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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 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 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 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 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 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 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 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 metaverse sentiments using machine learning approaches
Purpose: The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers ones intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience. Design/methodology/approach: The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently. Findings: The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models. Research limitations/implications: Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverses experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverses economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust. Social implications: In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators. Originality/value: The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models. 2024, Emerald Publishing Limited. -
Unveiling metaverse sentiments using machine learning approaches
Purpose: The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers ones intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience. Design/methodology/approach: The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently. Findings: The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models. Research limitations/implications: Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverses experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverses economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust. Social implications: In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators. Originality/value: The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models. 2024, Emerald Publishing Limited. -
Unveiling mental health nuances of male Indian classical dancers.
This study explores the lives of male Indian classical dancers, highlighting the duality of dance as a sanctuary and a stressor. As male Indian classical dancers negotiate and redefine norms of masculinity, the study calls for recognition of diverse masculine identities within traditionally feminized spaces. (PsycInfo Database Record (c) 2026 APA, all rights reserved) 2025 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved.; This research explores the mental health nuances of male Indian classical dancers (MICDs), through a lens of redefining masculinity, focusing on their perceived quality of life, psychosocial challenges, and coping strategies. This study follows an interpretive phenomenological approach to follow the lived experiences of MICDs. The participants are male, fluent in English, and pursuing Indian classical dance styles professionally, like Kathak, Bharatanatyam, Chhau, etc. Six participants were recruited for personal, semistructured, in-depth interviews, whereas, a focus group discussion with four participants was conducted to explore the stigma. The data were analyzed using interpretive phenomenological analysis, revealing themes of (a) identity fragmentation and negotiation in gendered social contexts, (b) gendered experiences, (c) emotional distress and psychological challenges, (d) coping mechanisms and resilience, and (e) stigmatization and social integration dynamics. MICDs grapple with identity formation, navigating a paradox of self-perception, artistic identity, and societal expectation. They reported experiencing emasculation, compromising artistic expression, and struggling with gender norms and gendered training constraints. They have faced name-calling, bullying, taunting, slandering, and discrimination leading to psychological challenges and distress. However, the paradox continues as male dancers use adaptive coping strategies despite the adversities that intertwine self-perception, societal pressures, and their passion for dance. These findings provide a strong foundation for making changes in the dance community for acceptance of male dancers, policy making for better job opportunities for male dancers, and mental health services to be provided to help them deal with distress. (PsycInfo Database Record (c) 2026 APA, all rights reserved) 2025 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Unveiling Green Supply Chain Practices: A Bibliometric Analysis and Unfolding Emerging Trends
Supply chain management is a multi-dimensional approach. Growing eco-consciousness has forced businesses to optimize operations and incorporate green practices across all the stages of supply chain in manufacturing and service sectors. Reviewing the past research literature propels us to understand its current and future prospects. Employing a systematic analysis, this research explores the intellectual structure of green supply chain practices and their connection to performance outcomes in various industries. This study covers a systematic literature review, content analysis, and bibliometric analysis on green supply chain management using VosViewer. It utilizes a PRISMA-guided screening method for identification, screening, eligibility and inclusion of literature from the literature available since 1999. The bibliometric analysis reveals key contributors, thematic clusters, prevailing theoretical frameworks, and emerging research trends in the domain of green supply chain management. China, followed by the United States and the United Kingdom, emerged as leading contributors to research in this area, driven by rapid economic growth, heightened environmental concerns, and well-established academic and industrial infrastructures. The study identifies eight thematic clusters within green supply chain management, including the triple bottom line, circular economy, and carbon emissions. The most highly cited papers within these clusters were examined for their methodologies, tools, and key findings, highlighting the prominent theories utilized in this field. Moreover, the research discusses how advanced technologies such as AI, blockchain, and big data analytics are poised to transform supply chains by enhancing decision-making and mitigating risks, thus playing a pivotal role in the future of green supply chain management. Copyright 2024 CA Rajkiran, Shaeril Michel Almeida. -
