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69 articles for “performance benchmarking”
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An Investigative Study on Cache-Oblivious Data Structures
Abstract: Cache-oblivious data structures and data management systems have emerged as critical components in modern computing environments, aiming to optimize memory access patterns across different levels of the memory hierarchy without explicit knowledge of cache sizes or configurations. This study presents an overview of cache-oblivious techniques, including adaptive data structures, compression, parallel processing, and security considerations. The workexplores future directions in cache-oblivious systems, such as non-volatile memory support, graph processing, edge …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 2, 2023 · pp. 33–37 Read article
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Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning
Abstract: The accelerating deployment of ocean energy converters (OECs) across tidal, wave, osmotic, and thermal domains necessitates rigorous, data-intensive environmental impact assessment (EIA) frameworks capable of modelling multi-stressor marine ecosystems in real time. Classical machine learning approaches, while operationally mature, encounter scalability bottlenecks and feature correlation limitations when applied to the high-dimensional, non-linear datasets characteristic of offshore monitoring networks. This paper presents a comprehensive quantum machine learning (QML) framework for the …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 1, 2026 · pp. 22–31 Read article
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Comparative Analysis of Modern Programming Paradigms: Evaluating Language Efficiency and Compiler Design Technique
Abstract: This paper attempts to provide some insights into the efficiency of modern programming paradigms via a comparative study and explore the important role played by compiler design in the optimization of these languages. Programming languages have been evolving quickly over time and different paradigms: imperative, functional, or object-oriented programming come with their idiosyncrasies and optimization techniques. The study starts by defining the foundational principles of each paradigm. It then goes …
Published in Recent Trends in Programming languages · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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Enhancing MRO Documentation Through Automated Translation of Non-Standard English to Simplified Technical English Using Offline LLMS
Abstract: Maintenance, Repair, and Overhaul (MRO) operations rely heavily on accurate and consistent documentation to ensure operational safety and compliance. However, the presence of non-standard English in technical documents often leads to ambiguity, misinterpretation, and inefficiencies in maintenance processes. This study presents an innovative solution that leverages an offline Large Language Model (LLM) to automatically translate non-standard English in MRO documents into standardized and technically precise language. By integrating predefined linguistic …
Published in Journal of Aerospace Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 9–15 Read article
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Autonomous Agentic AI for Adaptive Cure Optimization and Defect Prevention in Thermoset Polymer Composite Manufacturing
Abstract: Thermoset polymer composites occupy a central position in modern structural manufacturing, from aircraft fuselages to wind-turbine blades. Despite progress in resin chemistry and fiber architecture, the “cure process” that transforms compliant preforms into load-bearing structures remains difficult to manage. Manufacturers encounter ‘voids’, “interlaminar delaminations”, and “spring-back distortion” when curing complex or thick-section parts. The cause is not ignorance of the relevant physics, but rather that ‘temperature’, ‘chemistry’, ‘rheology’, and ‘mechanics’ …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 301–320 Read article
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Educating Compilers to Learn: Utilizing Machine Learning for More Brilliant Code Optimization
Abstract: This study explores the use of machine learning (ML) approaches to compiler optimization. The now-traditional static compilation techniques are transformed into adaptive, dynamic systems capable of making context-specific advancements. Traditional compilers rely mostly on heuristic or rule-based optimization techniques. While these techniques work well in general cases, they consistently fail to adapt well within the limits of code structures that modern machines display. This limitation is especially acute in today's …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 50–54 Read article
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MIRDcalc & OLINDA/EXM Dosimetry Software Analysis by SPECT/CT Scan Data of Lu-177 DOTATATE Radionuclide Therapy of NET Patients
Abstract: Accurate dosimetry is essential in nuclear medicine for optimizing radionuclide therapies and ensuring patient safety. In Radiopharmaceutical dosimetry the Medical Internal Radiation Dosimetry (MIRD)Society is the pioneer in organ-level dosimetry providing the fundamental basis for commonly used clinical and research dosimetry software like MIRDOSE and OLINDA/EXM. Recently, in MIRD Pamphlet No. 28, Part 1, the MIRD committee of the Society of Nuclear Medicine and Medical Imaging presented a new Software …
Published in Journal of Nuclear Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 48–63 Read article
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Emerging Trends in Civil Engineering: Sustainability and Digital Transformation
Abstract: Civil engineering has long played a pivotal role in shaping society’s infrastructure, focusing traditionally on construction, design, and the structural integrity of buildings, roads, and bridges. However, recent years have witnessed a profound transformation in the field, driven by the urgent need for sustainable practices and the rapid evolution of technology. This paper provides an in-depth analysis of the latest trends that are redefining civil engineering, with a particular emphasis …
Published in Recent Trends in Civil Engineering & Technology · Vol. 14, Issue 3, 2024 · pp. 8–11 Read article
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Comprehensive Comparative Analysis of Intrusion Detection Systems: Evaluating Signature Based, Anomaly Based, and Hybrid Approaches
Abstract: In the fast-changing world of cybersecurity, Intrusion Detection Systems (IDS) play a vital role in protecting digital resources. This study offers an in-depth comparative analysis to evaluate the efficiency and performance of different IDS solutions. It examines a variety of both commercial and opensource platforms, encompassing signature based, anomaly based, and hybrid models, to assess their effectiveness in identifying and responding to a wide range of cyber threats. Methodologies for …
Published in International Journal of Information Security Engineering · Vol. 3, Issue 2, 2025 · pp. 39–44 Read article
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Enhancing Customer Engagement with AI-Driven Movie Recommenders: Integrating Neural Collaborative Filtering, Sentiment Analysis, and Conversational Agents
