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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Li, Han Men, Chaoguang Liu, Yongmei Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798331527624

Deep learning-based semantic segmentation of airborne laser scanning (ALS) point clouds receives extensive attention from researchers in the field of remote sensing and computing. However, due to the special properties of ALS point cloud data, it makes difficulties in the semantic segmentation task. Therefore, a new global geometric attention network (GGAN) for ALS point cloud data semantic segmentation is proposed in this paper. Firstly, a local geometric attention convolution module is proposed to embed local geometric feature information. Then, a global relationship aware attention module was inserted into the network to improve the global perception of the geometric information. The experimental results of semantic segmentation show that the GGPN method can achieve satisfactory classification results. Semantic segmentation experiments are conducted on the ISPRS benchmark point cloud dataset to evaluate the performance of the GGAN network, and the method in this paper achieves an overall accuracy of 81.6 and the average F1 of 71.4 which is better than most of the existing popular methods. © 2024 IEEE.

关键词： Semantic Segmentation

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Navigating Spatial Insights: Comparative Exploration of Deep Learning Algorithms for Flood Detection using Remote Sensing Data 2

Navigating Spatial Insights: Comparative Exploration of Deep...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Mittal, Bhavuk Vanzara, Jui Sajidha, S.A. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

Detecting floods, which are highly destructive natural disasters, is crucial for minimizing their impact on both life and infrastructure [1]. Timely identification is essential, and satellites play a key role in assessing the extent of damage and pinpointing areas in urgent need of assistance following events such as floods, earthquakes, or wildfires. The "SEN12-FLOOD"dataset has emerged as a vital resource for flood detection, utilizing both synthetic aperture radar (SAR) and optical imaging. However, a significant challenge lies in the clarity of the images, making it difficult to accurately identify the extent of damage. In response, we propose a comprehensive methodology that incorporates data preprocessing, the comparison of multiple models, and efficient labeling techniques to enhance the accuracy of flood detection. The data preprocessing phase involves the removal of empty folders and irrelevant images, streamlining the dataset for optimal analysis. Additionally, we introduce band stacking, combining satellite imagery bands B2, B3, B4, and B8 to improve image clarity, providing a refined input for flood detection models. The methodology extends to model comparison, wherein multiple models are evaluated to identify the most effective one for accurate flood detection. © 2024 IEEE.

关键词： Satellite imagery

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Machine Learning based Smart Crop Recommendation System 4

Machine Learning based Smart Crop Recommendation System

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Shwetha, A.N. Prabodh, C.P. Siddaganga Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349900

Many challenges are faced by agricultural sector due to various dependent factors like changes in climatic conditions, variation in soil properties and farming practices. What to plant in their agricultural land is the common question for all farmers. Many farmers are failing to identify the crop that suits their agricultural land to get good yields. This results in following the same methods and crops what their ancestors were following. The crop yield always depends on multiple parameters like soil properties, climate conditions, water level etc. So if the farmers get suggestions regarding which crop to grow in their agricultural land would be of great help. The government is also helping farmers in many ways, one such scheme is soil testing. Soil testing is very important because through testing we can identify nutritional level, pH level and moisture level of soil which can optimize crop prediction. To address the above mentioned issues, this paper proposes a Crop Recommendation System(CRS) to help farmers based on machine learning algorithms to take wise decision to select a crop for their agricultural land. The CRS considers historical data, soil type, pH level, season, water level and amount of pesticides to provide crop recommendation for agricultural plots. The CRS system uses Naive Bayes algorithm to select the crop suitability. The CRS is a user friendly system that can be accessed through web or mobile. The user can give necessary input regarding their agricultural land and get crop recommendation which suits their farming land. The proposed system aims to improve crop yield, minimize resources and environmental impact. © 2024 IEEE.

关键词： Smart agriculture

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A Neutrosophic Study for the Transmission of Infection with Pathogenic Fungi from Males of Olive Fly Insects to Their Females

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Neutrosophic Sets and Systems 2024年 64卷 38-45页

作者： Alhabib, Amani Alhabib, Rafif Khalid, Huda E. Salama, A.A. Faculty of Agricultural Engineering Albaath University Homs Syria Department of Mathematics Faculty of Science Hama University Hama Syria Telafer University Telafer Iraq Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt

This paper presents the study of the effectiveness of horizontal transfer of local isolates of the pathogenic fungus Beauveria bassiana (Balsamo) on adults of olive fruit fly Bactrocera oleae (Rossi) at a concentration of 106 spores/ml in laboratory conditions (this work was carried out in specialized scientific laboratories). In addition, it is not possible to reach the desired results in such experiments effectively when the data and observations of the study are not clear and accurate. For this reason, in this paper, experimental data will be presented with inaccurate or uncertain observations using neutrosophic statistics. The purpose is to know the success of males contaminated with pathogenic isolates in the transmission of infection to females. In laboratory conditions through a neutrosophic reading of the study data. This proposed presentation provides greater accuracy, flexibility, and applicability than the classic experimental design in the case of uncertainty. © (2023), (University of New Mexico). All Rights Reserved.

