Cloud computing has become an essential component of enterprises and organizations globally in the current era of digital technology. The cloud has a multitude of advantages, including scalability, flexibility, and cost-effectiveness, rendering it an appealing choice for data storage and processing. The increasing storage of sensitive information in cloud environments has raised significant concerns over the security of such systems. The frequency of cyber threats and attacks specifically aimed at cloud infrastructure has been increasing, presenting substantial dangers to the data, (...) reputation, and financial stability of enterprises. Conventional security methods can become inadequate when confronted with ever intricate and dynamic threats. Artificial Intelligence (AI) technologies possess the capacity to significantly transform cloud security through their ability to promptly identify and thwart assaults, adjust to emerging risks, and offer intelligent perspectives for proactive security actions. The objective of this research study is to investigate the utilization of AI technologies in augmenting the security measures within cloud computing systems. This paper aims to offer significant insights and recommendations for businesses seeking to protect their cloud-based assets by analyzing the present state of cloud security, the capabilities of AI, and the possible advantages and obstacles associated with using AI into cloud security policies. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes of data, resource management complexities in optimizing (...) computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)

The increasing complexity and scale of modern computing needs have led to the development and adoption of cloud computing as a ubiquitous paradigm for data storage and processing. The hybrid cloud model, which combines both public and private cloud infrastructures, has been particularly appealing to organizations that require both the scalability offered by public clouds and the security features of private clouds. Various strategies for configuring and managing resources have been developed to optimize the hybrid cloud environment. These strategies aim (...) to balance conflicting objectives such as cost-efficiency, performance optimization, security, and compliance with regulatory standards. This exploratory research focused on evaluating the efficiency and limitations of different configuration strategies in hybrid cloud environments. Findings indicate that each approach presents distinct advantages. Improving resource utilization and automating governance processes are significant advantages of Policy-based Resource Management, which leads to costeffectiveness. Intelligent routing of traffic is a feature of Cross-cloud Load Balancing, resulting in optimized performance and higher service availability. By centralizing control, the Hybrid Cloud Service Mesh allows for secure and streamlined cross-service communication. A notable feature of Cross-cloud Container Orchestration is its ability to simplify the migration of applications across diverse cloud environments. For immediate threat detection and regulatory compliance, real-time monitoring is facilitated by Log Management and Analytics. However, Policy-based Resource Management can be complex and inflexible. Extra costs for data transfer between different cloud providers are a drawback of Crosscloud Load Balancing. Additional network hops create latency issues in Hybrid Cloud Service Mesh configurations. If configured incorrectly, Cross-cloud Container Orchestration could expose the system to security risks. Finally, Log Management and Analytics require both ample storage and advanced analytical capabilities. (shrink)

Containerization in a multi-cloud environment facilitates workload portability and optimized resource uti-lization. Containerization in multi-cloud environments has received significant attention in recent years both from academic research and industrial development perspectives. However, there exists no effort to systematically investigate the state of research on this topic. The aim of this research is to systematically identify and categorize the multiple aspects of containerization in multi-cloud environment. We conducted the Systematic Mapping Study (SMS) on the literature published between January 2013 and March (...) 2023. Eighty-six studies were finally selected and the key results are: (1) Four leading themes on cloud computing and network systems research were identified: ‘Scalability and High Availability’, ‘Performance and Optimization’, ‘Security and Privacy’, and ‘Multi-Cloud Container Monitoring and Adaptation’. (2) Seventy-four patterns and strategies for containerization in multi-cloud environment were classified across 10 subcategories and 4 categories. (3) Ten quality attributes considered were identified with 47 associated tactics. (4) Four frameworks were introduced from the analysis of identified challenges and solutions: security, automation, deployment, and monitoring challenge-solution frameworks. The results of this SMS will assist researchers and practitioners in pursuing further studies on containerization in multi-cloud environment and developing specialized solutions for containerization applications in multi-cloud environment. (shrink)

Server-less computing, also known as Function as a Service (FaaS), revolutionizes cloud architecture by allowing developers to focus on code and functionality without managing underlying infrastructure. This paradigm enhances resource management efficiency by dynamically allocating resources only when needed, thus optimizing cost and performance. Server-less models, epitomized by platforms like AWS Lambda, Google Cloud Functions, and Azure Functions, provide automatic scaling and fine-grained billing, making them ideal for applications with variable workloads. While challenges such as cold start latency and complex (...) debugging exist, the benefits of server-less computing—including cost savings, simplified deployment, and accelerated innovation—make it a transformative approach in the cloud computing landscape. This paper explores the principles, benefits, challenges, and practical applications of server-less computing in modern cloud architectures, highlighting its potential to drive efficiency and innovation. (shrink)

This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces a secure cloud framework designed to accommodate both individuals (...) and organizations accessing applications and data in the cloud. While individual users may generate and share data, organizations often involve multiple users in data sharing to support their business processes. In these scenarios, multi-user data ownership and access management become critical, requiring secure sharing of cryptographic keys among the authorized users. To address these issues and ensure data confidentiality in multi-user cloud environments, the Improved Secure Cloud Data Storage Framework (ISCDSF) is introduced. This research not only enhances data security but also provides a comprehensive framework for secure data sharing in the cloud, catering to the needs of both individual users and organizations. (shrink)

