Capgemini (United States)

Explore the article titled Trustworthy Autonomous Cloud Operations: Self-Healing Azure Architecture Powered by Agentic AI from IJIRT Volume 12, Issue 10. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.

Healthcare is increasingly driven by data-intensive applications that span medical imaging, biosignal monitoring, and multilingual patient records. While cloud platforms provide scalable resources for storage and advanced analytics, latency and security concerns make it essential to integrate intelligent processing at the edge. Traditional edge-cloud healthcare pipelines face limitations in handling heterogeneous data modalities, supporting multilingual interpretation, and maintaining privacy under regulatory frameworks. To address these challenges, this paper proposes a novel multilingual Large Language Model (LLM)-enhanced framework for secure signal and image analytics in edge-cloud healthcare systems. The framework fuses multimodal data streams such as ECG signals, MRI scans, and multilingual clinical text-using LLM-based contextual reasoning to enable adaptive decision support. A security layer is embedded through federated edge training, encryption protocols, and differential privacy, ensuring compliance with HIPAA and GDPR. Experimental evaluation across public healthcare datasets demonstrates that the proposed system achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7-12 {\%}$</tex> higher diagnostic accuracy in multilingual cases compared to unimodal baselines, while reducing latency by 18% through edge-assisted inference. These results indicate that multilingual LLM integration into healthcare edge-cloud pipelines offers a resilient, secure, and globally inclusive approach for next-generation medical analytics.

The Data-Integrated Campaign Engagement (DICE) framework marks a significant advancement in customer relationship management and revenue optimization for enterprise organizations. It integrates four critical intelligence components: Customer Intelligence, Competitive Intelligence, Market Signals, and Augmented Criteria. Together, these elements form a unified architecture that transforms traditional campaign management into intelligent, adaptive market interaction systems. Through rigorous mathematical formalization and empirical validation across telecommunications networks serving over 150 million subscribers, the DICE methodology demonstrates provable performance bounds while establishing new theoretical foundations for intelligent market interaction. Its mathematical precision, combined with practical transformative impact, positions DICE as a breakthrough in computational social science and applied market theory. Real-world implementations further validate its effectiveness. Across telecom networks with more than 150 million subscribers, the framework has delivered measurable improvements: a 24.7% increase in campaign response rates, a 67.3% boost in operational efficiency, and a 43.8% rise in revenue, all with statistical significance (p &lt; 0.001). DICE addresses core challenges such as data silos, limited personalization, and slow campaign execution cycles, while laying the groundwork for intelligent product architecture and streamlined order-to-cash processes.

Wireless sensor networks (WSNs) are systems made up of sensor nodes that communicate wirelessly over short distances, with adaptability and functionality at their coreessence. These networks have traits. Face challenges in devising effective strategies for identifying and thwarting attacks due to limitations, in individual sensor node processing power and the essence of wireless communication. Securing WSN poses a challenge given the constrained processing abilities of sensor nodes and the inherent nature of communication while facing various security threats that have emerged alongside the widespread adoption of these networks. To protect against these dangers it's crucial to put in place the security measures Encryption's key, to keeping data safe from unauthorized users Nevertheless not all encryption methods are equally strong as some are vulnerable Elliptic Curve Cryptography (ECC) is considered the best option due to its smaller key sizes offering strong security with efficiency in space and energy As technology advances conventional encryption techniques are seen as inadequate, for ensuring data privacy and security Utilizing principles from physics to ensure data transmission, between the sender and receiver is a groundbreaking advancement in network security known as quantum cryptography. Quantum Key Distribution (QKD) combined with ECC is highlighted in this study, for authentication processes, key management and energy efficiency. A novel method called QKD ECC has been put into practice. Compared against ECC and other encryption methods to bolster the security of WSN and safeguard the confidentiality of data exchange.