Abstract: In today’s competitive digital landscape, user engagement is a critical factor for the success of entertainment platforms, especially those offering movie recommendations. This study introduces a comprehensive AI-driven framework designed to enhance customer interaction, satisfaction, and loyalty through the intelligent integration of multiple deep learning models. The system combines three core components: Neural Collaborative Filtering (NCF) for generating personalized movie recommendations based on user behavior and preferences, Long Short-Term Memory …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 45–54 Read article
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Hybrid Approach for Community Detection Using Deep Learning Techniques
Abstract: Community detection in complex networks is a fundamental problem with applications across diverse domains, ranging from social networks to biological systems and beyond. Traditional methods based on graph theory have been widely used for identifying communities within networks. However, the intricate and evolving nature of modern networks demands more sophisticated approaches. This research work proposes a hybrid approach that combines the strengths of deep learning techniques with traditional community detection …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 3, 2024 · pp. 18–26 Read article
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A Review of Blocking Side-Channel Threats in Parallel Cloud Systems
Abstract: Side-channel attacks (SCAs) pose a critical security threat to parallel computing systems, particularly in shared cloud environments where multi-tenancy and resource contention create exploitable vulnerabilities. This study presents a comprehensive review of SCAs in parallel architectures, analyzing attack vectors such as cache-based exploits (e.g., Prime + Probe, Flush + Reload), timing attacks, power analysis, and network-based covert channels. We examine real-world cases including Spectre and Meltdown vulnerabilities that exposed fundamental …
Published in Recent Trends in Parallel Computing · Vol. 12, Issue 2, 2025 · pp. 15–25 Read article
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Quasi-Sobol-Based Optimization and Sigmoid Fuzzy Logic for Efficient Task Clustering in Decentralized Edge-Cloud Architectures
Abstract: Decentralized edge-cloud systems face a lot of issues in efficient task clustering, resource allocation, and real-time decision-making. Conventional methods mostly fail to work well under dynamic workloads and uncertain conditions. This study intends to optimize task clustering through quasi-Sobol-based optimization and Sigmoid Fuzzy Logic for resource allocation, enhancing decision-making accuracy and achieving efficiency in the system at the edge-cloud environment. A hybrid technique that has incorporated optimization via Quasi-Sobol sequences …
Published in Trends in Machine design · Vol. 12, Issue 2, 2025 · pp. 9–18 Read article
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Cloud-based Application Development and Optimization
Abstract: As cloud computing powers today’s applications, optimizing cloud-based development is crucial to achieve performance, cost effectiveness, and scalability. This research focuses on enhancing the design, deployment, and maintenance of cloud applications, tackling challenges in resource management, scalability, and resilience. We specifically explore dynamic resource allocation algorithms that use predictive analytics for auto-scaling based on workload variations, aiming to cut costs while preserving high performance. The study also investigates cross-cloud optimization …
Published in Journal of Open Source Developments · Vol. 12, Issue 1, 2025 · pp. 37–42 Read article
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Understanding Sentiment Trends Through Zero-Shot and Few-Shot Learning Models
Abstract: The requirement for large, manually labeled datasets is one of the main barriers to applying sentiment analysis algorithms in specialized or rapidly evolving disciplines in the present natural language processing (NLP) landscape. This work investigates a paradigm shift from traditional fully supervised learning to data-efficient methods, specifically zero-shot learning (ZSL) and few-shot learning (FSL). This study uses the advanced capabilities of instruction-tuned large language models (LLMs), like GPT-4, to assess …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 01–08 Read article
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Randomized Latent Vectors for Enhanced Reinforcement Learning Exploration
Abstract: This paper investigates Random Latent Exploration (RLE), a novel reinforcement learning technique that enhances exploration using randomized latent vector conditioning. I evaluate RLE’s performance across various environments, including discrete control tasks (FourRoom), continuous control (IsaacLab), and complex visual domains (Atari games). The core approach augments traditional reward functions with intrinsic rewards, calculated as the dot product between state features and periodically resampled latent vectors. The policy and value networks are …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 19–25 Read article
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Nanoparticle-Based Early Diagnostic Tools for Tuberculosis and Malaria in Rural India
Abstract: Malaria and Tuberculosis continue to be two major public health challenges in rural India. The interplay between an inefficient health care system, late diagnosis, and under-identification of cases contributes to the high morbidity and mortality associated with TB and Malaria. Conventional methods, such as sputum microscopy for TB, microscopy and rapid diagnostic tests (RDTs) for malaria, have a higher benchmark of sensitivity that requires a certain amount of time, skilled …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 45–51 Read article
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Feasibility Analysis of Wireless Power Transfer Technologies for Electric Vehicle Charging Applications
Abstract: The rapid pace of electric vehicles (EVs) has heightened the pressure on the need to have flexible, safe, and efficient infrastructures in addition to the plug-in infrastructures. The Wireless Power Transfer (WPT) has become a promising alternative that provides automated charging, better convenience, and a possible integration into the smart highways and urban environment. The present paper provides a feasibility study of the key WPT technologies in EV charging namely, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 27–38 Read article
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Combining Unstructured and Structured Clinical Data in a Hybrid Transformer Model to Enhance Cardiovascular Analytics and Clinical Decision- Making
Abstract: Since cardiovascular disease (CVD) continues to be a major global cause of morbidity and mortality, early and accurate risk prediction is essential for prompt intervention and individualized treatment. This study introduces a new hybrid transformer-based model that combines unstructured clinical narratives, structured data, and customized lifestyle characteristics. A comprehensive understanding of disease progression is made possible by the model's ability to capture contextual, temporal, and patient- specific insights through the …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 1, 2026 · pp. 30–37 Read article