Review
Computer Science, Artificial Intelligence
M. Sujatha, C. D. Jaidhar
Summary: Agriculture is crucial for many economies, but traditional soil fertility classification and fertilizer application methods are expensive and pollute the environment. This study explores the use of machine learning and deep learning techniques in soil fertility classification and examines their potential to reduce costs and improve soil fertility.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Peng Han, Qingxi Zhang, Wei He, Yuwang Chen, Boying Zhao, Yingmei Li, Guohui Zhou
Summary: This paper introduces a belief rule base (BRB) model for oil pipeline leakage prediction and proposes a double inference engine BRB-DI model. Compared with traditional BRB models, the new model improves the modeling capability and interpretability through rule reduction and the design of a double inference engine.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Mahmoud Hussein, Mohammed Elnahas, Arabi Keshk
Summary: Breast cancer is a serious disease that poses a threat to the lives of women worldwide. Early prediction of its occurrence or recurrence is crucial for improving the cure rate. This paper proposes a framework for improving the prediction of breast cancer recurrence and achieves significant improvements in prediction performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Arun Singh Bhadwal, Kamal Kumar, Neeraj Kumar
Summary: Designing an optimal and desired drug molecule structure is a challenging problem. This paper proposes a method based on normalized reparameterized conditional variational autoencoder and beam search in the latent space to address the challenges. The method can generate drug molecules with desired properties while maintaining diversity in the generation process.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Radhika Malhotra, Barjinder Singh Saini, Savita Gupta
Summary: This study introduces a radiomics-based model for predicting the survival of HGG patients by extracting features from MRI images. The study explores and evaluates the extracted features in depth and proposes a novel feature enhancement strategy to improve data separability.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Niranjan Kumar, Bhagyashri R. Hanji
Summary: This article presents a predictive framework for aspect-based extraction and classification to estimate users' optimal travel destinations. It utilizes key information extraction from reviews and categorizes emotions using Glove word vector representation, along with aspect-based sentiment analysis using Multinomial Logistic Regression and Fuzzy Domain Ontology algorithms. Simulated results and real-world data analysis show that the proposed strategy outperforms in terms of classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Benedetta Tafuri, Giammarco Milella, Marco Filardi, Alessia Giugno, Stefano Zoccolella, Ludovica Tamburrino, Valentina Gnoni, Daniele Urso, Roberto De Blasi, Salvatore Nigro, Giancarlo Logroscino
Summary: This study investigates the usefulness of radiomics analysis on T1-weighted MRI in diagnosing and phenotyping ALS patients. Machine learning algorithms were used to distinguish ALS patients from controls and Classic from non-Classical ALS motor phenotypes with high accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zeqiu Chen, Kaiyi Zhao, Shulin Sun, Jiayao Li, Shufan Wang, Ruizhi Sun
Summary: This study presents an end-to-end deep learning method for novelty detection in multi-view data, which trains multiple deep neural networks and optimizes data-enclosing hyperspheres in each view. The proposed method effectively learns the target class and outperforms state-of-the-art methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jayr Alencar Pereira, Jaylton Alencar Pereira, Cleber Zanchettin, Robson do Nascimento Fidalgo
Summary: This study introduces PrAACT, a transformer-based method that assists individuals with complex communication needs in sentence construction. It utilizes language models to predict communication cards and can easily adapt to user-specific vocabularies. Evaluation shows that PrAACT outperforms pre-trained models for this task. The main advantage of PrAACT is its ability to quickly adapt a language model according to the user's vocabulary.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Esra Sivari, Zafer Civelek, Seda Sahin
Summary: This study successfully achieved automatic, objective and reliable determination of fetal sex using deep transfer learning techniques on ultrasound images, which can serve as an auxiliary system for specialists and patients.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Heng Yao, Tong Liu, Guihao Li, Chuan Qin
Summary: This paper focuses on the forensic problem of screen-shot and demoired image operation. The network designed combines DenseNet and DeepViT structure, generates datasets for training and detecting demoired images by selecting typical demoireing algorithms, and adds the re-attention mechanism to extract global features of the image. The method achieves the best detection results and network performance in comparison experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Mohammad Majid Abedi, Emanuele Sacchi
Summary: This research aims to extract, classify, and study drivers' affective states from road safety-related tweets using keyword filtering, geo-boundaries, natural language processing, and machine-learning classification. The results showed that the trained RF model with count vector, and SVM classifier with word-level TF-IDF performed best in separating road safety-related from unrelated tweets and determining the proposed classification tags.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhaoxun Wang, Yushan Li, Yang Liu, Fanyu Meng
Summary: In this study, Large Kernel Attention (LKA) technology is introduced to achieve accurate and real-time object detection for autonomous vehicles. A new module called Res-VAN is designed to reduce the computational effort of the model while maintaining its accuracy. Experimental results show that the proposed LKA-YOLO model performs well on multiple datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Hui Yu, Jing Wang, Shi-Yu Zhao, Omayo Silver, Zun Liu, Jingtao Yao, Jian-Yu Shi
Summary: Deep learning-based models have limited interpretability in predicting drug-drug interactions (DDIs). We propose a novel approach that uses granular computing to identify key substructures and achieves high accuracy in predicting DDIs.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Pere Marti-Puig, Moises Serra-Serra, Francesca Ribas, Gonzalo Simarro, Miquel Caballeria
Summary: This article presents a new automatic shoreline detection method using a bidirectional LSTM network. The method processes images column by column, which improves its robustness and accuracy. The approach is shown to be effective in different video stations and can also be applied to satellite shoreline detection.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Ruijun Feng, Mingzhou Chen, Yaqi Song
Summary: Short-term traffic flow prediction is of great significance for intelligent transportation systems and traffic network management. Existing methods often use convolutional neural networks to capture spatiotemporal correlations, but face challenges due to complex factors. This paper proposes a hybrid deep learning method that improves prediction accuracy by designing a video-shaped multi-channel data structure and introducing a new mixed-pointwise convolution method.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yanmin Luo, Hongwei Lin, Wenlin Huang, Youjie Wang, Jixiang Du, Jing-Ming Guo
Summary: Human pose estimation in crowded scenes is challenging due to overlap and occlusion. We proposed PFFormer, a new transformer-based approach that treats pose estimation as a hierarchical set prediction problem. PFFormer focuses on human windows and coarsely predicts whole-body poses globally within them. It uses Windows Clustering Transformer and a global transformer to filter out interference from the background and capture inter-part correlation. Experimental results demonstrate the robustness of PFFormer in handling occlusion in crowded scenes.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Huseyin Bakir
Summary: The present study redesigned the exploration operator of the Artificial Rabbits Optimization (ARO) algorithm using fitness-distance balance strategies, resulting in the development of three versions of the Fitness-Distance Balance-based Artificial Rabbits Optimization (FDBARO). Experimental evaluation and analysis on benchmark functions showed that the FDBARO-3 algorithm, designed with dynamic fitness-distance balance selection, outperformed other algorithms in successfully exploring the search space. The proposed dynamic FDBARO (dFDBARO) algorithm demonstrated competitiveness in solving global optimization and constrained Optimal Power Flow (OPF) problems formulated with renewable energy sources and flexible transmission system devices.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Chhavi Dixit, Shashank Mouli Satapathy
Summary: This project aims to develop an efficient real-time multimodal emotion recognition model for analyzing emotion expression in human oration videos. Different models were trained for text, audio, images, and multimodal analysis using separate datasets. The models were tested and combined on the CMU-MOSEI dataset to find the most effective architecture. The proposed architecture achieved high accuracy and F1-score on the CMU-MOSEI dataset.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
