In the era of data-driven decision-making, business intelligence (BI) tools play a crucial role in enabling organizations to derive actionable insights from their data. However, the complexity of data queries and the manual nature of report generation often hinder user experience. This article explores how SQL automation can enhance the business user experience by streamlining data access, improving report generation speed, and increasing overall usability within BI tools and dashboards. By automating repetitive SQL tasks, organizations can empower business users to engage with data more effectively, ultimately leading to better decision-making and strategic outcomes.
In the field of marketing, customer segmentation is crucial for targeted work and improved customer engagement. Traditional customer segmentation mainly focuses on age, gender, location etc., which mainly misses the emotional context of customer feedback. To improve the efficiency of this segmentation, we are using Azure Cognitive Services’ sentiment analysis. This paper dwells into implementation details, outcomes and analysis. We found that sentiment analysis can substantially enhance precision and accuracy of segmentation, leading to more effective marketing approach.
Artificial intelligence has been a revolution in industries since it has injected new changes and increased organizational performance in different companies and organizations. However, it is equally important to note that intelligent machine-learning systems have raised concerns about the important ethical issues of bias, fairness, and transparency. This problem has called for the creation of ethical AI, an important branch that focuses on addressing such issues in a bid to enhance the right use of AI solutions. There are different types of bias, which are derived from the data set used, the algorithm and the absence of diversity in the AI development teams.
Artificial Intelligence (AI) is rapidly transforming the insurance industry by enhancing the efficiency of claims processing, under writing, decision making, document process, and improving fraud detection. The public cloud providers such as Amazon Web Services (AWS) plays a pivotal role in this revolution, by offering the advanced AI and machine learning services that streamline operations and provide actionable insights. This paper investigates the innovations and applications of AWS AI in the insurance industry, focusing on how services like Amazon Bedrock, Knowledge base and agents empower insurers to automate complex processes and ensure data security and compliance.
The consolidation and broad use of Customer Relationship Management (CRM) systems, which help businesses integrate and enhance their interaction with clients, has given rise to the demand for newer analytical capabilities. There is actually Salesforce CRM, which is among the most used cloud-based CRM applications that have shifted the limelight as it comes with powerful analytical tools that act as a processing center and are capable of turning raw information into valuable knowledge. Thus, this paper aims to discuss the general approach of the engineering and integration of an advanced analytics engine with the Salesforce CRM Cloud to help in decision-making, customer engagement and customer operation.
One of the main threats that networks have faced in medieval time is Distributed Denial of Service (DDoS) attacks especially, behavioral DDoS as it uses benign traffic patterns which makes the system crush under performance pressure. Traditional DDoS mitigation techniques frequently fall short in edge computing environments because they rely on a centralized control approach, which is not suitable for edge proximity to end users. This paper proposes a continuous behavioral DDoS mitigation framework by applying the Zero Trust (ZT) principles in edge computing.
Quantum computing addresses a change in perspective in computational capacities, offering exceptional potential for project management. This paper investigates the incorporation of quantum computing with project management strategies. I show the way that quantum calculations can upgrade asset designation, planning, risk appraisal, and partner commitment. My discoveries propose that quantum computing in project management can open paths for productivity and adequacy driving better results.
As organizations increasingly depend on data-driven decision-making, the complexity of Business Intelligence (BI) systems and their data pipelines has grown exponentially. This complexity introduces significant challenges in maintaining data quality, ensuring traceability, and guaranteeing system reliability. Unmanaged data dependencies in upstream BI components can lead to data inconsistencies, system failures, and compromised analytics. This paper presents a comprehensive framework for monitoring and managing data dependencies in upstream BI systems, with a primary focus on the Dependency Discovery Engine utilizing Static Code Analysis.
Software architecture has experienced quite significant changes in the last decades thanks to advancements such as multi-cloud, edge computing, and the need for global-scale applications. To this end, this paper discusses how software architectures have evolved to harness the various characteristics of modern computing models. Whereas multi-cloud is flexible and redundant, it also brings new complexities of how to manage it, how to keep it secure, and how to manage data within it. On the other hand, edge computing minimizes latency and optimizes offline data analytics by decentralizing workloads nearer to the user ends.
Rapid evolution of cloud-native technologies demands seamless migration strategies for enterprises to keep operational continuity alive while embracing modern platforms. The migration of cloud-native applications from VMware Tanzu Application Service (TAS) towards Red Hat OpenShift discusses challenges, best practices, and tools to help make migration successful. In comparison, TAS's strong deployments and microservices support place its worlds apart in architectural and operational paradigm versus OpenShift, a Kubernetes-based platform that is known to scale and have an excellent ecosystem. The migration journey involves careful consideration of application dependencies, containerization approaches, and re-architecting of workloads to align with Kubernetes-native patterns.
