Browse Items (14421 total)
Sort by:
-
Unveiling Patterns, Visualizations, and Trends from Patient Diabetes Data
The important role that exploratory data analysis, or EDA, plays in the context of diabetes prediction is explored in this work. EDA is used as a key component of a multimodal strategy to identify unique characteristics linked to diabetes. EDA offers insights that aid in the creation of prediction models by sifting through the complex patterns present in the medical data. The focus is on using EDA to fully grasp the data landscape while also comprehending the distinct features of diabetes. This investigation is critical to accurately categorizing people into discrete risk groups and emphasizes the use of domain-specific knowledge in enhancing diabetes prediction techniques. The research suggests using specific EDA techniques to gain deep insights and lead proactive responses. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Unveiling Powerful Machine Learning Strategies for Detecting Malware in Modern Digital Environment
Machine learning has emerged as formidable instrument in realm of malware detection exhibiting capacity to dynamically adapt to ever-shifting topography of digital hazards. This study presents an exhaustive comparative analysis of four intricate machine learning algorithms namely XGBoost Classifier, K-Nearest Neighbors (KNN) Classifier, Binomial Logistic Regression and Random Forest with primary objective of assessing their effectiveness in domain of malware detection. Conventional signature-based detection methodologies have struggled to synchronize with rapid mutations exhibited by malware variants. In sharp contrast machine learning algorithms proffer data-centric approach adept at unraveling intricate data patterns thereby enabling identification of both well-known and hitherto uncharted threats. To meticulously appraise efficacy of these machine learning models we employ stringent set of evaluation metrics. Precision, recall, F1 Score, testing accuracy and training accuracy are meticulously scrutinized to ascertain distinctive strengths and frailties of these algorithms. By providing comparative analysis of machine learning algorithms within milieu of malware detection this research engenders significant contribution to ongoing endeavor of fortifying cybersecurity. Resultant analysis elucidates that each algorithm possesses its unique competencies. XGBoost Classifier showcases remarkable precision (Benign files: 99%, Malicious files: 99%), recall (Benign files: 97%, Malicious files: 99%) and F1 Score (Benign files: 98%, Malicious files: 99%) implying its aptitude for precise malware identification. KNN Classifier excels in discerning benign software exhibiting precision (Benign files: 90%) and recall (Benign files: 91%) to mitigate likelihood of erroneous positives. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Unveiling Sentiment Trends: An Approach to Utilize Machine Learning in Studying User Activities on New Social Applications
Sentiment analysis is the examination of textual data to determine the writer's attitude, which can be positive, negative, or neutral. In the context of social media analysis, sentiment analysis is peculiar as it helps to identify trends in large amounts of data that are posted by social media users. In the case of sentiment analysis algorithms, the text is categorized into positive, negative, and neutral. Classification of sentiments involves the use of several algorithms such as the decision tree, support vectors, and neural networks. In other words, the paper intends to determine the users sentiment using the decision tree model. Some of the common data sets that have been utilized in this study include the COVID-19 pandemic data, movie reviews, and product ratings. What is tried to be accomplished in this type of case is to determine the efficiency and stability of the decision trees, as well as their optimum success region. Based on the results, it can be pointed out that the accuracy is the highest for the COVID-19 Tweets dataset when referring to the simulation model, which is 98%; hence, the decision tree is best used in the context of the health sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Unveiling stabilization mechanisms in a chaotic fractional Cryosphere model
This article investigates the influence of chaos control and time delay on a chaotic surface energy balance-mass balance model of the Cryosphere. This research delves into the effectiveness of employing the active control method to stabilize chaotic systems with different fractional orders. Moreover, the investigation uncovers a noteworthy aspect of system dynamics, highlighting the role of time delay as a stabilizing element in chaotic systems. The generalized predictor-corrector method has been used to solve the fractional delayed and non-delayed systems. Numerical simulations show that the addition of time lag confers stability to the chaotic model for fractional orders ? = 1 and 0.95. Remarkably, for ? = 0.90, 0.85, 0.80 and 0.75, the delayed model transitions into an asymptotically stable state, revealing the significant stabilizing effect of time delay. CSP - Cambridge, UK; I&S - Florida, USA, 2024 -
Unveiling the bulge-disc structure, AGN feedback, and baryon landscape in a massive spiral galaxy with Mpc-scale radio jets
