Intelligent Systems with Applications最新文献

Consumers are increasingly demanding high-quality food, which presents significant challenges for agricultural supply chains. While the majority of research in the agri-food sector has concentrated on optimizing logistics costs and meeting demand by focusing on minimizing the last mile, the complexity of the first mile in the agricultural supply chain has been less explored. Farmers must efficiently manage the harvesting process and the transportation of harvested produce to consolidation centers to ensure the delivery of high-quality products. This paper addresses this research gap by introducing a mixed-integer programming model that leverages vehicle routing problem concepts to optimize the logistics processes involved in transporting harvested products from various fields to a central depot. The primary objective is to minimize total logistics costs associated with visiting different fields during a pick-up round using multiple vehicles. The model has been applied to a case study involving an agricultural cooperative in Greece as part of the European BBTWINS project, which aims to enhance agri-food value chain digitalization for improved resource efficiency. The results demonstrate that organizing the first mile of the agri-food supply chain with a cooled vehicle for pick-up rounds can reduce logistics costs by up to 40% compared to the current practices.

Conventional machine learning techniques in diagnosing cardiovascular disease have a limitation owing to the lack of interpretability of models. This study utilised an explainable machine learning approach to predict the likelihood of having CVD. Four machine learning models were employed for CVD diagnosis: Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme Gradient Boost (XGB). Shapley Additive Explanations (SHAP) were used to provide reasoning for the models' predictions. Using these models and explanations, a user interface was developed to assist in diagnosing CVD. All four classification models demonstrated good accuracy in diagnosing CVD, with the KNN model showcasing the best performance (Accuracy: 71 %). SHAP provided the reasoning behind KNN predictions, and the predictive interface was developed by embedding these explanations to provide transparency behind the model's decisions.

In deterministic mobile edge computing (MEC) networks, accurately predicting latency is critical for optimizing resource allocation and enhancing quality of service (QoS). This paper introduces a novel approach leveraging attention mechanism enhanced long short-term memory (LSTM) networks to predict latency in MEC networks. The proposed model integrates attention mechanisms into LSTM networks to capture temporal dependency and emphasize relevant features in the input data, thereby improving the prediction accuracy. T extensive experiments are conducted by using practical MEC network data, demonstrating that the proposed approach significantly outperforms traditional LSTM and other baseline models in terms of prediction accuracy and computational efficiency. Additionally, we analyze the impact of various configurations in the attention mechanism and LSTM on the model performance, providing insights into the optimal settings. The findings of this study contribute to the advancement of latency prediction techniques in deterministic MEC networks, facilitating more efficient and reliable network management.

The advent of the Internet of Things (IoT) has revolutionized the field of intelligent system development by providing an extensive amount of data from IoT devices, essential for the management of these systems and the creation of innovative services. This data covers various aspects, including creation at the physical layer, transmission through the network layer, and processing within the application layer. This study presents a groundbreaking approach to amalgamating multi-source and varied data within intelligent systems leveraging IoT technology. Our approach seeks to optimize the integration of diverse datasets by examining the correlations between different data types using a novel mixed information gain strategy, leading to effective data fusion. It capitalizes on the computational and storage capacities of systems for seamless integration and augments the analysis of information, thereby improving the useability of data in intelligent systems. Simulation tests confirm the superiority of our method, demonstrating a remarkable improvement in performance in the fusion of dynamic, multi-source heterogeneous data compared to conventional techniques.

Smart grid is a transformative advancement that modernized the traditional power system for effective electricity management, and involves optimized energy distribution by load forecasting. Precise load forecasting provides the best utilization of energy resources and increases sustainability. Dynamic changes of several connected factors, such as temporal and geographical variability, pose challenges to accurate load prediction. Integrating Artificial Intelligence (AI) in the smart grid can enhance the performance of the forecasting process by capturing these changes. This study investigated load forecasting tasks on four different datasets. Several preprocessing and augmentation techniques are applied to increase the data quality. An attention-based 1D-CNN-GRU model is proposed to capture the temporal patterns from the time-series data, and the hyperparameters of the model are optimized using a particle swarm optimization (PSO) algorithm that also accelerates the convergence and results in an efficient training session. Empirical evaluations highlight that the proposed model substantially minimizes the loss, reflecting the ability to make accurate predictions. It obtains MAE values of 0.12, 0.8, 16.48, and 82.64 for the four datasets. Moreover, the explainable AI (XAI) technique is applied using Shapley Additive explanations (SHAP) to interpret the model prediction, providing the feature ranking based on their prediction score. Moreover, this study utilizes federated learning, enables collaborative training, maintains the privacy of the grid data, and secures the process comprehensively. The aggregation mechanism in federated learning is modified using pruning-based methods that reduce the parameters and computational cost, resulting in a more efficient framework. Integrating all these approaches provides valuable insights for developing a load forecasting model and outlines potential contributions in the smart grid domain.

Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.

The current study examines the factors influencing investor opinions on issues related to listed firms during the first day of Initial Public Offerings (IPOs), focusing on a sample of 350 fixed-priced IPOs listed on the Malaysian stock exchange (Bursa Malaysia) from 2004 to 2021. This research contributes to existing literature by employing various machine learning methods, which address the limitations of traditional linear regression models commonly used in previous studies. Specifically, five methods—extra tree regressor (ETR), single feature selection (SFS), reverse single feature (RSF), recursive feature elimination (RFE), and sequential modelling feature adding (SMFA)—are utilized to assess the importance of features in predicting the investor opinion gap within the dataset.

The study's experiments indicate that these methods effectively mitigate noisy data, enhancing their reliability for this type of analysis. The findings provide valuable insights for regulators regarding safeguarding investors' rights to information disclosed in prospectuses.

Power forecasting plays a crucial role in the operation of smart grid system, which is indispensable for making the operation plan of power system, improving economic efficiency and ensuring the quality of power supply. In order to enhance the accuracy of power load forecasting, a hybrid intelligent power load forecasting system is proposed in this paper. The system first preprocesses the raw data using Savitzky-Golay smoothing technology to eliminate noise and improve data quality. Then, a long and short term memory network with attention mechanism is used to enhance the generalization ability of the model. In addition, in order to further improve the prediction performance, an improved genetic algorithm is integrated to optimize the model parameters. Finally, a data set is used to verify the proposed prediction method. In terms of short-term forecasting ability of experiment of the testing data set, compared with LSTM model, the proposed method shows superior performance in root mean square error and mean absolute error indicators, with root mean square error reduced by 18.7 % and mean absolute error reduced by 26.2 %. In terms of long-term prediction ability of experiment of the testing data set, compared with GBRT model, the proposed method reduces root mean square error and mean absolute error by 24.8 % and 30.7 %, respectively. The experimental results show that compared with the existing benchmark algorithm, the proposed method is significantly improved in two key indexes of prediction accuracy, which proves its effectiveness and superiority in power load prediction.

In today’s information-driven world, open data is crucial in making valuable structured data freely accessible to the public. However, the absence of quality metadata often hinders the findability and representation of this data. In this study we specifically focus on keywords, proposing a strategy for their automatic generation. In particular, we employed five existing keyword extraction methodologies (BERT, RAKE, YAKE, TEXTRANK, and ChatGPT) and proposed a novel hybrid methodology, named BRYT (read as bright). Our evaluation of these algorithms was conducted using Gestalt String Matching and Jaccard Similarity techniques. We validated our study using a selection of datasets from the EU data portal, specifically choosing those that exhibited potentially high-quality metadata. This included datasets that contained a substantial number of keywords and had comprehensive, relevant metadata. The results showed that 69.1% of the dataset keywords majorly matched (more than 50% or 5 keywords), 24.7% minorly matched (up to 50% or 5 keywords), and 6.2% did not match. The proposed hybrid model, BRYT, outperformed other algorithms in the major matches, while ChatGPT was a close second. YAKE outperformed the others in minor matches, and ChatGPT was again a close second. The evaluations concluded that BRYT consistently extracted more representative keywords in major matches, highlighting its effectiveness in improving findability. This study sets up a favorable field for further representative metadata extraction and population, making the data more findable, discoverable, and accessible.

Given the Amharic language has a lot of perplexing terminology since it features duplicate homophone letters, fidel's ሀ, ሐ, and ኀ (three of which are pronounced as HA), ሠ and ሰ (both pronounced as SE), አ and ዐ (both pronounced as AE), and ጸ and ፀ (both pronounced as TSE). The WSD (Word Sense Disambiguation) model, which tackles the issue of lexical ambiguity in the context of the Amharic language, is developed using a deep learning technique. Due to the unavailability of the Amharic wordnet, a total of 1756 examples of paired Amharic ambiguous homophonic words were collected. These words were ድህነት(dhnet) and ድኅነት(dhnet), ምሁር(m'hur) and ምሑር(m'hur), በአል(be'al) and በዢል(be'al), አቢይ (abiy) and ዐቢይ(abiy), with a total of 1756 examples. Following word preprocessing, word2vec, fasttext, Term Frequency-Inverse Document Frequency (TFIDF), and bag of words (BoW) were used to vectorize the text. The vectorized text was divided into train and test data. The train data was then analysed using Naive Bayes (NB), K-nearest neighbour (KNN), logistic regression (LG), decision trees (DT), random forests (RF), and random oversampling technique. Bidirectional Gate Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) improved to 99.99 % accuracy even with limited datasets.