As agile practices continue to evolve, teams often face the challenge of maintaining an ever-growing regression test suite while ensuring timely test execution and consistent quality. Regression testing plays a critical role in verifying that new code changes do not negatively affect existing functionality. As new features are developed, the regression suite expands, leading to challenges with test maintenance, execution time, and reporting efficiency. This article explores strategies for managing the growing regression test suite without sacrificing speed or quality.
The rapid adoption of battery-powered devices, particularly in electric vehicles and energy storage systems, has highlighted the need for efficient and accurate battery health monitoring methods. Traditional approaches, based on basic parameters like voltage and capacity, are limited in their ability to predict long-term performance and degradation. This research explores the application of artificial intelligence (AI) in developing predictive models to assess battery health and longevity. By analysing real-time data from various battery parameters, machine learning algorithms, including decision trees, neural networks, and support vector machines, are utilized to predict battery performance over time.
As the global building sector uses approximately 30% of global energy and is a major contributor to greenhouse gas emissions, the fight against climate change requires such approaches. Energy efficiency in residential and commercial buildings has become critical to ensure the achievement of sustainable development goals and diminish climate change. Different strategies for improving energy efficiency will be presented through smart technologies, implementation of renewable energy systems and energy efficient building design. Potential for minimizing energy demand is evaluated in terms of smart HVAC systems; Internet of Things (IoT) enabled building management systems, and passive design strategies.
Efficient decision-making during crises such as natural disasters, pandemics, and large-scale emergencies requires rapid access to reliable and contextually relevant information. This paper presents a novel Retrieval-Augmented Generation (RAG) framework designed to integrate, synthesize, and analyze diverse data sources for real-time crisis management. By combining structured inputs, such as weather reports and resource inventories, with unstructured data, including social media updates and news articles, the system generates actionable insights tailored to specific emergency scenarios.
Complex digital payment systems make them more prone to fraud, raising the need for advanced fraud detection solutions. Since rule-based systems cannot keep up with fraudsters' ever-changing schemes, AI is needed to prevent fraud. This research examines how AI could be used to detect fraud in future payment processing systems to improve efficiency, accuracy, and security. AI models can evaluate enormous information in real time using deep learning, decision trees, and neural networks to detect fraudulent activities that people neglect. The study uses mixed methods to combine quantitative model performance indicators (F1 score, recall, accuracy, and precision) with qualitative financial case study findings.
For the conventional retaining wall in a kind of gravity wall, the weight of the structure influences the stability of the retaining wall. Moreover, the weight of the retaining wall is determined by its dimension. The assumptions of retaining wall dimension are determined by trial and error. If the retaining wall is not enough to bear the load, the dimension of the retaining wall must be changed. In this study, we analyze a gravity retaining wall according to Rankine’s theory. The active earth pressure and passive earth pressure have been calculated using Rankine’s theory and the stability of the retaining wall against sliding, overturning, and bearing capacity is also calculated. Finally, the stability is compared through discrete element modeling simulation. As a limitation of this study, there is only one layer of backfill material considered due to simplicity.
In the following report, the author examines the application of A/B Testing and Synthetic Control methods for model evaluation in the domain of retail analytics. One of the most popular experimental methods is called A/B testing, and it is used in retail to determine the success of particular marketing campaigns and both the location and the style of products within the store as well as website layouts. But it may be confined in specific situations, including cases with more than two treatment groups and non-observable kinds of variables.
This paper aims at discussing the analysis of health insurance claim through risk classification, fraudulence and cost prediction models. Combined with state-of-art data preprocessing and modelling techniques, insurers can better drive decision, minimize fraud, and better plan for financials. Logistic regression, random forest, gradient boost and models of similar category help in pattern analysis and cost of claim forecasting. They further effectiveness, equity and customer relations for implementing sound insurance that is sustainable.
The rapid evolution of digital payment solutions has necessitated seamless integration of diverse financial systems and Application Programming Interfaces (APIs) to facilitate online banking, wire transfers, and related services. Enterprise integration plays a pivotal role in overcoming interoperability challenges, ensuring secure transactions, and enhancing customer experience. This paper explores the integration challenges, proposes potential solutions, and examines the impact and scope of enterprise integration in digital payment solutions. By addressing these aspects, the research aims to provide a comprehensive understanding of how enterprise integration transforms financial ecosystems.
