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Landslide susceptibility assessment along the major transport corridor using decision tree model: a case study of Kullu-Rohtang Pass

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International Journal of Business Intelligence and Data Mining 2023年第1期24卷 1-24页

作者： Nirbhav Malik, Anand Maheshwar Prasad, Mukesh Department of Geography Delhi School of Economics University of Delhi Delhi India Swami Shraddhanand College University of Delhi Delhi India TGT Computer Science Directorate of Education Delhi Government Delhi India Faculty of Engineering and Information Technology School of Computer Science Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

To lessen damages from landslides, the key challenge is to predict the events precisely and accurately. The objective of this study is to assess landslide susceptibility in the study area. To achieve this objective, a detailed landslide inventory has been prepared based on imagery data and frequent field visits of 153 rock slides and 44 debris slides. Nine landslide factors were prepared initially and their relationships with each other and with the type of landslide was analysed. information gain ratio measure is used to eliminate triggering factors with least score. Train_test_split method was used to classify the dataset into training and testing groups. Decision tree classification model of machine learning was applied for landslide susceptibility model (LSM). The performance was evaluated using classification report and receiver operating characteristic (ROC) curve. Results obtained have proven that the decision tree classification model performed well with good accuracy in forecasting landslide susceptibility. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Landslides

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Revolutionizing Medical Diagnostics: Exploring Creativity in AI for Biomedical Image Analysis

Revolutionizing Medical Diagnostics: Exploring Creativity in...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Krishnamoorthy, P. Swetha, Devulapally Geetha, Pamidimukkala Sai Karunambiga, K. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Thiruthangal Nadar College PG Department of Computer Science Tamil Nadu Chennai India Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bolli Kunta India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Karpagam Institute of Technology Department of CSE Tamil Nadu Coimbatore India Faculty of Information and Communication Technology Kampar Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

Biomedical image analysis plays a crucial role in the early and accurate diagnosis of diseases, significantly impacting patient outcomes. This study presents an innovative approach to biomedical image analysis by integrating machine learning techniques with a user-friendly web interface. The system takes input images uploaded through the web interface and processes them using advanced machine learning algorithms implemented in Python. The goal is to automate the detection of diseases from these images, enhancing the efficiency and precision of diagnosis. Convolutional Neural Networks (CNNs), a specialized class of deep learning models designed for image analysis, are employed to learn and characterize disease-specific patterns. The proposed approach is evaluated using diverse biomedical images encompassing various diseases and conditions. Extensive experiments demonstrate the system's effectiveness, achieving high accuracy, sensitivity, and specificity in disease detection. Integrating machine learning with a web interface simplifies the diagnostic process and enables remote consultations, making healthcare services more accessible and timelier. In conclusion, this study presents a robust framework for automated biomedical image analysis, demonstrating the potential of machine learning and web technologies in revolutionizing healthcare diagnostics. The system's accuracy and user-friendly interface make it a valuable tool for healthcare professionals, contributing to early disease detection and improved patient care. © 2024 IEEE.

关键词： Convolutional neural networks

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Corona Virus Chest CT Scan Classification Using Deep Learning 1

Corona Virus Chest CT Scan Classification Using Deep Learnin...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Ishwarya, K. Arunadevi, R. Nandhini, G. Gurumoorthy, Sasikumar Abinaya, K. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India J.J. College of Engineering and Technology Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350323795

People can perceive an occasion from this project investigates how well profound learning models prepared on chest CT pictures can analyze Coronavirus tainted individuals in a quick and mechanized process. To this end, we embraced progressed profound organization structures and proposed a consecutive convolutional brain organization to accomplish the best presentation. We led broad arrangements of tests on two CT picture datasets, specifically, the SARS-CoV-2 CT-filter and the Coronavirus CT. The outcomes illustrate predominant exhibitions for our models contrasted and past investigations. Our best models accomplished normal exactness, accuracy, review, and F1-score upsides of 99.2%, 93.9%, 92.0%, 92.9. For better understandable of the outcomes, applied perception procedures to give graphic clarifications to models' expectations. Include perceptions of the learned elements show all around isolated bunches addressing CT pictures of Coronavirus and non-Coronavirus cases. Additionally, the representations demonstrate that our models are equipped for recognizing Coronavirus cases as well as give precise confinement of the Coronavirus related locales, as shown by thoroughly prepared radiologists. © 2023 IEEE.