We study the bulge-disc components and stellar mass distribution in the fast-rotating, highly massive spiral galaxy 2MASX J23453268-0449256, which is distinguished by extraordinary radio jets extending to Mpc scales. Using high-resolution multiwavelength Hubble Space Telescope (HST) observations and multiparameter panchromatic spectral energy distribution (SED) fitting, we derive estimates of key properties, such as the star formation rate, total baryonic mass in stars, and the characteristics of warm dust. Our findings, validated at a spatial resolution of approximately 100 pc, reveal a pseudo-bulge rather than a classical bulge, as well as a small nuclear bar and resonant ring, challenging traditional models of galaxy formation. Furthermore, the absence of tidal debris and the highly symmetric spiral arms within a rotationally supported stellar disc suggest a peaceful co-evolution of the galactic disc and its central supermassive black hole (SMBH). Notably, the galaxy exhibits suppressed star formation in its central region, which may be influenced by feedback from the central accreting SMBH, producing powerful radio jets. Detailed multiwavelength studies of potential star-forming gas show that while hot X-ray gas cools in the galaxy's halo, new stars do not form in the centre, likely due to this feedback. This study raises important questions about the efficient fuelling and sustained collimated jet activity in J2345-0449, highlighting the need for a better understanding of the central black hole's properties. The exceptional rarity of galaxies like 2MASX J23453268-0449256 presents intriguing challenges in uncovering the physical processes behind their unique characteristics. 2025 The Author(s). -
Unveiling the Dual Potential of the MoS2@VS2 Nanocomposite as an Efficient Electrocatalyst for Hydrogen and Oxygen Evolution Reactions
Clean and reliable energy sources are essential amidst growing environmental concerns and impending energy shortages. Creating efficient and affordable catalysts for water splitting is a challenging yet viable option for renewable energy storage. Traditional platinum-based catalysts, while highly active, are quite expensive. Our study introduces two-dimensional (2D) MoS2@VS2 nanocomposites, developed using hydrothermal technique, as a bifunctional catalyst for the electrolysis of water into valuable products. Structural studies revealed the formation of MoS2@VS2 nanocomposites with a nanoflake-like structure, where MoS2 nanosheets grow on the VS2 surface. This 2D-based electrocatalyst demonstrated exceptional reaction kinetics, with low overpotentials of 265 mV for the hydrogen evolution reaction (HER) and 300 mV for the oxygen evolution reaction (OER) at 10 mA/cm2. Furthermore, the electrocatalyst displayed small Tafel slopes of 65 mV/dec and 103 mV/dec for HER and OER, respectively, along with excellent stability. The unprecedented catalytic activity stems from the synergistic effect between semiconducting MoS2 and metallic VS2. Density functional theory calculations confirmed that this synergy enhances the electrical conductivity, facilitating efficient electron transfer during the reaction and providing an abundance of exposed active sites. These results mold MoS2@VS2 nanocomposites as promising electrocatalysts for overall water splitting, paving the way for sustainable energy future. 2024 American Chemical Society. -
Unveiling the Dynamics of Initial Public Offerings: A Comprehensive Review of IPO Pricing, Performance, and Market Trends
Initial Public Offerings (IPOs) serve as pivotal moments in the financial markets, representing a company's transition from private to public ownership. The importance of IPOs lies in their capacity to raise substantial capital, facilitating business expansion and development. This paper conducts an in-depth analysis of Initial Public Offerings (IPOs) in India spanning the period from 2018 to 2022, with a particular focus on their listing day performance. The study categorizes IPOs into various issue price ranges, revealing substantial variability in listing day returns across these categories. It underscores the importance of pricing strategy, emphasizing the need for companies to carefully assess their issue prices to align with market demand. Furthermore, the analysis highlights the varying levels of risk associated with IPO investments based on issue price ranges, advocating for diversification and thorough due diligence. In addition, the paper emphasizes the dynamic nature of IPO markets, influenced by factors beyond pricing, and encourages a balanced approach that considers both potential rewards and challenges. This research provides valuable insights for stakeholders, guiding companies, investors, and analysts in making informed decisions in the dynamic world of IPOs. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Unveiling the Dynamics: A Performance Analysis of RPL under Congestion in IoT Network
The Routing Protocol for Low Power and Lossy Network (RPL) is a standardized routing protocol for resource constraint devices deployed in diverse applications in Internet of Things (IoT). RPL is the most efficient protocol which is carefully designed to meet energy efficiency of sensor nodes. However, this protocol is prone to network congestion which is one of most crucial bottlenecks of this protocol. In the current study a thorough analysis of effect of congestion on RPL routing metrics are analyzed. We have designed a congestion scenario using Cooja simulator and analyzed its effects on ETX, Power, Duty Cycle through graphs. The results of the experiments finally outline the critical parameters affected due to congestion in RPL. Grenze Scientific Society, 2024. -