The rapid growth and increasing complexity of e-commerce platforms have driven the need for innovative methods to enhance product recommendations. This research builds on earlier studies that utilized machine learning algorithms and investigates how Large Language Models (LLMs) can further improve recommendation systems. By leveraging their superior natural language comprehension, LLMs can provide personalized recommendations tailored to individual customer inquiries, purchase histories, and product information.
Chronic back pain is a widespread problem that affects many people’s lives by limiting their ability to move and causing constant discomfort, while surgery is also an option for severe cases, non-surgical treatments, particularly physical therapy, have proven effective for managing pain and improving quality of life. Physical therapy helps to reduce pain, improve te movement, and strengthen muscles through targeted exercises, manual therapy, and education, and so it gives long-term benefits by addressing the root causes of pain and teaching patients how to prevent future issues.
The pharmaceutical manufacturing industry is characterized by strict quality control requirements and a requirement for close equipment calibration for achieving the product integrity and successful regulatory compliance. In multiple manufacturing site manufacturing, managing calibration intervals is a huge challenge leading to downtime and inconsistency. In this study we investigate the use of Artificial Intelligence (AI) for optimizing calibration schedules in multi-site operations. The proposed AI framework uses machine learning algorithms to process historical calibration data and real time equipment performance metrics to forecast optimal interval between calibrations.
The rapid development of Gen AI and LLM opens a new avenue for application possibilities in both IR and QA. Since large language models are computation-intensive, avoiding latency to guarantee real-time performance can often take time and effort. The work, bearing this in mind, tries to minimize this considerable latency challenge for several applications of emergent-gen AI by studying the exploitation of the concept of edge-cloud collaboration. This paper presents a framework that efficiently deploys LLMs by leveraging strengths from both edge and cloud to balance loads, reduce latency, and provide responsiveness for IR and QA applications.
Despite generative AI's tremendous transformational potential in NLP, low-resource languages remain significantly behind in data scarcity and computational load. It is envisioned that the present study will investigate whether cloud infrastructure can be gainfully employed toward exploring and training generative models for low-resource languages so they can surmount both their data and computational limitations. It starts by reviewing related work in generative AI and techniques optimized for the low-resource context to identify such languages' challenges and unique demands.
AI growth is so fast-paced that it revolutionizes BI in making informed data-driven decisions across industries. In line with this, the study also studies the integrations of both Tableau and Einstein Discovery into Salesforce for AI-enabled capabilities that enhance BI. On the one hand, the vastness of Tableau's scope consists of advanced data visualization which enables businesses to generate dynamic dashboards even in such complex datasets; Einstein, on the other hand, uses machine learning and predictive analytics to uncover actionable insights complete with recommended actions. These tools, when combined, offer a revolution in decision-making due to real-time data analyses that are accurate and project forward.
Enhancing Business User Experience: By Leveraging SQL Automation through Snowflake Tasks for BI Tools and Dashboards
Ankit Bansal
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In the era of data-driven decision-making, business intelligence (BI) tools play a crucial role in enabling organizations to derive actionable insights from their data. However, the complexity of data queries and the manual nature of report generation often hinder user experience. This article explores how SQL automation can enhance the business user experience by streamlining data access, improving report generation speed, and increasing overall usability within BI tools and dashboards. By automating repetitive SQL tasks, organizations can empower business users to engage with data more effectively, ultimately leading to better decision-making and strategic outcomes.
Enhancing Customer Segmentation with Azure Cognitive ServicesAjinkya P. Chatur, Shiragi S. Pattalwar
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In the field of marketing, customer segmentation is crucial for targeted work and improved customer engagement. Traditional customer segmentation mainly focuses on age, gender, location etc., which mainly misses the emotional context of customer feedback. To improve the efficiency of this segmentation, we are using Azure Cognitive Services’ sentiment analysis. This paper dwells into implementation details, outcomes and analysis. We found that sentiment analysis can substantially enhance precision and accuracy of segmentation, leading to more effective marketing approach.
Ethical AI: Navigating the Challenges of Bias, Fairness, and Transparency in Machine LearningManoj Boopathi Raj, Sneha Murganoor
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Artificial intelligence has been a revolution in industries since it has injected new changes and increased organizational performance in different companies and organizations. However, it is equally important to note that intelligent machine-learning systems have raised concerns about the important ethical issues of bias, fairness, and transparency. This problem has called for the creation of ethical AI, an important branch that focuses on addressing such issues in a bid to enhance the right use of AI solutions. There are different types of bias, which are derived from the data set used, the algorithm and the absence of diversity in the AI development teams.
