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Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization

IEEE Access 2025年 13卷 72375-72388页

作者： Aldhahri, Eman Ali Almazroi, Abdulwahab Ali Alkinani, Monagi Hassan Ayub, Nasir Alghamdi, Elham Abdullah Janbi, Nourah Fahad University of Jeddah Collage of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah21959 Saudi Arabia University of Jeddah College of Computing and Information Technology at Khulais Department of Information Technology Jeddah21959 Saudi Arabia Air University Islamabad Department of Creative Technologies Islamabad44000 Pakistan

Optimal agricultural methods need precise crop health and ecological strain monitoring. This study proposes a novel data science strategy to improve crop health prediction and stress assessment. ResXceNet-HBA is a cutting-edge classification model that uses ResNet blocks, Xception modules with Adaptive Depthwise Separable Convolutions, and HBA-optimized parameters. This model uses HBA's Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. Our system also employs Adaptive Synthetic Resampling with Environmental Context. Using novel measures including the Crop Type Generalisation Score (CTGS) and Environmental Sensitivity Index (ESI), the ResXceNet-HBA model achieved 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-Score. These results beat ResNet, CNN, and Inception V2. The model executed in 50.9 seconds, faster than the alternatives. The confusion matrix exhibits minimal false positives and negatives, suggesting good prediction accuracy. ResXceNet-HBA's statistics and resource optimization value increases. Precision farming and sustainable agriculture benefit from our strategy's significant environmental stress and crop health assessments. © 2013 IEEE.

关键词： Monitoring Crops Temperature measurement Adaptation models Temperature sensors Plants (biology) Accuracy Predictive models Urban agriculture Deep learning

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Enhanced Click Fraud Detection in Digital Advertising Through Ensemble Deep Learning 14th

Enhanced Click Fraud Detection in Digital Advertising Throug...

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14th International Conference on Frontier Computing, FC 2024

作者： Batool, Amreen Kim, Jisoo Byun, Yung-Cheol Department of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of Collage of Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

As digital marketing becomes more and more crucial in today’s business strategies, the issue of click fraud. Which aims to deplete advertising budgets through activities. Has grown, leading to the need, for defenses. This research introduces a blend of Convolutional Neural Networks (CNN) Bidirectional Long Short Term Memory networks (BiLSTM) and Random Forest (RF) classifiers to effectively combat click fraud. This innovative model combines the power of learning for extracting features with RFs classification capabilities. By incorporating CNN and BiLSTM our method improves the identification of spatial features from data, which are then classified using an RF model. This ensemble approach is designed to address shortcomings in existing models that depend mainly on machine learning methods and often demand extensive feature manipulation. We showcase the efficiency of our model through testing on a dataset, achieving an impressive accuracy rate of 99.19% surpassing previous techniques. Additionally, we evaluate metrics such as precision, recall, and F1 score all highlighting the superiority of our approach. This research does not only push forward the tools for detecting click fraud but also offers a scalable framework for potential adaptation and implementation, in real world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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EEG-Based Age and Gender Prediction Using Deep Neural Network

EEG-Based Age and Gender Prediction Using Deep Neural Networ...

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Computing and information technology (ICCIT-1441), 2020 International Conference on

作者： Kaltham Alshayeb Turky Alotaiby Walaa Alsumari Gaseb Alotibi Waleed Almutairi Dept. Collage of Computer Science and Engineering University of Jeddah Saudi Arabia King Abdulaziz City for Science and Technology KACST Saudi Arabia Dept. Collage of Computer Science and Information Technology University of Tabuk Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Brain-computer interfaces (BCIs) have witnessed remarkable progress and are finding applications in diverse domains. This research paper introduces a novel application of BCI technology, focusing on the prediction of age and gender via EEG analysis using Deep CNN and ResNet models. A widely adopted EEG recording device was employed to capture cerebral activities from 64 subjects, encompassing both male and female participants, in a comfortable situation with eyes closed and eyes opened. The CNN model achieved an accuracy of 99.8% for age classification and 99.93% for gender classification. On the other hand, the ResNet model attained an accuracy of 99.05% for age and 99.32% for gender classification. Furthermore, our analysis indicates that utilizing all channels of the EEG signals yields superior performance in predicting age and gendercompared to brain regions.

关键词： Biometrics Hands Deep learning Accuracy Focusing Predictive models Brain modeling Electroencephalography Recording information technology

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Tourist Attraction Recommendation Using a Word2Vec-Based Approach Enhanced by XGBoost Classifier 14th

Tourist Attraction Recommendation Using a Word2Vec-Based App...

