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Multilevel and Multi-granularity of Remote Sensing Imagery application based on Deep learning and machine learning Algorithm 21

Multilevel and Multi-granularity of Remote Sensing Imagery A...

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4th international conference on machine learning and machine Intelligence, MLMI 2021

作者： Jean Bosco, Musabe Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence School of Computer Science and Technology Chongqing University of Posts and Telecommunication China

ISBN: (纸本)9781450384247

Deep learning classification has state-of-the-art machine learning approaches. Earlier work proves the deep convolutional neural network was successful and brilliant in different tasks such as image classification and image processing in remote sensing datasets. To recognize and clarify the physical aspect of the earth's surface land cover and exploit the land use is an exciting issue in environmental monitoring analyzing remote sensing data that is free and easy to get in different areas without time-consuming. To improve the quality of data sources and cooperating with image representation is still lack methods. We started with the traditional segmentation approach to divide an image into a single and compare multi-level into multiple segmentation for remote sensing imagery. We review the traditional machine learning and deep learning neural network classification tasks. We collected a dataset from six countries of Eastern Africa Communities with nine categories. We proposed an ensemble average model neural network with three models, combined as a single model trained, and achieved 96.5% validation accuracy. Finally, we compared state-of-the-art and extracted features pre-trained weight ImageNet and used traditional machine learning algorithms to improve accuracy. © 2021 ACM.

关键词： Image segmentation

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Mobile Medical Assistant System for Laboratory Report Analysis and Medical Drug Identification

Mobile Medical Assistant System for Laboratory Report Analys...

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Advancements in Computing (ICAC), international conference on

作者： Deshani Warnakulasuriya tharushi Dewangi Navodya Sewwandi Minoli Rathnayake Nuwan Kodagoda Kushanra Suriyawansha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

It is quite common for medical drugs and prescriptions to be misidentified by hospitals and after drugs are being dispensed to the patients. Misidentification of medical drugs is more common among elderly and visually impaired patients. In hospital organizations, the leading medical error is adverse drug events. Another most common issue patients face is keeping track of medical lab reports. Our proposed mobile medical assistant system uses image processing to identify drugs with or without packaging, identifying prescription and medical lab reports. Furthermore, the mobile application will identify the trends of medical lab reports and predict next month’s results of the medical lab report of the patient using machine learning.

关键词： Drugs Hospitals Image processing Organizations machine learning Packaging Market research

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

5th International Conference on Innovative Intelligent Indus...

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

ISBN: (纸本)9783031807596

the proceedings contain 61 papers. the special focus in this conference is on Innovative Intelligent Industrial Production and Logistics. the topics include: An Out-of-Sample Clustering Ensemble Method for Defect Detection and Classification in Metal Additive Manufacturing;Advanced Process Monitoring and OEE Metrics: Leveraging AASs for Efficiency;obsolescence Forecasting of Intel Processors: A Transformers Approach;machine learning Tool for Yield Maximization in Cream Cheese Production;comparative Evaluation of Irregular Shape Strip-Packing Algorithms;an Inventory Management Support Tool through Indirect Q-Value Estimation: A Combined Optimization and Forecasting Approach;multiscale Clustering to Improve Anomaly Detection in Nuclear Equipments;clustering Analysis for Forecasting Medicine Consumption;a Decision Support System of the Configuration of a Supermarket in a Components Company for the Automotive Industry;a Distributed Framework for Cooperative Scheduling of Production, Transportation, and Maintenance Using Multi-Agent Systems;the Role of Actor’s Creative Self-efficacy in AI Enabled Value Creation;building Realistic Environment from computer Vision Approach Applied to Manufacturing Simulation in the Digital Twin Context;towards a Process-Based Industry 5.0 Maturity Model: A Feasibility Study in Supply Chain;a Multi-objective Genetic Algorithm Approach for Multi-component Products Recovery and Remanufacturing Planning;reinforcement learning for Optimizing Routing in the Production Supply of Matrix Production Systems;an Innovative Fault Detection Robotic Tool for Overhead Cranes in Industries: Magnetic Wheel Modelling and Experimental Validation;current Trends and Future Challenges to Put Circular Manufacturing in Practice;enhancing Human – Robot Collaboration in the Industry 5.0 Framework with Physics-Informed Neural Networks: application to Collision Detection;AI-Driven Smart Air Conditioning System for a Sustainable and Energy-Efficient Industrial Future.