Integrating AWS AI for Automated Insurance Claims ProcessingHarshavardhan Nerella, Praveen Borra, Mahidhar Mullapudi
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Artificial Intelligence (AI) is rapidly transforming the insurance industry by enhancing the efficiency of claims processing, under writing, decision making, document process, and improving fraud detection. The public cloud providers such as Amazon Web Services (AWS) plays a pivotal role in this revolution, by offering the advanced AI and machine learning services that streamline operations and provide actionable insights. This paper investigates the innovations and applications of AWS AI in the insurance industry, focusing on how services like Amazon Bedrock, Knowledge base and agents empower insurers to automate complex processes and ensure data security and compliance.
From Data to Insights: Engineering the Analytics Engine in Salesforce CRM Cloud to Drive IntelligenceAtul Gupta
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The consolidation and broad use of Customer Relationship Management (CRM) systems, which help businesses integrate and enhance their interaction with clients, has given rise to the demand for newer analytical capabilities. There is actually Salesforce CRM, which is among the most used cloud-based CRM applications that have shifted the limelight as it comes with powerful analytical tools that act as a processing center and are capable of turning raw information into valuable knowledge. Thus, this paper aims to discuss the general approach of the engineering and integration of an advanced analytics engine with the Salesforce CRM Cloud to help in decision-making, customer engagement and customer operation.
Behavioral DDoS Prevention through Zero Trust Principles in Edge ComputingHariprasad Sivaraman
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One of the main threats that networks have faced in medieval time is Distributed Denial of Service (DDoS) attacks especially, behavioral DDoS as it uses benign traffic patterns which makes the system crush under performance pressure. Traditional DDoS mitigation techniques frequently fall short in edge computing environments because they rely on a centralized control approach, which is not suitable for edge proximity to end users. This paper proposes a continuous behavioral DDoS mitigation framework by applying the Zero Trust (ZT) principles in edge computing.
Quantum Computing in Project Management: Unlocking New Frontiers for Optimization and Decision-Making
K.C. Lakshminarasimham
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Quantum computing addresses a change in perspective in computational capacities, offering exceptional potential for project management. This paper investigates the incorporation of quantum computing with project management strategies. I show the way that quantum calculations can upgrade asset designation, planning, risk appraisal, and partner commitment. My discoveries propose that quantum computing in project management can open paths for productivity and adequacy driving better results.
A Comprehensive Framework for Data Dependency Monitoring in Upstream Business Intelligence Systems
Naveen Edapurath Vijayan
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As organizations increasingly depend on data-driven decision-making, the complexity of Business Intelligence (BI) systems and their data pipelines has grown exponentially. This complexity introduces significant challenges in maintaining data quality, ensuring traceability, and guaranteeing system reliability. Unmanaged data dependencies in upstream BI components can lead to data inconsistencies, system failures, and compromised analytics. This paper presents a comprehensive framework for monitoring and managing data dependencies in upstream BI systems, with a primary focus on the Dependency Discovery Engine utilizing Static Code Analysis.
Taming Complexity: The Evolution of Software Architecture in the Age of Multi-Cloud, Edge Computing, and Global-Scale
ApplicationsDevisharan Mishra
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Software architecture has experienced quite significant changes in the last decades thanks to advancements such as multi-cloud, edge computing, and the need for global-scale applications. To this end, this paper discusses how software architectures have evolved to harness the various characteristics of modern computing models. Whereas multi-cloud is flexible and redundant, it also brings new complexities of how to manage it, how to keep it secure, and how to manage data within it. On the other hand, edge computing minimizes latency and optimizes offline data analytics by decentralizing workloads nearer to the user ends.
Navigating The Shift: Seamless Migration of Cloud Native Applications from Tanzu Application Service to OpenShift
Swetha Sistla
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Rapid evolution of cloud-native technologies demands seamless migration strategies for enterprises to keep operational continuity alive while embracing modern platforms. The migration of cloud-native applications from VMware Tanzu Application Service (TAS) towards Red Hat OpenShift discusses challenges, best practices, and tools to help make migration successful. In comparison, TAS's strong deployments and microservices support place its worlds apart in architectural and operational paradigm versus OpenShift, a Kubernetes-based platform that is known to scale and have an excellent ecosystem. The migration journey involves careful consideration of application dependencies, containerization approaches, and re-architecting of workloads to align with Kubernetes-native patterns.