Unveiling Future Trends in Employer Branding: Systematic Review and Bibliometric Analysis
Employer branding, an emerging area in Human Resource Management (HRM), has gained significant importance. Despite its importance, the literature on employer branding remains fragmented due to the absence of a comprehensive review that consolidates the intellectual structure of the field. This study addresses the existing knowledge gap by conducting a systematic literature review accompanied by bibliometric analysis utilizing performance analysis and science mapping through the Tableau software package. Through a comprehensive review of 27 articles, this study reveals the key branding elements, top journals, contributing countries, industries, citation trends, sample statistics, theoretical contribution, and six key themes (i.e., Employer branding attributes, sustainable employer branding, employee-centric employer branding, social media employer branding, recruitment strategies, HRM practices of employer branding) that characterize the body of the employer branding. Finally, the study has identified an integrative framework and set the direction for future research. It offers actionable recommendations for HR practitioners, emphasizing technology integration in employer branding initiatives and incorporating sustainable practices to enhance organizational attractiveness. This research contributes to a deeper understanding of the concept of employer branding. It provides valuable guidance for organizations seeking to navigate and optimize their employer branding strategies for the future. (2025), (Regional Inform. Center for Sci. and Technol.). All rights reserved. -
Unveiling Dynamics of Structural Breaks in Global Stock Markets and Implications for Forecasting Accuracy
This research investigates structural breaks in global stock markets, employing the Chow test on major indices from January 2013 to November 2023. Results reveal significant breaks in NYSE (November 2020) linked to the US election and positive vaccine trials, Nasdaq (May 2020) amidst global concerns over COVID-19, and Euronext 100 (February 2021), suggesting market shifts. Notably, Shanghai Stock Exchange experienced a robust break in December 2014, contrasting with SZSE's non-significant break. HKEX experiences a significant shift in June 2020, possibly influenced by US regulatory policies and COVID-19. The Nifty index shows a profound break in December 2020, correlated with pandemic severity. LSE Group evidences a break in July 2019, while the Saudi Exchange shows non-significant evidence in March 2021. The study underscores the importance of considering structural breaks for accurate market forecasting and decision-making. Descriptive statistics provide insights into market characteristics. The methodology integrates the Chow test and CUSUM squares for break detection. Findings contribute to understanding global market dynamics and emphasize the impact of external events on structural stability. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Unveiling Cutting Edge Innovations in the Catalytic Valorization of Biodiesel Byproduct Glycerol into Value Added Products
The increasing production of biodiesel has led to a glut in the production of glycerol, which is a byproduct. This has resulted in the quest for alternative applications using glycerol as a cheap and readily available starting material. One promising approach is the catalytic valorization of glycerol, which converts glycerol into valuable chemicals such as 1,2-propanediol, lactic acid, and acrolein. The glycerol formed affects the efficiency of the biodiesel, and hence it must be removed. Different processes can convert glycerol to various useful products like glycerol carbonate, glycidol, solketal, lactic acid, and glyceric acid. These different products, the processes used for synthesis, and the various catalysts used have been discussed. The most effective methods for the syntheses, the numerous catalyst systems, mechanisms of the reactions, and applications of these products in different fields are discussed in this review. The paper also discusses the challenges and opportunities of glycerol valorization, including the need for improved catalyst selectivity and activity and the potential for integrating glycerol valorization with other biorefinery processes. Overall, the catalytic valorization of glycerol offers a promising pathway for utilizing this abundantly available resource, and this review provides valuable insights for researchers and practitioners working in this area. 2023 Wiley-VCH GmbH. -
Unusual Generation of Filament-Like Crystal on Vapor-Deposited Sb2Se3 Whiskers Under Ambient Atmosphere
This research article proposes a novel strategy to explore the nucleation and growth mechanism of a filamentary spike-like feature (secondary growth) on vapor-deposited antimony selenide (Sb2S3) whiskers (primary crystallization) due to the influence of electric fields, defects, and ambient atmosphere. Small, ultra-long, branched whiskers were produced by the physical vapor deposition (PVD) method utilizing a homemade tubular furnace. In order to grow these crystal features, a temperature difference (?T) of 180C was maintained by adjusting the temperature in the hot (710C) and cold zones (530C), followed by a fast cooling rate of 12C/min. Optical and scanning electron microscopy, three-dimensional (3D) profilometry, and Raman imaging analysis were utilized to investigate the surface features of the as-grown and electrically activated whiskers under ambient atmosphere. A possible crystallization (secondary growth) mechanism of the filamentary crystals in the defective region under the influence of an electric field was proposed. We noted that the effect of extrinsic impurities like oxygen coupled with an electric field promoted the growth of filamentary crystals on the whiskers, which were probed utilizing x-ray diffraction (XRD), energy-dispersive x-ray analysis (EDAX), x-ray photoelectron spectroscopy (XPS), Raman analysis, thermogravimetric analysis (TGA), and differential thermal analysis (DTA). An orthorhombic crystal structure with unit dimensions of a = 11.632 b = 11.798 and c = 3.987was calculated from the XRD results. This research provides a new growth mechanism and a comprehensive picture of nucleation followed by branching of filamentary crystals on the primary crystallized Sb2Se3 whisker surface. The research output with regard to layered chalcogenide materials (LCMs) will undoubtedly help researchers focus on removing secondary/whisker growth from LCM-based optoelectronic devices. The Minerals, Metals & Materials Society 2025. -
