Results for 'Data Center Optimization'

976 found
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  1.  92
    ENHANCED SLA-DRIVEN LOAD BALANCING ALGORITHMS FOR DATA CENTER OPTIMIZATION USING ADVANCED OPTIMIZATION TECHNIQUES.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):369-376.
    In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm (...), and simulated annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. (shrink)
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  2.  85
    Efficient Data Center Management: Advanced SLA-Driven Load Balancing Solutions.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):368-376.
    In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm (...), and simulated annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. (shrink)
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  3.  79
    Optimizing Data Center Operations with Enhanced SLA-Driven Load Balancing".S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):368-376.
    The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. Extensive simulations are conducted using synthetic and real-world datasets to evaluate the performance of the proposed algorithm. The results demonstrate that the optimized load balancing approach outperforms traditional algorithms in terms of SLA compliance, resource utilization, and energy efficiency. The findings suggest that the integration of optimization techniques into load balancing algorithms can significantly (...)
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  4. OPTIMIZATION TECHNIQUES FOR LOAD BALANCING IN DATA-INTENSIVE APPLICATIONS USING MULTIPATH ROUTING NETWORKS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):377-382.
    In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of (...)
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  5.  91
    Optimization Algorithms for Load Balancing in Data-Intensive Systems with Multipath Routing.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):377-382.
    : In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume (...)
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  6. Multipath Routing Optimization for Enhanced Load Balancing in Data-Heavy Networks.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):377-382.
    In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of (...)
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  7. Big Data Optimization in Machine Learning.Xiaocheng Tang - 2015 - Disertation 1.
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  8. Latency-Aware Packet Transmission Optimization in Duty-Cycled WSNs.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):444-459.
    Wireless Sensor Networks (WSNs) have become increasingly prevalent in various applications, ranging from environmental monitoring to smart cities. However, the limited energy resources of sensor nodes pose significant challenges in maintaining network longevity and data transmission efficiency. Duty-cycled WSNs, where sensor nodes alternate between active and sleep states to conserve energy, offer a solution to these challenges but introduce new complexities in data transmission. This paper presents an optimized approach to aggregated packet transmission in duty-cycled WSNs, utilizing advanced (...)
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  9. Enhancing Water Productivity of Alfalfa (Medicago Sativa) Under Centre Pivot Irrigation System.Amir Mustafa Abd Aldaim, Adam Bush Adam & Abdelmoneim Elamin Mohamed - 2018 - International Journal of Engineering and Information Systems (IJEAIS) 2 (12):24-30.
    Abstract: The objective of this study was to evaluate water productivity of alfalfa (Medicago sativa) under centre pivot irrigation system. The experimental works were conducted at three centre pivot irrigation projects (Indian, Arab Authority for Agricultural Investment and Development (AAAID) and Sedonix projects) located at Khartoum State during the period from April 2011 to April 2013. In each project, three irrigation systems were randomly selected for the study treatments. Crop water requirement was obtained using CROPWAT 8 computer model. The parameters (...)
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  10. Attitude and Ethical Behaviors of Healthcare Providers as Antidotes of Health Service Consumer Satisfaction in Mgbuoshimini Primary Health Centre, Port Harcourt, Nigeria.Justina Ikpoko-Ore-Ebirien Dike Isaruk, Ikpoko-Ore-Ebirien Dike Isaruk & Deborah Thelma George - 2023 - Journal of Health, Applied Sciences and Management 6 (3):24-33.
    Health service consumers' satisfaction with the services they receive has been a challenge over the past decade, and this has been attributed to many factors that diverse scholars have investigated using different variables. In this study, the attitude and ethical behaviours of healthcare providers as antidotes to health service consumers' satisfaction in the Primary Health Centre at Mgbuoshimini, Port Harcourt, Nigeria, were investigated. A cross-sectional descriptive research design was used to select participants from pregnant women, nursing mothers, couples for family (...)
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  11.  59
    Machine Learning-Driven Optimization for Accurate Cardiovascular Disease Prediction.Yoheswari S. - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-359.
