of entrepreneurial activity

Modern society is experiencing a digital transformation of various spheres associated with the development of artificial intelligence and big data. The introduction of large language models (hereinafter referred to as LLM) into scientific research opens new opportunities, but also raises a number of questions, which makes it relevant to study the peculiarities of their application for qualitative data analysis in sociology. The purpose of this article is to explore how LLM can influence the methodology and practice of sociological research, and to identify the advantages and disadvantages of their application. The authors rely on the use of the Calude-3 LLM to qualitatively analyse empirical data from a sociological study of students’ perception of ­entrepreneurship. The possibilities of LLM in the analysis of qualitative data are revealed: analysis of sentiment, construction of logical conclusions, classification, clustering, and formation of typologies. The advantages of using LLM are shown: increased data processing speed, saving time and resources. The application of LLM becomes a tool to optimise the research process in sociology, allowing to deepen the analysis of qualitative data, but it also has a number of limitations: social and political bias, difficulties with hallucinations. It is necessary to increase the transparency of models, improve their interpretability and explainability and reduce their social and political bias as well as ethical and legal regulation of the use of LLM models.

Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.

High-throughput sequencing (HTS) technologies have been instrumental in investigating biological questions at the bulk and single-cell levels. Comparative analysis of two HTS data sets often relies on testing the statistical significance for the difference of two negative binomial distributions (DOTNB). Although negative binomial distributions are well studied, the theoretical results for DOTNB remain largely unexplored. Here, we derive basic analytical results for DOTNB and examine its asymptotic properties. As a state-of-the-art application of DOTNB, we introduce DEGage, a computational method for detecting differentially expressed genes (DEGs) in scRNA-seq data. DEGage calculates the mean of the sample-wise differences of gene expression levels as the test statistic and determines significant differential expression by computing the P-value with DOTNB. Extensive validation using simulated and real scRNA-seq data sets demonstrates that DEGage outperforms five popular DEG analysis tools: DEGseq2, DEsingle, edgeR, Monocle3, and scDD. DEGage is robust against high dropout levels and exhibits superior sensitivity when applied to balanced and imbalanced data sets, even with small sample sizes. We utilize DEGage to analyze prostate cancer scRNA-seq data sets and identify marker genes for 17 cell types. Furthermore, we apply DEGage to scRNA-seq data sets of mouse neurons with and without fear memory and reveal eight potential memory-related genes overlooked in previous analyses. The theoretical results and supporting software for DOTNB can be widely applied to comparative analyses of dispersed count data in HTS and broad research questions.

A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

The Analytics Paradigm: Balancing Innovation and Ethics in a Data-Centric World

This article examines data analytics's profound and multifaceted influence on contemporary society, exploring its transformative impact across various sectors and the challenges it presents. By synthesizing current research and case studies, we demonstrate how data analytics enhances decision-making through personalization and predictive insights, drives innovation in business and technology, and improves public services, particularly in healthcare and urban planning. The article also critically addresses the ethical implications and privacy concerns associated with the proliferation of data-driven approaches, including data security, algorithmic bias, and fairness. As data analytics continues to reshape societal structures and individual experiences, this article argues for a balanced approach that maximizes its potential benefits while mitigating risks. Our analysis concludes by considering the long-term societal implications of this data revolution, emphasizing the need for adaptive policies, education, and ethical frameworks to guide the future development and application of data analytics in an increasingly connected world.

Governmentality in construction claim management: Role of smart data initiatives

Despite efforts to identify and prevent causes, construction claims remain inevitable and repetitive. Therefore, focus has shifted to managing claims with systematic processes, yet challenges related to claim preparation persist. Drawing from political governance, where data analytics promote self-governance to overcome similar challenges, this study contrasts politics with construction claims, aiming to investigate potential for adopting similar data-driven approaches in the construction claim context. Through a systematic literature review in conjunction with ten industry interviews, we present similarities in control mechanisms and disciplinary powers as regulatory elements but reveal dissimilarities in self-governance and utilisation of data analytics. This study proposes categorizing initiatives in claim preparation to people-led, organisational-led, and data-led with data analytics pivotal in each. Through the application of data analytics resembling governmentality within the realm of political governance, the study reveals that the integration of self-governance and smart data analytics presents a potential solution for effectively addressing the challenges inherent in the preparation of construction claims.