The word “privilege” has become a part of our everyday conversations. However, it is not evident whether the various interlocutors in discussions on privilege are using it in the same sense. While different instances of privilege like white, male, or caste privilege have been discussed in contemporary academic discourses, we believe there is a lack of clarity regarding the notion of privilege. We critically analyse existing accounts of privilege to show that they leave some room for improvement. We offer an (...) alternative account of privilege as “entitlements that fail to track deserts” that circumvents prevalent definitional ambiguities and emphasises the inherent undeserving nature of privilege. The hitherto underexplored links between privileges, deserts, entitlements, and rights can help us formulate a more accurate grasp of privilege. (shrink)

The current discourse on moral epistemology (ME), has hardly paid any attention to the question concerning the demarcation of the domain of ME within epistemology. Neither is the subject matter of ME considered unique, nor is the methodology adopted in its investigations considered distinct. We attempt to show in this paper that this omission does not restrict itself to a mere taxonomical oversight but rather leads to certain deeper conceptual concerns. We argue that a casual and porous understanding of the (...) subject matter of ME is the result of conflating moral beliefs and justification with non-moral beliefs and justification. If ME doesn't merit a clear demarcation within epistemology, then the very legitimacy of ME is brought under a cloud, thereby threatening the distinction between ethics and epistemology. We believe G. E Moore foresaw this predicament and our interpretation of his work could offer a possible solution. (shrink)

The increasing computational demands of artificial intelligence (AI) workloads have significantly escalated energy consumption in data centers. AI-driven applications, including deep learning, natural language processing, and autonomous systems, require substantial computing power, primarily provided by Graphics Processing Units. These GPUs, while enhancing computational efficiency, contribute to significant power consumption and heat generation, necessitating advanced cooling strategies. This study provides a quantitative assessment of AI-specific hardware power usage, focusing on the NVIDIA H100 GPU. The analysis compares AI data center energy consumption (...) to the average US household power usage, demonstrating that a single AI rack consumes approximately 39 times the energy of a typical household. Additionally, a scalability analysis estimates that approximately 87 new hyper-scale data centers consume the electricity as much as consumed by New York City. This emphasizes that with rapid growth of AI Data Centers, the large-scale deployment could lead to an unprecedented rise in global energy demand. Furthermore, the study evaluates the impact of heat dissipation on cooling requirements, highlighting the need for energy- efficient cooling solutions, including liquid and immersion cooling techniques. Future research directions include energy- efficient AI models, renewable energy integration, sustainable AI accelerator designs, and intelligent workload optimization to mitigate the environmental impact of large-scale AI adoption. This research provides critical insights for designing more sustainable AI-driven data centers while maintaining high-performance computing efficiency. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet it (...) is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

Agriculture is the field that assumes a significant part in improving our nation’s economy. Farming is the one that brought forth human advancement. India is an agrarian country and its economy generally dependent on crop productivity. Agriculture is the spine of all business in our country. Choosing a crop is vital in agriculture planning. The determination of crops will rely on various boundaries, for example, market value, production rate and distinctive government policies. Numerous progressions are needed in the agriculture field (...) to improve changes in our Indian economy. Improvements in agriculture can be done utilizing machine learning techniques which are applied effectively on cultivating area. Alongside all advances in the machines and innovations used in cultivating, valuable and exact data about various matters likewise assumes a huge part in it. The aim of the proposed system is to carry out the yield determination technique using Decision Tree Regressor, Random Forest with the goal that this strategy helps in taking care of numerous agriculture and farmers issues. This improves our Indian economy by expanding the yield rate of crop production. (shrink)

In an era where digital interactions shape perceptions, sentiment analysis emerges as a powerful tool to decode emotions embedded in text. It empowers businesses, researchers, and decision-makers to transform raw opinions into actionable insights, fostering stronger connections with audiences. This project harnesses the potential of Google’s Vertex AI Auto ML to revolutionize sentiment analysis, delivering an intelligent, automated, and explainable system capable of deciphering emotional tones in reviews, social media posts, and beyond. This approach enhances sentiment-based insights for businesses, researchers, (...) and organizations, improving decisionmaking processes based on user feedback. By integrating cloud-based AutoML capabilities, this project enhances classification accuracy, scalability, and deployment efficiency, making sentiment analysis accessible for businesses, researchers, and organizations. The model’s ability to extract actionable insights from user-generated content supports real-time decision-making, social media monitoring, and customer feedback analysis across various industries. (shrink)

This study proposes a novel approach for multi-cancer detection utilizing the VGH-6 algorithm coupled with an efficient neural network model and SHAP (Shapley Additive Explanation) AI method. The VGH-6 algorithm is a sophisticated computational tool known for its accuracy in identifying various types of cancer. In this research, it is combined with an efficient neural network architecture to enhance the classification performance and improve the overall detection process. Additionally, the SHAP AI technique is incorporated to provide insightful explanations for the (...) model's predictions, thus increasing the interpretability and trustworthiness of the results. By leveraging these advanced technologies in tandem, the proposed methodology aims to achieve a robust and reliable multi-cancer detection system. The integration of VGH-6, efficient neural networks, and SHAP AI offers a comprehensive framework for accurate cancer identification, which is crucial for early diagnosis and effective treatment planning. Ultimately, this study introduces a promising approach that may significantly impact the field of cancer detection and pave the way for improved healthcare outcomes. (shrink)