Unveiling the Ecological and Geopolitical Contradictions of Large-Scale Development Initiatives: A Critical Analysis of the Belt and Road Initiative
This chapter critically examines the ecological and geopolitical contradictions within large-scale development initiatives, focusing on China's Belt and Road Initiative (BRI) as a paradigmatic case. While framed as a vehicle for sustainable growth, the BRI often instrumentalizes sustainability discourse to legitimize extractive development, suppress dissent, and extend geopolitical influence. Using interdisciplinary approaches from political economy, post-development theory, and critical discourse analysis, the chapter deconstructs narratives such as 'Green BRI,' ecological civilization, and win-win cooperation. Case studies from Southeast Asia and Africa expose a stark contrast between rhetorical commitments and realities of environmental degradation, displacement, and debt dependency. The chapter advocates for an eco-centric, justice-oriented development paradigm and offers policy recommendations that emphasize accountability, participation, and ecological integrity in future infrastructure planning. 2026, IGI Global Scientific Publishing. All rights reserved. -
Unveiling the Emotions: A Sentiment Analysis of Amazon Customer Feedback
This study explores sentiment analysis in the context of diverse regions and contemporary customer feedback, aiming to address research questions related to consolidation based on polarity scores and sentiments. The research utilizes multinomial regression for a comprehensive analysis of customer feedback worldwide. The investigation incorporates confusion matrices, statistics, and class-specific metrics to evaluate the models performance. Results indicate a highly accurate model with perfect sensitivity, specificity, and overall accuracy. The analysis further includes a breakdown of key metrics such as accuracy, confidence intervals, no information rate, p-value, kappa, and prevalence, emphasizing the models robustness. In conclusion, the multinomial logistic regression model demonstrates exceptional performance in predicting sentiment across diverse classes, highlighting its effectiveness in sentiment analysis on a global scale. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Unveiling the Factors of Women Entrepreneurs on Social Media to Achieve Enterprise Sustainability
The research studies in the area of womens entrepreneurship (WE) has received more attention in the last decade due to its impact on bringing balanced development. On one hand, the growth of digital innovation has changed the landscape of entrepreneurship in emerging markets and on the other hand, the advocacy on business sustainability has increased. Prior studies are limited to understand the role of WE in this changing landscape. This study aims to identify the most relevant factors that influence the women entrepreneurs on social media to develop sustainable enterprise. An extensive literature review has been conducted to advance the knowledge on the WE and has been presented in form of a conceptual model to present a comprehensive perspective. Further, the research identifies social factors, psychological factors, resource factors, financial factor, firm-performance related factors, and technological factors. These factors are linked with entrepreneurial orientation among women on social media and therefore this helps in gaining sustainability. These study further present implications, strategies and agenda for future research in the area of WE. 2025 selection and editorial matter, Esra Sipahi Dongul, Serife Uguz Arsu, Richa Goel, and Tilottama Singh; individual chapters, the contributors. -
Unveiling the Future of Business Success: Integrating Environmental and Sustainable Criteria through Business Cases
This chapter explores how environmental and sustainable criteria can enhance organizational success in rapidly changing economic and societal trends. It also focuses on future value creation. The continuing fight is leading to the obsolescence of traditional tools for management due to the increased level of competition, rapid rates of change, and time compression in society. To address urgent demands for sustainability, this research examines the blending of ecological and sustainable standards with corporate objectives, illustrating how businesses can benefit from such integration. The existing business practices are presented with evidence showing their impact on the environment and society, thereby stressing the need for sustainability measures that ensure long-term success. Through an exploration of peer-reviewed literature on this topic, a variety of ways are examined. Different companies can incorporate these criteria into their operations by an analysis of best practice followed by market leaders that involve setting clear objectives and identifying key performance indicators (KPIs) to measure progress. In addition, the chapter investigates the strategies that corporations need to implement in order to adopt environmental integrity principles within their company policies, such as renewable energy sources, intelligent buildings and circular economy models, as well as other solutions that can reduce their ecological footprints, leading to organizational excellence. 2026 selection and editorial matter, Sonal Trivedi, Balamurugan Balusamy, Krishnaraj Nagappan, Dinesh Krishnan Subramaniam and Daniel Arockiam; individual chapters, the contributors. All rights reserved. -
