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Journal articles on the topic 'Computing Methodologies'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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1

Ogala, Justin Onyarin, and Ese Sophia Mughele. "Agile Software Development Methodologies in Cloud Computing." Advances in Multidisciplinary and scientific Research Journal Publication 1 (July 30, 2022): 67–80. http://dx.doi.org/10.22624/aims/rebk2022-p7.

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Department of Computer Science Faculty of Computing, University of Delta, Agbor, Nigeria E-mails: justin.ogala@unidel.edu.com; s.mughele@unidel.edu.ng ABSTRACT In the software sector, agile development approaches have a lot of promise. Agile development methodologies are highly realistic in their awareness of the fact that business requirements change all the time. By executing software releases iteratively and collecting user feedback more often, agile development approaches maximize the benefits of cloud computing. This study is a cloud computing and Agile Methods analysis. This article examines Agile management and development approaches, as well as their benefits when used in conjunction with cloud computing. By executing software releases iteratively and obtaining user feedback very often, agile development practices maximize the benefits of cloud computing. The basic proposition of this study is that Small and medium enterprises should use a framework that guides their migration to cloud computing to reduce all the issues that come with cloud computing infrastructures Keywords: Agile Methodologies, Cloud Computing, Software Development, Integration

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Furuhashi, Takeshi. "Soft Computing Methodologies for Intelligent Systems." IEEJ Transactions on Industry Applications 118, no. 3 (1998): 280–83. http://dx.doi.org/10.1541/ieejias.118.280.

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Papadrakakis, Manolis, and Nikos D. Lagaros. "Soft computing methodologies for structural optimization." Applied Soft Computing 3, no. 3 (2003): 283–300. http://dx.doi.org/10.1016/s1568-4946(03)00040-1.

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Sarpudin, Sarpudin, Fajri Rahmadeli, and Hadi Supratikta. "The Impact of Cloud Computing on System Lifecycle Methodologies: an Innovative Approach to Scalability Flexibility." Indonesian Development of Economics and Administration Journal 3, no. 1 (2024): 73–81. http://dx.doi.org/10.70001/idea.v3i1.213.

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This research analyzes the impact of cloud computing on traditional system lifecycle methodologies and offers an innovative approach that leverages the advantages of cloud computing. Qualitative research was conducted with a literature study on conventional system lifecycle methodologies and cloud computing. The results show that cloud computing enables increased scalability and flexibility through a service-based approach, automation, and elastic management of resources. This research provides recommendations for implementing a hybrid lifecycle methodology that leverages the advantages of both cloud and traditional systems.This paper discusses the impact of cloud computing on system lifecycle methodologies. Cloud computing offers an innovative approach to system scalability and flexibility by allowing computing and storage resources to scale dynamically according to demand. Traditional system lifecycle methodologies are becoming less relevant for rapidly changing cloud environments, so they need to be adapted to respond to the scalability and flexibility capabilities offered by cloud computing.This paper evaluates the impact of the shift towards cloud computing on the methodologies used in the system development lifecycle. Cloud computing offers the ability to scale elastically according to demand, which opens up opportunities to adopt a more adaptable and flexible approach to system development. This paper discusses how traditional system lifecycle methodologies can be adapted to accommodate key cloud computing characteristics such as scalability, flexibility, and virtualization of computing resources. This paper reviews the implications of the move towards a cloud computing paradigm on the methodologies used in developing and maintaining software systems. The shift towards cloud-based environments offers the advantages of scalability and flexibility in dynamically managing system resources. However, these key characteristics of cloud computing pose challenges to traditional system lifecycle methodologies designed for on-premise environments. This paper discusses how system lifecycle methodologies can be adapted to better support system development in cloud computing environments.

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Yoshida, Makoto, and Kazumine Kojima. "High Performance Computing Design by Code Migration for Distributed Desktop Computing Grids." International Journal of Grid and High Performance Computing 3, no. 4 (2011): 53–70. http://dx.doi.org/10.4018/jghpc.2011100105.

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Large scale loosely coupled PCs can organize clusters and form desktop computing grids on sharing each processing power; power of PCs, transaction distributions, network scales, network delays, and code migration algorithms characterize the performance of the computing grids. This article describes the design methodologies of workload management in distributed desktop computing grids. Based on the code migration experiments, transfer policy for computation was determined and several simulations for location policies were examined, and the design methodologies for distributed desktop computing grids are derived from the simulation results. The language for distributed desktop computing is designed to accomplish the design methodologies.

