The automobile service industry's explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance System, integ...
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ISBN:
(纸本)9798331513894
The automobile service industry's explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance System, integrating Classical Machine Learning (ML) techniques with Generative AI to optimize garage service discovery and analysis. The system employs sophisticated data processing methods, including Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and regex-based service detection, to extract actionable insights from unstructured garage *** to the system are machine learning models Random Forest (RF) and XGBoost (XGB) which achieve high precision and recall in classifying garage services. A hybrid search mechanism, combining cosine similarity with ML-driven predictions, ensures the delivery of highly personalized search results. To further refine decision-making, the system incorporates Generative AI models such as Perplexity for web-based research, Gemini for location-specific analysis, Mistral for email sending and GPT-4 for detailed service recommendations and dall-e for creating user specific parts images. These advanced tools provide users with comprehensive information that enables them to make well-informed decisions about garage *** evaluation of the system is conducted using robust metrics, including precision, recall, F1-score, and system latency. Experimental results reveal a precision of 85%, recall of 70.8%, and an F1-score of 77.2%, demonstrating the efficacy of integrating classical ML with generative AI. The system's average latency of 5.9 seconds ensures a seamless and responsive user *** hybrid framework highlights the potential of blending classical ML and Large Language Models (LLMs) to enhance search and recommendation functionalities, offering a scalable and robust blueprint for future advancements in the automotive service sector. The system's Propose a Multi-Agent System With high accuracy,
In today's world, many technologically innovative solutions are developed to prioritize women's safety solutions, especially during nighttime. This research's main aim is to focus on addressing crucial saf...
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Sentiment analysis is a natural language processing technique for extracting sentimental, opinion, or emotional information from text data. Sentiment analysis of natural language is a complex task that requires h...
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While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis spe...
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The Industrial Internet of Things (IIoT) has revolutionized industrial operations by enhancing connectivity and automation. However, this interconnectivity also introduces significant vulnerabilities, particularly to ...
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Renal irregularities are serious medicinal condition that is becoming more common and killing more people each year. In its early stages, renal irregularities are curable, but it can progress irreversibly and result i...
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Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine ...
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This article investigates large batch training techniques using layer-wise adaptive scaling ratio (LARS) across diverse settings. In particular, we first show that a state-of-the-art technique, called LARS with the wa...
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The accumulation of trash in aquatic and marine environments has severe and significant consequences on marine ecosystems, resulting in a persistent environmental hazard. While volunteer trash pickup efforts have grow...
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In urban environments, efficient ambulance response times are critical for saving lives. This paper proposes a novel approach utilizing a multi-sensor integration system for improving ambulance control and traffic man...
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ISBN:
(纸本)9798350364828
In urban environments, efficient ambulance response times are critical for saving lives. This paper proposes a novel approach utilizing a multi-sensor integration system for improving ambulance control and traffic management. The system combines Radio Frequency Identification (RFID) sensors, cameras, and microphones to enhance the responsiveness of ambulance drivers and alleviate traffic congestion. The RFID sensors are strategically placed along the ambulance route to facilitate seamless communication between the ambulance and traffic signals. When an ambulance approaches, the RFID sensors trigger pre-programmed traffic signal adjustments, such as extending green lights or halting conflicting traffic flow, to expedite the ambulance's passage. Simultaneously, the camera-based detection system identifies the presence of ambulances in traffic and assesses congestion levels in real-time. Utilizing computer vision algorithms, the system analyzes live camera feeds to detect ambulance vehicles and evaluate traffic density and movement patterns. This information enables dynamic rerouting of ambulances to less congested routes, optimizing response times and minimizing delays. Furthermore, a microphone array is employed to detect the distinct audio signature of ambulance sirens. By leveraging sound analysis techniques, the system accurately identifies the approach of an ambulance and triggers additional traffic management measures, such as prioritizing ambulance lanes or temporarily rerouting vehicles to clear a path. Integration of these sensor technologies into a unified control system offers a comprehensive solution for improving ambulance navigation through urban traffic. Through proactive traffic signal adjustments, dynamic route optimization, and real-time siren detection, the proposed system enhances overall emergency response effectiveness while reducing the risk of traffic-related delays and accidents. Moreover, the system's adaptability and scalability make it suit
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