关键词： Transmissions

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Neuroimaging-based Machine Learning for Early Alzheimer's Disease Prediction 3

Neuroimaging-based Machine Learning for Early Alzheimer's Di...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Jerome, Jim Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509675

Early detection of Alzheimer's disease (AD) is crucial for timely intervention and slowing its progression. This research leverages neuroimaging-based machine learning to classify cognitive impairment levels using MRI images. The proposed approach involves preprocessing MRI images, converting them into a numerical format, and training a BEiT (BERT for Image Transformation) model to classify individuals into one of four categories: No Impairment, Very Mild, Mild, or Moderate Impairment. A web interface, developed using Streamlit, enables user-friendly access, allowing real-time predictions by uploading MRI images. This system demonstrates the potential of integrating advanced machine learning techniques and neuroimaging for early Alzheimer's diagnosis, paving the way for personalized interventions and improved outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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A Study on extraction QA-model based on disturbing word embedding and adversarial self-attention mechanism 9

A Study on extraction QA-model based on disturbing word embe...

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9th International Symposium on Advances in Electrical, Electronics, and computer engineering, ISAEECE 2024

作者： Yu, ZhuoPei Department of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9781510683303

Question-and-answer model research has recently been continuously explored in the NLP field, such as the development of human-machine interaction. But the extent to which these models truly understand language remains questionable, and in the face of antagonistic examples, relying too much on some of the word characteristics, makes the model unable to accomplish the tasks that require language understanding and reasoning ability, and the deficiencies of the model are magnified. Therefore, this paper proposes a method (PEC) based on disturbing word embedding and antagonizing self-attention mechanism, which is to obtain semantically retaining adversarial samples by disturbing verb embedding and reduce the noise effect of word embedding process on model training. Through contextual global constraint countermeasure training to enhance the model's deep mining of semantic information, which adds anti-self-attention mechanism to reduce the model's dependence on false features to improve the model's prediction of answers using simple matching strategies. The effectiveness and superiority of PEC method in enhancing the accuracy and robustness of question and answer model are verified by numerous experiments on multiple datasets. © 2024 SPIE.

关键词： Adversarial machine learning

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Brain Tumor Identification Using a Deep Learning Approach

Brain Tumor Identification Using a Deep Learning Approach

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2024 International Conference on computer, Electrical and Communication engineering, ICCECE 2024

作者： Sarkar, Priya Saha, Ashim Pal, Tannistha National Institute of Technology Agartala Computer Science and Engineering Agartala India

ISBN: (纸本)9798350386479

The health of people is seriously threatened by brain tumours, and the prognosis of patients is greatly enhanced by early identification. Convolutional Neural Networks (CNNs) have demonstrated impressive performance in a range of computer vision applications, such as the processing of medical images. This work proposes an effective brain tumor detection system using the VGG16 (Visual Geometry Group 16-layer) CNN architecture. A dataset of brain magnetic resonance imaging (MRI) scans is used by the proposed system to identify characteristics of the existence of tumors. Utilizing pre-trained weights on ImageNet, transfer learning is used to improve the model's performance. Our results found VGG16 model is a good option for precisely identifying brain tumors from MRI images. As a dependable instrument for early diagnosis, the proposed system has promise for improving patient care and neuroimaging results. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Review On Text Classification Using Improved Deep Learning Models 2

Review On Text Classification Using Improved Deep Learning M...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gaikwad, Rajni Mulchandani, Mona Thakur, Reena Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387933

Modern deep learning models have exhibited better performance than conventional machine learning methods across a range of text classification tasks, encompassing natural language inference, sentiment analysis, news categorization, and question answering. The application of text classification in natural language processing extends to tasks like spam detection, news categorization, information retrieval, sentiment analysis, and intent assessment. Traditional text classifiers utilizing machine learning techniques encounter challenges such as sparse data, dimensionality issues, and limited generalization capabilities. In contrast, classifiers based on deep learning effectively tackle these limitations, eliminating the need for intricate feature extraction processes, and providing improved learning capabilities along with enhanced prediction accuracy. One notable instance of Convolutional neural networks are used in this way (CNNs). This paper delineates the text classification process, emphasizing the application of deep learning methodologies for text classification. © 2024 IEEE.

关键词： Sentiment analysis

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A Transfer Learning-Based DenseNet with Multi kernel aggregated Channel Attention Block for COVID-19 Classification using CT scan images

A Transfer Learning-Based DenseNet with Multi kernel aggrega...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Girepunje, Shashank Singh, Pradeep National Institute of Technology Department of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350373783

Accurate diagnosis of COVID-19 using chest X-ray imaging is pivotal for large-scale screening and epidemic containment efforts. However, discerning COVID-19 from CT scan images poses formidable challenges due to the heterogeneous and intricate radiographic features, such as widespread ground-glass opacities and diffuse reticular-nodular opacities. Secondly, the limited number of samples also poses a big challenge for deep learning models that require large number of samples to perform efficiently. In this study, we propose a novel approach for COVID-19 classification using a pre-trained DenseNet architecture combined with a MKACAB (multi-kernel aggregated Channel attention Block). Leveraging the expressive power of DenseNet and the attention mechanism's ability to highlight informative features, our model aims to effectively discriminate between COVID-19 and non-COVID-19 cases. Comprehensive experimental evaluations conducted on publicly available dataset attest to the superior performance of our architecture in the domain of COVID-19 classification. © 2024 IEEE.

关键词： COVID-19

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