Many factors shape agricultural economic behaviour, but large datasets on crop yields, market price volatilities and climate change — perhaps too much data, in fact — are the most prominent In this regard, the devel...
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ISBN:
(纸本)9798400712425
Many factors shape agricultural economic behaviour, but large datasets on crop yields, market price volatilities and climate change — perhaps too much data, in fact — are the most prominent In this regard, the development of models that accurately identify and predict agricultural economic behaviour, is quite topical. In this paper, a patternrecognition method for agricultural economic behaviour based on Long Short-Term Memory Network (LSTM) is presented, its performance is improved through multiple optimisation methods. In prior work, LSTM networks have demonstrated an outstanding performance in processing time series data, making them appropriate to capture dynamic features such as seasonal variations, cyclical patterns, and market trends in agricultural economic activities. Finally, to improve the model performance, this paper combines data preprocessing methods, Dropout regularization technology, and PSO algorithm for hyperparameter tuning. Such optimising features have proven effectively increasing model capacity to find latent-augmented patterns in agriculture econ space and provide more robust and accurate prediction results. Experimental results show that the model proposed in this paper achieves a significant accuracy improvement over traditional methods for agricultural economic forecasting tasks.
the proceedings contain 98 papers. the topics discussed include: self-supervised learning for point clouds through multi-crop mutual prediction;assessment and prediction of corrosion rate of marine railway bridges bas...
ISBN:
(纸本)9798350324303
the proceedings contain 98 papers. the topics discussed include: self-supervised learning for point clouds through multi-crop mutual prediction;assessment and prediction of corrosion rate of marine railway bridges based on ridge regression model;Chunk-BERT: boosted keyword extraction for long scientific literature via BERT with chunking capabilities;chunking of Setswana noun and verb phrase;RarKGQA: multi-hop question and answering method based on knowledge graph embedding;diagnosis and identification of citrus canker growth rate using machine learning;joint spatial similarity-based attention for retina vessel segmentation with super resolution encoding;mixed attention interleaved execution cascade architecture for fittings instance segmentation in overhead transmission line images;and RFBiCF: a relation-first bidirectional cascade framework for relational triple extraction.
Property theft, including vehicle theft, has become a significant concern globally. Technological advancements are providing smart solutions to address issues like vehicle theft. this research aims to enhance indoor l...
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ISBN:
(数字)9798350353839
ISBN:
(纸本)9798350353846
Property theft, including vehicle theft, has become a significant concern globally. Technological advancements are providing smart solutions to address issues like vehicle theft. this research aims to enhance indoor localization by developing a solution for asset tracking. the emergence of micro-mobility vehicles, such as e-scooters and e-bikes, presents an opportunity to promote sustainable urban transportation. However, micro-mobility businesses—direct-to-consumer companies and sharing platforms—face significant challenges in fleet management and theft prevention. the present study proposes a “Bluetooth Low Energy (BLE) beacon” solution to help these businesses protect their fleets, even in worst-case scenarios. the research objectives are twofold: to track stolen micro-mobility vehicles and to trace lost assets accurately, using a long transmission range of up to 500 meters. the proposed approach enables operators to accurately locate stolen scooters through indoor mapping, leveraging the BLE beacon's extended transmission range and a battery life of up to 20 years. Research designs consist of artifacts such as constructs, models, methods, and instantiations created to achieve the goals of scientific inquiry into the design. the artifact represents a comprehensive framework for improving asset tracing in challenging environments.
Advanced machine learning techniques have shown significant promise in predicting student performance in E-Iearning systems, but challenges such as handling imbalanced datasets, integrating multimodal behavioral data,...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
Advanced machine learning techniques have shown significant promise in predicting student performance in E-Iearning systems, but challenges such as handling imbalanced datasets, integrating multimodal behavioral data, and managing the complexities of feature selection persist. Recent techniques, such as SMOTE for class balancing and BorutaNetCV for feature selection, have made strides in improving prediction accuracy; however, they often fail to address real-time cognitive state integration and the influence of diverse engagement patterns. To overcome these limitations, this study employs a comprehensive methodology that combines robust preprocessing methods, advanced feature selection, and ensemble machine learning models such as Random Forest, AdaBoost, and XGBoost. the findings demonstrate that predictive accuracy can be significantly enhanced through these techniques, and the proposed framework enables the development of personalized interventions that can foster improved student engagement and success in dynamic E-learning environments.
Generative steganography is a steganography method that uses a generator to convert secret messages into realistic images. It has received widespread attention due to its ability to resist steganalysis. However, exist...
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ISBN:
(纸本)9798400712425
Generative steganography is a steganography method that uses a generator to convert secret messages into realistic images. It has received widespread attention due to its ability to resist steganalysis. However, existing methods suffer from poor quality of generated stego images and the inability to withstand losses during complex social media transmission processes. In response to these issues, this article proposes a new frequency-domain diffusion generative steganography method that can achieve secure and robust steganography without the need for training or fine-tuning the network. In addition, we also studied the inherent errors in the bidirectional mapping of diffusion models and proposed solutions. the experimental results demonstrate the excellent performance of our method in terms of extraction accuracy, robustness, security, and image quality.
the proceedings contain 106 papers. the topics discussed include: an unmanned aerial vehicle video object tracking algorithm based on Siamese attention network;improved U-Net network for infrared small target detectio...
