The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing (...) transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, (...) recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing (...) transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a (...) 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based (...) duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based (...) duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (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)

The fictional worlds of science fiction can stimulate philosophical speculation towards socio-technical scenarios and trends that are extrapolated from our physical reality. This widely accepted observation highlights but one of the ways to pursue philosophy with the aid of fiction and science fiction in particular. In this paper, we argue that fiction can in itself constitute a philosophical, academic work and need not merely represent the subject about which such work speculates. This idea questions the currently predominant, institutional paradigm which (...) identifies philosophical works with written texts and specifically with a single kind of text: the paper-format (§1). To suggest an alternative way of doing philosophy, we analyse the structure and content of the recent, experimental science fiction book, The Clouds, and propose that its hybrid textuality – which leverages at once fiction, theory, and meta-commentary – offers a possible corrective to the cognitive closure and rigidity of expression encouraged by the institutionally accepted approach to philosophical work (§ 2). (shrink)

Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated (...) by deep belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm. (shrink)

Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this concern, philosophers of (...) science have started analyzing scientific representations in terms of fictionalization strategies. Many arguments center on a dyadic relation between the model and its target system, focusing on structural resemblances and “as if” scenarios. This chapter provides a different approach. It looks more closely at model building to analyze the interpretative strategies dealing with the representational limits of models. How do we interpret ambiguous elements in models? Moreover, how do we determine the validity of model-based inferences to information that is not an explicit part of a representational structure? I argue that the problem of ambiguous inference emerges from two features of representations, namely their hybridity and incompleteness. To distinguish between fictional and non-fictional elements in scientific models my suggestion is to look at the integrative strategies that link a particular model to other methods in an ongoing research context. To exemplify this idea, I examine protein modeling through X-ray crystallography as a pivotal method in biochemistry. (shrink)

COVID-19 and military actions on the territory of Ukraine fundamentally changed the organization of the educational process, forcing educational institutions to transfer classroom learning to virtual space. To organize the educational process remotely, we need to organize an educational environment in which we can ensure the implementation of all types of educational activities, as well as creation and selection of e-resources depending on their types. Having guidelines in the form of modes, forms and learning outcomes, it is necessary to structure (...) the content of educational disciplines, corresponding curricula, to ensure the activity of students during lectures and the implementation of practical tasks with the necessary tools, to determine and carry out the selection of electronic educational resources for the effective organization of the educational process in conditions of open education. First of all, it is important to take into account all types of student activities, namely: those ones, which are usually implemented in class and determine the possibility of their implementation in a remote format using cloud services. The monograph considers the problem of effective use of Microsoft 365 services for the organization of e-learning through the prism of the competency approach. In particular, the study established a correspondence between training activities and M365 services, which can provide tools support for their implementation. Chapter 1 analyzes the components of digital competence and the abilities necessary for the organization of the e-learning process. Based on the analysis, the correspondence between the components of digital competence and M365 services, the possession of which indicates the existing level of digital competence, was established. Chapter 2 of the monograph describes the digital competences necessary for the organization of various types of training using Microsoft 365 services. It studies features of using digital identity cloud services, Microsoft 365 services for digital data management and collaboration, e-learning management, design and creation of digital content, problem solving and innovation, digital communication, cooperation and interaction. Chapter 3 offers a developed model of hybrid educational environment architecture based on Microsoft Teams platform and analyzes digital competences for the organization of asynchronous interaction between students and teachers using MS Teams tools. Chapter 4 is devoted to the results of an experimental study of the effectiveness of the use of Microsoft 365 services for the organization of e-learning and the readiness of teachers and students to use them. (shrink)

