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检索条件"任意字段=3rd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2021"

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Optical Remote Sensing and machine learning Algorithm Used to Access the Leaf Area Index and Canopy Water Content 3rd

Optical Remote Sensing and Machine Learning Algorithm Used t...

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3rd international conference on Intelligent Systems and applications, ICISA 2024

作者： Vinta, Surendra Reddy Kambala, Gireesh Ahmad, Anzar Patro, Pramoda School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Teach For America 2140 Hillside Derby Run CummingGA30040 United States Department of Electronics and Communication Graphic Era to be deemed University Dehradun India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India

ISBN: (纸本)9789819798384

Precise evaluation of crop canopy characteristics requires optical remote sensing (RS) equipped with solid procedures. Despite much research on the subject, algorithm performance employing RS still needs improvement. In a short amount of time, five distinct algorithms partial-least-squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR) with feature selection were used to evaluate the LAI and CWC of rice canopy data. Results from the visible to short-wave electromagnetic spectrum of canopy reflectance were input into the algorithms. Researchers used two sets of repeated field trials in the 2014–15 and 2015–16 rice growing seasons to gather data on leaf area index (LAI) (600) and Canopy water content (CWC) (480). The coefficient of determination (R2) was used to assess the efficiency of each method. Compared to other algorithms, PLSRLW outperformed them with R2 values of 0.77 for LAI and 0.66 for CWC. In addition, for every model, created a kernel concentration estimator of the root mean formed error values using a bootstrapping strategy. By giving calibration samples, which have the same canopy assembly as the test samples, more weight, the findings indicated that an appropriate method might be used to increase the forecast accuracy of LAI and CWC. Calibration of the model should be done using an entire season's worth of canopy spectral data since sub-setting it leads to high error values in the test dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optical remote sensing

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Predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive learning Framework 3rd

Predicting Falls Through Muscle Weakness from a Single Who...

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3rd international Workshop on applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Zhang, Xia Saleem, Afsah Ilyas, Zaid Suter, David Nadeem, Uzair Prince, Richard L. Zhu, Kun Lewis, Joshua R. Sim, Marc Gilani, Syed Zulqarnain Centre for AI and ML School of Science Edith Cowan University Joondalup Australia Nutrition and Health Innovation Research Institute Edith Cowan University Joondalup Australia Computer Science and Software Engineering The University of Western Australia Perth Australia Medical School The University of Western Australia Perth Australia Department of Endocrinology and Diabetes Sir Charles Gairdner Hospital PerthWA Australia

ISBN: (纸本)9783031820069

Falls are often attributed to poor muscle function, with weak hand grip strength clinically recognized as a major risk factor. However, grip strength is rarely assessed clinically. Low radiation dual-energy X-ray absorptiometry (DXA) whole-body scans, that can be obtained during routine osteoporosis screening, offer a comprehensive overview of body composition, thereby providing valuable information for musculoskeletal health. Here, we propose a machine learning technique, exploiting image and clinical data to classify weak grip strength ( © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Big data and machine learning for Climate Change Prediction: An Integrated Approach to Environmental Monitoring 3

Big Data and Machine Learning for Climate Change Prediction:...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Ramesh, Azmeera Gulmira, Zufarova Shnain, Ammar Hameed Ramya, R. Nagaveni, P. Aanandha Saravanan, K. CMR TECHNICAL CAMPUS Telangana India Andijan Machine-Building University Faculty of Economics 56 Boburshah Prospect Andijan Uzbekistan College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Psna College of Engineering and Technology Department of Information Technology India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331542375

The integration of big data and machine learning (ML) in climate change prediction offers a transformative approach to environmental monitoring, enabling improved accuracy, scalability, and actionable insights. This study presents an integrated framework that combines the strengths of big data technologies and advanced ML algorithms to address the complexities of climate systems. Diverse datasets from satellites, IoT sensors, and historical records were preprocessed using normalization, feature engineering, and dimensionality reduction techniques to ensure data quality and compatibility. ML models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures, were developed to tackle spatial and temporal prediction tasks. The models demonstrated robust performance, with CNNs achieving 93.5% accuracy in deforestation detection and hybrid CNN-RNN models delivering 95% accuracy in cyclone trajectory prediction. Real-time monitoring applications, such as flood risk assessment and air quality forecasting, showcased low latency and scalability, reinforcing the framework's operational viability. Despite these advancements, challenges such as data bias, computational demands, and algorithmic transparency remain significant. To address these, solutions like dataset augmentation, edge computing, and explainable AI are proposed. This research highlights the potential of integrating big data and ML for dynamic and precise climate predictions while emphasizing the need for ethical and equitable deployment of these technologies. By fostering interdisciplinary collaboration and leveraging emerging computational innovations, this study contributes to advancing climate resilience and sustainable environmental management. © 2025 IEEE.

