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New Trends in Computer Sciences
ISSN 2783-6851 · Vilnius Gediminas Technical University · 39 artikel di indeks
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Artikel Jurnal
AI-enabled software requirements specification for banking chatbot systems
Software Requirements Specification plays an important role in the success of software development projects by providing clear, structured, and consistent documentation of requirements. However, traditional SRS processes are manual, time-consuming, ambiguous, and inconsistent. This paper proposes…
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Discovering disease risk trends by applying Latent Dirichlet Allocation sentiment analysis to social media health discussions
Social media platforms generate large volumes of health-related content that can provide valuable insights into emerging disease concerns and public perceptions. This study explores disease-risk trends through topic modeling and sentiment analysis of social media discussions. We used Latent…
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Candidate assessment in asynchronous video interviewing: a hybrid hyperbolic fuzzy integrated MCDM framework with generative AI expertise
Asynchronous Video Interviewing (AVI) is increasingly used in large-scale recruitment; however, its evaluation remains constrained by opaque predictive models, limited interpretability, and reliance on subjective human judgement. Interview data is often vague and complex. In this study we propose a…
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Distinguishing lexical deviation from semantic resilience in LLM-based round-trip translation
Round-trip translation (RTT) is widely used for data augmentation and paraphrasing, yet its impact on meaning preservation remains poorly understood. We evaluate RTT systematically across two phases totaling 80,000 translation operations: 200 synthetic English passages are routed through single-…
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Hybrid clustering framework for large-scale scientific literature structuring
This study addresses the challenge of organizing and interpreting large collections of scientific literature, focusing on a rapidly growing and heterogeneous AI research domain derived from object-detection-with-small-data literature. First, based on the set of keywords using Clarivate Analytics…
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Impact of unit and item nonresponse on population total estimation in asymmetric business survey data
Nonresponse is one of the main sources of nonsampling error affecting the accuracy of survey estimates, particularly in business surveys characterized by highly asymmetric data distributions. This study investigates the impact of unit and item nonresponse on the estimation of population totals…
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A secure restful deployment pattern for course recommendation microservices using proof-of-work blockchain authentication
Deploying machine learning models across distributed environments requires robust architectural patterns that ensure both cross-platform interoperability and secure access control. While Representational State Transfer (REST) APIs are widely adopted for service exposure, existing implementations in…
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Evaluating knowledge graph enhanced retrieval augmented generation for automated functional requirements extraction
The automation of requirements engineering using large language models offers significant potential for efficiency but struggles with hallucinations and a lack of domain-specific precision in highly regulated fields such as healthcare. While retrieval augmented generation (RAG) addresses some of…
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Interpretable machine learning approaches for student dropout prediction in higher education
Student dropout remains one of the most significant challenges in higher education, affecting academic performance, financial sustainability, and strategic planning within universities. This study presents an approach to predicting student dropout risk using machine learning methods and educational…
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Wearable physiological sensor fusion for pain episode detection: a review
Pain episode detection and forecasting from physiological signals collected by wearable devices is becoming increasingly important. Self-reported pain ratings in daily life are recorded irregularly, leaving episode onset, duration, and dynamics incompletely documented. The reviewed literature shows…
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Framework for deep reinforcement learning in Webots virtual environments
Reinforcement learning (RL) algorithms, particularly deep reinforcement learning (DRL), have shown transformative potential in robotics by enabling adaptive behaviour in virtual environments. However, a comprehensive framework for efficiently testing, training, and deploying robots in these…
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Dynamic multi-scale simulation for evaluating combat effectiveness against aerial threats
The development and assessment of modern weapon systems require efficient and flexible simulation tools. This paper introduces a multi-scale discrete-event simulation framework designed to evaluate the dynamic combat effectiveness of weapon systems. The framework combines high-resolution and…
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Enhancing prediction of ride-hailing fares using advanced deep learning techniques
Fare prediction is a critical component of online ride-hailing services, as it significantly influences consumer decision-making and enhances operational efficiency for service providers. Reliable fare prediction is especially important in dynamic pricing environments, where fares are affected by…
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Enhancing land use classification with hybrid machine learning and satellite imagery
The growing accessibility of satellite imagery and the rapid evolution of machine learning (ML) techniques have significantly advanced land use classification for environmental monitoring. However, challenges such as cloud coverage, varying image resolutions, and seasonal changes continue to hinder…
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Effect of noise reduction on PLSR modeling in near infrared spectroscopy using denoising autoencoder
In this study, a deep learning-based denoising autoencoder approach is proposed to increase the robustness of near-infrared spectroscopy data to random noise and improve quantitative modeling accuracy. Artificial Gaussian noise at four different levels (10, 15, 20, and 25 dB) was added to the…
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Artikel Jurnal
A rapid review on ontology- and data-driven business process modelling
In modern organizations, the ability to efficiently manage and adapt business processes is essential. Business process modelling (BPM) is widely used to visualize, analyse, and improve operational processes. As the complexity of business environments increases, the integration of ontological…
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Improving AHP consistency through cognitive collaboration with large language models
The Analytic Hierarchy Process (AHP) is a human-centered method designed to structure complex problems and extract the authentic, consistent opinions of decision-makers. However, its practical application is often limited by inconsistency in human judgments, often caused by the respondent’s…
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Lightweight deep models for video anomaly detection: a comparative study of autoencoders and MobileNetV2 on the avenue dataset
Video anomaly detection aims to identify unusual events in surveillance footage, yet many existing deep learning solutions remain too computationally heavy for real-time deployment on resource-limited hardware. This study presents a systematic comparison of three lightweight deep learning models…
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Artikel Jurnal
Bit error rate characterization of LoRa chirp spread spectrum modulation across spreading factors: a Simulink-based analysis
Low-Power Wide-Area Networks (LPWANs) based on LoRa technology have gained prominence in Internet of Things (IoT) deployments due to their long-range communication capabilities and low power consumption. The physical layer performance of LoRa’s Chirp Spread Spectrum (CSS) modulation under varying…
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Artikel Jurnal
Fuzzy-based assessment of stakeholder feedback in software requirements
Requirements engineering is essential for aligning software systems with stakeholder needs, yet incorporating stakeholder feedback into existing software requirements remains challenging due to ambiguity, inconsistency, and limited validation mechanisms. This paper proposes an AI-based evaluation…