    The research methodology involves data preprocessing, feature engineering, model training, and performance evaluation. We employ optimization methods such as Genetic Algorithms and Grid Search to fine-tune model parameters, ensuring robust and generalizable models. The dataset used includes patient medical records, with features like age, blood pressure, cholesterol levels, and lifestyle habits serving as inputs for the ML models. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC), assess the model's predictive power.
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  12. National Center for Biomedical Ontology: Advancing biomedicine through structured organization of scientific knowledge.Daniel L. Rubin, Suzanna E. Lewis, Chris J. Mungall, Misra Sima, Westerfield Monte, Ashburner Michael, Christopher G. Chute, Ida Sim, Harold Solbrig, M. A. Storey, Barry Smith, John D. Richter, Natasha Noy & Mark A. Musen - 2006 - Omics: A Journal of Integrative Biology 10 (2):185-198.
    The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) (...)
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  13. Innovative Approaches in Cardiovascular Disease Prediction Through Machine Learning Optimization.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-359.
    Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, (...)
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  14. Efficient Aggregated Data Transmission Scheme for Energy-Constrained Wireless Sensor Networks.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):445-460.
    Optimization algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to determine the optimal aggregation and transmission schedules, taking into account factors such as network topology, node energy levels, and data urgency. The proposed approach is validated through extensive simulations, demonstrating significant improvements in energy consumption, packet delivery ratio, and overall network performance. The results suggest that the optimized aggregated packet transmission method can effectively extend the lifespan of duty-cycled WSNs while ensuring reliable (...)
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  15. OPTIMIZING DATA SCIENCE WORKFLOWS IN CLOUD COMPUTING.Tummalachervu Chaitanya Kanth - 2024 - Journal of Science Technology and Research (JSTAR) 4 (1):71-76.
    This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes of data, (...)
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  16. System availability optimization for production and embedding of bitumen bounded materials.Milan Mirkovic - 2016 - Dissertation, University of Belgrade
    Application of the reliability of repairable systems on solving problems from constructing production systems takes an important place in the process of finding the optimal solution among the suggested system choices. The basic hypothesis when using the reliability of the repairable systems is that every machine is representing a component, a fact that is debatable when talking about technical sciences. However, considering the second assumption of the stationary process, the function of the availability is introduced. It represents the measure between (...)
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  17. Attack Prevention in IoT through Hybrid Optimization Mechanism and Deep Learning Framework.Regonda Nagaraju, Jupeth Pentang, Shokhjakhon Abdufattokhov, Ricardo Fernando CosioBorda, N. Mageswari & G. Uganya - 2022 - Measurement: Sensors 24:100431.
    The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge (...)
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  18. A Sustainability Improvement Strategy of Interconnected Data Centers Based on Dispatching Potential of Electric Vehicle Charging Stations.Xihao Wang, Xiaojun Wang, Yuqing Liu, Chun Xiao, Rongsheng Zhao, Ye Yang & Zhao Liu - 2022 - Sustainability 14 (11):6814.
    With the rapid development of information technology, the electricity consumption of Internet Data Centers (IDCs) increases drastically, resulting in considerable carbon emissions that need to be reduced urgently. In addition to the introduction of Renewable Energy Sources (RES), the joint use of the spatial migration capacity of IDC workload and the temporal flexibility of the demand of Electric Vehicle Charging Stations (EVCSs) provides an important means to change the carbon footprint of the IDC. In this paper, a sustainability improvement (...)
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  19. The National Center for Biomedical Ontology.Mark A. Musen, Natalya F. Noy, Nigam H. Shah, Patricia L. Whetzel, Christopher G. Chute, Margaret-Anne Story & Barry Smith - 2012 - Journal of the American Medical Informatics Association 19 (2):190-195.
    The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop and use (...)
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  20. Data, Privacy, and the Individual.Carissa Véliz - 2020 - Center for the Governance of Change.
    The first few years of the 21st century were characterised by a progressive loss of privacy. Two phenomena converged to give rise to the data economy: the realisation that data trails from users interacting with technology could be used to develop personalised advertising, and a concern for security that led authorities to use such personal data for the purposes of intelligence and policing. In contrast to the early days of the data economy and internet surveillance, the (...)
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  21.  70
    Efficient Cryptographic Methods for Secure Searchable Data in IoT Frameworks.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):409-415.
    As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the (...)