In this chapter, it is suggested that our epistemic access to metaphysical modality generally involves rationalist, a priori elements. However, these a priori elements are much more subtle than ‘traditional’ modal rationalism assumes. In fact, some might even question the ‘apriority’ of these elements, but I should stress that I consider a priori and a posteriori elements especially in our modal inquiry to be so deeply intertwined that it is not easy to tell them apart. Supposed metaphysically necessary identity statements (...) involving natural kind terms are a good example: the fact that empirical input is crucial in establishing their necessity has clouded the role and content of the a priori input, as I have previously argued. For instance, the supposed metaphysically necessary identity statement involving water and its microstructure can only be established with the help of a controversial a priori principle concerning the determination of chemical properties by microstructure. The Kripke-Putnam framework of modal epistemology fails precisely because it is unclear whether the required a priori element is present. My positive proposal builds on E. J. Lowe’s work. Lowe holds that our knowledge of metaphysical modality is based on our knowledge of essence. Lowe’s account strives to offer a uniform picture of modal epistemology: essence is the basis of all our modal knowledge. This is the basis of Lowe’s modal rationalism. I believe that Lowe’s proposal is on the right lines in the case of abstract objects, but I doubt that it can be successfully applied to the case of natural kinds. Accordingly, the case of natural kinds will be my main focus and I will suggest that modal rationalism, at least as it is traditionally understood, falls short of explaining modal knowledge concerning natural kinds. Yet, I think that Lowe has identified something of crucial importance for modal epistemology, namely the essentialist, a priori elements present in our modal inquiry. The upshot is that rather than moving all the way from modal rationalism to modal empiricism, a type of hybrid approach, ‘empirically-informed modal rationalism ’, can be developed. (shrink)

The COVID-19 pandemic that began in 2020 has significantly impacted businesses, regardless of size or industry. The hybrid and remote working models have moved all meetings with potential and existing suppliers to an online environment. This also applies to small- and medium-sized enterprises (SMEs), which have had to adapt themselves to the new situation and implement the solutions necessary to survive on the market. On the other hand, clients have become more aware of the environment and its changes. Customers (...) are trying to be more eco-friendly, by choosing and moving towards Green IT. Thus, this needs to be considered. The acquisition of management information systems (MIS) in the pandemic era is based only on virtual meetings. The main goals of this paper were the identification of the changes in the negotiations caused by the COVID-19 pandemic, the transformation of this process into virtual environment, discussion of the possibility of using Green IT in addition to Management Information Systems, and the changes caused by the pandemic. The article was prepared based on the results of qualitative research using the case study method. The comparative analysis includes purposely selected cloud-based Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems’ acquisition processes, presented from the clients’ perspective. The research was conducted in 2021, based on the authors’ practical experience, and presents four cases. This research illustrates the negotiations concerning an acquisition transaction pre-pandemic and during the pandemic. Finally, the conclusions and main differences caused by the pandemic in the acquisition transaction process of management information systems (MIS) are presented. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of nodes. This results (...) in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for (...) predictive search improvements a. (shrink)

Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds (...) an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. (shrink)

Cloud computing is rapidly gaining traction in business. It offers businesses online services on demand (such as Gmail, iCloud and Salesforce) and allows them to cut costs on hardware and IT support. This is the first paper in business ethics dealing with this new technology. It analyzes the informational duties of hosting companies that own and operate cloud computing datacenters (e.g., Amazon). It considers the cloud services providers leasing ‘space in the cloud’ from hosting companies (e.g, (...) Dropbox, Salesforce). And it examines the business and private ‘clouders’ using these services. The first part of the paper argues that hosting companies, services providers and clouders have mutual informational (epistemic) obligations to provide and seek information about relevant issues such as consumer privacy, reliability of services, data mining and data ownership. The concept of interlucency is developed as an epistemic virtue governing ethically effective communication. The second part considers potential forms of government restrictions on or proscriptions against the development and use of cloud computing technology. Referring to the concept of technology neutrality, it argues that interference with hosting companies and cloud services providers is hardly ever necessary or justified. It is argued, too, however, that businesses using cloud services (banks, law firms, hospitals etc. storing client data in the cloud, e.g.) will have to follow rather more stringent regulations. (shrink)

Data security is, protecting data from ill- conceived get to, utilize, introduction, intrusion, change, examination, recording or destruction. Cloud computing is a sort of Internet-based computing that grants conjoint PC handling resources and information to PCs what's more, different gadgets according to necessity. It is a model that empowers universal, on-request access to a mutual pool of configurable computing resources. At present, security has been viewed as one of the best issues in the improvement of Cloud Computing. The (...) key issue in effective execution of Cloud Computing is to adequately deal with the security in the cloud applications. This paper talks about the part of cryptography in cloud computing to improve the data security. The expectation here is to get bits of knowledge another security approach with the usage of cryptography to secure information at cloud data centers. (shrink)