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Taylor, M. J., E. P. Moynihan, and A. T. Wood-Harper. "End-user computing and information systems methodologies." Information Systems Journal 8, no. 1 (1998): 85–96. http://dx.doi.org/10.1046/j.1365-2575.1998.00005.x.

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Roibas, Anxo Cereijo. "Teaching appropriate ethnographic methodologies for pervasive computing." International Journal of Teaching and Case Studies 1, no. 1/2 (2007): 135. http://dx.doi.org/10.1504/ijtcs.2007.014215.

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Gilbert Fakeyede, Ololade, Evelyn Chinedu Okeleke, Patrick Azuka Okeleke, and Olubukola Rhoda Adaramodu. "A COMPREHENSIVE REVIEW OF IT AUDIT METHODOLOGIES IN THE AGE OF QUANTUM COMPUTING." JOURNAL OF TECHNOLOGY & INNOVATION 3, no. 2 (2023): 85–92. http://dx.doi.org/10.26480/jtin.02.2023.85.92.

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The emergence of quantum computing presents a profound challenge and opportunity for information technology (IT) audit methodologies and IT security. Quantum computing’s potential to break classical encryption methods and its promise of exponential computational power necessitate a proactive response. This comprehensive review explores the fundamentals of quantum computing, the vulnerabilities of classical encryption, the transition to quantum-safe encryption, and the role of IT auditors in navigating this quantum landscape. Additionally, we address emerging quantum technologies, ethical considerations, and the interplay between quantum computing and IT security. To thrive in the quantum era, organisations are advised to plan for quantum-safe transitions, invest in quantum-resistant cryptography, monitor evolving regulations, develop quantum-ready workforces, integrate Quantum Key Distribution (QKD), and adopt a long-term security strategy. Adapting IT audit methodologies to the quantum era requires a multidisciplinary approach, and the recommendations provided here aim to guide organisations in securing their digital assets in this transformative quantum-empowered future.

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AR, Manu, Dr Vinod Kumar Agrawal, and Dr K. N. Bala Subramanya Murthy. "Representation of cloud ecosystem using engineering methodologies." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (2013): 518–32. http://dx.doi.org/10.24297/ijct.v7i1.3482.

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Cloud Computing has fascinated massive consideration for business in spite of lot of technologies and business models in the market. The operational particulars within the cloud are not coherent enough to customers. Hence, this paper provides the method to derive trusted cloud computing scaffold. This scaffold is represented using the engineering methodologies. In this work we use elementary engineering principles of computation to represent the cloud eco-system, comprising of complete cloud system from the end-user perspective.

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Kumar Ale, Narendar. "Enhancing Business Operations: Importance and Methodologies of Cloud Computing Security Testing." International Journal of Science and Research (IJSR) 9, no. 10 (2020): 1797–99. http://dx.doi.org/10.21275/sr24619055641.

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Tagliaferri, R., N. Pelosi, A. Ciaramella, G. Longo, M. Milano, and F. Barone. "Soft computing methodologies for spectral analysis in cyclostratigraphy." Computers & Geosciences 27, no. 5 (2001): 535–48. http://dx.doi.org/10.1016/s0098-3004(00)00166-7.

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Pugliano, A., R. Santamaria, S. Troisi, and L. Turturici. "LATITUDE BY TIME DIFFERENCES (OPERATIVE AND COMPUTING METHODOLOGIES)." Survey Review 31, no. 245 (1992): 413–20. http://dx.doi.org/10.1179/sre.1992.31.245.413.

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BONISSONE, PIERO, and KAI GOEBEL. "Soft Computing for diagnostics in equipment service." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 4 (2001): 267–79. http://dx.doi.org/10.1017/s0890060401154028.

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We present methods and tools from the Soft Computing (SC) domain, which is used within the diagnostics and prognostics framework to accommodate imprecision of real systems. SC is an association of computing methodologies that includes as its principal members fuzzy, neural, evolutionary, and probabilistic computing. These methodologies enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. We outline the advantages and disadvantages of these methodologies and show how they can be combined to create synergistic hybrid SC systems. We conclude the paper with a description of successful SC case study applications to equipment diagnostics.

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Subramani, B. S., Prasanna P. Tejas, R. Rohan, and Pavan P. "Cloud Computing - Cryptography: A Comprehensive Study on Methodologies and Applications." Recent Trends in Computer Graphics and Multimedia Technology 7, no. 1 (2025): 29–36. https://doi.org/10.5281/zenodo.14886405.