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14th International Conference on Frontier Computing, FC 2024

作者： Ko, Jiyoung Kim, Jisoo Byun, Yung-Cheol Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Collage of Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

Recently, online recommendation systems that recommend travel products or services have been widely used. Many companies are working to develop effective recommendation systems to improve customer interaction and gain competitiveness. This paper is a study on the development of a tourist attraction recommendation system in Jeju Island using embedding based on the Word2Vec algorithm. In this study, we propose a method to predict the place to be visited next by considering the places the traveler has visited as words and the entire itinerary as a sentence. We propose a study that recommends nearby tourist destinations by applying the Word2Vec algorithm to tourists’ travel data and then learning the pattern by Xgboost Classifier model. Based on the results predicted through a machine learning model, nearby vectors are found and the five optimal tourist destinations are recommended. It was confirmed that the proposed method has higher accuracy when the mouth bedding vector has a 4-dimensional dimension than when it is 1-dimensional. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Leveraging Data Augmentation to Improve Deep Transfer Learning Models for Diabetic Retinopathy Severity Classification 14th

Leveraging Data Augmentation to Improve Deep Transfer Learni...

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14th International Conference on Frontier Computing, FC 2024

作者： Nazir, Kinza Kim, Jisoo Byun, Yung-Cheol Department of Electronic Engineering Jeju National University Jeju63243 Korea Republic of Collage of Engineering Jeju National University Jeju Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

Diabetic Retinopathy (DR) has become one of the major reasons for vision loss worldwide. It is a retinal complication resulting from diabetes. DR can lead to ultimate blindness if not detected in the early stages. Since the symptoms don’t show till the late stages, it is very important to detect the disease early on to prevent vision loss. Ophthalmologists use fundus images to find internal scars or lesions. For the past few years, machine learning specifically deep learning has played an important role in the automation of medical screening. One of the limitations of using machine learning for medical imaging is a shortage of large medical datasets. Data augmentation plays a vital role in avoiding overfitting problems. This study used six pre-trained models MobileNet, DenseNet201, InceptionV3, VGG16, Xception, and ResNet50 with suitable data augmentation techniques and compared the results. The results showed that the deep learning models trained on augmented DR images performed better compared to the models without prior augmentation. Inception-V3 in particular trained on an augmented dataset achieved the highest 96.02% validation accuracy and 98.06% AUC hence out-performed other pre-trained models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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VLSI Design of LSTM-Based ECG Classification for Continuous Cardiac Monitoring on Wearable Devices

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Electronics Letters 2025年第1期61卷

作者： Akhtar, Nousheen Fan, Jiancun Buzdar, Abdul Rehman Ahmed, Muaz Raza, Ali School of Information and Communications Engineering Xi'an Jiaotong University Xi'an China Department of Computer Engineering National University of Technology Islamabad Pakistan Collage of Science and Engineering Hamad Bin Khalifa University Doha Qatar

A portable and efficient electrocardiogram (ECG) classification system is essential for continuous cardiac monitoring in wearable healthcare devices. This paper presents a highly efficient very large scale integration architecture optimized for real-time ECG classification. The proposed system integrates a feature extraction module that utilizes a four-level daubechies discret wavelet transform and a classification module comprising multiple long-short-term memory recurrent neural networks, fully connected layers, and a multilayer perceptron. The design achieves a classification accuracy of (Formula presented.). The hardware architecture demonstrates low resource utilization and operates at a power consumption of 41 mW with a clock frequency of 54 MHz, ensuring real-time classification. The presented design is verified on a Xilinx field-programmable gate array and tested using the publicly available ECG data set. Compared to state-of-the-art implementations, our approach achieves a superior balance between classification accuracy, power efficiency, and hardware resource optimization, making it suitable for wearable cardiac monitoring applications. © 2025 The Author(s). Electronics Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Recurrent neural networks

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EmoSyn: Adaptive Emotion Framework for Sentiment Analysis and Internet of Medical Things

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Awan, Kamran Ahmad Alqahtani, Abdullah M. Cengiz, Korhan Alshammari, Alya Alrashdi, Ibrahim The University of Haripur Faculty of Information Technology and Numerical Sciences Department of Information Technology Khyber Pakhtunkhwa Pakistan Jazan University Department of Electrical and Electronic Engineering Collage of Engineering and Computer Science Saudi Arabia Prince Mohammad Bin Fahd University Department of Electrical Engineering Al Khobar31952 Saudi Arabia Princess Nourah bint Abdulrahman University Department of Applied Linguistics College of Languages Riyadh11671 Saudi Arabia Jouf University Department of Computer Science College of Computer and Information Sciences Al-Jouf Sakaka73211 Saudi Arabia