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  22.  74
    Enhanced Secure Cloud Storage: An Integrated Framework for Data Encryption and Distribution.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):416-427.
    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 (...)
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  23. Intelligent Encryption and Attribute-Based Data Retrieval for Secure Cloud Storage Using Machine Learning.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):415-425.
    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 (...)
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  24. A conceptual framework for data-driven sustainable finance in green energy transition.Omotayo Bukola Adeoye, Ani Emmanuel Chigozie, Ninduwesuor-Ehiobu Nwakamma, Jose Montero Danny, Favour Oluwadamilare Usman & Kehinde Andrew Olu-Lawal - 2024 - World Journal of Advanced Research and Reviews 21 (2):1791–1801.
    As the world grapples with the urgent need for sustainable development, the transition towards green energy stands as a critical imperative. Financing this transition poses significant challenges, requiring innovative approaches that align financial objectives with environmental sustainability goals. This review presents a conceptual framework for leveraging data-driven techniques in sustainable finance to facilitate the transition towards green energy. The proposed framework integrates principles of sustainable finance with advanced data analytics to enhance decision-making processes across the financial ecosystem. At (...)
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  25. Synthetic Health Data: Real Ethical Promise and Peril.Daniel Susser, Daniel S. Schiff, Sara Gerke, Laura Y. Cabrera, I. Glenn Cohen, Megan Doerr, Jordan Harrod, Kristin Kostick-Quenet, Jasmine McNealy, Michelle N. Meyer, W. Nicholson Price & Jennifer K. Wagner - 2024 - Hastings Center Report 54 (5):8-13.
    Researchers and practitioners are increasingly using machine‐generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic (...)
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  26. Data-Driven HR Strategies: AI Applications in Workforce Agility and Decision Support.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):520-530.
    By embracing AI-driven HR analytics, organizations can anticipate market shifts, prepare their workforce for future challenges, and stay ahead of the competition. This study outlines the essential components of AI-driven HR analytics, demonstrates its impact on workforce agility, and concludes with potential future enhancements to further optimize HR functions. Key words: Predictive Workforce Analytics, Talent Optimization, Machine Learning in.
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  27.  88
    Advanced Phishing Content Identification Using Dynamic Weighting Integrated with Genetic Algorithm Optimization.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):500-520.
    The Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and (...)
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  28.  77
    OPTIMIZED ENCRYPTION PROTOCOL FOR LIGHTWEIGHT AND SEARCHABLE DATA IN IOT ENVIRONMENTS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):408-414.
    As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the (...)
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  29. AI-Driven Healthcare Optimization in Smart Cities.Eric Garcia - manuscript
    Urbanization poses significant challenges to healthcare systems, including overcrowded hospitals, inequitable access to care, and rising costs. Artificial Intelligence (AI) and the Internet of Things (IoT) offer transformative solutions for optimizing healthcare delivery in smart cities. This paper explores how AI-driven predictive analytics, combined with IoT-enabled wearable devices and telemedicine platforms, can enhance patient outcomes, streamline resource allocation, and reduce urban health disparities. By analyzing real-time health data and predicting disease outbreaks, this study demonstrates the potential of AI to (...)
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  30.  86
    Cloud-Based Secure Storage: A Framework for Efficient Encryption, Decryption, and Data Dispersion.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):427-434.
    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, (...)
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  31. (1 other version)The Czech Republic: From the Center of Christendom to the Most Atheist Nation of the 21st Century. Part 1. The Persecuted Church: The Clandestine Catholic Church (Ecclesia Silentii) in Czechoslovakia During Communism 1948-1991.Scott Vitkovic - 2023 - Occasional Papers on Religion in Eastern Europe (Opree) 43 (1):18 - 59.
    This research examines the most important historical, political, economic, social, cultural, and religious factors before, during, and after the reign of Communism in Czechoslovakia from 1918 to 2021 and their effect on the extreme increase in atheism and decrease in Christianity, particularly Roman Catholicism, in the present-day Czech Republic. It devotes special attention to the role of the Clandestine Catholic Church (Ecclesia Silentii) and the changing policies of the Holy See vis-à-vis this Church, examining these policies' impact on the continuing (...)
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  32.  72
    Real-Time Phishing Detection Using Genetic Algorithm-Based Ranking and Dynamic Weighting Optimization.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):491-500.