The paper analyses six ethical challenges posed by cloud computing, concerning ownership, safety, fairness, responsibility, accountability and privacy. The first part defines cloud computing on the basis of a resource-oriented approach, and outlines the main features that characterise such technology. Following these clarifications, the second part argues that cloud computing reshapes some classic problems often debated in information and computer ethics. To begin with, cloud computing makes possible a complete decoupling of ownership, possession and use of (...) data and this helps to explain the problems occurring when different providers of cloud computing retain or relinquish the right to use or own users‘ data. The problem of safety in cloud computing is coupled to that of reliability, insofar as users have to trust providers to preserve their data, applications and content in a reliable manner. It is argued that, in this context, data insurance could play an important role. Regarding fairness, the paper argues that cloud computing is already reshaping the nature of the Digital. Responsibility, accountability and privacy close the ethical analysis of cloud computing. In this case, the thesis is that the necessity to account for the actions of cloud computing users imposes delicate trade-offs between users‘ privacy and the traceability of their operations. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, (...) recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. (shrink)

Air pollution is a significant environmental concern that affects human health, ecosystems, and climate change. Effective monitoring and management of outdoor air quality are crucial for mitigating its adverse effects. This paper presents an advanced approach to outdoor pollution measurement utilizing Internet of Things (IoT) technology, combined with optimization techniques to enhance system efficiency and data accuracy. The proposed framework integrates a network of IoT sensors that continuously monitor various air pollutants, such as particulate matter (PM), carbon monoxide (CO), sulfur (...) dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃), across different geographic locations. The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. (shrink)

Dworkin’s famous argument from legal disagreements poses a problem for legal positivism by undermining the idea that the law can be (just) the result of the practice and attitudes of norm-applying officials. In recent work, the chapter author argued that a hybrid contextualist theory paired with a dispositional theory of value—a hybrid dispositionalism, for short—offers the resources to respond to similar disagreement- based arguments in other evaluative and normative domains. This chapter claims that the theory the author advocates (...) can extend to legal statements and disputes, and shares some important features with Toh’s (2011) idea that legal statements express shared acceptance of norms. The chapter proposes that a contextualist semantics for legal statements paired with the pragmatic communication of implicatures that express shared acceptances of norms, achieves the same goal that Toh aims at. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that (...) no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. (shrink)

This chapter surveys hybrid theories of well-being. It also discusses some criticisms, and suggests some new directions that philosophical discussion of hybrid theories might take.

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that (...) no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. Future enhancements may include integrating machine learning algorithms to predict and adapt to new security threats in real time, further solidifying the reliability of cloud storage solutions. (shrink)

The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be (...) made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

This is a draft of a chapter for the Routledge Handbook of Metaethics, edited by David Plunkett and Tristram McPherson. I offer an overview of hybrid views in metaethics, with main focus on hybrid cognitivist views such as those defended by Daniel Boisvert and David Copp, and on hybrid expressivist views such as those defended by Michael Ridge and myself.

The transgressive ontological character of hybrids—entities crossing the ontological binarism of naturalness and artificiality, e.g., biomimetic projects—calls for pondering the question of their ethical status, since metaphysical and moral ideas are often inextricably linked. The example of it is the concept of “moral considerability” and related to it the idea of “intrinsic value” understood as a non-instrumentality of a being. Such an approach excludes hybrids from moral considerations due to their instrumental character. In the paper, we revisit the boundaries of (...) moral considerability by reexamining the legitimacy of identifying intrinsic value with a non-instrumental one. We offer the concept of “functional value,” which we define as a simultaneous contribution to the common good of the ecosystem and the possibility to disclose the full variety of aspects of a being’s identity. We argue that such a value of hybrids allows us to include them into the scope of moral considerability. (shrink)

This paper is a survey of recent ‘hybrid’ approaches to metaethics, according to which moral sentences, in some sense or other, express both beliefs and desires. I try to show what kinds of theoretical issues come up at the different choice points we encounter in developing such a view, to raise some problems and explain where they come from, and to begin to get a sense for what the payoff of such views can be, and what they will need (...) to do in order to earn that payoff. (shrink)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over encrypted data. (...) This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

