Browse Items (11809 total)
Sort by:
-
Popularity Prediction of Online Social Media Content: A Bibliometric Analysis
An online social network is a platform that enables individuals to interact with others who have similar backgrounds, preferences, activities, and associations. The number of features available and the format of each online social network range widely. Users of online social networks, such as Twitter, Instagram, Flicker, and Pinterest, have increased dramatically in recent years. Content sharing is the most popular feature of online social networks, used by both specific users and big enterprises. This study has used bibliometric methods to analyze the growth of the social media popularity prediction on online social network content from 2013 to 2022. The publications have been extracted from the dimensions database, and the VOS viewer software was used to visualize research patterns. The finding provides valuable information on the publication year, authors, authors country, authors organizational affiliations, publishing journals, etc. Based on the findings of this analysis, researchers will be able to design their studies better and add more insights into their empirical studies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Intelligent Model forPost Covid Hearing Loss
Several viral infections tend to cause Sudden Sensorineural Hearing Loss (SSNHL) in humans. Covid-19 being a viral disease could also cause hearing deficiencies in people as a side effect. There have been pieces of evidence from various case studies wherein covid infected patients have reported to be suffering from sudden sensorineural hearing loss. The main objective of this study is to inspect the phenomenon and treatment of SSNHL in post-COVID-19 patients. This study proposes a mathematical model of hearing loss as a consequence of covid-19 infection using ordinary differential equations. The solutions obtained for the model are established to be non-negative and bounded. The disease-free equilibrium, endemic equilibrium and basic reproductive number have been obtained for the model which helps analyse the models trend through stability analysis. Moreover, numerical simulations have been performedfor validating the obtained theoretical results. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
AI Healthcare Industry in Life Science Industry: A Perspective View
The main goal of this study is to look at how well the innovation system for AI healthcare technology is working in the life science business and find things that are getting in the way of progress. A lot of different types of research were used for this study. It combines both quantitative and qualitative data from tertiary studies, business-related written sources, and conversations with 21 experts and 25 life science management leaders to get new ideas. The results make it clear that innovation system performance is being held back by a lack of resources and poor communication from top healthcare experts about what they need to improve healthcare with AI technology innovations. The study says that to deal with these problems, policymakers need to make changes that increase the resources that are available and come up with clear goals and visions for how AI technology can improve healthcare. Using the socio-technical technological advancement System (TIS) approach in the healthcare setting, the study adds to our knowledge of how the innovation system works and how different parts of it affect each other. Overall, this study throws light on the complicated ways that innovation works in the life science field. It gives lawmakers, industry workers, and other interested parties useful information for pushing AI healthcare technology forward in a sociotechnical framework. 2024 IEEE. -
Exploring Ethical Considerations: Privacy and Accountability in Conversational Agents like ChatGPT
In recent years, advances in artificial intelligence (AI) and machine learning have transformed the landscape of scientific study. Out of all of these, chatbot technology has come a long way in the last few years, especially since ChatGPT became a well-known artificial intelligence language model. This comprehensive review investigates ChatGPT's background, applications, primary challenges, and possible future advancements. We first look at its history, progress, and fundamental technology before delving into its many applications in customer service, health care, and education. We also discuss potential countermeasures and highlight the major challenges that ChatGPT faces, including data biases, moral dilemmas, and security threats. Finally, we go over our plans for ChatGPT's future, outlining areas that need further research and development, improved human-AI communication, closing the digital gap, and ChatGPT integration with other technologies. This study offers useful information for scholars, developers, and stakeholders interested in the rapidly evolving subject of artificial intelligence-powered conversational bots. This study looks at the ways that ChatGPT has changed scientific research in several domains, such as data processing, developing hypotheses, collaboration, and public outreach. In addition, the paper examines potential limitations and ethical quandaries associated with the use of ChatGPT in research, highlighting the importance of striking a balance between human expertise and AI-assisted innovation. The paper addresses multiple ethical issues with the state of computers today and how ChatGPT can cause people to oppose this notion. This study also has a number of ChatGPT biases and restrictions. It is noteworthy that in a very short period, ChatGPT has garnered significant interest from academics, research, and enterprises, notwithstanding several challenges and ethical issues. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Harnessing Medical Databases and Data Mining in the Big Data Era: Advancements and Applications in Healthcare