Sentiment analysis, or more specifically, the integration of IoMT into healthcare systems, requires frameworks that must adapt at runtime with high precision. Most existing methods have several limitations of either latency or accuracy issues, and therefore perform less effectively in dynamic scenarios. This study aims to address these challenges by proposing EmoSyn, a novel framework that incorporates Emotion Wave Modulation (EWM), Neuro-Cognitive Language Dynamics (NCLD), and the Sentient IoMT Interaction Protocol (SIP). EWM generates dynamic emotional waveforms using high-dimensional feature vectors and kernelized mappings, while NCLD employs synthetic neural mappings and adaptive linguistic modeling to capture semantic transitions. SIP facilitates real-time IoMT recalibration through bidirectional sentiment-driven feedback. Implemented using mathematical frameworks and a custom Emotion-Aware Predictive Synthesis Algorithm (EAPSA), EmoSyn ensures precise sentiment interpretation and efficient IoMT interactions. The framework was evaluated using MOSEI and MIMIC-III datasets in a Python-based simulation environment. The results showed EmoSyn achieving 91% precision for MOSEI and 86% for MIMIC-III, with average latencies of 29 ms and 27 ms. © 2013 IEEE.

关键词： Semantics

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Time Series Facebook Prophet Model and Python for COVID-19 Outbreak Prediction

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computers, Materials & Continua 2021年第6期67卷 3781-3793页

作者： Mashael Khayyat Kaouther Laabidi Nada Almalki Maysoon Al-zahrani Department of Information Systems and Technology Collage of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia Department of Computer and Network Engineering Collage of Computer Science and EngineeringUniversity of JeddahJeddah23218Saudi Arabia and University of TunisElmanarTunisia

COVID-19 comes from a large family of viruses identied in 1965;to date,seven groups have been recorded which have been found to affect *** the healthcare industry,there is much evidence that Al or machine learning algorithms can provide effective models that solve problems in order to predict conrmed cases,recovered cases,and *** researchers and scientists in the eld of machine learning are also involved in solving this dilemma,seeking to understand the patterns and characteristics of virus attacks,so scientists may make the right decisions and take specic ***,many models have been considered to predict the Coronavirus outbreak,such as the retro prediction model,pandemic Kaplan’s model,and the neural forecasting *** research has used the time series-dependent face book prophet model for COVID-19 prediction in India’s various ***,we proposed a prediction and analysis model to predict COVID-19 in Saudi *** time series dependent face book prophet model is used to t the data and provide future *** study aimed to determine the pandemic prediction of COVID-19 in Saudi Arabia,using the Time Series Analysis to observe and predict the coronavirus pandemic’s spread daily or *** found that the proposed model has a low ability to forecast the recovered cases of the COVID-19 *** contrast,the proposed model of death cases has a high ability to forecast the COVID-19 ***,obtaining more data could empower the model for further validation.

关键词： COVID-19 time series analysis prediction face book prophet model python

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Context-Based Visual Sentiment Analysis for Social Media Data

Context-Based Visual Sentiment Analysis for Social Media Dat...

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2022 International Conference on Artificial Intelligence of Things, ICAIoT 2022

作者： Mohammed, Dunya Jasim Aleqabie, Hiba J. University of Kerbala Collage of Computer Science and Information Technology Computer Science Department Karbal Iraq

ISBN: (纸本)9798350396768

Social networking sites have recently grown in importance and popularity, so the field of textual sentiment analysis has emerged and attracted a great deal of research interest, additionally, sentiment analysis in images is still in its infancy and little research has been conducted in this area;listing text or visual content alone is insufficient to convey And the opposite of the feelings of the published content;therefore, it was proposed to analyze the visual feelings based on the content of the image. In this paper, a system was proposed to determine the polarity of posts and tweets on social networking sites using textual analysis and visual analysis. A system or model was proposed that integrates these different properties using a proposed neural network (DVSF) that integrates the text model and the visual model to provide a final decision to indicate the polarity of these posts. Twitter (MVSA) and Flickr(EmotionROI) datasets were utilized, and the results were encouraging overall. © 2022 IEEE.

关键词： Sentiment analysis

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