关键词： Coronavirus

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Large Capacity Image Watermarking Model using SWT and Laplacian Pyramid 25

Large Capacity Image Watermarking Model using SWT and Laplac...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wang, Baowei Yang, Fan Wu, Yufeng Dai, Changyu Huang, Wenjue Zhao, Xingyuan Engineering Research Center of Digital Forensics Ministry of Education China Waterford Institute Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350330014

Image watermarking algorithms have developed rapidly in recent years. Most of the Deep Neural Network(DNN)-based image watermarking algorithms embed only 30 bits or 64 bits messages in 128 × 128 images, while few algorithms embed large capacity messages. This is mainly because embedding large capacity messages will significantly influence the performance of algorithms. However, the limited capacity of these messages inhibits their effectiveness in certain data protection scenarios. Therefore, we propose a DNN-based large capacity image water-marking model. By using Stationary Wavelet Transform(SWT) and Laplacian pyramid transform, we expand the embedding capacity and achieve embedding 128 bits messages in 128 × 128 images. Experimental results show that under the same conditions, the proposed model is superior to state-of-the-art models, and has excellent performance. © 2023 IEEE.

关键词： Deep neural networks

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Emerging Wireless Technologies for Efficient Routing for Internet of Things 11th

Emerging Wireless Technologies for Efficient Routing for Int...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： John, Jomy Dhamodaran, S. Ramesh, J.V.N. Anand, Rohit Namdev, Arpit Gupta, Ankur Department of Computer Science P M Government College Kerala Chalakudy India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur Vaddeswaram India New Delhi110020 India Department of Information Technology University Institute of Technology RGPV Madhya Pradesh Bhopal India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

ISBN: (纸本)9789819717231

Many types of wireless networks and technologies, including those used for longer distances, shorter distances, and in the personal area are all included into the designs of IoT applications. This opens up a wide range of commercial possibilities in sectors as varied as e-health, smart cities, and smart homes. This article takes a close look at some key wireless technologies for the IoT, as well as several others that are currently undergoing rapid development. Notable technologies including ZigBee, 6LoWPAN, Bluetooth Low Energy, LoRa, and several variants of Wi-Fi, including the cutting-edge IEEE 802.11ah standards are addressed. The data range and rate, network size, RF channels and bandwidth, power consumption, and other relevant aspects are all studied in depth in the research in order to assess the capabilities and behaviors of these technologies. Evidence suggests that a holistic technology approach is required for the IoT to provide interoperable and secure communications. In this paper, we investigate how recent advances in wireless technology can provide answers to long-standing problems in efficient routing. Several alternative optimization methods, such as PSO and ACO, inspired by natural events have been applied throughout the routing process in previous studies. In this research, we use an MVO to find the optimal connection establishment route for wireless nodes. The simulation results validate that the proposed adjustment improves throughput. In addition, the ratio of packets lost by the conventional method to those lost by the proposed method is larger than that of the latter. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans

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Multimedia Tools and Applications 2024年第28期83卷 71885-71908页

作者： Das, Suchismita Dubey, Rajni Jena, Biswajit Tsai, Lung-Wen Saxena, Sanjay Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei111031 Taiwan Department of Computer Science and Engineering Institute of Technical Education and Research SOA Deemed to Be University Bhubaneswar India Department of Medicine Research Taipei Medical University Hospital Taipei111031 Taiwan Department of Information Technology Office Taipei Medical University Hospital Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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Intrusion Detection System for Vehicle Network based on Embedded Convolutional LSTM 24

Intrusion Detection System for Vehicle Network based on Embe...

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2024 2nd International Conference on Internet of Things and Cloud Computing technology, IoTCCT 2024

作者： Wang, Hongyun Zhou, Kun Deng, Hongli Luo, Chuan Hou, Xinlong School of Computer Science China West Normal University Nanchong China Education and Information Technology Center China West Normal University Nanchong China School of Electronic Information Engineering China West Normal University Nanchong China