In the contemporary period of Big Data, the healthcare industry is witnessing a transformative paradigm shift, propelled by the convergence of medical databases and data mining technology. This research paper delves into the multifaceted application of this synergy, offering a comprehensive overview of its implications and opportunities. With the exponential growth of healthcare data, the utilisation of medical databases serves as the bedrock for data mining techniques, fostering critical advancements in diagnosis, treatment, and patient care. Through this research, we explore the integration of electronic health records, genomic data, and clinical databases, unveiling new dimensions of predictive analytics, patient profiling, and disease monitoring. Moreover, we assess the ethical and privacy concerns entailed in this data-rich landscape, emphasising the need for robust governance and security measures. Our paper encapsulates the evolving landscape of health care, demonstrating the immense potential and the ethical responsibilities accompanying this groundbreaking merger of technology and medicine in the period of Big Data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
The Various Challenges Involved in Sensor Based Cloud System to Protect the Data and to Avoid Attacks: A Technical Review
In these studies, we introduce a unique protection framework for the integration of Wireless Sensor Networks (WSN) with cloud computing, aimed closer to enhancing statistics-centric programs consisting of far-flung healthcare structures. The framework's cornerstone is a robust, bendy safety version that ensures immoderate-degree information confidentiality, integrity, and terrific-grained get proper of access to control, addressing the important protection demanding situations in WSN-Cloud integration. By the use of a hybrid encryption mechanism that mixes the strengths of symmetric and uneven encryption techniques, our method gives a entire safety answer that protects information during transmission and garage. Furthermore, the version includes an efficient key manipulate gadget, facilitating the dynamic era and relaxed distribution of encryption keys. This contemporary framework is designed to mitigate common safety threats, such as Man-in-the-Middle (MITM) and Denial of Service (DoS) attacks, even as preserving the overall performance and standard performance of the blanketed gadget. Our research offers a massive contribution to securing statistics-centric packages in WSN-Cloud ecosystems, making sure dependable and comfortable facts verbal exchange and get right of entry to for a way off healthcare programs and past. 2024 IEEE. -
Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management
This research looked at the transformative potential of cutting-edge machine learning algorithms in various areas of precision agriculture, with an emphasis on enhancing smart irrigation systems for onion farming. Using a vast sensor network and real-time monitoring, we investigated the performance of CNN, ANN, and SVM, three well-known machine learning algorithms. After extensive testing and investigation, our results reveal that CNN beats ANN and SVM in terms of outstanding accuracy in predicting plant water requirements. Because of CNN's superior predictive powers, our intelligent irrigation system maintains perfect soil conditions, resulting in increased agricultural yields and resource savings. The study's findings have important implications for modern agriculture, paving the way for data-driven, sustainable agricultural methods that address global concerns such as food security and environmental sustainability. As we approach the era of smart agriculture, our research demonstrates how technology has the potential to alter crop farming and aid in the development of a more resilient and successful agricultural industry. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Exploring the Opportunities of AI Integral with DL and ML Models in Financial and Accounting Systems
