Language models excel in linguistic processing but often face challenges with complex reasoning tasks that require real-world interaction and multi-step logic. This paper presents the Cognitive Adaptive Reasoning Arch...
详细信息
ISBN:
(数字)9798331517878
ISBN:
(纸本)9798331517885
Language models excel in linguistic processing but often face challenges with complex reasoning tasks that require real-world interaction and multi-step logic. This paper presents the Cognitive Adaptive Reasoning Architecture (CARA), a framework that enhances reasoning by integrating a modular swarm architecture with knowledge graphs (KGs) and specialized agents. These agents, equipped with diverse tools, dynamically adapt to various tasks, actively engaging with real-world data. CARA's continuous memory update cycle ensures each reasoning step is based on up-to-date, contextual KG knowledge, enhancing decision-making. Incorporating human insights further aligns outcomes with nuanced cognition. Experimental results demonstrate CARA's superior performance over larger models, advancing language models' reasoning capabilities for more context-aware, informed decision-making in dynamic environments.
Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aer...
详细信息
Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retriev...
详细信息
ISBN:
(数字)9798350364699
ISBN:
(纸本)9798350364705
Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment.
Clinical notes containing valuable patient information are written by different health care providers with various scientific levels and writing styles. It might be helpful for clinicians and researchers to understand...
详细信息
Transfer learning is a powerful technique for image classification, especially when dealing with limited data. However, selection of the best transfer learning approach and model remains challenging, since the strateg...
详细信息
ISBN:
(数字)9798331529048
ISBN:
(纸本)9798331529055
Transfer learning is a powerful technique for image classification, especially when dealing with limited data. However, selection of the best transfer learning approach and model remains challenging, since the strategy is highly influenced by the scenario. This work conducted a thorough analysis of four transfer learning approaches for binary classification, and multiclass classification using four pretrained models i.e. MobileNetV2, MobileNetV3Large, EfficientNetB0, and EfficientNetB5. The test accuracy was measured with respect to the sample size considering the effects of data augmentation and fine tuning. The key finding of the study revealed that data augmentation has improved the test accuracy by up to $11.8 \%$. Moreover, the lightweight models marked a breakthrough by outperforming large scale models’ accuracy for sparse data settings when fine tuning (FT) and data augmentation (DA) are applied appropriately. Hence, this work provides a comprehensive overview on selecting different transfer learning approaches for sparse data classification by exploring diverse models and datasets, while discussing the consequences and challenges for future real-world applications.
In the field of crowd counting research, many recent deep learning based methods have demonstrated robust capabilities for accurately estimating crowd sizes. However, the enhancement in their performance often arises ...
详细信息
We report synthetic frequency dimension Su-Schrieffer-Heeger model and its band structure observation using coupled ring cavities on an integrated photonic chip. Intra-cell and inter-cell couplings between hybridized ...
详细信息
Ranong and Chumphon provinces in Thailand pos-sess significant tourism potential due to their natural beauty and archaeological heritage. However, tourism development in the region remains limited due to inadequate in...
详细信息
ISBN:
(数字)9798350381764
ISBN:
(纸本)9798350381771
Ranong and Chumphon provinces in Thailand pos-sess significant tourism potential due to their natural beauty and archaeological heritage. However, tourism development in the region remains limited due to inadequate information infrastructure and limited technology integration. This research presents a chatbot designed to promote tourism in these locations. The chatbot leverages a graph database built using data from the University to Tambon (U2T) project. Functioning as a virtual travel companion, it provides information on tourist attractions, accommodations, and dining options. Advanced natural language processing and machine learning enable the chatbot to under-stand user inquiries and effectively retrieve relevant information. Evaluation demonstrates strong performance with an F1-score of 99.56 for intent classification and 99.96 for named entity recognition. Testing with user-designed test cases confirmed the chatbot's functionality.
Plant diseases directly affect farm output by lowering grain, fruit, and vegetable quality, threatening global food security. Thus, farmers inspect plant leaves with their eyes. Plant leaf monitoring is unreliable and...
详细信息
ISBN:
(数字)9798331515683
ISBN:
(纸本)9798331515690
Plant diseases directly affect farm output by lowering grain, fruit, and vegetable quality, threatening global food security. Thus, farmers inspect plant leaves with their eyes. Plant leaf monitoring is unreliable and error-prone. A multitude of deep learning algorithms have been developed to detect plant leaf diseases; however, most rely on low-resolution photos utilizing convolutional neural nets (CNNs). This research seeks to create an improved nine-layer the CNN network model for the accurate classification of leaf-related illnesses. A contrast enhancement approach is used to pre-process plant leaf pictures. Following binary thresholding, the pre-processed pictures are separated into leaf images and abnormality segmented using the "Enhanced U-Net (EU-Net)" approach. A Multilevel Feature Fusion Network Convolutional Neural Network (MFFN-CNN) is employed to categorize illnesses of leaves based on segmented images. The "Hybrid Leader Cat Swarm Optimization(HLCSO)" method improves U-Net parameter optimization. The effectiveness of the leaf diagnostic model is demonstrated experimentally employing a variety of factors through the use of numerous baseline approaches.
Cloud computing is currently a popular research topic among academics. It is an internet-based resource pool with a wide range of resources. The cloud environment is extremely dependable in terms of making resources a...
详细信息
暂无评论