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<em>Pall computing is an Internet-based computing paradigm that pays cloud druggies (CU) on a pay-per-use basis and makes many coffers available to them through Cloud Service Providers (CSP) on demand without requiring them to purchase the underlying infrastructure. To increase efficiency and the ability to complete several activities at once, it facilitates the virtualization of physical funds. A variety of deployment options are offered by the Cloud Computing Environment (CCE) to reflect the many orders of pall that organizations or institutions possess. Service models and deployment models are two distinct categories of models. SaaS, PaaS, and IaaS are examples of service models. Public Cloud, Private Pall, Mongrel Pall, and Community Cloud make up the deployment or deployment paradigm. Although there are several distinct of cloud computing that make it very significant, sequestration appears to be a particular issue in pall. a variety of vibrant service model types under pall calculating grease colorful situations of sequestration services.</em>

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Wang, Yingxu, Victor Raskin, Julia Rayz, et al. "Cognitive Computing." International Journal of Software Science and Computational Intelligence 10, no. 1 (2018): 1–14. http://dx.doi.org/10.4018/ijssci.2018010101.

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Cognitive Computing (CC) is a contemporary field of studies on intelligent computing methodologies and brain-inspired mechanisms of cognitive systems, cognitive machine learning and cognitive robotics. The IEEE conference ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This article reports the plenary panel (Part II) in IEEE ICCI*CC'17 at Oxford University. The summary is contributed by distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of cognitive computing.

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Faheem, Muhammad, Tahar Kechadi, and Nhien An Le-Khac. "The State of the Art Forensic Techniques in Mobile Cloud Environment." International Journal of Digital Crime and Forensics 7, no. 2 (2015): 1–19. http://dx.doi.org/10.4018/ijdcf.2015040101.

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Smartphones have become popular in recent days due to the accessibility of a wide range of applications. These sophisticated applications demand more computing resources in a resource constraint smartphone. Cloud computing is the motivating factor for the progress of these applications. The emerging mobile cloud computing introduces a new architecture to offload smartphone and utilize cloud computing technology to solve resource requirements. The popularity of mobile cloud computing is an opportunity for misuse and unlawful activities. Therefore, it is a challenging platform for digital forensic investigations due to the non-availability of methodologies, tools and techniques. The aim of this work is to analyze the forensic tools and methodologies for crime investigation in a mobile cloud platform as it poses challenges in proving the evidence.

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Haveraaen, Magne. "Case Study on Algebraic Software Methodologies for Scientific Computing." Scientific Programming 8, no. 4 (2000): 261–73. http://dx.doi.org/10.1155/2000/482042.

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The use of domain specific languages and appropriate software architectures are currently seen as the way to enhance reusability and improve software productivity. Here we outline a use of algebraic software methodologies and advanced program constructors to improve the abstraction level of software for scientific computing. This leads us to the language of coordinate free numerics as an alternative to the traditional coordinate dependent array notation. This provides the backdrop for the three accompanying papers:Coordinate Free Programming of Computational Fluid Dynamics Problems, centered around an example of using coordinate free numerics,Machine and Collection Abstractions for User-Implemented Data-Parallel Programming, exploiting the higher abstraction level when parallelising code, andAn Algebraic Programming Style for Numerical Software and its Optimization, looking at high-level transformations enabled by the domain specific programming style.

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Kaynak, O., and I. Rudas. "Soft computing methodologies and their fusion in mechatronic products." Computing & Control Engineering Journal 6, no. 2 (1995): 68–72. http://dx.doi.org/10.1049/cce:19950202.

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Zhu, Lin, Vitoantonio Bevilacqua, and De-Shuang Huang. "Special issue on Advanced Intelligent Computing Methodologies and Applications." Neurocomputing 188 (May 2016): 1–2. http://dx.doi.org/10.1016/j.neucom.2015.11.047.

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Huang, De-Shuang. "Special issue on advanced intelligent computing theories and methodologies." Neurocomputing 137 (August 2014): 1–2. http://dx.doi.org/10.1016/j.neucom.2014.02.009.

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Gómez, Pilar, Angela Nebot, Sabrine Ribeiro, René Alquézar, Francisco Mugica, and Franz Wotawa. "Local Maximum Ozone Concentration Prediction Using Soft Computing Methodologies." Systems Analysis Modelling Simulation 43, no. 8 (2003): 1011–31. http://dx.doi.org/10.1080/0232929031000081244.