Optimizing Regression Test Suites in Agile: Strategies for Maintaining Speed and QualityAsha Rani Rajendran Nair Chandrika
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As agile practices continue to evolve, teams often face the challenge of maintaining an ever-growing regression test suite while ensuring timely test execution and consistent quality. Regression testing plays a critical role in verifying that new code changes do not negatively affect existing functionality. As new features are developed, the regression suite expands, leading to challenges with test maintenance, execution time, and reporting efficiency. This article explores strategies for managing the growing regression test suite without sacrificing speed or quality.
Battery Health Monitoring With AI: Creating Predictive Models to Assess Battery Performance and LongevityHari Prasad
Bhupathi, Srikiran Chinta
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The rapid adoption of battery-powered devices, particularly in electric vehicles and energy storage systems, has highlighted the need for efficient and accurate battery health monitoring methods. Traditional approaches, based on basic parameters like voltage and capacity, are limited in their ability to predict long-term performance and degradation. This research explores the application of artificial intelligence (AI) in developing predictive models to assess battery health and longevity. By analysing real-time data from various battery parameters, machine learning algorithms, including decision trees, neural networks, and support vector machines, are utilized to predict battery performance over time.
Optimizing Energy Efficiency in Residential and Commercial BuildingsHari Prasad
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As the global building sector uses approximately 30% of global energy and is a major contributor to greenhouse gas emissions, the fight against climate change requires such approaches. Energy efficiency in residential and commercial buildings has become critical to ensure the achievement of sustainable development goals and diminish climate change. Different strategies for improving energy efficiency will be presented through smart technologies, implementation of renewable energy systems and energy efficient building design. Potential for minimizing energy demand is evaluated in terms of smart HVAC systems; Internet of Things (IoT) enabled building management systems, and passive design strategies.
RAG-Powered Real-Time Intelligence for Crisis ManagementAbhinav Balasubramanian
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Efficient decision-making during crises such as natural disasters, pandemics, and large-scale emergencies requires rapid access to reliable and contextually relevant information. This paper presents a novel Retrieval-Augmented Generation (RAG) framework designed to integrate, synthesize, and analyze diverse data sources for real-time crisis management. By combining structured inputs, such as weather reports and resource inventories, with unstructured data, including social media updates and news articles, the system generates actionable insights tailored to specific emergency scenarios.
AI in Designing New Payment Processing Systems for Fraud DetectionDevendra Singh Parmar
Harshad Pitkar, Hemlatha Kaur Saran, Pankaj Gupta
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Complex digital payment systems make them more prone to fraud, raising the need for advanced fraud detection solutions. Since rule-based systems cannot keep up with fraudsters' ever-changing schemes, AI is needed to prevent fraud. This research examines how AI could be used to detect fraud in future payment processing systems to improve efficiency, accuracy, and security. AI models can evaluate enormous information in real time using deep learning, decision trees, and neural networks to detect fraudulent activities that people neglect. The study uses mixed methods to combine quantitative model performance indicators (F1 score, recall, accuracy, and precision) with qualitative financial case study findings.
Stability Analysis of Gravity Retaining Wall by DEM SimulationDhruval Jigar Shah
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For the conventional retaining wall in a kind of gravity wall, the weight of the structure influences the stability of the retaining wall. Moreover, the weight of the retaining wall is determined by its dimension. The assumptions of retaining wall dimension are determined by trial and error. If the retaining wall is not enough to bear the load, the dimension of the retaining wall must be changed. In this study, we analyze a gravity retaining wall according to Rankine’s theory. The active earth pressure and passive earth pressure have been calculated using Rankine’s theory and the stability of the retaining wall against sliding, overturning, and bearing capacity is also calculated. Finally, the stability is compared through discrete element modeling simulation. As a limitation of this study, there is only one layer of backfill material considered due to simplicity.
Leveraging A/B Testing and Synthetic Control Methods for Effective Model Evaluation in Retail AnalyticsBhageerath Bogi
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In the following report, the author examines the application of A/B Testing and Synthetic Control methods for model evaluation in the domain of retail analytics. One of the most popular experimental methods is called A/B testing, and it is used in retail to determine the success of particular marketing campaigns and both the location and the style of products within the store as well as website layouts. But it may be confined in specific situations, including cases with more than two treatment groups and non-observable kinds of variables.