The theory of collective agency and intentionality is a flourishing field of research, and our understanding of these phenomena has arguably increased greatly in recent years. Extant theories, however, are still ill-equipped to explain certain aspects of collective intentionality. In this article we draw attention to two such underappreciated aspects: the failure of the intentional states of collectives to supervene on the intentional states of their members, and the role of non-human factors in collective agency and intentionality. We propose a (...) theory of collective intentionality which builds on the ‘interpretationist’ tradition in metasemantics and the philosophy of mind as initiated by David Lewis and recently developed further by Robbie Williams. The collective-level analogue of interpretationism turns out to look different in some ways from the individual-level theory, but is well-suited to accommodating phenomena such as hybrid collective intentionality. Complemented with Kit Fine’s theory of variable embodiment, such a theory also provides a diachronic account of intentional collectives. (shrink)

Cloud computing is the web-based empowering agent for sharing of mechanical infrastructural assets, programming, and computerized content, permitting them Infrastructure, Platforms, Software) to be offered on a compensation-for-use premise, similar to any utility assistance. Dramatic development in Computer capacities, the extra-conventional pace of development in advanced substance utilization, trailed by unstable development of uses have brought forth the beginning of Cloud Computing. The bookmarks which are saved offline can be only accessed by the specific system. The bookmarks are (...) stored in the cloud using platform-as-a-service. Platform-as-a-service (PaaS) is the following layer up – just as the basic stockpiling, organizing, and virtual servers this will likewise incorporate the instruments and programming that designers need to assemble applications on top of: that could incorporate middleware, database management, operating system, and development tools. (shrink)

This paper proposes a novel account of the contents of memory. By drawing on insights from the philosophy of perception, I propose a hybrid account of the contents of memory designed to preserve important aspects of representationalist and relationalist views. The hybrid view I propose also contributes to two ongoing debates in philosophy of memory. First, I argue that, in opposition to eternalist views, the hybrid view offers a less metaphysically-charged solution to the co-temporality problem. Second, I (...) show how the hybrid view conceives of the relationship between episodic memory and other forms of episodic thinking. I conclude by considering some disanalogies between perception and memory and by replying to objections. I argue that, despite there being important differences between memory and perception, those differences do not harm my project. (shrink)

This essay characterizes the perception of the visual hybrid as nonconceptual, introducing the terminology of nonconceptual content theory to aesthetics. The visual hybrid possesses a radical but nonetheless exemplary aesthetic composition and is well established in culture, art, and even design. The essay supplies a philosophical analysis of the results of cross-cultural experiments, showing that while categorization or conceptual hierarchization kicks in when the visual hybrids are juxtaposed with linguistic descriptions, no conceptual scheme takes effect when participants are (...) presented with mere visual hybrids. In isolation, the hybrids do not lend themselves to classification. I draw four conclusions from these experimental outcomes: 1. The perception of visual hybrids follows the structure of a nonconceptual mental content, because the original categories or concepts of the hybrids’ components are not combined into one, and their properties are not applied to one another, therefore none of the components reconstructs the other such that it is introduced to a new category. 2. Language freezes the hybridity of the visual hybrid into conceptuality. 3. Given that language has a freezing effect in the case of an extreme visual phenomenon such as the hybrid, it is all the more restraining in moderate artistic compositions, such as visual metaphors, in which properties of one component (the source) are applied to the other (the target). In those, nonconceptuality emerges from relatively organized compositions, forms, and relations, and from the dependence of objects and their properties on perceptual context. 4. Thus, the nonconceptualist terminology is suitable for the analysis of aesthetic perception in general and aesthetic perception’s relation to language. Keywords aesthetics, perception, nonconceptual content, hybrids, visual metaphors. (shrink)

The oil and gas industry is a complex data-driven industry with compute-intensive, data-intensive and business-intensive features. Cloud computing and big data have a broad application prospect in the oil and gas industry. This research aims to highlight the cloud computing and big data issues and challenges from the informatization in oil and gas industry. In this paper, the distributed cloud storage architecture and its applications for seismic data of oil and gas industry are focused on first. Then, (...) class='Hi'>cloud desktop for oil and gas industry applications are also introduced in terms of efficiency, security and usability. Finally, big data architecture and security issues of oil and gas industry are analyzed. Cloud computing and big data architectures have advantages in many aspects, such as system scalability, reliability, and serviceability. This paper also provides a brief description for the future development of Cloud computing and big data in oil and gas industry. Cloud computing and big data can provide convenient information sharing and high quality service for oil and gas industry. (shrink)