    The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection system's decision-making process. The Genetic Ranking Optimization Algorithm (GROA) is used to rank (...)
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  33. Conceptual Spaces for Space Event Characterization via Hard and Soft Data Fusion.Jeremy R. Chapman, David Kasmier, David Limbaugh, Stephen R. Gagnon, John Crassidis, James Llinas, Barry Smith & Alexander P. Cox - 2021 - AIAA (American Institute of Aeronautics and Astronautics) Scitech 2021 Forum.
    The overall goal of the approach developed in this paper is to estimate the likelihood of a given kinetic kill scenario between hostile spacebased adversaries using the mathematical framework of Complex Conceptual Spaces Single Observation. Conceptual spaces are a cognitive model that provide a method for systematically and automatically mimicking human decision making. For accurate decisions to be made, the fusion of both hard and soft data into a single decision framework is required. This presents several challenges to this (...)
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  34.  49
    ntelligent Hybrid Cloud Data Deduplication for Optimized Storage Utilization.A. Manoj Prabaharan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):625-633.
    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 (...)
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  35. Britchenko Igor. University as a core of e-learning ecosystem/Polishchuk Y., Kornyliuk A., Britchenko I.//14th conference reader, Prague: Center for Higher Education Studies Location: Microsoft, Prague, CZECH REPUBLIC Date: JUN 20-21, 2019. – P. 309-319.Igor Britchenko, Polishchuk Yevhenia & Kornyliuk Anna - 2019 - In Igor Britchenko, Polishchuk Yevhenia & Kornyliuk Anna (eds.), 14th conference reader, Prague: Center for Higher Education Studies. Praga, Czechy: pp. 309-319.
    The concept and the main stakeholders of E-learning ecosystem are investigated at the article. University is regarded as a center of such ecosystem due to skilled knowledge providers and technical equipment availability. Studying different cases authors prove that higher educational institution plays a driver role in different projects, especially social start-up projects. Different models of partnership between universities and other stakeholders are considered. In authors’ opinion, one of the most perspective collaborative projects are in frame of “students – schoolchildren” (...)
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  36. SAR-BSO meta-heuristic hybridization for feature selection and classification using DBNover stream data.Dharani Talapula, Kiran Ravulakollu, Manoj Kumar & Adarsh Kumar - forthcoming - Artificial Intelligence Review.
    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 (...)
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  37. Social Implications of Big Data and Fog Computing.Jeremy Horne - 2018 - International Journal of Fog Computing 1 (2):50.
    In the last half century we have gone from storing data on 5-1/4 inch floppy diskettes to cloud and now fog computing. But one should ask why so much data is being collected. Part of the answer is simple in light of scientific projects but why is there so much data on us? Then, we ask about its “interface” through fog computing. Such questions prompt this chapter on the philosophy of big data and fog computing. After (...)
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  38. Analysis of the amount of latent carbon in the reconstruction of residential buildings with a multi-objective optimization approach.Nima Amani, Abdulamir Rezasoroush & Ehsan Kiaee - 2024 - International Journal of Energy Sector Management (Ijesm) 18 (6):2408-2434.
    Purpose: Due to the increase in energy demand and the effects of global warming, energy-efficient buildings have gained significant importance in the modern construction industry. To create a suitable framework with the aim of reducing energy consumption in the building sector, the external walls of a residential building were considered with two criteria of global warming potential and energy consumption. -/- Design/methodology/approach: In the first stage, to achieve a nearly zero-energy building, energy analysis was performed for 37 different states of (...)
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  39. La situation professionnelle : entre invariance et perspective?Paul Olry - 2012 - Revue Phronesis 1 (1):68-84.
    This contribution is an invitation to consider the professional situation in a way that goes beyond a social meaning or a subjective approach. Understood as an intermediate object, the professional situation is studied as a result of tension between invariance and perspective. The data centre on the activity of counselors whose role is to guide farmers confronted with agro-environmental standards. This text brings into question on one hand the attributions qualifying the situation as «professional» and that attest to a (...)
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  40. Disaster Data Centre—An Innovative Educational Tool for Disaster Reduction through Education in Schools.Lekkas Efthymis - 2014 - Journal of Power and Energy Engineering 2:25-40.
