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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 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 Effects of Heavy Metals : A Comprehensive Study On Physiological, Phytochemical, and Anatomical Responses in Jacobaea Maritima (L.) Pelser and Meijden
Modernization and industrialization have been of great importance in the recent past, yet newlinethere are a few disadvantages including the release of harmful effluents into the environment. The medicinal plants that are found in these heavy metal polluted soils can have positive and negative effects as well. In this study the plant Jacobaea maritima (L.) Pelser and Meijden an important medicinal plant in the field of homeopathy was subjected to three different heavy metalscadmium, chromium and lead at a concentration range of 50, 100, 150, 200 and 250ppm.The morphological parameters, shoot length and root length reduced more than 50% and 25% incase of shoot and root respectively. Phytochemical analysis shows significant variation inchlorophyll. newlineThe highest of protein was found in Cr 100ppm (7.47mg) and the least was found inCd 250ppm newline(0.38mg). Proline was found to be high in Pb 200ppm (1.242mg/ml) and least in Cr50ppm newline(0.368mg/ml). The Total Phenolic Content (TPC) was found to be highest in Cr 250ppm newline(3.229mg/g) and least in control (0.57mg/g) and Total Flavonoid Content (TFC) was found to be highest in Cd 100 ppm and least in Control (0.04mg/g) plant which include root, shoot and leaf. The growth of the plant was observed along with a few parameters like net photosynthetic rate (Pn), transpiration rate (E), leaf stomatal conductance (C), and the photosynthetic active radiation (PAR). It has been found that the chlorophyll values in the Cr 150 (5.470.4) concentration are high. The chlorophyll values in Pb100 (9.40.35) and Pb250 (9.80.26) are in close proximity to newlineeach other. The highest chlorophyll was found in Cd100 concentration. The net photosynthetic rate was less affected in Pb150 (30.980.75), and most in Cr100 (4.050.09). The Cr 50 (0.190.02) showed the least. The Leaf stomatal conductance was drastically reduced in all the treated plants only Cr 100 (2298.251.85) showed minimal variation. In morphology shoot length newlineand root length were reduced more than 50% and 25% in case of shoot and root respectively. -
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 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 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 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 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 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. -
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. -
Unstructured data extraction system using multi head attention and a novel language model /
Patent Number: 202141056398, Applicant: K. P. Kavitha.
A system 100 for Offline handwritten text recognition (HTR) of a scanned handwritten text input image leveraging Modern Deep Recurrent Neural Network (RNN). System 100 comprises (RNN) is proposed with the help of the present's embodiments disclosure (RNN). A cursive eliminated handwritten text image is mapped to a multi-head attention-based sequence-to-sequence learning applying the beam search technique and employing an RNN-based variable-length encoder-decoder architecture. -
Unstructured data extraction system using multi head attention and a novel language model /
Patent Number: 202141056398, Applicant: K. P. Kavitha.
A system 100 for Offline handwritten text recognition (HTR) of a scanned handwritten text input image leveraging Modern Deep Recurrent Neural Network (RNN). System 100 comprises (RNN) is proposed with the help of the present's embodiments disclosure (RNN). A cursive eliminated handwritten text image is mapped to a multi-head attention-based sequence-to-sequence learning applying the beam search technique and employing an RNN-based variable-length encoder-decoder architecture. -
Unsteady three-dimensional MHD flow of a nano Eyring-Powell fluid past a convectively heated stretching sheet in the presence of thermal radiation, viscous dissipation and Joule heating
The purpose of this study is to investigate the unsteady magnetohydrodynamic three-dimensional flow induced by a stretching surface. An incompressible electrically conducting Eyring-Powell fluid fills the convectively heated stretching surface in the presence of nanoparticles. The effects of thermal radiation, viscous dissipation and Joule heating are accounted in heat transfer equation. The model used for the nanofluid includes the effects of Brownian motion and thermophoresis. The highly nonlinear partial differential equations are reduced to ordinary differential equations with the help of similarity method. The reduced complicated two-point boundary value problem is treated numerically using RungeKuttaFehlberg 45 method with shooting technique. A comparison of the obtained numerical results with existing results in a limiting sense is also presented. At the end, the effects of influential parameters on velocity, temperature and nanoparticles concentration fields are also discussed comprehensively. Further, the physical quantities of engineering interest such as the Nusselt number and Sherwood number are also calculated. 2016 University of Bahrain -