ISBN:
(纸本)9781450384087
the proceedings contain 106 papers. the topics discussed include: an unmanned aerial vehicle video object tracking algorithm based on Siamese attention network;improved U-Net network for infrared small target detection;an improved long-term anti-masking object tracking algorithm;oriented target detection algorithm based on transformer;long time target tracking algorithm based on multi feature fusion and correlation filtering;weakly supervised object detection for auroral vortex structure in all-sky image;FCOS small target detection algorithm combined with multi-layer hybrid attention mechanism;integrating line weber local descriptor and deep feature for tire indentation mark image classification;and unsupervised feature learning for temporal segmentation of auroral image sequences.
the proceedings contain 17 papers. the topics discussed include: image data augmentation method based on style transfer;FPGA hardware implementation of Q-learning algorithm with low resource consumption;a machine-lear...
ISBN:
(纸本)9781450396080
the proceedings contain 17 papers. the topics discussed include: image data augmentation method based on style transfer;FPGA hardware implementation of Q-learning algorithm with low resource consumption;a machine-learning pipeline for semantic-aware and contexts-rich video description method;multi-scale and kernel-predicting convolutional networks for Monte Carlo denoising;single image dehazing algorithm based on generative adversarial network;biparted hyperboloid and sphere intersection algorithm;optimal sensor deployment in complex 3D terrain surface;weakly supervised road garbage quantification system based on WCCA;online detection of character and 3D surface defect in steel rail production;a credit scoring ensemble framework using Adaboost and multi-layer ensemble classification;and an LSTM-based method for recognition and prediction of aircraft formation.
the proceedings contain 28 papers. the special focus in this conference is on Human Brain and Artificial Intelligence. the topics include: Uncovering Cognitive Taskonomy through Transfer Learning in Masked Autoen...
ISBN:
(纸本)9789819640003
the proceedings contain 28 papers. the special focus in this conference is on Human Brain and Artificial Intelligence. the topics include: Uncovering Cognitive Taskonomy through Transfer Learning in Masked Autoencoder-Based fMRI Reconstruction;Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal patterns;Effect of Music Training in Neural Responses to Emotional Speech Prosody: Insights from EEG and Brain Network Analysis;potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony;Exploring EEG-Based Neural Correlates of Multivariate Ordinal Emotion Representations;the Co-varying Multimodal pattern in Treatment-Resistant and Non-treatment-Resistant Schizophrenia;investigating the Dynamics of Seizure Neuroactivities Using Hidden Markov Model;suppressing Seizure via Optimal Electrical Stimulation to the Hub of Epileptic Brain Network;SVFormer: A Direct Training Spiking Transformer for Efficient Video Action recognition;BL-BERT: Extracting Body Language from Behavior Sequences in Freely Moving Mice;Benchmarking Neural Decoding Backbones Towards Enhanced On-Edge iBCI Applications;Enhanced Local Attention with Deep Neural Networks for EEG Decoding;Mirror Contrastive Loss Based Sliding Window Transformer for Subject-Independent Motor Imagery Based EEG Signal recognition;Active Urination Detection Using EEG Based on FBCNet;D2CAN: Domain-Guided Contrastive Adversarial Network for EEG-Based Cross-Subject Cognitive Workload Decoding;group-Specific Fusion Model and Its Application in Identifying Multimodal Co-varying Diagnostic patterns for Psychiatric Disorders;multi-category Brain Tumor Segmentation via Multi-scale and Cross-category Relation Modeling;consistent Brain Age Difference in Childhood Autism Spectrum Disorder and its Subtypes;Brain-Aware Readout Layers in GNNs: Advancing Alzheimer’s Early Detection and Neuroimaging;TSICNet: Importance of Connectome Information for Epilepsy Classification;a Brain-Inspired Distr
the proceedings contain 33 papers presendted at a virtual meeting. the special focus in this conference is on Recent Trends in Image Processing and patternrecognition. the topics include: Real-Time Face recognition f...
ISBN:
(纸本)9783031070044
the proceedings contain 33 papers presendted at a virtual meeting. the special focus in this conference is on Recent Trends in Image Processing and patternrecognition. the topics include: Real-Time Face recognition for Organisational Attendance Systems;Harnessing Sustainable Development in Image recognitionthrough No-Code AI Applications: A Comparative Analysis;evaluating Performance of Adam Optimization by Proposing Energy Index;an Alignment-Free Fingerprint Template Protection Technique Based on Minutiae Triplets;early Prediction of Complex Business Processes Using Association Rule Based Mining;A Framework for Masked-Image recognition System in COVID-19 Era;A Deep-Learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video;Detection of Male Fertility Using AI-Driven Tools;face Mask Detection Using Deep Hybrid Network Architectures;a Super Feature Transform for Small-Size Image Forgery Detection;UHTelHwCC: A Dataset for Telugu Off-line Handwritten Character recognition;inflectional and Derivational Hybrid Stemmer for Sentiment Analysis: A Case Study with Marathi Tweets;adaptive threshold-Based Database Preparation Method for Handwritten Image Classification;a Graph-Based Holistic recognition of Handwritten Devanagari Words: An Approach Based on Spectral Graph Embedding;Imagined Object recognition Using EEG-Based Neurological Brain Signals;a Fast and Efficient K-Nearest Neighbor Classifier Using a Convex Envelope;single Channel Speech Enhancement Using Masking Based on Sinusoidal Modeling;extraction of Temporal Features on Fibonacci Space for Audio Based Vehicle Classification;an Empirical Study of Vision Transformers for Cervical Precancer Detection;An Improved Technique for Preliminary Diagnosis of COVID-19 via Cough Audio Analysis;agricultural Field Analysis Using Satellite Hyperspectral Data and Autoencoder;Development of NDVI Prediction Model Using Artificial Neural Networks;time Series Forecasting of Soil Moisture Using Sa
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