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22

Ide, Nancy, and Donald Walker. "Introduction: Common methodologies in humanities computing and computational linguistics." Computers and the Humanities 26, no. 5-6 (1992): 327–30. http://dx.doi.org/10.1007/bf00136978.

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Bipin Gajbhiye, Shalu Jain, and Pandi Kirupa Gopalakrishna Pandian. "Penetration Testing Methodologies for Serverless Cloud Architectures." Innovative Research Thoughts 8, no. 4 (2022): 347–59. http://dx.doi.org/10.36676/irt.v8.i4.1456.

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As organizations increasingly adopt serverless cloud architectures to enhance scalability and reduce operational costs, the security landscape has evolved, introducing new challenges and vulnerabilities. Serverless computing, characterized by its abstraction of infrastructure management and dynamic resource allocation, presents unique security concerns that traditional penetration testing methodologies may not adequately address. This research paper explores penetration testing methodologies specifically tailored for serverless cloud environments, aiming to identify effective strategies for evaluating and mitigating security risks in these modern architectures. The paper begins by defining serverless computing and its key characteristics, such as event-driven execution, automatic scaling, and micro-billing models. Unlike traditional server-based environments, serverless architectures often rely on Functions-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) components, which can obscure the underlying infrastructure and introduce complex attack vectors. Consequently, traditional penetration testing approaches, designed for monolithic or microservices-based systems, may fall short in identifying and exploiting vulnerabilities specific to serverless environments.

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Aruna, L. "Effective Methodologies for Enhancing Cloud Management in New Modern Enterprises." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 133. http://dx.doi.org/10.23956/ijarcsse.v7i8.39.

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Today Cloud computing is altering the way industries and enterprises do their businesses in that with passion scalable and virtualized assets are provided as a service over the Internet. This new model creates a brand new chance for enterprises. In this paper, some of the essential features of effective methodologies for cloud computing in the modern enterprise are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud Computing is capable as one of the main enablers for the all new manufacturing industry, it can transform the usual manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that support successful collaboration. Cloud computing has been in some of the major key areas of manufacturing industries such as IT, pay-as-you-go business models, productions calling up and down per require, and elasticity in deploying and customizing solutions. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all others tags of a product life cycle. Cloud Managed service provider (CMSP) is a company that distantly manages a customer's IT communications and/or end-user systems, typically on a proactive basis and under a subscription model.

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Jagtap, Sharayu Manohar. "Exploring the Intersection of Sustainable Computing and Intelligent Systems." International Journal of Advance and Applied Research S6, no. 22 (2025): 671–74. https://doi.org/10.5281/zenodo.15532783.

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<em>Green computing, integrated with artificial intelligence (AI), presents a trans formative pathway to mitigate the environmental impact of modern computing infrastructures. This research paper investigates the strategies and methodologies employed in leveraging AI to enhance green computing practices. Focusing on energy efficiency, resource optimization, and sustainable infrastructure, the study explores the development of eco-friendly algorithms, sustainable data centers, and green cloud computing. Rigorous data analysis methodologies—incorporating statistical models and case studies—demonstrate the significant potential of AI-driven solutions in reducing energy consumption and carbon emissions. In doing so, the paper lays out a research framework for future studies aiming at the dual goals of technological advancement and environmental sustainability.</em>

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Deepak Kumar Lnu. "AI-Driven Verification for Compute Express Link (CXL): Challenges, Innovations, and Future." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2540–57. https://doi.org/10.32628/cseit25112728.

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This comprehensive article explores the evolution and challenges of Compute Express Link (CXL) verification methodologies in modern computing environments. The article examines the critical aspects of cache coherency testing, compliance validation, and debugging strategies while highlighting the transformative role of artificial intelligence in enhancing verification processes. The article demonstrates how advanced methodologies address the complexities of heterogeneous computing systems by analyzing various verification approaches, including AI-driven compliance automation, predictive debugging, and adaptive testbenches. The article encompasses memory device verification performance, system-level integration, and future directions in CXL verification, providing insights into emerging technologies and methodologies for ensuring robust system validation.

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Shyam, Gopal, and Shankar Ramamoorthy. "Performance Management in Cloud Computing." ECS Transactions 107, no. 1 (2022): 1245–53. http://dx.doi.org/10.1149/10701.1245ecst.