With the integration of artificial intelligence (AI), today's fast financial landscape increasingly promises the most efficient and accurate processes for decision-making in accounting practices. On the other hand, the opacity of models represents a truly difficult challenge, given that transparency and accountability are key for using AI in making financial decisions. This is a research paper that focuses on the explanation of an XAI model application as a way of improving transparency in financial decision-making within the accounting field. The paper begins by outlining how transparency is important and opens the room for trust and understanding in the process of financial decision-making. Traditional black-box AI models, although able to provide remarkable predictions, usually exhibit low interpretability; this entails that stakeholders may have a small degree of understanding regarding the rationale behind the decisions. This provides a cloudy appearance not to hamper trust and supports compliance with regulatory standards like GAAP (Generally Accepted Accounting Principles) and IFRS (International Financial Reporting Standards). The proposed work applies to the accounting domain and brings about some of the different XAI techniques that are designed under this domain. The following techniques aim at demystifying the AI algorithms for effective AI stakeholders' understanding of the model predictions and underlying decision-making processes. 2024 IEEE. -
Comparative Analysis of Predictive Models for Customer Churn Prediction in the Telecommunication Industry
To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. The results indicate that the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition. 2024 IEEE. -
Exploring the Adoption Readiness of the Indian Generation for Social Media Payments: An In-Depth Analysis of WhatsApp Payments
Advancements in technologies always get higher acceptance among people. Regarding payment technologies, integrating payment facility in the Social Media platform are considered a second-generation payment technology. With the introduction of Hike wallets and WhatsApp payment, unprecedented opportunities are available to the users. In India, with the introduction of WhatsApp on November 2020, the users of FinTech got opened a gateway to social media payment. Social Media payments are considered easy and convenient, but is the Indian generation, especially people born in the internet phase (Gen Y and Gen Z), ready to adopt WhatsApp payment. The current study was done to investigate the elements that contribute to the acceptance and use of the WhatsApp payment service in India. To attain this objective, we used an extended UTAUT2 model with the moderating effect of generation. The data was gathered from 265 respondents and analyzed using the PLS-SEM method. The results of the study outlined that Gen Z is strengthening the moderating effect only between the facilitating conditions of the users and the actual usage of WhatsApp payment. The practical implications and directions for the further research are mentioned in the study. ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024. -
Optimization of Friction Stir Welding of AlCu Butt Joint Using Taguchi Method
In this work, the 5mm thickness of base metals AA6101 and C11000 was welded using a hardened OHNS steel tool by FSW mechanism. The Taguchi method involves the optimization of welding mechanism variables tool rotation speed (rpm), feed rate (mm/min), and tool offset (mm) to gain extremely rigid joints. The ANOVA reveals the percentage contribution of the three welding mechanism variables can be examined. From the Taguchi design of optimization technique, at 1000rpm, 40mm/min, andtool offset towards softer metal will possess maximum impact load. The tools rotating speed produced the greatest contribution to the impact load. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
The Impactful Role of ML Algo in the Field of Enactment Nostrum: An Intensive/Deep Review
Machine translation (MT) research has explored a variety of models, including statistical machine translation (SMT), rule-based machine translation (RBMT), and hybrid approaches. Hybrid MT systems aim to improve translation quality by using the strengths of different models. However, the complex set of functions associated with MT systems is still difficult to understand and optimize. This instant study propose an approach based on ML with respect to hybrid MT that addresses these issues by automatically interpreting and weighting features using ML tools. This research framework includes a classification approach to classify and compare translations from multiple black-box A system that uses ML classifiers trained on feature vectors derived from natural language processing tools. This study presents a method to train and use an SVM-based classifier to generate hybrid interpretations. The test results for English-Chinese pairs show the potential of this research approach to improve translation quality. The proposed framework is a simple and efficient way to combine different MT systems, improving translation results without manual intervention. 2024 IEEE. -
The Development of ID System for Detecting Attacks in WSN Through Ontology Method and its Strategy
