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84 articles for “evaluation benchmarks”
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LLM-Assisted Programming: Trends, Evaluation Gaps, and Software Assurance
Abstract: Large language models (LLMs) are increasingly used for code generation, software testing, program repair, and security analysis. However, their evaluation still relies heavily on functional benchmarks that often focus on small, isolated programming tasks. Such benchmarks provide limited insight into security, test quality, repository-level dependencies, and maintainability. This study examines how the evaluation of LLM-assisted programming has developed across these application areas. The literature shows a gradual shift from function-level …
Published in Recent Trends in Programming languages · Vol. 13, Issue 2, 2026 · pp. 28–42 Read article
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Linux Operating System Performance Evaluation and States
Abstract: Linux has made great growth recently, and as a result of its many useful features for businesses and home users, it has grown to be one of the maximum broadly used working structures with inside the IT industry. Three Linux desktop distributions were chosen for further assessment based on their popularity among available Linux distributions. The goal of this article is to assess and analyse the performance of three different …
Published in Journal of Advances in Shell Programming · Vol. 8, Issue 2, 2021 · pp. 1–9 Read article
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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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Building Scalable Microservices with Micronaut, Kotlin, and AWS DynamoDB: A Comprehensive Architecture Study
Abstract: The evolution of enterprise software has trended steadily toward microservice architectures due to their inherent scalability and resilience advantages over monolithic systems. This research explores a comprehensive implementation approach using Micronaut, an innovative JVM-based framework specifically designed for resource-efficient microservices. The study combines Micronaut with Kotlin programming language and leverages AWS DynamoDB as a scalable NoSQL persistence layer, with Apache Kafka providing event-driven communication capabilities. We explore the critical role …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 2, 2025 · pp. 40–57 Read article
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Path Lab-AI: An Autonomous Framework for Error-Free Histopathology Slide Interpretation
Abstract: Path Lab-AI represents a fully autonomous platform for the analysis of histopathology slides with circumscribed structures, designed to obtain highly accurate results using diagnostic methods and avoiding the usual limitations of standard microscopy-based pathology. Leveraging recent deep learning and whole slide image (WSI) analysis innovations, our system takes advantage of automated WSI ingestion along with pre-processing steps to account for staining variability, remove artifacts, and localize tissue from background. Such …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 1, 2026 · pp. 19–30 Read article
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Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications
Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 · pp. 10–22 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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Explainable Sentiment Mining Model in Mental Health Forums for Emotion Classification and Justification
Abstract: Understanding and interpreting emotions expressed in online mental health discussions plays a crucial role in enabling early detection of psychological distress and facilitating timely interventions. As individuals increasingly turn to digital platforms to share personal experiences and seek support, automated systems capable of accurately identifying emotional states can significantly assist clinicians, moderators, and support communities. This paper presents a deep learning–based sentiment mining and emotion classification framework specifically designed to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 22–32 Read article
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Neuro-Symbolic Agentic AI for Autonomous Scientific Discovery: Integrating Deep Reinforcement Learning, Quantum Simulation, and XAI-Audited LLM Hypothesis Generation in Drug Target Identification
Abstract: The exponential growth of multi-omics data and the increasing complexity of disease-associated protein interactomes have rendered conventional drug target identification pipelines computationally and epistemologically inadequate. This paper presents the Neuro-Symbolic Agentic AI for Scientific Discovery (NS-AASD) framework, a unified architecture that cohesively integrates deep reinforcement learning (DRL) exploration strategies, variational quantum simulation (VQS) of protein conformational dynamics, and XAI-audited large language model (LLM) hypothesis generation within an autonomous scientific discovery …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 · pp. 15–24 Read article
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A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 Read article
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Palm Print Recognition Based on PCA and Its Variants
Abstract: In present era, automatic personal recognition is a serious problem that needs to be overcome properly. Palm print recognition is one of the most reliable and successful biometric solutions due to its numerous advantages such as stable line features, low resolution imaging, low cost capturing device, being non-intrusive and user friendly. In this article, performance comparisons of palm print recognition techniques based on subspace approaches (PCA, 2DPCA, 2D²PCA) have been …
Published in Current Trends in Signal Processing · Vol. 5, Issue 3, 2015 · pp. 7–14 Read article
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Redefining Road Safety: A Comprehensive Literature Review on Computer Vision for Speed Breaker Detection in Vehicle