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

5th International Conference on Innovative Intelligent Indus...

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

ISBN: (纸本)9783031807749

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9th international conference on Computing and Informatics, ICOCI 2023

9th International Conference on Computing and Informatics, I...

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9th international conference on Computing and Informatics, ICOCI 2023

ISBN: (纸本)9789819995882

the proceedings contain 55 papers. the special focus in this conference is on Computing and Informatics. the topics include: Sentiment Analysis of Arabic Dialects: A Review Study;charting Inclusive Digital Society Research Trends: A Bibliometric Analysis of E-Participation through Social Media;systematic Literature Review and Bibliometric Analysis on Addressing the Vanishing Gradient Issue in Deep Neural Networks for Text Data;a Test Dataset of Offensive Malay Language by a Cyberbullying Detection Model on Instagram Using Support Vector machine;tikTok Video Cluster Analysis Based on Trending Topic;a Video Summarization Method for Movie Trailer-Genre Classification Based on Emotion Analysis;e-Nose: Spoiled Food Detection Embedded Device Using machine learning for Food Safety application;an Analysis of Objective Function Modification Approaches in Routing Protocols for Low Power and Lossy Networks: A Fuzzy Logic-Based Perspective;an Empirical Study of Label Size Effect on Classification Model Accuracy Using a Derived Rule from the Holy Quran Verses;large Scale Web Crawling and Distributed Search Engines: Techniques, Challenges, Current Trends, and Future Prospects;anomalies in Mooring (thin) Lines: Causes, Risk Mitigations, and Real Time Consequences of Failure – A Comprehensive Review;data Analytics Modelling System for Short Courses at Seberang Jaya Community College;examining the Software Developers’ Perception in Open-Source Software of Blockchain Project Using Association Rules Mining;support Vector machine for Satellite Images Classification Using Radial Basis Function Kernel Method;a Regression Test Case Prioritization Framework for Software Sustainability;towards a Sustainable Digital Society: Supporting Producer Mobility in Named Data Networking through Immobile Anchor-Based Mechanism.

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Towards Eco-Resilient Networks: CNN-Enabled Anomaly Detection for Green Cybersecurity

Towards Eco-Resilient Networks: CNN-Enabled Anomaly Detectio...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Arman Kalra Kanwarpartap Singh Gill Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Cybersecurity is a major concern across various industries today. It is directly related to global goals such as enhancing infrastructure and industrial innovation (SDG 9) and fostering peace, justice, and strong institutions (SDG 16). Traditional problem-detection methods are becoming less effective as cyber threats evolve and increase in frequency, necessitating new approaches. this article introduces a novel technique for detecting abnormal patterns in network traffic using Convolutional Neural Networks (CNNs). the model is trained and tested on the CICIDS2017 dataset, which includes a wide range of both normal and suspicious network activities. Our CNN-based approach achieved a remarkable accuracy of 93.64%, outperforming conventional techniques. this method not only enhances cybersecurity but also contributes to SDG 9 by promoting resilient infrastructure. Additionally, it aids in building robust institutions by swiftly detecting and mitigating cyber threats, aligning with SDG 16. the performance of the model, including metrics like accuracy, recall, F1 score, and confusion matrices, has been thoroughly analyzed. these results underscore the importance of leveraging advanced machine learning techniques, especially deep learning, in cybersecurity solutions. Creating secure and sustainable digital environments depends heavily on integrating such innovations. Our model’s ability to accurately detect network anomalies demonstrates the potential of CNNs to revolutionize cybersecurity practices. this study advocates for the continued application of deep learning methods to address the growing challenges in cybersecurity, contributing to the achievement of key sustainable development goals while ensuring the stability and security of digital systems.