Cybercriminals are becoming increasingly targeted by the rapid expansion of the Internet of Things (IoT), leading to an increase in cyberattacks targeting IoT devices and their communication channels These attacks, if failure to detect may result in significant service disruption, financial loss or damage to sensitive data. Real-time intrusion detection is essential to ensure reliability, security and profitability of IoT applications. This study introduces a new intrusion detection system designed for IoT devices that uses deep learning (DL). Utilizing ontology in wireless sensor networks (WSN), this intelligent system detects suspicious activities that pose a threat to connected IoT devices with configuration-neutral design provides ease of use, while the test performance analysis is simulated and real-world. It highlights its strong performance in determining admissions. The effectiveness of the system against many types of attacks such as denial of service, workholes, blackholes, opportunistic service attacks, etc. is confirmed by experimental research and furthermore, the system achieves F1 scores, accuracy and the number of memories. This advanced deep learning intrusion detection system shows great promise to improve IoT network security due to its high detection rate. 2024 IEEE. -
Oil Price Volatility and Its Impact on Industry Stock Return Bi Variate Analysis
Oil price volatility impacts industries differently depending on a countrys status as a net oil importer or exporter. In oil-importing nations like India, sectors such as banking, energy, materials, retailing, transportation, and manufacturing are adversely affected by price fluctuations, while industries like food, beverages, and pharmaceuticals tend to be more resilient. Conversely, oil-exporting countries experience milder effects, with the oil and gas sector bearing the brunt of supply disruptions while other industries remain insulated. Over time, the correlation between oil prices and stock market performance has strengthened, making oil price volatility a systemic risk factor. The source of oil price shocks, whether from demand changes or supply disruptions, significantly influences their impact on stock returns. Notably, there are substantial volatility spillovers between oil and stock markets. This study aims to explore the relationship between oil shocks and industry returns using various multivariate models, highlighting the importance of considering oil as a relevant risk factor in portfolio management. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Tracking Sigmoid Regression with Multicollinearity in Phase I: An Approach Incorporating Control Charts
Regression and quality control are two crucial techniques that data analysis employs in improving the decision-making process. We use the sigmoid function to model the connection between independent factors and the dependent variable in sigmoid regression. When there is a significant correlation among the independent variables in a regression model, multicollinearity a statistical phenomenon exists. Multicollinearity presents problems with higher uncertainty when estimating individual coefficients possibly making it harder to identify each variable's distinct contribution to the model. By suggesting a control chart specifically designed for the sigmoid regression model, this research presents a strategy to address the impact of influential observations using regression control charts, by making use of principal component regression class estimators. Principal component regression merges from the principal component analysis and linear regression methodologies, aiming to alleviate multicollinearity issues and enhances the stability of regression models. The performance of the model is evaluated using Pearson's residuals, Deviance residuals, and residuals. This strategy is proven to be useful in real world situations demonstrated through an application in the field of sleep wellness disorder. In conclusion, this study introduces a unique control chart to manage multicollinearity in sigmoid regression, providing a new perspective on the topic to spot differences in the underlying process by highlighting trends in the residuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Examining the Impacts and Obstacles of AI-Driven Management in Present-Day Business Contexts
This paper explores the growing role of Artificial Intelligence (AI) in the management structures of modern business organizations. In order to improve operational effectiveness and overall success, it focuses on the integration and effects of AI within Management Information Systems (MIS). The study finds the many advantages artificial intelligence (AI) offers to knowledge management, resource management systems, and organizational effectiveness through a thorough analysis. The paper uses a wide range of scholarly references to explain different aspects of AI-powered management, such as strategic planning, decision-making, and sustainable marketing tactics. The study highlights a notable void in the all-encompassing comprehension of artificial intelligence's concrete contribution to business improvement, thereby promoting a deeper and more empirical investigation of AI's incorporation into business operations. 2024 IEEE. -
Algorithmic Trading: Financial Markets Using Artificial Intelligence