IMPLEMENTASI ALGORITMA KNN UNTUK DETEKSI GENDER DAN USIA BERBASIS PENGENALAN WAJAH

In the modern era, face recognition is one of the important technologies used in various security and data analysis applications. This paper aims to use Python to implement the K-Nearest Neighbors (KNN) algorithm to detect gender and age through face recognition. The main problem faced is the accuracy in classifying gender and estimating age from various facial images with lighting, pose, and expression. This study uses various steps to collect facial data, use image processing techniques to extract features, and use the KNN algorithm for classification. The results show that applying KNN can detect gender and age from faces with sufficient accuracy, although there are some problems with extreme lighting and pose conditions. These findings indicate that the KNN algorithm can be used in facial recognition applications to detect gender and age. By fixing parameters and improving data quality, further improvements can be achieved.

Compositional-geochemical characterization and multi-element pollution and risk assessment of potentially toxic elements in the urban landfill soil

Urban areas are characterized by the presence of various pollution sources including landfills. During the recent decade, urban landfills were investigated as a source of pollution by potentially toxic elements and potential deposits for landfill mining. The contents of Cr, V, Ti, Ca, K, Mo, Zr, Sr, Rb, As, Zn, Cu, Co, Fe, Mn, Pb, Ba in soils of Vanadzor city landfill site (VL) were determined using the X-ray fluorescence spectrometer to assess multi-element pollution and ecological risk and to identify potentially toxic elements geochemical associations through the application of compositional data analysis. The results showed that Cr, Mo, Zn, Cu, and Pb displayed significant values (>100%) of coefficient of variation suggesting their anthropogenic origin. Moreover, these elements, except Cr, displayed outliers and extreme values in the same sampling sites located directly on the reclaimed part of the landfill. The median contents of Zn, Pb, Cu, Cr, Cd, and As exceeded the global median values by 8.2, 9.8, 19.0, 4.6, 6.1, and 4.5 times, respectively. The multi-element pollution assessment showed that an extremely hazardous level of pollution was observed on the reclaimed part of the VL. From this site, a trend of decrease in the pollution level was observed in the eastern and western directions. In contrast, an allowable level of pollution was observed in the northern part - toward the direction of the above-located hill. The VL was found to be in an inferior ecological condition (a very high level of potential ecological risk was observed), which might also have a negative impact on the ecosystems located in its vicinity. The hierarchical clustering has revealed that the studied elements are grouped in two geochemical associations: a naturally formed - Co, Fe, V, Ti, Mn, Sr, Zr, Rb, K, Ba, As; and an anthropogenic one - Zn, Pb, Cu, Ca.

The Shape(s) of Information Practice: Using Radial Mapping Qualitatively

ABSTRACTInformation practices comprise both seeking and avoidance. Although information practices scholars use qualitative and visual methods to understand seeking practices, they rarely do so to understand avoidance. This paper proposes a new visual method that supports revealing and explaining the complex interplay of seeking and avoidance in rich qualitative data. We introduce seven dimensions of information seeking and avoidance practice (intensity, granularity, engagement, control, relevance, quality, and timeliness). We conceptualize these visually as axes radiating outward from a central origin. We use Excel radar charts to depict these dimensions, allowing us to identify and characterize the shapes of information seeking and avoidance in everyday information practices. We apply our approach to two cases from published literature to show how mapping reveals the interplay between seeking and avoidance at one point in time and over time. We propose potential data collection and analysis applications of this method for information practices research.