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Cloud service providers encounter a challenge in managing remote resources due to the dynamic nature of the cloud environment. The complexity of the process is increased by the requirement of maintaining service quality in line with customer expectations, as well as the extremely dynamic nature of cloud-hosted applications. As a result of developments in big data learning methodologies, traditional systems have given way to intricate systems. In the existing studies, it is shown that the resource adjustment decision-making process is intimately linked to the system's behavior, including resource utilization and application components. The most essential requirements and restrictions in cloud resource management, as well as workload and anomaly analysis approaches in the context of cloud performance management, are discussed in this paper. The related works are provided, with major methodologies in current studies ranging from data analysis to performance techniques. Finally, a list of open challenges is compiled, taking into account the identified gaps in the overall direction of the tasks under consideration.

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Devineni, Sreenath, and Bhargavi Gorantla. "Energy-Efficient Computing and Green Computing Techniques." 4 1, no. 4 (2023): 37–45. http://dx.doi.org/10.46632/cset/1/4/5.

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Energy-efficient computing and strategies for environmentally sustainable computing have become indispensable in the contemporary world, given the escalating demand for computing power and the growing emphasis on ecological conservation. This summary delves into diverse facets of energy efficient computing and green computing methodologies, elucidating their significance, hurdles, and potential remedies. The primary objective of energy-efficient computing is to curtail the energy consumption of computing systems while either maintaining or enhancing their performance. This proves pivotal in alleviating the ecological ramifications of computing, especially as data centers and similar facilities consume substantial amounts of energy. Green computing techniques encompass a broad spectrum of practices geared towards diminishing the environmental footprint of computing. These encompass the utilization of renewable energy sources, the crafting of energy-efficient hardware, the optimization of software algorithms, and the cultivation of energy consciousness among users. Despite the advantages associated with energy-efficient computing and green computing methodologies, various challenges necessitate attention. These encompass the delicate balance between energy efficiency and performance, the intricacies involved in designing energy-efficient systems, and the absence of standardized metrics for gauging and comparing energy efficiency. Exploration in this domain propels technological progress, resulting in the creation of more effective hardware, software, and systems. These advancements not only positively impact the environment but also propel the overall evolution of computing technology. The Information and Communication Technology (ICT) sector substantially adds to worldwide carbon emissions. Energy-efficient computing and environmentally conscious computing approaches can play a crucial role in mitigating this impact, decreasing energy usage, and advocating for the adoption of renewable energy sources. The aim of this research is to investigate the complexities associated with multiple attribute decision-making when confronted with intuitionist fuzzy information. In this context, the weights of attributes are not entirely known, and the attribute values are expressed using intuitionist fuzzy numbers. To ascertain the attribute weights, an optimization model is formulated based on the foundational principles of traditional grey relational analysis (GRA). The proposed approach entails computing the grey relation degree between each alternative and the positive-ideal solution as well as the negative-ideal solution. This degree is subsequently utilized to establish a relative relational degree, facilitating the simultaneous ranking of all alternatives concerning both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result Efficient Algorithms is ranked at first position and Green Data Centers is ranked at fifth position.

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Ward, Aengus. "Humanities Computing? La corónica's 50 Years of Innovation." La corónica: A Journal of Medieval Hispanic Languages, Literatures, and Cultures 50, no. 1-2 (2021): 197–205. http://dx.doi.org/10.1353/cor.2021.a910121.

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Abstract: Iberomedievalism in the United States has always been at the cutting edge of innovative approaches and methodologies. The development of computational, and subsequently digital, techniques corresponds almost exactly with the existence of La corónica . This article, in part a response to Kathleen Kish's update on the activities of the Wisconsin Seminary of Medieval Spanish Studies published in 1979, traces the significance of the journal as an important part of the dissemination of new techniques and methodologies in the study of medieval Iberia.

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Kotiyal, Saurabh, and Himanshu Thapliyal. "Design Methodologies for Reversible Logic Based Barrel Shifters." Journal of Circuits, Systems and Computers 25, no. 02 (2015): 1650003. http://dx.doi.org/10.1142/s0218126616500031.