This research study gives an in - depth view of the recent developments in the fields of Machine Learning (ML) and Reinforced Learning (RL) techniques as they are related to various models for forecasting and systems for financial trading. The practical usage of deep learning models, that incorporates Neural Networks such as Recurrent, Convolutional along with hybrid models integrating genetic algorithms with LSTM networks, for forecasting the stock market patterns as well as bank failures, and fluctuations in exchange rate which is addressed in this study in an in - depth review analysis of the latest literature. In addition to this it also investigates how trading algorithm performance as well as risk management can be enhanced by applying techniques of deep reinforcement learning. This study also demonstrates the enhanced, efficacy, precision and the profitability achieved by using these artificial intelligence methods as compared with conventional economic modelling and detailed technical study models by analysing a number of stock markets and different kinds of assets. 2024 IEEE. -
Tomato Plant Disease Classification Using Transfer Learning
Detecting and categorizing diseases in tomato plants poses a significant hurdle for farmers, resulting in considerable agricultural losses and economic harm. The prompt underscores the significance of promptly identifying and classifying diseases to enact successful management strategies. Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in tasks involving image classification, notably in categorizing diseases that impact tomato plants. However, CNN models can be computationally expensive to train and require large datasets of labeled images. Utilizing advanced CNN models can enhance the efficacy of classification models for tomato plant diseases, simultaneously decreasing computational expenses and the demand for extensive training data. Enhanced CNN models can be developed using a variety of techniques, such as transfer learning, data augmentation, and residual networks. This project aims to implement a tomato plant disease classification model using an enhanced convolution neural network. This work uses the lifelong learning method which is the model that allows one to learn new tasks without forgetting previous knowledge. Leveraging sophisticated CNN models can improve the effectiveness of classification models for tomato plant diseases, while also reducing computational costs and the need for extensive training data. It is beneficial for tasks where there is limited data available to train a model from scratch. 2024 IEEE. -
Allometry Authentication in the Field of Finance: Creation of Well Secured System using AI Algo Based Systems
It is true the banking sector is increasingly under pressure to tighten security in an ever-changing digital arena, even as the customer experience needs to be strengthened. Thus, the use of biometric authentication through enhanced AI-driven systems that would enhance the security protocols while at the same time smoothening the users' interactions was a promising way in response. The paper that follows explores the integration of biometric authentication within banking systems in a bid to make clear its effectiveness in relation to reinforcing security and enhancing user experience. Accordingly, bijson etal. argue that biometric security fits perfectly in banks, since with the increasing cyber threats, banks are bound to deploy more advanced security mechanisms. These traditional means, suchjson, use of passwords and PINs, have shown vulnerabilities that are liable to exploitation and should be changed into something much more resilient. The authentication under biometrics also validates a user's identity by basing it on unique physiological or behavioral traits, such as a fingerprint, features of the face, patterns of the iris, and the voice. Biometric systems authenticate users with a very high level of confidence through AI-based algorithms, averting the security risks associated with unauthorized access and identity theft. Further, biometric authentication overcomes the flaws that prevail with the traditional mode of methods and hence, it ensures a very comfortable and user-friendly mode of system security. 2024 IEEE. -
Automated Verification of Open/Closed Principle: A Code Analysis Approach
The SOLID principles are foundational to software engineering, focusing on the maintainability, scalability, and extensibility of software systems. The Open/Closed Principle (OCP), a pivotal element among these principles, underscores the need to design software modules that are open for extension yet closed for modification. This research explores automated verification techniques for OCP, addressing the validation of software modules through extensibility and adaptability assessments. The principal objectives involve the development of a code analysis approach and a methodology capable of automating the verification of adherence to OCP in developed codes, providing actionable insights to software developers. The system focuses on specific aspects of OCP, including inheritance, abstraction, and polymorphism, and aims to provide clear indications of where violations occur within a codebase. The implementation uses the Abstract Syntax Tree (AST) analysis to examine class definitions. The automated analysis of Python code using the defined rules offers a clear understanding of OCP adherence. Results are presented in Pandas DataFrames, indicating potential violations and providing developers with actionable insights to enhance code quality and maintainability. Overall, the automated code verification system aims to enhance code quality and adherence to fundamental design principles, paving the way for advancements in automated code analysis and software engineering practices. 2024 IEEE.