Abstract: This literature review focuses on the use of computer vision techniques for detecting speed breakers on roads, with the objective of improving road safety. Speed breakers play a crucial role in controlling vehicle speeds and ensuring pedestrian safety, but their sudden appearance can lead to accidents, discomfort, and damage to vehicles. The review highlights five key points derived from the analysis of existing literature. Firstly, various computer vision algorithms, including …
Published in Journal of Computer Technology & Applications · Vol. 14, Issue 1, 2023 · pp. 52–63 Read article
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Reward-Aligned Reinforcement Learning from Human Feedback for Emotion-Sensitive Large Language Model Therapists: Balancing Empathetic Engagement, Boundary Safety, and Clinical Accountability
Abstract: The deployment of large language models (LLMs) in mental health therapy contexts introduces a critical alignment challenge: these systems must simultaneously cultivate genuine empathic rapport, observe clinically grounded safety boundaries, and remain auditable under institutional accountability frameworks. Existing reinforcement learning from human feedback (RLHF) pipelines optimize for a scalar reward signal that is demonstrably insufficient for the multi-objective, temporally extended nature of therapeutic conversation. This paper presents RA-RLHF-T (Reward-Aligned RLHF …
Published in International Journal of Tropical Medicines · Vol. 3, Issue 2, 2026 Read article
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AI-Powered Prompt-to-Production Platform for Automated React Application Generation and Cloud Deployment
Abstract: The proliferation of large language models (LLMs) has fundamentally altered the landscape of automated software engineering. However, a significant “deployment chasm” persists between the generation of code artifacts and their realization as production-grade cloud applications. Traditional workflows are marred by the “DevOps Tax”: the high operational overhead of environment configuration, dependency resolution, and infrastructure provisioning. This paper presents a novel, technically adept architecture that bridges this gap through a unified …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 13, Issue 2, 2026 · pp. 1–16 Read article
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Cascade CNN Framework for Low Resolution Image Classification
Abstract: Abstract: Low resolution images contain less visual information, so classifications of these images are difficult. For overcoming this problem, cascade CNN framework for low resolution image classification is proposed. In this framework, super resolution CNN (SRCNN) enlarges low resolution image into super resolution image. The convolutional features of these super resolution images are fused with low resolution convolutional features which are extracted by low resolution CNN (LRCNN) feature extractor. A …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 6, Issue 1, 2019 · pp. 39–43 Read article
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A Survey on Designing of OpenMP, UPC and MPI
Abstract: AbstractThe present pattern to multicore designs underscores the need of parallelism. While new dialects and options for supporting all the more proficiently, these frameworks are proposed, MPI confronts this new test. Along these lines, execution assessments of current alternatives for programming multicore frameworks are required. This paper assesses MPI execution against Unified Parallel C (UPC) and OpenMP on multicore designs. From the investigation of the outcomes, it can be reasoned …
Published in Recent Trends in Parallel Computing · Vol. 4, Issue 2, 2017 · pp. 58–63 Read article
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Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning
Abstract: A machine learning (ML)-based framework is developed and validated through experimental analysis and comparative modeling to enhance system dependability and improve prediction performance. The proposed framework includes key stages such as data preprocessing, feature evaluation, model training, and performance benchmarking to determine the most effective prediction technique. Several machine learning models were evaluated, including Ensemble models, Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 275–296 Read article
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Open-Source AI Agent Frameworks: A Comparative Performance Analysis
Abstract: The rapid growth of agentic AI has led to a diverse ecosystem of open-source frameworks for developing LLM-based agents. These frameworks differ in their approaches to state management, multi-agent coordination, tool orchestration, memory, and workflow execution. This paper presents a comparative analysis of prominent open-source AI agent frameworks, including LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, and related systems. The study synthesizes a common architectural model to identify the core components …
Published in Journal of Open Source Developments · Vol. 13, Issue 2, 2026 · pp. 23–33 Read article
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Development and Performance Evaluation of a Multi-Purpose Power Tiller for Small-Scale Farming Applications
Abstract: This project outlines the development and testing of a multi-purpose power tiller tailored for small-scale farmers. Designed to reduce labor and enhance productivity, the machine integrates features for ploughing, seedbed preparation, and lightweight transportation. Key parameters including depth of tillage, working width, and fuel consumption were evaluated during field trials and benchmarked against manual operations. Results highlight its efficiency and potential for improving rural agricultural practices. Along with its primary …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 2, 2026 · pp. 14–19 Read article
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Improving The Accuracy of Medical Diagonosis Detection Using Machine Learning
Abstract: While accurate and timely medical diagnosis is a fundamental aspect of effective health care delivery, traditional methods have not been able to overcome major hurdles such as inefficiencies in data analysis with Gi Human Error as well as limitations in scalability. The “Improved Accuracy of Medical Diagnosis Detection Using Machine Learning” project seamlessly integrates advanced machine learning (M L) technologies with efficient preprocessing and feature selection techniques to outperform all …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 1–8 Read article