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Barrel shifter is an integral component of processor datapaths in computing systems since it can shift and rotate multiple bits in a single cycle. Furthermore, reversible logic has applications in emerging computing paradigms such as quantum computing, quantum dot cellular automata, optical computing, etc. In this work, we propose efficient methodologies for designing reversible barrel shifters. The proposed methodologies are designed using Fredkin gate and Feynman gate (FG). The Fredkin gate is used because it can implement a 2:1 MUX with minimum quantum cost, minimum number of ancilla inputs and garbage outputs, and the Feynman gate is used to avoid a fanout since a fanout is not allowed in reversible logic. In the existing literature, design methodologies are limited to the design of a ([Formula: see text]) reversible left rotator that can only perform the left rotate operation. This work explores the other primary functionalities of a reversible barrel shifter such as the design of a reversible: (i) logical right shifter, (ii) universal right shifter that supports logical right shifter, arithmetic right shifter and right rotate operation, (iii) bidirectional logical shifter and (iv) universal bidirectional shifter that supports bidirectional logical and arithmetic shifter and rotate operations. The other types of reversible barrel shifters can also be easily designed by making minor modifications in the proposed methodologies. The proposed design methodologies are generic in nature and can be implemented using any barrel shifter of ([Formula: see text]) size, where n and k are the number of data bits and shift value, respectively. In order to minimize the number of ancilla inputs and garbage outputs, strategies such as the implementation of an n number of 2:1 MUXes as a chain of n Fredkin gates and the mapping of the two different 2:1 MUXes that are controlled by a common control signal but having the swapped controlled signals on a single Fredkin gate, are utilized. The design methodologies are evaluated in terms of the number of garbage outputs, the number of ancilla inputs and quantum cost. For a ([Formula: see text]) reversible barrel shifter, the relations between the varying values of n and k and their impact on the number of garbage outputs, the number of ancilla inputs and quantum cost are also established to help the designers in choosing an efficient barrel shifter according to their design needs.

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Grittmann, Pascal, Arsène Pérard-Gayot, Philipp Slusallek, and Jaroslav Křivánek. "Efficient Caustic Rendering with Lightweight Photon Mapping." Computer Graphics Forum 37, no. 4 (2019): 133–42. https://doi.org/10.1111/cgf.13481.

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Robust and efficient rendering of complex lighting effects, such as caustics, remains a challenging task. While algorithms like vertex connection and merging can render such effects robustly, their significant overhead over a simple path tracer is not always justified and – as we show in this paper – also not necessary. In current rendering solutions, caustics often require the user to enable a specialized algorithm, usually a photon mapper, and hand-tune its parameters. But even with carefully chosen parameters, photon mapping may still trace many photons that the path tracer could sample well enough, or, even worse, that are not visible at all. Our goal is robust, yet lightweight, caustics rendering. To that end, we propose a technique to identify and focus computation on the photon paths that offer significant variance reduction over samples from a path tracer.We apply this technique in a rendering solution combining path tracing and photon mapping. The photon emission is automatically guided towards regions where the photons are useful, i.e., provide substantial variance reduction for the currently rendered image. Our method achieves better photon densities with fewer light paths (and thus photons) than emission guiding approaches based on visual importance. In addition, we automatically determine an appropriate number of photons for a given

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Singh, Dr Satinderjeet. "Methodologies for Resolving Data Security and Privacy Protection Issues in Cloud Computing Technology." Asian Journal of Applied Science and Technology 06, no. 04 (2022): 40–49. http://dx.doi.org/10.38177/ajast.2022.6404.

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Because of its accessibility and flexibility, cloud technology is among the most notable innovations in today's world. Having many service platforms, such as GoogleApps by Google, Amazon, Apple, and so on, is well accepted by large enterprises. Distributed cloud computing is a concept for enabling every-time, convenient, on-demand network access to processing resources including servers, storage devices, networks, and services that may be mutually configured. The major security risks for cloud computing as identified by the Cloud security alliance (CSA) have been examined in this study. Also, methods for resolving issues with cloud computing technology's data security and privacy protection were systematically examined.

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Dr., Satinderjeet Singh. "Methodologies for Resolving Data Security and Privacy Protection Issues in Cloud Computing Technology." Asian Journal of Applied Science and Technology (AJAST) 6, no. 4 (2022): 40–49. https://doi.org/10.38177/ajast.2022.6404.

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Because of its accessibility and flexibility, cloud technology is among the most notable innovations in today's world. Having many service platforms, such as GoogleApps by Google, Amazon, Apple, and so on, is well accepted by large enterprises. Distributed cloud computing is a concept for enabling every-time, convenient, on-demand network access to processing resources including servers, storage devices, networks, and services that may be mutually configured. The major security risks for cloud computing as identified by the Cloud security alliance (CSA) have been examined in this study. Also, methods for resolving issues with cloud computing technology's data security and privacy protection were systematically examined

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Nazir, Ambreen, Ayesha Raana, and Muhammad Fahad Khan. "Cloud Computing ensembles Agile Development Methodologies for Successful Project Development." International Journal of Modern Education and Computer Science 5, no. 11 (2013): 28–35. http://dx.doi.org/10.5815/ijmecs.2013.11.04.