Unveiling the Future: Exploring Stock Price Prediction in the Finance Sector through Machine Learning and Deep Learning - A Comprehensive Bibliometric Analysis
The investigation of predicting share prices is a captivating and beneficial area of study within the realm of economic research. precise projections and findings can potentially benefit shareholders by reducing the risk of making suboptimal investment selections. The objective of this investigation is to examine the present state of research pertaining to the prognostication of share price predictions through the utilization of Machine Learning (ML) and Deep Learning techniques. The present study examined the existing body of scientific works on methods involving DL and ML in the context of predicting the value of stocks. This study presents a comprehensive overview of research trends, methodologies, and applications in a particular field by conducting a bibliometric analysis of publications indexed in the Scopus database. Drawing from the presented data, recommendations for optimal methodologies can be formulated. The data was visually represented through the utilization of the R programming language and Vos Viewer software. The investigation additionally discerns the primary authors, institutions, and nations that are making contributions to this particular field of research. The outcomes of this investigation possess the potential to guide future research trajectories and offer significant perspectives for professionals and policymakers who are keen on utilizing machine learning and deep learning in the financial sector. 2024 IEEE. -
Unveiling the Impact of Adverse Childhood Experiences on Adult Criminal Behavior: A Qualitative Enquiry
This qualitative study explored how adverse childhood experiences contribute to criminal behavior among 20 male prisoners (aged 2040) in Kerala. Using semi-structured interviews, thematic analysis revealed seven key themes: family dysfunction, emotional struggle, abuse, economic struggle, peer pressure, coping mechanisms, and sensation seeking. Findings showed that family dysfunction creates baseline trauma, fostering emotional voids and maladaptive coping. The study emphasizes the interconnectedness of multiple adversities in shaping criminality. It highlights the need for early interventions addressing trauma, emotional dysregulation, and unhealthy coping patterns through supportive networks to prevent criminal behavior later in life. 2025 Taylor & Francis Group, LLC. -
Unveiling the Indian REIT narrative-qualitative insights intoretail investors perspectives
Purpose: The present study delves into the causes of relatively lower retail participation in the Indian REIT market. Specifically, it investigates investors' attitudes and perceptions towards REITs as a unique asset class. This paper provides a comprehensive understanding of the perception and factors influencing Indian retail investors' reluctance to participate in the REIT market. Design/methodology/approach: Qualitative research was conducted through semi-structured interviews to gather insights from non-investors in REITs. The data were transcribed and analyzed using content analysis techniques. Finally, coding techniques were used to identify broad study themes. Findings: According to the study results, many retail investors are unfamiliar with REITs. Even among those knowledgeable about REITs and with a favorable view, it is not commonly seen as a feasible investment option due to its early stage, unattractive returns and limited number of REITs. Practical implications: Developed countries have established REIT markets, while it is still in its infancy in developing countries such as India. Financial advisors, fund houses and the media should focus on educating investors to increase awareness. Originality/value: The study is the first qualitative investigation into the perception of retail investors to understand the reasons for lower retail engagement in the Indian REIT market. 2024, Emerald Publishing Limited. -
Unveiling the kinetics of oxygen evolution reaction in defect-engineered B/P-incorporated cobalt-oxide electrocatalysts
Defect-rich transition-metal oxide electrocatalysts hold great promise for alkaline water electrolysis due to their enhanced activity and stability. This study presents a new strategy that significantly improve the OER activity of Co-oxide nanosheets through incorporation of B and P (B/P-CoOx NS), eventually leading to abundant surface defects and oxygen vacancies. The B/P-CoOx NS demonstrates low overpotential of 220 mV to achieve 10 mA/cm2. The electrochemical and kinetic studies coupled with conventional morphological and structural characterizations, reveal that various crystalline defects like vacancies, dislocations, twin planes, and grain boundaries play crucial roles in promoting the OH? ion adsorption, the formation of intermediates, and the desorption of oxygen molecules. The industrial viability of the developed electrocatalyst is substantiated through assessments under harsh industrial conditions of 6 M KOH at 60 C in a zero-gap single-cell alkaline electrolyzer which achieves 1 A/cm2 at 1.95 V. Chronoamperometry tests (100 h) highlight remarkable robustness of the electrocatalyst. This work establishes a new strategy to fabricate defect-rich OER electrocatalysts, setting a precedent to achieve better OER rates with non-noble materials. 2024 -
Unveiling the Landscape: A Comparative Study of U-Net Models for Geographical Features Segmentation
