Integrating Self-Attention Mechanisms For Contextually Relevant Information In Product Management

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2024, Volume and Issue: 10(4)

Published: Dec. 11, 2024

GPT-Product is an innovative AI solution that aims to transform product management and development by using sophisticated natural language processing (NLP) abilities. Building on Transformer architecture, frameworks like as BERT, GPT, T5 have greatly enhanced applications, thereby allowing more efficient chatbots, translation services, content generating tools, so on. utilises the advanced GPT-3.5 architecture provide full solutions for market evaluation, interpretation of client input, automated development. This enhances decision-making processes. self-attention mechanism model precise contextually appropriate information, enabling effective lifetime. uses deep learning optimise processes, decrease time-to-market, enhance quality. It positions itself essential tool firms striving maintain competitiveness in a rapidly changing industry.

Language: Английский

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Next-Generation Spam Filtering: Comparative Fine-Tuning of LLMs, NLPs, and CNN Models for Email Spam Classification

Electronics, Journal Year: 2024, Volume and Issue: 13(11), P. 2034 - 2034

Published: May 23, 2024

Spam emails and phishing attacks continue to pose significant challenges email users worldwide, necessitating advanced techniques for their efficient detection classification. In this paper, we address the persistent of spam by introducing a cutting-edge approach filtering. Our methodology revolves around harnessing capabilities language models, particularly state-of-the-art GPT-4 Large Language Model (LLM), along with BERT RoBERTa Natural Processing (NLP) models. Through meticulous fine-tuning tailored classification tasks, aim surpass limitations traditional systems, such as Convolutional Neural Networks (CNNs). an extensive literature review, experimentation, evaluation, demonstrate effectiveness our in accurately identifying while minimizing false positives. showcases potential LLMs specialized tasks like classification, offering enhanced protection against evolving attacks. This research contributes advancement filtering lays groundwork robust security systems face increasingly sophisticated threats.

Language: Английский

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SMART Restaurant ReCommender: A Context-Aware Restaurant Recommendation Engine

AI, Journal Year: 2025, Volume and Issue: 6(4), P. 64 - 64

Published: March 25, 2025

With the rise of e-commerce and web application usage, recommendation systems have become important to our daily tasks. They provide personalized suggestions assist with any task under consideration. While various machine learning algorithms been developed for tasks, existing still face limitations. This research focuses on advancing context-aware sytems by leveraging capabilities Large Language Models (LLMs) in conjunction real-time data. The exploits integration data APIs LLMs enhance already integrated into smart societies. experimental results demonstrate that hybrid approach significantly improves user experience quality, ensuring more relevant dynamic suggestions.

Language: Английский

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LLMs for product classification in e-commerce: A zero-shot comparative study of GPT and claude models

Natural Language Processing Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100142 - 100142

Published: March 1, 2025

Language: Английский

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Industrial applications of large language models

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 21, 2025

Large language models (LLMs) are artificial intelligence (AI) based computational designed to understand and generate human like text. With billions of training parameters, LLMs excel in identifying intricate patterns, enabling remarkable performance across a variety natural processing (NLP) tasks. After the introduction transformer architectures, they impacting industry with their text generation capabilities. play an innovative role various industries by automating NLP In healthcare, assist diagnosing diseases, personalizing treatment plans, managing patient data. provide predictive maintenance automotive industry. recommendation systems, consumer behavior analyzers. facilitates researchers offer personalized learning experiences education. finance banking, used for fraud detection, customer service automation, risk management. driving significant advancements tasks, improving accuracy, providing deeper insights. Despite these advancements, face challenges such as ethical concerns, biases data, resource requirements, which must be addressed ensure impartial sustainable deployment. This study provides comprehensive analysis LLMs, evolution, diverse applications industries, offering valuable insights into transformative potential accompanying limitations.

Language: Английский

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Enabling Design of Secure IoT Systems with Trade-Off-Aware Architectural Tactics

Sensors, Journal Year: 2024, Volume and Issue: 24(22), P. 7314 - 7314

Published: Nov. 15, 2024

The increasing use of the Internet Things (IoT) in homes and industry brings significant security privacy challenges, while also considering trade-off for performance, energy consumption, processing capabilities. Few explicit specific guidelines exist to help architects these trade-offs designing secure IoT systems. This article proposes address this situation by extending well-known architectural tactics taxonomies with IoT-specific trade-offs; preserving auditability, quality characteristics ISO 25010:2023 standard. proposed technique catalog are illustrated design Nunatak environmental monitoring system. proposal was empirically validated a controlled experiment, where balanced mix 12 novice expert practitioners had Environmental Monitoring System; they used similar catalogs, versus without information. Results suggest that having information yield improvements decision-making effectiveness (Precision) usefulness (F1-Score), particularly benefiting less experienced designers. Wider adoption trade-off-aware catalogs will allow systematic, auditable systems, especially so architects.

Language: Английский

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