A Hybrid Knowledge Graph-Deep Learning Model for Predictive Analytics in Healthcare Worker Stress Management with Cloud Environments

The integration of machine learning (ML) models with cloud computing has transformed the landscape of predictive analytics, offering scalable, efficient, and flexible solutions for organizations. Cloud platforms such as AWS, Google Cloud, and Microsoft Azure enable businesses to deploy and manage complex ML models without the need for extensive on-premise infrastructure. However, optimizing these ML models for performance and cost-efficiency in cloud environments presents unique challenges, including resource management, latency, scalability, and data security. This paper focuses on strategies to optimize machine learning models specifically for predictive analytics in cloud environments. It explores key techniques such as auto-scaling, model compression, and hyperparameter tuning, which are critical for improving the accuracy and speed of predictions while minimizing computational costs. The research also examines advanced tools such as containerization, serverless computing, and cloud-native services that further streamline the deployment and management of ML models. In the Indian context, where cloud adoption is growing rapidly, optimizing ML models is crucial for businesses across various sectors, including finance, healthcare, and e-commerce. By leveraging cloud-based ML solutions, Indian companies can enhance their predictive analytics capabilities, driving smarter decision-making and operational efficiency. This abstract presents an overview of how optimized machine learning models can unlock the full potential of predictive analytics in cloud environments, leading to better business outcomes. Through case studies and practical applications, this paper provides actionable insights into the best practices for optimizing ML models in a cloud-based setting.

Resource allocation in the cloud environment for 5G applications can be explained by referring to the strategic distribution and necessary assignment of computing resources such as virtual machines, storage, and network bandwidth that meet the dynamic demands of applications and services. The framework proposed is on resource allocation in the cloud environment by BRoML for 5G applications. In the proposed BRoML model, the Backtracking Regularized model is incorporated for the effective calculation of the resources in the cloud environment. The optimization is performed for the effective computation of resources in the cloud environment through the computed resources. Using the estimated optimized values, a machine learning model can be trained and tested to classify resource allocation. In this regard, the simulation analysis is compared to BRoML with traditional schemes like SVM and RF. The result shows that BRoML has a higher resource utilization while exhibiting lower latency, higher increased throughput, and a better efficiency score overall. Machine learning techniques and optimization mechanisms give flexibility and intelligence to BRoMl in solving resource allocation issues within cloud computing. These results reinforce the view that BRoML can create a strong impact on the development process of cloud computing with its dynamic, intelligent solution in resource allocation optimization under various scenarios.

The integration of artificial intelligence with Financial Operations (FinOps) is revolutionizing cloud cost optimization for enterprises. This scholarly article explores how AI-enabled FinOps transforms financial governance in cloud environments by providing enhanced visibility, automated anomaly detection, and intelligent optimization recommendations. The evolution from reactive cost management to proactive governance models has enabled organizations to address challenges in cloud spending through sophisticated machine learning algorithms, predictive analytics, natural language processing, and deep learning applications. Implementation frameworks incorporating enterprise architecture principles, comprehensive data integration strategies, real-time monitoring systems, and effective change management practices are driving significant improvements across industry verticals. Case studies demonstrate varying levels of success across sectors, with documented implementation challenges and best practices providing valuable insights for organizations embarking on AI-FinOps journeys. The combination of technological capabilities with organizational strategies creates sustainable financial governance that supports both innovation and fiscal responsibility in increasingly complex cloud environments.

The rapid growth of cloud computing has led to an increased demand for security mechanisms to safeguard sensitive data and resources from cyber threats. Intrusion detection systems (IDS) play a crucial role in identifying unauthorized access or malicious activities within cloud environments. This paper presents a comparative study of machine learning (ML) techniques used in intrusion detection for cloud computing platforms. Various ML algorithms, including decision trees, support vector machines, k-nearest neighbors, and neural networks, are evaluated based on their performance in detecting different types of attacks. The study assesses the accuracy, efficiency, and scalability of these techniques in cloud environments, highlighting their strengths and limitations. The findings provide valuable insights into the selection of appropriate machine learning models for effective intrusion detection in dynamic and scalable cloud systems.