关键词： Deep learning Training Technological innovation Accuracy Telecommunication traffic Convolutional neural networks computer security Sustainable development Anomaly detection Resilience

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An Approach to Detect Air Quality using machine learning and Data Analysis Tools

An Approach to Detect Air Quality using Machine Learning and...

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international conference on Computing and Networking Technology (ICCNT)

作者： Anirban Roy Mehnaz Rashid Mim Department of Computer Science & Engineering Daffodil International University Dhaka Bangladesh

After the headway of the computerized structure, Information science hopes to be a fundamental part here. Information science stores and investigates information of various sorts of banks, foundations, workplaces and others. the framework answers in this manner subject to learning. Precise learning gives the exact outcome in any case its produce blended forecast. Along these lines, this learning is the most significant part for computerized framework. Information science is basically expectation based. machine can imagine result without help from some other individuals rely on the given dataset. Generally, that predicted outcome is more accurate, once in a while not. the certified guess is obligated to learning accuracy. thusly, Information science is distributed four frameworks reliant upon learning, for example, Ostensible, Ordinal, Discrete, Ceaseless. Directed systems are reliant upon marked information. Also, unstructured dataset is utilized in unaided realizing, where model information is given as of now doesn't yield. In this work we attempted to anticipate lessen air contamination in various nations with python programming language, Google studio and RFM run application. For that we gathered a dataset from public outflows answered to the Show on Lengthy reach Transboundary Air Contamination (LRTAP Show). Air poison discharges — EEA datasets. A gigantic extent of individuals kicks the bucket purposes behind vascular disease at of air contamination. Early discovery of air contamination might help the patient defeat the infection. this might be an approach to forestalling this sickness. the Information science method is essentially usable systems in this manner. In this paper the result is the gamble variables of air toxins which is in table 4 and the aftereffect of RMSE in XG Lift calculation.

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Expression of Concern for: machine learning techniques for identifying Cyberbullying on digital networks

Expression of Concern for: Machine Learning techniques for i...

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Innovative Trends in Information Technology (ICITIIT), international conference on

作者： N. Gayathri Prawin R Ranjith kumar A Manikandan R Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9781665494144

ISBN: (纸本)9781665494151

With the proliferation of virtual entertainment platforms around the world, particularly among young people, digital taunting and enmity have become real and annoying problems that networks must address. threats can use these levels to attack and weaken others in their networks. To combat digital tormenting, various strategies and tactics have been used or proposed, including early detection and alarms that both detect and protect victims from such attacks. machine learning techniques with Artificial Intelligence Framework are being widely used to identify specific linguistic patterns that danger uses to hunt for their victims. the Feeling Analysis (FA) of virtual entertainment content is one of the expanding fields of research in AI. FA makes it possible to gradually identify online harassment and continuously recognize cyberbullying. this study recommends a SA model to identify cyberbullying messages in Facebook web-based entertainment. SVM and MAXENT classifier, controlled AI arrangement tools, are employed in this model. When a higher n-grams language model is applied to such texts in correlation with analogous prior research, the findings of the investigations carried out using this model showed encouraging results. Similar patterns in the results showed that these classifiers preferred execution measures over other classifiers on such remarks.

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Deep learning Forecast of Cognitive Workload Using fNIRS Data

Deep Learning Forecast of Cognitive Workload Using fNIRS Dat...