Unsteady thin film flow with ohmic heating and chemical reactions
In this study, we have analyzed magnetohydrodynamic (MHD) consequences on the heat and mass transmission within unsteady dissipated liquid film flow. Flow is generated due to stretchable surface accompanied with effects of ohmic heating, chemical reaction and heat absorption. Moreover, the flow governing partial differential equations (PDEs) are further modified into equivalent ordinary differential equations (ODEs) by applying regular perturbation method to get its analytical solution after that we have applied sixth-order RungeKutta technique to get its numerical solution. These two solutions are validating each other in the simulations. Figures are plotted to study the changes in physical quantities like skin friction coefficient, concentration, velocity, temperature, Sherwood and Nusselt number with the variations of Prandtl numbers Pr, parameters of chemical reaction ?, Eckert numbers Ec, magnetic parameter Ha (also known as Hartman number) Schmidt number and coefficient of heat absorption ?. World Scientific Publishing Company. -
Unsteady squeezing flow of a magnetized nano-lubricant between parallel disks with Robin boundary conditions
The aim of the present work is to examine the impact of magnetized nanoparticles (NPs) in enhancement of heat transport in a tribological system subjected to convective type heating (Robin) boundary conditions. The regime examined comprises the squeezing transition of a magnetic (smart) Newtonian nano-lubricant between two analogous disks under an axial magnetism. The lower disk is permeable whereas the upper disk is solid. The mechanisms of haphazard motion of NPs and thermophoresis are simulated. The non-dimensional problem is solved numerically using a finite difference method in the MATLAB bvp4c solver based on Lobotto quadrature, to scrutinize the significance of thermophoresis parameter, squeezing number, Hartmann number, Prandtl number, and Brownian motion parameter on velocity, temperature, nanoparticle concentration, Nusselt number, factor of friction, and Sherwood number distributions. The obtained results for the friction factor are validated against previously published results. It is found that friction factor at the disk increases with intensity in applied magnetic field. The haphazard (Brownian) motion of nanoparticles causes an enhancement in thermal field. Suction and injection are found to induce different effects on transport characteristics depending on the specification of equal or unequal Biot numbers at the disks. The main quantitative outcome is that, unequal Biot numbers produce significant cooling of the regime for both cases of disk suction or injection, indicating that Robin boundary conditions yield substantial deviation from conventional thermal boundary conditions. Higher thermophoretic parameter also elevates temperatures in the regime. The nanoparticles concentration at the disk is boosted with higher values of Brownian motion parameter. The response of temperature is similar in both suction and injection cases; however, this tendency is quite opposite for nanoparticle concentrations. In the core zone, the resistive magnetic body force dominates and this manifests in a significant reduction in velocity, that is damping. The heat build-up in squeeze films (which can lead to corrosion and degradation of surfaces) can be successfully removed with magnetic nanoparticles leading to prolonged serviceability of lubrication systems and the need for less maintenance. IMechE 2021. -
Unsteady natural convection in a liquid-saturated porous enclosure with local thermal non-equilibrium effect
Stability analysis of free convection in a liquid-saturated sparsely-packed porous medium with local-thermal-non-equilibrium (LTNE) effect is presented. For the vertical boundaries freefree, adiabatic and rigidrigid, adiabatic are considered while for horizontal boundaries it is the stress-free, isothermal and rigidrigid, isothermal boundary combinations we consider. From the linear theory, it is apparent that there is advanced onset of convection in a shallow enclosure followed by that in square and tall enclosures. Asymptotic analysis of the thermal Rayleigh number for small and large values of the inter-phase heat transfer coefficient is reported. Results of DarcyBard convection (DBC) and RayleighBard convection can be obtained as limiting cases of the study. LTNE effect is prominent in the case of BrinkmanBard convection compared to that in DBC. Using a multi-scale method and by performing a non-linear stability analysis the GinzburgLandau equation is derived from the five-mode Lorenz modal. Heat transport is estimated at the lower plate of the channel. The effect of the Brinkman number, the porous parameter and the inter-phase heat transfer coefficient is to favour delayed onset of convection and thereby enhanced heat transport while the porosity-modified ratio of thermal conductivities shows the opposite effect. 2020, Springer Nature B.V.