Efficient resource scheduling is a foundational challenge in cloud computing environments, where dynamic workloads, heterogeneous resources, and stringent service-level agreements (SLAs) converge to complicate optimal task allocation. Traditional scheduling algorithms such as Round-Robin, Min-Min, and other heuristic/metaheuristic approaches often struggle to adapt in real time to fluctuations in demand, resource availability, and cost-energy trade-offs [1],[2]. In contrast, artificial intelligence (AI) techniques including supervised learning, reinforcement learning (RL), and hybrid optimisation models offer adaptive and predictive capabilities that significantly enhance scheduling performance by learning from past workload patterns and system feedback [3],[4]. This paper investigates the application of AI-driven resource scheduling in cloud environments, proposing a framework that integrates workload forecasting, dynamic resource allocation, and SLA compliance optimisation. Experimental evaluation, based on simulated and real-trace datasets, demonstrates that the proposed AI-based scheduling approach can improve resource utilisation, reduce energy consumption, and lower SLA violation rates compared to baseline methods. The findings highlight the potential of AI to reshape scheduling in modern cloud infrastructures and underscore the need for further research into scalability, transparency of AI decisions, and hybrid edge-cloud deployment scenarios.

In order to effectively manage varying workloads, serverless computing in cloud settings requires dynamic resource allocation, which is addressed by the deployment of an auto-scaling system. Serverless computing is an affordable option for cloud-based apps since users are only charged for the resources that are really used. However, complex techniques are needed to dynamically scale resources up or down in response to real-time traffic patterns in order to manage the scalability of serverless functions, particularly when demand is variable. In order to optimize resource allocation in serverless systems, this study suggests an adaptive auto-scaling method that makes use of real-time monitoring and predictive analytics. In order to minimize idle resources and guarantee high availability, the suggested system dynamically modifies the number of function instances. Workload monitoring, performance metrics analysis, and the use of machine learning models to forecast future demand and optimize scaling choices are important elements of the mechanism. The system is made to manage different computing job levels while striking a balance between cost effectiveness and responsiveness. When compared to static scaling models, the outcomes of putting this auto-scaling mechanism into practice show better responsiveness, cost savings, and resource usage. Furthermore, the suggested method easily adjusts to the evolving needs of the application, which makes it appropriate for a variety of cloud-based applications, ranging from tiny services to major business solutions. This study demonstrates how intelligent scaling can improve serverless computing's adaptability and effectiveness in cloud settings.

Anomaly detection is critical for network security, fraud detection, and system health monitoring applications. Traditional methods like statistical approaches and distance-based techniques often struggle with high-dimensional and complex data, leading to high false positive rates. This study addresses the challenge by investigating advanced AI-driven techniques to reduce false positives and enhance data security within cloud computing environments. This study employs deep learning models, integrates contextual data, and incorporates comprehensive security measures to enhance anomaly detection performance. Data from synthetic sources, such as the NSL-KDD dataset and real-world cloud environments, were utilized to capture user behavior logs, system states, and network traffic. Over 50 academic journals were reviewed, and 21 were selected based on inclusion criteria, such as relevance to AI-driven anomaly detection, empirical performance metrics, and the focus on cloud environments, and exclusion criteria that filtered out studies lacking empirical data or not specific to cloud-based systems. Methodologically, the research involves a comparative analysis of different AI techniques and their impact on false positive rates, accuracy, precision, and recall. The findings demonstrate that deep learning techniques significantly outperform traditional methods, achieving a lower false positive rate and higher accuracy. The results underscore the importance of contextual data and robust security protocols in reliable anomaly detection. This research fills a gap by thoroughly evaluating advanced AI techniques for reducing false positives in cloud environments. The study's significance lies in guiding the development of more effective anomaly detection systems, thereby enhancing security and reliability across various applications. Additionally, organizations should invest in continuously developing and integrating AI-driven anomaly detection systems with comprehensive security measures to improve their effectiveness the study suggests that further study be conducted with large datasets to evaluate the effectiveness of Hybrid anomaly detection systems in detecting and addressing false positives.

The growing sophistication of cyber threats has increased the need for advanced detection techniques, particularly in cloud computing environments. Zero-day threats pose a critical risk due to their ability to bypass traditional security mechanisms. This study proposes a deep learning model called mixed vision transformer (MVT), which converts binary files into images and applies deep attention mechanisms for classification. The model was trained using the MaLeX dataset in a simulated Docker environment. It achieved an accuracy between 70% and 80%, with better performance in detecting malware compared with benign files. The proposed MVT approach not only demonstrates its potential to significantly enhance zero-day threat detection in cloud environments but also sets a foundation for robust and adaptive solutions to emerging cybersecurity challenges.