关键词： Environmental monitoring

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The Impact of Ultra-Wideband Sensors in Industrial Automation and Robotics applications 3

The Impact of Ultra-Wideband Sensors in Industrial Automatio...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Chaudhary, Pavan Sidhu, Amritpal Nayak, Chinmayee Das, Sidhant Kantharaj, I.K. Thapa, K.Naresh Kumar Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Centre for Artificial Intelligence and Machine Learning Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

Ultra-Wideband Technology has garnered a reputation as an enabler of advancing industrial automation and robotics applications. UWB sensors emit short pulses of electromagnetic radiation to measure and can deliver highly accurate and real-time data over long distances. Compared to other sensing technologies, UWB sensors have a remarkably broad frequency spectrum and provide high data rates at low power. There are several advantages of UWB sensors implemented in industrial automation and robotics. These are done in a lab with some reference;you can take the data offline. UWB sensors can complement and be used in integration with other technologies, such as lidar or vision systems, to achieve higher performance results for a complete perception of the environment. This allows better and more efficient decision-making for autonomous systems. UWB sensors deliver highly accurate and repeatable measurements for robotic movement monitoring and control. They are also used for asset monitoring and inventory control, which finally help logistics & SCM. UWB sensors also improve workplace safety by warning about potential collisions or other hazardous conditions. © 2025 IEEE.

关键词： Electromagnetic pulse

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An Analysis of Generative Adversarial Networks for Intrusion Detection System

An Analysis of Generative Adversarial Networks for Intrusion...

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Disruptive Technologies (ICDT), international conference on

作者： Rakesh Kumar Veerendra Yadav Harvendra Kumar Research Scholar School of Engineering & Technology Noida International University Noida India Department of Computer Science & Engineering School of Engineering & Technology Noida International University Noida India Department of Computer Science & Engineering IIMT College of Engineering Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Intrusion Detection System (IDS) is an important part of networks that checks network traffic and identifies mistrustful activity. IDS can be software applications, specialised hardware devices or cloud services. They use one or both of two primary methods to detect threats: signature-based or anomaly-based detection. Generative Adversarial Networks (GANs) are machine learning mechanisms that have been used to develop training examples for Intrusion Detection Systems (IDSs). GAN s are adversarial because they train two networks against each other, one of which generates new data and the other predicts whether the data comes from the original data set. GANs are increasingly used in cyber security, especially for tasks that require training on unbalanced data sets of attack classes.

关键词： Training machine learning algorithms Reviews Intrusion detection Telecommunication traffic Generative adversarial networks Software Hardware Disruptive technologies Image classification

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SRv6-INT Enabled Network Monitoring and Measurement: Toward High-Yield Network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

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3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

Network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band Network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined Networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

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machine learning Techniques for Pose Based Human Activity Recognition

Machine Learning Techniques for Pose Based Human Activity Re...

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Disruptive Technologies (ICDT), international conference on

作者： Ravindra Singh Koranga Arun Kumar Rai Lalit Mohan Shubham Singh Pritika Priya School of Computer Science and Engineering Galgotias University Greater Noida India CSE Birla Institute of Applied Sciences Bhimtal India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Activity recognition is the task of identifying human actions and physical activities. It can have applications in broad areas including medical, sports and fitness, healthcare, behaviour analysis and security. Activity recognition is a widely sought after area of research in both machine learning (ML) and smart devices. The activity recognition plays an important role in the field of machine learning. There are several machine learning techniques which can be applied for pose based activity recognition. These techniques involve training machine learning models for the dataset. The dataset is obtained by using OpenPose framework. This framework provides keypoints from an input image. These keypoints represent the raw data determining the pose of a human. The machine learning model can predict the pose after training on the dataset. models