As next-generation networks evolve to integrate terrestrial, wireless, and edge technologies, resource management becomes increasingly complex. This paper presents an AI-powered orchestration system that autonomously configures and manages network slices across hybrid access environments. Leveraging machine learning and predictive analytics, the system dynamically optimizes latency, throughput, and service-level performance in real time, achieving up to 35 % reduction in average latency and a 28 % improvement in throughput across test scenarios. At its core is a smart feedback loop that fuses multiple AI models to continuously adapt to changing network conditions. Experimental evaluations demonstrate that the system not only simplifies configuration and reduces operational overhead by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 0 \%}$</tex> but also ensures consistent service quality across diverse deployment contexts. This work contributes a scalable, selfmanaging framework that advances the state of intelligent, responsive service delivery in future communication systems.

AI which is reshaping the landscape of quality assurance (QA) by enhancing efficiency, accuracy, and coverage. Traditionally, software testing has relied heavily on manual and semi-automated processes, often leading to time-consuming and error-prone tasks. AI introduces a new paradigm in software testing, offering advanced methodologies and tools that significantly improve the effectiveness and efficiency of QA practices. Additionally, AI enhances test maintenance and evolution. Traditional test suites require manual updates as software evolves, which can be labor-intensive and prone to oversight. AI-powered tools can automatically adjust test cases and scripts to accommodate changes in the software, reducing the manual effort required to maintain and update tests. By leveraging AI to detect changes in the software and adjust tests accordingly, organizations can ensure that their test suites remain relevant and effective throughout the development lifecycle.Another significant advancement brought about by AI is the optimization of test execution.

This article examines the implementation of the PTC Windchill PLM system at AutoTech Solutions Inc., a leading Tier-One automotive supplier, focusing on the challenges and solutions in deploying a global PLM system.The article investigates the transformation of product development capabilities through process standardization, Nishant Kapoor https://iaeme.com/Home/

In the era of cloud computing, securing access to cloud instances is paramount for protecting sensitive data and ensuring compliance with industry standards. Secure Shell (SSH) access management plays a critical role in safeguarding cloud environments by controlling how users interact with cloud-based systems. However, traditional SSH access management methods can often be cumbersome, error-prone, and vulnerable to breaches. This paper explores the implementation of Privileged Access Management (PAM) solutions for enhancing the security of SSH access to cloud instances. By integrating PAM systems with cloud infrastructure, organizations can enforce stricter access controls, enable detailed session auditing, and reduce the risk of unauthorized access. The paper discusses best practices for managing SSH keys, monitoring user activity, and automating access management workflows using PAM solutions, ultimately improving the security posture of cloud environments.

Thereis a possibility that the reliability and accessibility of voting systems will be significantly improved because of developments in technology. To solve the problem of providing voters with an identity that is safe and accurate, the research suggests combining the technologies of the Internet of Things (IoT) with biometric authentication. Using Internet of Things devices, voters can have their identities verified in real-time at voting sites if the user registers distinctive biometric characteristics, such as fingerprints, facial features, and iris patterns. Increasing safety is the goal of this multifactor authentication method, which incorporates voter IDs, secure QR codes, and biometric data. Privacy concerns may be alleviated using biometric template protection, which conceals real biometric data while allowing for precise identification matching. The system prioritizes scalability and ease of use to ensure that it can support many voters and a wide variety of user requirements. The proposed voting system has the goals of reducing instances of voter impersonation, improving accessibility for people of all demographics, and contributing to the development of a more open and trustworthy voting environment.

The deep learning model (DL) S-ResNet-152 (Squeeze-based ResNet-152) is used to pull out the features. Then, a bidirectional gated auto network (Bi-GRU-AN) is used to predict software faults based on a mixed DL model. A modified whale optimization algorithm and a crisscross optimization algorithm (MWOA-CS) are used to find the best weight for the suggested classifier. In MWOA-CS, the position of each dimension of the optimization problem is changed by a random better WOA optimization method while the repetitive process is going on. In this work, experiments are done on two software defect data sets (Kamei and PROMISE) to show how well the model classifies the data. The precision, recall, accuracy, and F1-score are the four complete evaluation measures used. The results show that the framework projected in this paper does a great job of classifying the two types of software fault data sets and is much better than the baseline models. then precision rate as 98.32 and then recall rate as 93.24 and then F-1 score as 94.53 correspondingly.