Enhancing fundus images is crucial for early diagnosis and monitoring of retinal diseases. Although CNN and Transformer-based methods have made great progress, CNNs struggle with long-range dependencies, and Transform...
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ISBN:
(数字)9798350363203
ISBN:
(纸本)9798350363210
Enhancing fundus images is crucial for early diagnosis and monitoring of retinal diseases. Although CNN and Transformer-based methods have made great progress, CNNs struggle with long-range dependencies, and Transformers face challenges with large model sizes and low computational efficiency. To address these issues, Selective Structured State-Space Models (SSMs) offer an efficient solution with linear complexity. However, standard Mamba models still suffer from local pixel information loss and channel redundancy, limiting their performance in fundus image enhancement. We propose MambaFE, which incorporates localized refinement and an advanced channel attention mechanism to resolve challenges such as pixel detail loss and excessive channel information. Considering that retinal structures play a vital role in fundus analysis, retaining these structures during the enhancement process is crucial. As retinal structures are predominantly located within the high-frequency components of fundus images, we introduce a frequency-guided approach to capture reliable representations of these features, ensuring improved retention of essential information during enhancement. Comprehensive experimental findings indicate that the proposed MambaFE outperforms contemporary fundus image enhancement techniques, significantly enhancing the quality of clinically low-quality and synthetically degraded fundus images, while offering robust support for real-world medical applications.
Recent evidence shows that high-intensity exercises reduce tremors and stiffness in Parkinson's disease (PD). However, there is insufficient evidence on the types of exercises; in effect, high-intensity may be a p...
Recent evidence shows that high-intensity exercises reduce tremors and stiffness in Parkinson's disease (PD). However, there is insufficient evidence on the types of exercises; in effect, high-intensity may be a personalized measure. Recent progress in automated Human Activity Recognition using machine learning (ML) models shows potential for better monitoring of PD patients. However, ML models must be calibrated to ignore tremors and accurately identify activity and its intensity. We report findings from a study where we trained ML models using data from medically validated triple synchronous sensors connected to 8 non-PD subjects performing 32 exercises. We then tested the models to identify exercises performed by 8 PD patients at different stages of the disease. Our analysis shows that better data preprocessing before modeling can provide some model generalizability. However, it is extremely challenging, as the models work with high accuracy on one group (Healthy or PD patients) (F1=0.88-0.94) but not on both *** relevance-Patients with Parkinson's and other motor-generative diseases can now accurately measure physical activity with machine learning approaches. Clinicians, caregivers, and apps can make accurate, personalized exercise recommendations to augment medications that reduce tremors and stiffness.
Finding relevant information in a vast and growing amount of data has become significant since the arrival of the internet. An Information Retrieval System is described as searching and retrieving a list of documents,...
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ISBN:
(数字)9798350363203
ISBN:
(纸本)9798350363210
Finding relevant information in a vast and growing amount of data has become significant since the arrival of the internet. An Information Retrieval System is described as searching and retrieving a list of documents, such as web pages or other items, in response to a user query. There are many weighting schemes, such as Boolean, Term Frequency or TF, and Term-Frequency-Inverse-Document-Frequency or TFIDF. In Boolean, a ‘1’ indicates the presence of a term in the document, whereas a ‘0’ indicates its absence. In TF, the term is represented by the number of occurrences in a document. TFIDF is the most popular one. It combines TF with an inverse document frequency IDF; IDF gives less weight to terms that frequently occur in many documents. With reference to the information retrieval system literature, TFIDF gives more accurate results on the computationally expensive expenses. Term Frequency is less expensive than TFIDF, and the least expensive one is the Boolean weighting scheme. In this paper, we investigated the effect of different weighting schemes and found out that, in many cases, Boolean and TF performed the same as TFIDF and, in a few cases, outperformed them. The experiments in this paper were conducted on many datasets of variant sizes and types. The dataset documents were indexed using TFIDF, TF, and Boolean. Then, using the queries that come with the dataset, we computed the precision and recall by comparing the results of different weighting schemes; the queries were also indexed using TFIDF, TF, and Boolean weighting schemes. The objective is to show that the use of the Boolean or TF weighting scheme, which is considered not computationally expensive, instead of TFIDF, which is regarded as very computationally expensive, does not significantly affect the results and, in a few cases, gives better results.
Considerable resources are wasted on projects that deliver few or no benefits. The main objective is to better understand the characteristics of projects that are successful in delivering good client benefits. We aske...
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ISBN:
(纸本)9781728199269
Considerable resources are wasted on projects that deliver few or no benefits. The main objective is to better understand the characteristics of projects that are successful in delivering good client benefits. We asked 71 Norwegian software professionals to report information about projects completed between 2016 and 2018. We found that both benefits management and agile practices have a significant relationship with perceived realisation of client benefits. This includes the benefits management practices of having a plan for benefits realisation, individuals with assigned responsibility for benefits realisation, benefits management during project execution, quantification of realised benefits, evaluation of realised benefits, re-estimation of benefits during project execution, and the agile practices of a flexible scope and frequent deliveries to production. The software projects that were successful in delivering client benefits adopted benefits management and agile practices to a larger extent than the less successful ones. Future studies are required to establish more comprehensive understanding of what distinguishes projects that deliver good client benefits from the rest, including studies of the realisation of client benefits in agile software projects.
The ability to recognize and respond to human emotions has become a crucial component in enhancing humancomputer interaction, with the development of speech emotion recognition technologies playing a pivotal role in t...