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Ahn, Moon-Youl, and Se-J. Lee. "Performances of Multidisciplinary Design Optimization Methodologies in Parallel Computing Environment." Transactions of the Korean Society of Mechanical Engineers A 31, no. 12 (2007): 1150–56. http://dx.doi.org/10.3795/ksme-a.2007.31.12.1150.

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Kodogiannis, V. S., M. Boulougoura, E. Wadge, and J. N. Lygouras. "The usage of soft-computing methodologies in interpreting capsule endoscopy." Engineering Applications of Artificial Intelligence 20, no. 4 (2007): 539–53. http://dx.doi.org/10.1016/j.engappai.2006.09.006.

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Shama, Firdous. "Cognitive Computing." Research and Applications: Emerging Technologies 3, no. 1 (2021): 1–8. https://doi.org/10.5281/zenodo.4730213.

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<em>Cognitive computing is the new wave of Artificial Intelligence (AI), relying on traditional techniques based on expert systems and also exploiting statistics and mathematical models. In particular, cognitive computing systems can be regarded as a “more human” artificial intelligence. In fact, they mimic human reasoning methodologies, showing special capabilities in dealing with uncertainties and in solving problems that typically entail computation consuming processes. Moreover, they can evolve, exploiting the accumulated experience to learn from the past, both from errors and from successful findings. From a theoretical point of view, cognitive computing could replace existing calculators in many fields of application but hardware requirements are still high. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and de-notational mathematics.</em>  

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Rahardja, Dewi. "Computational statistics for time-series forecasts via SAS/ETS 9.4 drop-down menu." Journal of Statistics and Management Systems 26, no. 4 (2023): 787–800. http://dx.doi.org/10.47974/jsms-877.

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In this paper, using the Statistical Time Series (TS) methodologies reviewed in the literature, we present a step-by-step computational statistics procedure to execute TS forecasts computing via a point-and-clicks (semi-automatic) option of SAS (Statistical Analysis Software). Such point-and-clicks pathway of computing is via the SAS Econometrics Time Series 9.4 (SAS-ETS) drop-down menu. This paper will be very beneficial for forecasters in multiple areas of applications (e.g., business, epidemiology, sociology, etc.) without much expertise in the Statistical TS methodologies.

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Bai, Junjie, Kan Luo, Jun Peng, et al. "Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 4 (2017): 80–92. http://dx.doi.org/10.4018/ijcini.2017100105.

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Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.

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Devadass, Lingkiswaran, Sugalia Santhira Sekaran, and Rajermani Thinakaran. "CLOUD COMPUTING IN HEALTHCARE." International Journal of Students' Research in Technology & Management 5, no. 1 (2017): 25. http://dx.doi.org/10.18510/ijsrtm.2017.516.

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Information Technology (IT) has modernizedhealthcare sector via the newest development. Cloud computing in healthcare is budding and charming as crucial methodologies by most of the stakeholders. It has the special ability to offer infinite capacity and power of process in the e-healthcare sector.This leads computer to be used efficiently and exclusively by the sharing of resources in healthcare. This paper provide a review of some proposed cloud based e-healthcare architectures edge along with issues inbothtechnologies and the crucial reasons of enhancingforward to a cloud based e-healthcare especially in Malaysia.

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Wang, Yingxu, George Baciu, Yiyu Yao, et al. "Perspectives on Cognitive Informatics and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 1 (2010): 1–29. http://dx.doi.org/10.4018/jcini.2010010101.

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Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.

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Brandao, Pedro Ramos. "Computer Forensics in Cloud Computing Systems." Budapest International Research in Exact Sciences (BirEx) Journal 1, no. 1 (2019): 71–86. http://dx.doi.org/10.33258/birex.v1i1.141.

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The challenges on traditional Computer Forensic mythology in facing with the growing adoption of Cloud Computing services and models by corporations and organizations. We analyse the results of recent research in analysis of computers’ physical memory, new methodologies and tools, and how these technologies can be applied to computer forensics in this specific context.