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IEEE international conference on Human-machine Systems

作者： Nicolas Grimaldi Yunmei Liu Ryan McKendrick Jaime Ruiz David Kaber Industrial and Systems Engineering University of Florida Gainesville FL USA Industrial Engineering University of Louisville Louisville KY USA Mission Systems Northrop Grumman Falls Church VA USA Computer & Information Science & Engineering University of Florida Gainesville FL USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Introduction: In the domain of helicopter piloting, the pilot’s performance is driven by many cognitive processes, demanding substantial cognitive resources. the pilot must maintain situation awareness and perform rapid decision-making. An objective of integrated helicopter technologies is to predict and effectively manage pilot cognitive workload to ensure safety and efficiency throughout flight. Methods: In this study, we collected data on seven participants, including three experienced pilots, using a UH-60V cockpit simulator to perform 46 distinct trials under various flight conditions. fNIRS neuroimaging was used to collect high-resolution neurophysiological data for exploring and forecasting cognitive workload using a collection of deep learning models. Model implementation: three deep learning architectures are detailed in this work: a stacked LSTM model, a CNN-LSTM hybrid, and a transformer model. Results: An evaluation of three Seq2Seq models, each with two distinct forecasting lengths (10s and 30s), revealed LSTM-based architectures as superior performers for 10s forecasting tasks. Discussion: the LSTM-based models’ superior performance suggested potential limitations with the transformer’s self-attention mechanisms for our specific application. Surprisingly, the CNN-LSTM architecture did not surpass the stacked LSTM model’s performance during forecasting tasks. Conclusion: Future research directions include exploring diverse time-series Seq2Seq methods and forecasting cognitive workload as ordinal measures, offering insights into shifting cognitive demands.

关键词： Deep learning Analytical models Predictive models Transformers Data models Real-time systems Safety

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Single-Molecule Bioelectronic Sensor: Improving Reliability with machine learning Approaches 5

Single-Molecule Bioelectronic Sensor: Improving Reliability ...

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5th IEEE international conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Sarcina, L. Scandurra, C. Caputo, M. Catacchio, M. Di Franco, C. Bollella, P. Chironna, M. Torricelli, F. Esposito, I. Osterbacka, R. Scamarcio, G. MacChia, E. Torsi, L. University of Bari Dept. Chemistry Bari Italy University of Bari Dipartimento di Scienze Biomediche e Oncologia Umana Bari Italy University of Brescia Brescia Italy University of Dusseldorf Dusseldorf Germany Abo Akademi Faculty of Science and Engineering Turku Finland University of Bari Dept. of Physics Bari Italy University of Bari Dept. of Pharmaceutical Sciences Bari Italy

ISBN: (纸本)9781665457330

Digitizing biomarkers analysis by quantifying them at the single-molecule level is the new frontier for advancing the science of precision health. the enhancement of the technical capabilities of bioelectronics systems, by giving clinicians the possibility to rely on biomarkers quantifications down to the single-molecule, holds the potential to revolutionize the way healthcare is provided. Such an analytical tool will indeed enable clinicians to associate a biomarker tiniest increase to the progression of a disease, particularly at its early stage.1 Eventually, physicians will be able to identify the very moment in which the illness state begins. Such an occurrence will enormously enhance their ability of curing diseases by supporting better prognosis and permitting the application of precise treatment methods. the single molecule bio-electronic smart system array for clinical testing - SiMBiT - technology has been developed within the blooming field of precision medicine, leveraging on the single molecule with large transistor (SiMoT)2 lab-based technology that can perform single-molecule detection of both proteins and DNA bio-markers.3,4 Specifically, the SiMBiT technology has lately developed the SiMoT lab-based device into a cost-effective portable prototype multiplexing array that integrates, with a modular approach, standard components and interfaces with novel materials and exhibits enhanced sensing capabilities. the SiMBiT prototype has proven its potency in early detection of pancreatic cancer, being capable to discriminate among low-grade and high-grade mucinous cyst's lesions in peripheral biofluids, such as plasma samples. In this perspective, machine learning approaches play a pivotal role in developing classifiers for a fast, reliable multiparametric biosensors output. Supervised model based on multivariate data processing has been undertaken to enable multiplexing, i.e. the simultaneous quantification of three biomarkers, namely MUC1 and CD55 proteins

关键词： Biomarkers

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