In virtue of what is something a reason for action? That is, what makes a consideration a reason to act? This is a metaphysical or meta-normative question about the grounding of reasons for action. The answer to the grounding question has been traditionally given in ‘pure’, univocal terms. This paper argues that there is good reason to understand the ground of practical normativity as a hybrid of traditional ‘pure’ views. The paper 1) surveys the three leading ‘pure’ answers to (...) the question of a normative ground, 2) examines one or two of the most difficult problems for each, proposing along the way a new objection to one, and 3) argues that a particular hybrid view about normative grounds –‘hybrid voluntarism’ – avoids each of the main problems faced by the three leading ‘pure’ views. (shrink)

Cloud computing (ClC) has become a more popular computer paradigm in the preceding few years. Quality of Service (QoS) is becoming a crucial issue in service alteration because of the rapid growth in the number of cloud services. When evaluating cloud service functioning using several performance measures, the issue becomes more complex and non-trivial. It is therefore quite difficult and crucial for consumers to choose the best cloud service. The user's choices are provided in a quantifiable (...) manner in the current methods for choosing cloud services. Hence, this study attempts to achieve this objective through construction. decision-making framework so-called cloud services evaluator (CSsEv). The main indicator and its sub-indicators are formed as nodes at levels(n) in tree soft sets (TSSs). Thereafter Single Value Neutrosophic Sets (SVNSs) as branch of neutrosophic sets which conjunction with the Multi-Criteria Decision Making (MCDM) technique to facilitate analysis and evaluation process for the available Cloud services providers. Hence, entropy is employed to obtain indicators and sub_indicators weights and Complex Proportional Assessment utilizes these weights to facilitate the decision process of selecting optimal ClSPs. (shrink)

Cloud computing has transformed data management and storage by providing unmatched scalability, flexibility, and cost-effectiveness. However, rising cloud storage use has raised data security and privacy issues. As sensitive data being outsourced to third-party cloud providers, security is crucial. Traditional encryption methods secure data but make data recovery difficult. Specifically, efficiently searching encrypted data without compromising security is difficult.

Philosophers should consider a hybrid meta-ethical theory that includes elements of both moral expressivism and moral error theory. Proponents of such an expressivist-error theory hold that all moral utterances are either expressions of attitudes or expressions of false beliefs. Such a hybrid theory has two advantages over pure expressivism, because hybrid theorists can offer a more plausible account of the moral utterances that seem to be used to express beliefs, and hybrid theorists can provide a simpler (...) solution to the Frege-Geach problem. The hybrid theory has three advantages over pure error theory, because hybrid theorists can offer a more plausible account of the moral utterances that seem to be used to express attitudes, hybrid theorists can more easily explain moral motivation, and hybrid theorists can avoid the implausible claim that all moral discourse is radically mistaken. Accordingly, such a hybrid theory should be more attractive than pure expressivism or pure error theory to philosophers who are skeptical about moral facts and truth. (shrink)

The rapid adoption of cloud computing has revolutionized the way organizations operate, offering unparalleled flexibility, scalability, and efficiency. However, it also introduces a new set of vulnerabilities and security challenges. This manuscript explores the integration of artificial intelligence (AI) in cybersecurity solutions to address these cloud vulnerabilities. By examining the current landscape, AI methodologies, and practical implementation strategies, we aim to provide a roadmap for enhancing cloud security through AI-powered solutions. -/- .

cloud computing is a general term used to depict another class of system based computing that happens over the web. The essential advantage of moving to Clouds is application versatility. Cloud computing is extremely advantageous for the application which are sharing their resources on various hubs. Scheduling the errand is a significant testing in cloud condition. Typically undertakings are planned by client prerequisites. New scheduling techniques should be proposed to defeat the issues proposed by organize properties amongst (...) client and resources. New scheduling systems may utilize a portion of the customary scheduling ideas to consolidate them with some system mindful procedures to give answers for better and more effective employment scheduling. Scheduling technique is the key innovation in cloud computing. This paper gives the study on scheduling calculations. There working regarding the resource sharing. We systemize the scheduling issue in cloud computing, and present a cloud scheduling pecking order. (shrink)