ISBN: (纸本)9798400710148

The CAN bus is a mainstream explicit protocol for data transmission in intelligent vehicles. Therefore, the secure transmission of CAN bus is an essential means to ensure the safety of intelligent vehicles. Traditional models ignore the CAN message-to-message's time sequence and the continuity of the internal characteristics of CAN data frames. To this end, this paper proposes an intrusion detection system for vehicle network based on an embedded convolutional LSTM (EC-LSTM-VNIDS). Firstly, a new embedded convolutional gate is inserted between the cellular state and the implicit state to extract the correlation of the internal features of the CAN data frame via a one-dimensional convolutional operation. Secondly, the model extracts the time relationship of CAN message-to-message by LSTM. Finally, the model analyzes the continuity of the internal features of the CAN data frames and the time relationship of the message-to-message to classify them by the fully connected layer. Experiments show that the EC-LSTM-VNIDS proposed in this paper can achieve more than 99% in all four evaluation metrics on four real-world in-vehicle CAN bus message datasets. © 2024 Copyright held by the owner/author(s).

关键词： Network intrusion

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Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 642-656页

作者： Madhavi, Macharapu Kolikipogu, Ramakrishna Prabakar, S. Banerjee, Sudipta Maguluri, Lakshmana Phaneendra Raj, G. Bhupal Balaram, Allam Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India School of Commerce and Management Studies Dayananda Sagar University Bangalore India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Maharashtra Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram Guntur India School of Agriculture SR University Warangal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Climate change is one of the most pressing global challenges of our time, with far-reaching impacts on ecosystems, economies, and human societies. Accurate prediction of climate change patterns is crucial for developing effective mitigation and adaptation strategies. Remote sensing data, with its ability to provide comprehensive and continuous observations of the Earth’s surface, plays a vital role in monitoring and predicting these changes. However, the complexity and high dimensionality of remote sensing data present significant challenges for traditional predictive models. In this study, we present an Enhanced Deep Learning Strategy for climate change prediction using remote sensing data, integrating a Cascaded Inception-LGBM model. The proposed model combines the feature extraction capabilities of the Inception module with the predictive power of the Light Gradient Boosting Machine (LGBM). The methodology was evaluated on various climate variables, including temperature, precipitation, and CO₂ levels, achieving an accuracy of 97.22%. Comparative analysis with state-of-the-art models demonstrated the superior performance of our approach, particularly in terms of RMSE, MAE, and R² metrics. Robustness tests further confirmed the model’s generalization capabilities under different data conditions. This study underscores the potential of advanced deep learning techniques in enhancing climate change prediction accuracy and offers insights into the key drivers of climate variability. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change prediction Deep learning Environmental monitoring Feature extraction Inception module Light Gradient Boosting Machine Model accuracy NDVI Remote sensing Spectral indices

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3D Object Detection with Attention:Shell-Based Modeling

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computer Systems Science & engineering 2023年第7期46卷 537-550页

作者： Xiaorui Zhang Ziquan Zhao Wei Sun Qi Cui School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box *** address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate *** improve the detection accuracy,a shell-based modeling method is *** roughly determines which spherical shell the coordinates belong ***,the results are refined to ground truth,thereby narrowing the localization range and improving the detection *** improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection *** of these features are highlighted by weighting them on the feature *** training,it makes the feature weights that are favorable for object detection get ***,the extracted features are more adapted to the object detection *** comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.

关键词： 3D object detection autonomous driving point cloud shell-based modeling self-attention mechanism

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Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

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computer Modeling in engineering & Sciences 2023年第4期135卷 91-108页

作者： Zhichen Hu Huali Ren Jielin Jiang Yan Cui Xiumian Hu Xiaolong Xu School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Institution of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou515021China School of Earth Sciences and Engineering Nanjing UniversityNanjing210023China College of Mathematics and Information Science Nanjing Normal University of Special EducationNanjing210023China

An obviously challenging problem in named entity recognition is the construction of the kind data set of *** some research has been conducted on entity database construction,the majority of them are directed at Wikipedia or the minority at structured entities such as people,locations and organizational nouns in the *** paper focuses on the identification of scientific entities in carbonate platforms in English literature,using the example of carbonate platforms in ***,based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts,this paper designs a literature content extraction method that allows dealing with complex text ***,based on the literature extraction content,we formalize the entity extraction task(lexicon and lexical-based entity extraction)for entity ***,for testing the accuracy of entity extraction,three currently popular recognition methods are chosen to perform entity detection in this *** show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition *** study presents a pilot study of entity extraction,which involves the use of a complex structure and specialized literature on carbonate platforms in English.

关键词： Named entity recognition carbonate platform corpus entity extraction english literature detection

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