The current cross-camera multi-person identity association method is limited to pairwise association between cameras, lacking multi-view collaborative processing, which restricts its application in complex surveillanc...
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Current research on millimeter-wave radar gesture recognition mostly focuses on gestures with larger amplitudes, which are difficult to meet the needs of human-computer interaction on the mobile terminal. There are al...
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In road surface crack segmentation algorithms, obtaining comprehensive contextual information is crucial. While many solutions use the Transformer architecture for global information, its computational complexity requ...
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作者：
Arthan, SudaratTamee, KreangsakNaresuan University
Department of Computer Science and Information Technology Phitsanulok Thailand Naresuan University Research
Center for Academic Excellence in Nonlinear Analysis and Optimization Naresuan University Department of Computer Science and Information Technology Phitsanulok Thailand
This study presents a solution to the data sparsity issue in POI recommendation systems by introducing a multi-modal user profile approach called Visual-Textual Collaborative Filtering (VTCF). VTCF integrates textual ...
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In this paper, a LVDS parallel data re-calibration circuit for network interconnection on multi-chip is proposed. The operation states of the re-calibration circuit can be divided into four states: normal, empty, cali...
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This paper presents a multi-agent hierarchical workflow tailored for automating data analysis, code generation, and visualization, focusing specifically on user-provided CSV datasets. The workflow integrates AlphaCodi...
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High dropout rates are a common challenge in higher education, particularly in computerscience degree programs. These dropouts impede our ability to produce a greater number of graduates in this highly demanded field...
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ISBN:
(纸本)9783031533815;9783031533822
High dropout rates are a common challenge in higher education, particularly in computerscience degree programs. These dropouts impede our ability to produce a greater number of graduates in this highly demanded field due to the limitation of the capacity of students enrolled in the first semester. Identifying the reasons behind dropout is a complex task, as the individuals involved are often inaccessible or unwilling to reflect on their lack of success. This paper aims to investigate the factors contributing to dropout rates in computerscience degree programs. Through a comprehensive literature survey and qualitative interviews, we have identified the underlying causes of dropout in our computerscience bachelor's degree program. Our findings reveal a range of reasons, with time constraints and misaligned expectations of the degree program emerging as the most frequently mentioned factors in our interviews. Based on these insights, we present several recommendations aimed at reducing the dropout rate.
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,
作者：
Mbuli, NhlanhlaCollege of Science
Engineering and Technology Florida Campus University of South Africa Department of Electrical Engineering Roodepoort1709 South Africa
The compare projects that are characterized by many attributes, with these attributes either qualitative or quantitative, and these attributes potentially conflicting, may be achieved by utilizing multi-criteria decis...
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This research uses real-world images with complicated disturbance information (the backdrop was identical to the apples' surfaces) to identify and categorize apple quality. This research introduces a new model for...
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