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Alsadie, Deafallah. "Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects." PeerJ Computer Science 10 (June 17, 2024): e2128. http://dx.doi.org/10.7717/peerj-cs.2128.

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Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

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Rahul Kumar Jha. "An In-Depth Evaluation of Hybrid Approaches in Soft Computing for the Identification of Social Engineering." Journal of Soft Computing Paradigm 5, no. 3 (2023): 232–48. http://dx.doi.org/10.36548/jscp.2023.3.002.

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Social engineering attacks continue to pose significant threats to information security by exploiting human psychology and manipulating individuals into divulging sensitive information or performing actions that compromise organizational systems. Traditional defense mechanisms often struggle to detect and mitigate such attacks due to their dynamic and deceptive nature. In response, the integration of hybrid soft computing techniques has developed as a promising method to enhance the accuracy and effectiveness of social engineering detection systems. This study provides an in-depth exploration of the various hybrid soft computing methodologies applied to the detection of social engineering attacks. It discusses the synergistic combination of different soft computing techniques, such as genetic algorithms, neural networks, swarm intelligence and fuzzy logic along with their integration with other security measures. The study presents a comprehensive survey of recent research advancements, methodologies, datasets, performance metrics, and challenges in the domain of hybrid soft computing for social engineering detection. Furthermore, it offers insights into potential future directions and applications for advancing the field.

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Rodrigues, Gennaro, Fernanda Lima Kastensmidt, and Alberto Bosio. "Survey on Approximate Computing and Its Intrinsic Fault Tolerance." Electronics 9, no. 4 (2020): 557. http://dx.doi.org/10.3390/electronics9040557.

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This work is a survey on approximate computing and its impact on fault tolerance, especially for safety-critical applications. It presents a multitude of approximation methodologies, which are typically applied at software, architecture, and circuit level. Those methodologies are discussed and compared on all their possible levels of implementations (some techniques are applied at more than one level). Approximation is also presented as a means to provide fault tolerance and high reliability: Traditional error masking techniques, such as triple modular redundancy, can be approximated and thus have their implementation and execution time costs reduced compared to the state of the art.

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OSTROWSKI, DAVID ALFRED. "SEMANTIC COMPUTING IN SOCIAL MEDIA." International Journal of Semantic Computing 07, no. 03 (2013): 325–47. http://dx.doi.org/10.1142/s1793351x13500062.

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The ever-increasing amount of information flowing through Social Media presents numerous opportunities for the generation of Business Intelligence. Challenges exist in the leveraging of these data sources due to their heterogeneity and unstructured content. This paper presents the application of Semantic Computing to Social Media for industrial application, focusing on topic identification and behavior prediction. The methodologies described can benefit many areas of an organization including support of marketing, customer service, engineering and public relations. Results demonstrate that business operations can be substantially enhanced through application of Semantic Computing to Social Media.

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Tripathi, Ashutosh. "Serverless Deployment Methodologies: Smooth Transitions and Improved Reliability." International Journal of Innovative Research in Advanced Engineering 09, no. 12 (2022): 510–14. http://dx.doi.org/10.26562/ijirae.2022.v0912.10.

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The advancement of cloud computing has opened doors to serverless architectures, granting developers the freedom to create and launch applications without the burden of server management. This paper explores different approaches to deploying serverless applications, namely All-At-Once Deployment, Blue-Green Deployment, Canary Deployment, A/B Testing, and Shadow Deployment. Each method is thoroughly examined to grasp its approach, advantages, and ideal usage scenarios, offering a detailed roadmap for organizations seeking to enhance their deployment workflows for improved efficiency, dependability, and reduced downtime.

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Vucha, Mahendra, and Arvind Rajawat. "A Case Study: Task Scheduling Methodologies for High Speed Computing Systems." International Journal of Embedded Systems and Applications 4, no. 4 (2014): 01–12. http://dx.doi.org/10.5121/ijesa.2014.4401.

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Yamada, Kazuhito, Ikuko N. Motoike, Tetsuya Asai, and Yoshihito Amemiya. "Design methodologies for compact logic circuits based on collision-based computing." IEICE Electronics Express 3, no. 13 (2006): 292–98. http://dx.doi.org/10.1587/elex.3.292.

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Spitaleri, Rosa Maria. "Applied scientific computing IX: Modeling, numerical methods, algorithms and simulation methodologies." Mathematics and Computers in Simulation 82, no. 12 (2012): 2889–90. http://dx.doi.org/10.1016/j.matcom.2012.10.001.

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