This study explores the integration of AIdriven techniques into cloud orchestration, emphasizing the transformative impact on resource optimization, automation, and scalability. The paper presents realworld case studies across industries, highlighting the benefits and challenges of AI in cloud environments, such as data privacy and computational power requirements. By leveraging machine learning models for workload distribution and dynamic scaling, organizations can achieve significant efficiency and cost-effectiveness. The findings underline AIs crucial role in driving innovation and enabling responsive, secure cloud management in the digital age.

Cloud computing alters the way organizations manage and deploy their IT resource. It provides an organization with scalable, inexpensive, and flexible options. The complexity and dynamic nature of cloud environments pose a challenge to maintaining high availability at all times, especially when the system fails or a disaster arises. The legacy techniques of disaster recovery, fault tolerance, and high availability leave behind much to be desired. These techniques are mostly static, slow to respond, and have a dismal ability to adapt to continuously changing conditions in contemporary cloud systems. Such techniques largely depend on manual configurations and predefined policies; resulting in lots of inefficiencies and increases in the risk of service downtime. This research investigates the way Artificial Intelligence (AI) changes the paradigm on cloud resilience to promote adoption of intelligent systems for guaranteed disaster recovery, better fault-tolerant behavior, or transparent high availability. With machine learning algorithms, AI-based cloud services utilize large data volumes to reveal patterns within system logs, performance metrics, and user behavior data; thereby offering real-time anomaly detection and predictive failure analysis. For example, techniques like predictive analytics help cloud providers predict likely system outages, optimize the resources to be used, and automate failover processes (Xu et al., 2021; Lee & Kumar, 2022). AI-aided disaster recovery techniques employ complex algorithms to produce an adaptive backup mechanism, thereby minimizing loss and reducing restoration time. Fault tolerance in AI cloud systems comes from intelligent error correction, automatic isolation of faults, and self-healing features, i.e. repair of faults without the need for human supervision (Chen et al., 2020). Besides that, AI also contributes to high availability through intelligent load balancing, which ensures that at any given time, resources are optimally distributed throughout the network to sustain continuous service even during peak demand or unanticipated failures (Patel & Zhang, 2023). The approach is a comprehensive review of the various existing literature on the topic, empirical analysis of the current AI-driven cloud solutions available in the market, and case studies for comparison analysis on the different AI systems. The study scenario reveals that AI-driven solutions noticeably reduce downtimes, lead to improved recovery times, and contribute to overall system reliability as compared to traditional methods. However, other areas include model bias, data privacy, and continuous training of AI models. This study expands the trends of AI in the field of cloud computing by documenting the significance of intelligent systems in bridging traditional weaknesses of resilience strategies. It further signifies the need for AI into predictive maintenance, automated disaster response, and proactive fault management of rapidly changing dynamic cloud environments. Future studies will focus on AI integration along with edge computing and blockchain technologies for even more robust and secure services in cloud operations.

The article is devoted to the development of a digital agriculture management system (DAMS) that combines monitoring of agricultural objects, analysis of long-term field experiment data and modern information technologies. The purpose of the study is to create a theoretical basis for optimizing agricultural production management by integrating artificial intelligence, machine learning, remote sensing, geographic information systems (GIS) and big data. The novelty of the work lies in the use of machine learning algorithms for yield forecasting, scaling of agricultural system archetypes based on spatio-temporal analysis, as well as the development of a spatial decision support system (DSS) that combines geographic information systems, modeling and knowledge bases. The technical implementation of monitoring includes the use of unmanned aerial vehicles, weather stations, soil sensors and satellite images, the data of which are processed in cloud environments. The research results demonstrate the possibility of reducing uncertainty in the management of agricultural technologies through predictive analytics, simulation modeling and standardization of multimodal data. The findings emphasize the practical importance of the digital agriculture management system for increasing sustainability, minimizing risks and profitability of crop production. The proposed system allows moving from generalized recommendations to individualized management that takes into account the spatio-temporal variability of agricultural landscapes. The key components of the digital agriculture management system - modules for yield forecasting, analysis of vegetation indices, differentiated fertilization and integration of field experiment data - are focused on solving the problems of digitalization of agriculture. The results of the work are of practical importance for the introduction of intelligent technologies in the agro-industrial complex.