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ISBN:
(数字)9798331508180
ISBN:
(纸本)9798331508197
The ability to recognize and respond to human emotions has become a crucial component in enhancing humancomputer interaction, with the development of speech emotion recognition technologies playing a pivotal role in this advancement. While deep learning models have driven significant progress in the field of speech emotion recognition, traditional machine learning algorithms offer a practical alternative, balancing performance with lower computational requirements. This study presents a comprehensive approach to speech emotion recognition using the Berlin Emotional Speech Database, classifying seven emotional states: anger, sadness, anxiety or fear, disgust, boredom, happiness, and neutrality. The study employs a range of acoustic features, including pitch, RMS energy, MFCCs, and formants, which are combined with 14 statistical descriptors and extracted using tools like OpenSMILE. Preprocessing steps, such as normalization, noise reduction, and silence removal, are applied to enhance the quality and reliability of the data. The performance of traditional machine learning models, including Support Vector Machine, Random Forest, and $k$ -Nearest Neighbors, is evaluated on the processed dataset. The results demonstrate the effectiveness of these traditional models, with Support Vector Machine achieving the highest classification accuracy of 90.65 %, followed by Random Forest and $k$ -Nearest Neighbors. The results of this study highlight the capacity of traditional machine learning techniques to effectively capture the complexities of emotional expression, while circumventing the computational burden associated with deep learning models. The practical relevance of this research extends to real-time applications across various domains, including healthcare, virtual assistants, and customer service, where the demand for efficient and reliable emotion recognition systems is paramount.
A Smart village is a concept as a modern global approach for the tourism development community and creative industries. The rapid development of technology makes information technology one of the primary needs in busi...
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ISBN:
(纸本)9798350345728
A Smart village is a concept as a modern global approach for the tourism development community and creative industries. The rapid development of technology makes information technology one of the primary needs in business. Technology utilization in Indonesia is a community-based tourism approach for a tourist village to be a smart village. Technology expects to facilitate collective information from various tourism awareness groups (or ‘POKDARWIS’ in Bahasa Indonesia) and business owners, such as culinary and merchandise businesses that can support the progress of tourist village (or ‘Desa Wisata’ in Bahasa Indonesia) destinations. However, when viewed from the optimization of the implementation of tourist villages, many still need more information about the attractiveness of tourist villages in Indonesia even though the tourist village itself is a form of village development innovation, one of Indonesia's targets of government programs. Moreover, due to this lack of knowledge, many tourists need more information regarding tourist village facilities and services, such as culinary and merchandise. In this paper, we present the development of a web-based tourism management platform that aims to promote culinary and local merchandise through a tourism management platform. This platform makes it easy for tourists or visitors to make online orders, purchases, and payments. Business actors can also manage culinary and merchandise businesses through the system. The prototype system of the tourism management platform develops using the agile method in six phases: concept, inception, iteration, testing, production, and review. The tourism management platform is tested on the User at the soft-launching events and runs as expected.
In practical applications, the acquisition cost of hyperspectral images is high, and acquiring substantial labeled data is difficult, which results in a restricted number of labeled samples using for training. Few-sho...
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ISBN:
(数字)9798350363203
ISBN:
(纸本)9798350363210
In practical applications, the acquisition cost of hyperspectral images is high, and acquiring substantial labeled data is difficult, which results in a restricted number of labeled samples using for training. Few-shot learning (FSL) serves as an efficient method to tackle the classification challenges of hyperspectral images (HSI) when labeled samples are scarce. It acquires transferable knowledge from abundant labeled data, that can be transferred to new domains. However, the distributional differences between domains with abundant labeled data and those with limited labeled data make the generalizable knowledge learned from the former less applicable to the classification tasks of the latter. To address this issue, this paper proposes a cross-domain few-shot hyperspectral image classification method based on label propagation. Firstly, the method employs a feature extraction module to capture complex information within samples, then applies a graph structure to the label propagation algorithm. The graph structure effectively captures local relationships between samples and avoids over-reliance on distant samples, thereby reducing the risk of overfitting. Furthermore, it incorporates a Tri-training structure to learn from different perspectives and complement each other, ultimately achieving classification of cross-domain few-shot data. Experimental results on hyperspectral datasets with Chikusei as the source domain, and Pavia University and Salinas as the target domains demonstrate that our method is effective and has good performance.
The development of 5G technology has led to the rapid construction of the Internet of Vehicles (IOV). The emergences of various IOV applications like autonomous driving require unlimited low-latency computation resour...
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The remarkable performance of Artificial Intelligence (AI) in the diagnosis and prediction of Alzheimer’s disease (AD) has attracted considerable attention in recent years. This study intends to outline the developme...
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ISBN:
(数字)9798350352009
ISBN:
(纸本)9798350352016
The remarkable performance of Artificial Intelligence (AI) in the diagnosis and prediction of Alzheimer’s disease (AD) has attracted considerable attention in recent years. This study intends to outline the development trends and research focus in the AI-Alzheimer field. The VOSviewer and bibliometrix R software package are employed for bibliometrics and visualization analysis of literature obtained from the Web of Science Core Collection database. There are 5,991 affiliations from 104 countries/regions that published 5,675 articles in this field by 2023. Results demonstrate that the number of AIAlzheimer-related publications has experienced slow progress for over twenty years before entering a period of exponential growth in 2016. The United States and China, contributing over 54% of the publications, stand out as leaders in technological innovation and international cooperation. The clustering analysis of keywords indicates that the major research domains are the utilization of machine learning and deep learning algorithms for AD and mild cognitive impairment (MCI) classification, early diagnosis of disease, and drug discovery. Generative adversarial networks (GANs), transfer learning, and Transformers are the emerging AI algorithms applied in the field of Alzheimer’s research, which also represent promising directions for future investigation. The findings provide a comprehensive summary of the AI-Alzheimer field and identify research frontiers, offering valuable references for scholars.
Peer assessment is a type of assessment that allows students in taking their initiative to manage their study under the supervision of their peers to achieve the learning outcome. Students constantly need to reflect o...
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