    During the last decades, mankind has suffered from devastation caused by natural disasters and technological accidents of increased frequency and children are among the most vulnerable population group, especially those attending school during times of disaster. The importance of education in promoting and enabling disaster risk reduction has already been identified by researchers. In this paper “Disaster Date Center (DDC)” is presented, a new, powerful and innovative tool for the study of and education on disasters. One noteworthy application of (...)
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  41. REVISITING THE HUMAN RESOURCE AND MANAGEMENT PROGRAM OF THE EARLY YEARS LEARNING CENTER IN MANDALUYONG CITY.Fe Jocelyn G. Dioquino, Albert S. Billones, Ana Katrina S. Caldeira, Melanie Carl T. Espe & Alfredo G. Sy Jr - 2023 - Get International Research Journal 1 (2).
    This study sought to investigate the Human Resource and Management (HRM) Program of a preschool hereinafter referred to as the Early Years Learning Center (EYLC) in Mandaluyong City for purposes of this research study. This is a qualitative case study that delved particularly into the issue of employee retention, especially of seasoned teachers and staff of the subject learning center. It used the interview method to generate an in-depth analysis as it revisited its HRM Program. To triangulate the (...)
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  42.  95
    Artificial Intelligence in HR: Driving Agility and Data-Informed Decision-Making.Madhavan Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):506-515.
    In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make proactive decisions (...)
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  43.  91
    Cloud-Based IoT System for Outdoor Pollution Detection and Data Analysis.Prathap Jeyapandi - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):424-430.
    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 (...)
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  44.  82
    Machine Learning for Optimized Attribute-Based Data Management in Secure Cloud Storage.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):434-450.
    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 (...)
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  45. Advanced Attribute-Based Keyword Search for Secure Cloud Data Storage Solutions.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):350-360.
    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 (...)
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  46.  81
    OPTIMIZED CLOUD SECURE STORAGE: A FRAMEWORK FOR DATA ENCRYPTION, DECRYPTION, AND DISPERSION.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):415-426.
    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, (...)
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  47. Real-Life Data of Neoadjuvant Chemotherapy in Breast Cancer: Aegean Region Experience.Atike Pınar Erdoğan, Ferhat Ekinci, Ahmet Özveren, Emine Bihter Eniseler, Bilgin Demir & Mustafa Şahbazlar - 2023 - European Journal of Therapeutics 29 (2):123-127.
    Objective: The use of neoadjuvant chemotherapy (NACT) in breast cancer is increasing. The management of locally advanced breast cancer differs due to the approach of the center to which the patient applied and the approach of the following physician. From this point of view, we aimed to evaluate the real life data of our region. -/- Methods: The study included 106 patients treated with neoadjuvant chemotherapy in the medical oncology clinic of two different university hospitals. Association between clinicopathological (...)
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  48.  78
    Automated Phishing Classification Model Utilizing Genetic Optimization and Dynamic Weighting Algorithms.M. Sheik Dawood - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):520-530.
    The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and reduced false positives. The proposed model outperformed traditional machine learning algorithms, showing promise for real-world deployment in phishing detection systems. We conclude with suggestions for future improvements, such as incorporating more behavioral data and deploying the system in realtime monitoring applications.
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  49.  74
    AI-Driven Deduplication for Scalable Data Management in Hybrid Cloud Infrastructure.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):587-597.
    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 (...)
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  50. OmniSearch: a semantic search system based on the Ontology for MIcroRNA Target Gene Interaction data.Huang Jingshan, Gutierrez Fernando, J. Strachan Harrison, Dou Dejing, Huang Weili, A. Blake Judith, Barry Smith, Eilbeck Karen, A. Natale Darren & Lin Yu - 2016 - Journal of Biomedical Semantics 7 (1):1.
    In recent years, sequencing technologies have enabled the identification of a wide range of non-coding RNAs (ncRNAs). Unfortunately, annotation and integration of ncRNA data has lagged behind their identification. Given the large quantity of information being obtained in this area, there emerges an urgent need to integrate what is being discovered by a broad range of relevant communities. To this end, the Non-Coding RNA Ontology (NCRO) is being developed to provide a systematically structured and precisely defined controlled vocabulary for (...)
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