Data Modelling For Health Insurance Claims AnalyticsNandish Shivaprasad
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This paper aims at discussing the analysis of health insurance claim through risk classification, fraudulence and cost prediction models. Combined with state-of-art data preprocessing and modelling techniques, insurers can better drive decision, minimize fraud, and better plan for financials. Logistic regression, random forest, gradient boost and models of similar category help in pattern analysis and cost of claim forecasting. They further effectiveness, equity and customer relations for implementing sound insurance that is sustainable.
The Role of Enterprise Integration in Digital Payment SolutionsGomathi Shirdi Botla
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The rapid evolution of digital payment solutions has necessitated seamless integration of diverse financial systems and Application Programming Interfaces (APIs) to facilitate online banking, wire transfers, and related services. Enterprise integration plays a pivotal role in overcoming interoperability challenges, ensuring secure transactions, and enhancing customer experience. This paper explores the integration challenges, proposes potential solutions, and examines the impact and scope of enterprise integration in digital payment solutions. By addressing these aspects, the research aims to provide a comprehensive understanding of how enterprise integration transforms financial ecosystems.
Enhanced Recommendations Based on Health in E-Commerce Using Large Language ModelsAditi Choudhary
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The rapid growth and increasing complexity of e-commerce platforms have driven the need for innovative methods to enhance product recommendations. This research builds on earlier studies that utilized machine learning algorithms and investigates how Large Language Models (LLMs) can further improve recommendation systems. By leveraging their superior natural language comprehension, LLMs can provide personalized recommendations tailored to individual customer inquiries, purchase histories, and product information.
Effectiveness of Non-Surgical Treatments for Chronic Back Pain: Physical Therapy TreatmentLaljibhai Makwana
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Chronic back pain is a widespread problem that affects many people’s lives by limiting their ability to move and causing constant discomfort, while surgery is also an option for severe cases, non-surgical treatments, particularly physical therapy, have proven effective for managing pain and improving quality of life. Physical therapy helps to reduce pain, improve te movement, and strengthen muscles through targeted exercises, manual therapy, and education, and so it gives long-term benefits by addressing the root causes of pain and teaching patients how to prevent future issues.
AI-Powered Optimization of Multi-Site Pharmaceutical Manufacturing Calibration IntervalsSrikanth Reddy Katta
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The pharmaceutical manufacturing industry is characterized by strict quality control requirements and a requirement for close equipment calibration for achieving the product integrity and successful regulatory compliance. In multiple manufacturing site manufacturing, managing calibration intervals is a huge challenge leading to downtime and inconsistency. In this study we investigate the use of Artificial Intelligence (AI) for optimizing calibration schedules in multi-site operations. The proposed AI framework uses machine learning algorithms to process historical calibration data and real time equipment performance metrics to forecast optimal interval between calibrations.
Edge-Cloud Collaboration for Low-Latency Gen AI ApplicationsRahul Vadisetty
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The rapid development of Gen AI and LLM opens a new avenue for application possibilities in both IR and QA. Since large language models are computation-intensive, avoiding latency to guarantee real-time performance can often take time and effort. The work, bearing this in mind, tries to minimize this considerable latency challenge for several applications of emergent-gen AI by studying the exploitation of the concept of edge-cloud collaboration. This paper presents a framework that efficiently deploys LLMs by leveraging strengths from both edge and cloud to balance loads, reduce latency, and provide responsiveness for IR and QA applications.
Training Generative AI for Low-Resource Languages in Cloud InfrastructureAnand Polamarasetti
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Despite generative AI's tremendous transformational potential in NLP, low-resource languages remain significantly behind in data scarcity and computational load. It is envisioned that the present study will investigate whether cloud infrastructure can be gainfully employed toward exploring and training generative models for low-resource languages so they can surmount both their data and computational limitations. It starts by reviewing related work in generative AI and techniques optimized for the low-resource context to identify such languages' challenges and unique demands.
AI-Powered Decision-Making: Enhancing Business Intelligence with Tableau and Einstein Discovery in Salesforce
Shalini Polamarasetti
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AI growth is so fast-paced that it revolutionizes BI in making informed data-driven decisions across industries. In line with this, the study also studies the integrations of both Tableau and Einstein Discovery into Salesforce for AI-enabled capabilities that enhance BI. On the one hand, the vastness of Tableau's scope consists of advanced data visualization which enables businesses to generate dynamic dashboards even in such complex datasets; Einstein, on the other hand, uses machine learning and predictive analytics to uncover actionable insights complete with recommended actions. These tools, when combined, offer a revolution in decision-making due to real-time data analyses that are accurate and project forward.