Untold Stories from the Slums: A Qualitative Exploration of the Lives of Female Informal Waste Workers
The present study investigates the nuanced experiences of urban informal waste workers, shedding light on the intricate realities shaping their daily lives. Employing purposive sampling, in-depth interviews were conducted, specifically targeting female participants aged eighteen and above, engaged in informal waste work for a minimum duration of one year. A total of ten in-depth interviews were meticulously executed, with recordings subsequently translated and transcribed for thorough analysis. Utilizing Braun and Clarks thematic analysis method, a comprehensive examination of the data yielded a hierarchical structure comprising codes, sub-themes, and overarching themes. The central themes identified encapsulate the multifaceted challenges encountered by urban informal waste workers, including Occupational Hardships and Vulnerability, Economic Struggles and Diminished Quality of Life, Commitment to Family, Emotional and Psychosocial Challenges, Appreciation and Acknowledgment of Support Received, Decreased Willingness to seek help, and Aspirations and Hopes for the future. By amplifying the voices of these marginalized workers, this study advocates for the implementation of inclusive policies and interventions tailored to address their diverse needs within the urban milieu. Through its findings, the study aims to cultivate a more compassionate and supportive environment that not only recognizes the invaluable contributions of urban informal waste workers but also strives to enhance their overall well-being and socio-economic standing. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Untold and Painful Stories of Survival: The Life of Adolescent Girls of the Paniya Tribes of Kerala, India
Tribal adolescent girls are vulnerable to neglect, abuse and exploitation across the world. Literature on the status of adolescents belonging to the Paniya tribe is scanty. However, limited information about the Paniya tribe of Kerala indicates that they are neglected and deprived from basic facilities. According to the Census Report of India (2011), 49.5% of the Paniya tribe members are literate. The lives of adolescents in the Paniya community are distinct from those of other sections of society, and they are yet to be addressed by the government or the media. The objective of this chapter is to discuss the issues and concerns of Paniya adolescent girls of Kerala. A Paniya girl from Vattachira (Calicut) treks around 2 km during her menstruation to fetch fresh and clean water. They use pieces of clothes to manage menstruation since they do not have access to pads or tampons. Drying their garments during the rainy season is difficult, which leaves them susceptible to rashes and infections. They are provided with residential educational facilities by the government, but they are unable to adjust to the lifestyles of other members of the society and are frequently bullied and discriminated, leading to school dropouts. Sexual exploitation by strangers and community members is widespread among Paniya girls, and unmarried mothers under the age of 18 are also prevalent among this community. The chapter highlights upon some of the challenges of the Paniya Tribal adolescent girls of Kerala and offers some suggestions for improving the quality of life of this marginalized group, which will assist the policymakers and government for taking need-based measures. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Unsupervised Learning for Understanding Diversity: Applying Feature Engineering and Cluster Analysis to Deaf and Hard of Hearing Data
As e-Learning emerges as a promising tool for instruction delivery, personalizing the e-Learning platform for DHH learners will benefit them to improve their learning engagement and educational attainment. This study aims to collect and analyze the different features unique to DHH learners and analyze the significant features among them. This study highlights the importance of addressing the diversity among DHH learners, while creating a personalized learning environment for them. With this focus, we employ the K-Means clustering algorithm to group the learners based on similar needs and preferences and identified that distinguishing clusters can be formed within the DHH group. We also tried to understand the significant features contributing to forming well separated groups. These results provide valuable insights into the diverse preferences and requirements when they interact with the learning materials. These findings emphasize the significance of personalized approach for DHH learners in educational settings and serve as the stepping stone to develop a personalized learning environment for them. 2024 IEEE. -
Unsupervised Feature Selection Approach for Smartwatches
Traditional feature selection methods can be time-consuming and labor-intensive, especially with large datasets. This studys unsupervised feature selection approach can automate the process and help identify important features preferred by a particular segment of users. The unsupervised feature selection method is applied for smartwatches. Smartwatches continue to gain popularity. It is important to understand which features are most important to users to design and develop smartwatches that are more engaging, user-friendly, and meet the needs and preferences of their target audience. The rapid pace of technological innovation in the smartwatch industry means that new features and functionalities are constantly being developed. Multi-cluster feature selection, Laplacian score, and unsupervised spectral feature are used. Conjoint analysis is done on the most common features in all three selection methods. The unsupervised feature selection technique is used for identifying the relevant and important features of new smartwatch users.The practical implication of the research is in the application of the technique in the new product design of smartwatches. The result of the study also informs smartwatch manufacturers and developers on the features they need to prioritize and invest in. This can ultimately result in better and more user-friendly smartwatches and a good overall experience for the user. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.