关键词： Training Pose estimation machine learning Medical services Predictive models Security Object recognition Human activity recognition Smart devices Sports

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12th international Workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on database and Expert Systems applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

引用

12th international Workshop on Biological Knowledge Discovery from data, BIOKDD 2021, 5th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2021, 3rd international Workshop on machine learning and Knowledge Graphs, MLKgraphs 2021, 1st international Workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international Workshop on Time Ordered data, ProTime2021 and 1st international Workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international conference on database and Expert Systems applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on database and Expert Systems applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired data-Sampling Prototype for Time-Series Analysis;membership-Mappings for data Representation learning: Measure Theoretic Conceptualization;membership-Mappings for data Representation learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep learning Processes;approximate Fault Tolerance for Edge Stream Processing;deep learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series Pattern Discovery by Deep learning and Graph Mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical Systems;integrating Gene Ontology Based Grouping and Ranking into the machine learning Algorithm for Gene Expression data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative Study of Deep learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case Study;Privacy Preserving machine learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction Strategies for Node Embeddings with rdF2Vec in Knowledge Graphs;bridging Semantic Web and machine learning: First Results of a Systematic Mapping Study.

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machine learning-Enabled Examination Feedback System

Machine Learning-Enabled Examination Feedback System

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Industrial Electronics: Developments & applications (ICIDeA), IEEE international conference on-

作者： Prashanth Ragam Botcha Taraka Rutesha Sai Anshita Priyadarshini Mekatotti Nallamala Ojasvi Adigoppula Jahnavi Pavithra Danaveni School of Computer Science & Engineering VIT-AP UNIVERSITY Inavolu Amaravati Vijayawada AP Department of Computer Science & Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The project proposes an Examination Feedback System utilizing machine learning to automate the evaluation of subjective answers in educational assessments. The system will analyze student responses and classify them under a number of predefined topics while making sure that the feedback it generates is tailored and specific to the needs of the students. The primary goal is to ensure improved outcomes through enhanced information to students, where improvement is needed, while helping educators identify performance trends. The system is powered by DistilBERT, which is a transformer-based natural language processing model which is known for its efficiency and contextual knowledge. The proposed system differs from previous manual grading systems in that it offers data-driven information while addressing confidentiality issues with a computationally efficient design. Thus, the examination process becomes scalable, accurate, and data-driven in satisfying the demands of upstream education systems with very limited computational infrastructure.

关键词： Training Accuracy Computational modeling Scalability machine learning Manuals Transformers Market research Natural language processing Context modeling

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Advanced AI & ML applications in Satellite Image Processing & Industry

Advanced AI & ML Applications in Satellite Image Processing ...

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Disruptive Technologies (ICDT), international conference on

作者： Raj Kishor Verma Ayush Chaudhary Harsh Bhati Gagan Bisht School of computer science & Engineering Galgotias University Gr. Noida Computer Science & Engineering Data Science ABES Institute of Technology Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

By integrating advanced artificial intelligence (AI) and machine learning (ML) into satellite image processing, a number of industries standard to benefit in kinds of artificial intelligence and machine learning used for geoanalysis. Decades-old image processing processes are beginning to be invaded by robots. Using ML and AI to process satellite images provides a lot of new AI powered imaginations that can monitor environmental change, support disaster response and plan city layouts. Such development in the precision of techniques such as object classification, object detection and pixel detection make the analysis of image sequences from satellites possible and rapid. Enhancement of task accuracy and observability. Widely used in fields like greenhouse gas surveillance, changes in land use trends and the natural habitats of animals. In addition to introducing AI methodologies into high resolved satellite data, obvious advantages are brought to both decision making and environmental sustainability in many fields. As countries put satellites in orbit and their images become more readily accessible to the public, the opportunities for AI-driven applications of satellite image data are expected to increase, offering significant advancements in such fields as global development and resource management. This paper discusses such earth-moving applications and looks at how the future of artificial intelligence (AI) is being impacted by all this.

关键词： Industries Satellites PSNR Image resolution Accuracy Surveillance Urban planning machine learning Satellite images Standards

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