Geographical features segmentation is a critical task in remote sensing and earth observation applications, enabling the extraction of valuable information from satellite imagery and aiding in environmental analysis, urban planning, and disaster management. The U-Net model, a deep learning architecture, has proven its efficacy in image segmentation tasks, including geographical feature analysis. In this research paper, a comparative study of various U-Net models customized explicitly for geographical features segmentation is presented. The study aimed to evaluate the performance of these U-Net variants under diverse geographical contexts and datasets. Their strengths and limitations were assessed, considering factors such as accuracy, robustness, and generalization capabilities. The efficacy of integrated components, such as skip connections, attention mechanisms, and multi-scale features, in enhancing the models performance was analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Unveiling the Motivation Drivers in Start-Up Workspace
This article delves into the relationship between workplace happiness and productivity in startup settings. Its primary objective is to dissect the multifaceted factors impacting employee well-being, aiming to enhance overall efficiency by customizing the work environment in myriad ways. For this study, a descriptive causal methodology was employed to investigate the impact of workplace happiness on productivity within start-up companies. A carefully structured questionnaire was administered to 256 employees within well-established organisations in Bangalore, India. Participants were selected through a Judgement sampling process to ensure impartial and unbiased representation. Survey respondents preferred the pre-COVID working conditions, acknowledging their advantages. However, the increased autonomy and flexibility in work arrangements have led to enhanced productivity under the new hybrid model. Notably, when employees are entrusted with greater responsibility, their job satisfaction rises, resulting in increased work output. organizations are now tasked with offering additional incentives to remote employees, thereby elevating the satisfaction and job fulfilment experienced by these individuals. Effectively tackling challenges necessitates the alignment of learning and development objectives with the internal business processes that maximize each employee's abilities and potential. This involves meeting the criteria outlined in the balanced scorecard components. 2024, Iquz Galaxy Publisher. All rights reserved. -
Unveiling the Necropolitics of Oil on Migrant Bodies in Deepak Unnikrishnans Birds
Oil played a significant role in fuelling the sociopolitical and economic development of Middle Eastern nations, attracting mass migration from South Asian nations. The article draws a nexus between the energy dynamics and labour exploitation within these petroleum-rich nations. It undertakes a close reading of the text Birds from Deepak Unnikrishnans novel Temporary People as it depicts the lives of migrant labourers who navigate an exploitative petro-capitalist system. The fictional text employs a narrative strategy juxtaposing elements of magic and realism, opening up a space for multilayered marginalised voices. The article engages with energy theories and interweaves Mbembes theory on necropolitics to grasp oils sovereign influence in delineating the boundaries between life and death in migrant lives. The surplus energy generated through fossil fuel extraction contributes to notions of boundless growth, coupled with technical and economic progress, which conceals the intensive manual labour underpinning these petrocultures. The magical property alluded to oil and the spectral absence of labour in the socio-cultural imagination co-constructs an exploitative and dehumanising labour regime for migrants. The migrant body is kept alive, and their existence is contingent upon the instrumental value of their labouring body, which constructs them as easily disposable and expendable as they are positioned outside the formal boundaries of citizenship. 2024 South Asian University. -
Unveiling the pattern of PhishingAttacks using the Machine Learning approach
This study introduces a unique approach to strengthening cybersecurity by combining advanced models for real-time detection of phishing websites. A classifier is trained to discern patterns associated with legitimate and phishing URLs, leveraging a carefully organized labeled dataset. The model in this paper forms the foundation for a real-time detection system, providing users with real-time information on potential phishing threats. Integrating an adaptive decision-making algorithm improves decision-making adaptability, particularly in scenarios challenging the model's confidence. A user feedback loop ensures the continuous learning and refinement of the system, aligning it more closely with user expectations. The future scope of this research involves exploring advanced models, improving explainability, and incorporating dynamic features for enhanced detection. Adaptive policies, large-scale deployment, and ethical implications are pivotal for real-world applicability. In conclusion, this study contributes to advancing phishing detection methodologies and lays the groundwork for future innovations in cybersecurity. The collaborative efforts of academia, industry, and cybersecurity stakeholders arenecessaryfor realizing the full potential of this paper and ensuring a safer online platform for users. 2024 IEEE.