In a very general sense, hybrid can be understood to be any organism that is the product of two (or more) organisms where each parent belongs to a different kind. For example; the offspring from two or more parent organisms, each belonging to a separate species (or genera), is called a “hybrid”. “Hybridity” refers to the phenomenal character of being a hybrid. And “hybridization ” refers to both natural and artificial processes of generating hybrids. These processes include (...) mechanisms of selective cross-breeding and cross-fertilization of parents of different species for the purpose of producing hybrid offspring. In addition to these processes, “hybridization ” also refers to natural and artificial processes of whole genome duplication that result in the doubling or trebling of the sets of chromosomes of the organism. This entry provides an overview of the impact of hybridity on agriculture. It begins with an historical sketch that traces the early horticulturalists’ and naturalists’ investigations of hybrids. This starts with the observations of Thomas Fairchild and Georges-Louis Leclerc, Comte de Buffon; and leads to the explanation of its mechanism by Gregor Mendel, James Watson and Francis Crick, and Ernst Mayr; and the eventual manipulation of hybrids and hybridization by Barbara McClintock. Following this, the reader is introduced to a number of key terms and concepts in use within current research as well as highlighting diverse ethical concerns that center on hybridization. Recent research that attempts to ascertain the role of hybridization in adaptive change will be introduced. This will include research on the evolution of crop species, increased biodiversity, and the use of hybrids to manipulate phenotypically desirable traits in agricultural crops. The focus of the discussion is on a particularly significant type of naturally occurring hybridization, polyploidy hybridization. Polyploids are organisms which have more than two complete genomes in each cell. This kind of hybridization is ubiquitous among crop plants. The role of polyploidy in plant evolution and the affects of polyploidy on plants and animals will be reviewed. A critical discussion of its agricultural value in the production of fertile polyploid hybrids highlights key epistemological, ontological, and ethical issues. These are illuminated with reference to the distinct processes of artificial and natural hybridization. A survey of these different kinds of hybridization includes the ethical and economic impacts of hybridity on global nutrition, the environment, and considerations of some practical implications for the agricultural industry. Tracking the role of hybrids, the process of hybridization, and the current impacts of it for agriculture requires knowledge of the history of its early conceptualization, understanding, and use. This is the topic of the following section. (shrink)

Disagreement is a pervasive feature of human discourse and a crucial force in shaping our social reality. From mundane squabbles about matters of taste to high-stakes disputes about law and public policy, the way we express and navigate disagreement plays a central role in both our personal and political lives. Legal discourse, in particular, is rife with disagreement - it is the very bread and butter of courtroom argument and legal scholarship alike. Consider a debate between two legal philosophers, Ronald (...) and Herbert, about the Eighth Amendment of the U.S. Constitution, which prohibits ‘cruel and unusual punishment’. Ronald asserts: ‘It is the law that capital punishment is prohibited’. In response, Herbert states: ‘It is not the law that capital punishment is prohibited’. We intuitively think Ronald and Herbert are disagreeing, which reveals in the fact that they are licensed to use ex¬pressions like no (it isn’t) and nuh-uh when responding to their opponent’s claim. But despite the ubiquity and significance of legal disagreement, its precise nature remains elusive. This chapter discusses what exactly is going on when two people disagree about what the law requires, and how can hybrid theory may answer this question. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, Support (...) Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, Support (...) Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, (...) security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud (...) storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. (shrink)

Grasping the identity of hybrids, that is beings which cross the binarism of nature and technology (e.g. genetically-modified organisms (GMOs), syn-bio inventions, biomimetic projects), is problematic since it is still guided by self-evident dualistic categories, either as artefacts or as natural entities. To move beyond the limitations of such a one-sided understanding of hybrids, we suggest turning towards the categories of affordances and the juxtaposition of needs and patterns of proper use, as inspired by the Heideggerian version of phenomenology. Drawing (...) upon selected concepts by Heidegger, we argue that hybrids can be conceptualised as a regenerative design and use to serve the planet. We argue that the ideal type of non-exploitative account of hybrids consists of the adaptive approach to the environment, which does not, however, exclude the possibility of designing and constructing new beings. We also point out that hybrids undermine the divide of being destructive/regenerative which marks the boundaries of nature and technology. (shrink)

knowing and not knowing in a regular social context and the emergente of monastic revolution of the self and its owner.

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over encrypted data.