For customers running their applications on Platform-as-a-Service (PaaS) cloud environments it is important to ensure the Quality-of-Service (QoS) of their applications. Knowing in advance if and when a potential problem is likely to occur allows the application owner to take appropriate countermeasures. Therefore, predictive analytics using machine learning could allow to be alerted in advance about potential upcoming QoS outages. In this context, mainly Infrastructure-as-a-Service (IaaS) or Software-as-a-Service (SaaS) have been studied in the literature so far. Studies about predicting QoS outages for the Platform-as-a-Service (PaaS) service model are sparse. Therefore, in this paper an approach for predicting response-time-related QoS outages of web services running in a PaaS cloud environment is presented. The proposed solution uses the open source Apache Spark platform in combination with MLib and binary classification by the naive Bayes algorithm. The approach is evaluated by using test data from a social app backend web service. The results indicate that it is feasible in practice.

Cloud computing infrastructure is an integral part of wireless communication applications, such as vRANs. Given the pervasive use of cloud computing, keeping track of its health is of utmost importance to its providers. Large scale of deployment of this aggravates the complexity. All vRAN based cloud applications generate logs which are raw text messages to reflect run-time execution. Analyzing such log messages and training a model can help an operator timely detect an anomaly, before it leads to service disruption. In this paper, we apply various natural language processing (NLP) models to detect system anomalies by analyzing logs and evaluate their precision and accuracy of anomaly detection. We make use of a machine learning (ML) model in form of a recurrent neural network (RNN) for anomaly detection. We have added attention layer to RNN models like LSTM and GRU models to improve accuracy. We found GRU models with attention are a good fit for logs dataset in cloud environment. Log messages are typically short sentences, linguistically much simpler, and don't suffer from long-range dependencies, hence adding attention layer didn't help for LSTM. GRU has a reduced instruction set, faster training time, hence are much more energy efficient without compromising on anomaly detection accuracy.

In the present internet world, accessing cloud resources for a low cost, according to their needs, is available to all users. Sharing resources is becoming increasingly necessary as people complete their activities in the cloud. It becomes essential for distributed workloads to be optimized to perform efficient workload scheduling and progressing resource utilization in a cloud environment. Scheduling cloud resources considerably benefits from the invention of machine learning and metaheuristic models to address this scenario. Though many existing algorithms are developed in cloud-based task scheduling using unsupervised clustering methods, the problem of unknown task requirements or resource availability in adverse conditions is still challenging. In this study, an uncertainty-based unsupervised technique is constructed to group incoming tasks according to the required resources, and it is scheduled to the most suitable resources more prominently. This paper introduced a Robust Neutrosophic C-Means Clustering boosted with the fish school search algorithm (RNCM-FSSA) for clustering the incoming tasks and the resources based on their requirement and availability. With the degree of indeterminacy, neutrosophic C-means discriminating the deterministic and indeterministic schemes and scheduling them to the optimal resources more effectively. Using the fitness value computed by FFSA, the potential cluster centroids are utilized for clustering, thus avoiding the early convergence in the grouping process. The simulation results explore that the robustness of the proposed RCNM-SSA achieves better resource utilization, the degree of imbalance is minimal, and computation complexity is also considerably decreased compared with other unsupervised models.

The complexity of the Internet and the volume of network traffic have dramatically increased in the last few years, making it more challenging to design scalable Network Traffic Monitoring and Analysis (NTMA) systems. Critical NTMA applications such as the detection of network attacks and anomalies require fast mechanisms for on-line analysis of thousands of events per second, as well as efficient techniques for off-line analysis of massive historical data. The high-dimensionality of network data provided by current network monitoring systems opens the door to the massive application of machine learning approaches to improve the detection and classification of network attacks and anomalies, but this higher dimensionality comes with an extra data processing overhead. In this paper we present Big-DAMA, a big data analytics framework (BDAF) for NTMA applications. Big-DAMA is a flexible BDAF, capable to analyze and store big amounts of both structured and unstructured heterogeneous data sources, with both stream and batch processing capabilities. Big-DAMA uses off-the-shelf big data storage and processing engines to offer both stream data processing and batch processing capabilities, decomposing separate engines for stream, batch and query, following a Data Stream Warehouse (DSW) paradigm. Big-DAMA implements several algorithms for anomaly detection and network security using supervised and unsupervised machine learning (ML) models, using off-the-shelf ML libraries. We apply Big-DAMA to the detection of different types of network attacks and anomalies, benchmarking multiple supervised ML models. Evaluations are conducted on top of real network measurements collected at the WIDE backbone network, using the well-known MAWILab dataset for attacks labeling. Big-DAMA can speed up computations by a factor of 10 when compared to a standard Apache Spark cluster, and can be easily deployed in cloud environments, using hardware virtualization technology.