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Automated Blood Cell Detection and Counting via Deep learning for Microfluidic Point-of-Care Medical Devices 3

Automated Blood Cell Detection and Counting via Deep Learnin...

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3rd International conference on Artificial Intelligence Applications and Technologies (AIAAT)

作者： Xia, Tiancheng Jiang, Richard Fu, YongQing Jin, Nanlin Univ Lancaster Sch Comp & Commun Lancaster LA1 4WY England Northumbria Univ Fac Engn & Enviorn Newcastle NE1 8ST England Northumbria Univ Comp & Informat Sci Newcastle NE1 8ST England

Automated in-vitro cell detection and counting have been a key theme for artificial and intelligent biological analysis such as biopsy, drug analysis and decease diagnosis. Along with the rapid development of microfluidics and lab-on-chip technologies, in-vitro live cell analysis has been one of the critical tasks for both research and industry communities. However, it is a great challenge to obtain and then predict the precise information of live cells from numerous microscopic videos and images. In this paper, we investigated in-vitro detection of white blood cells using deep neural networks, and discussed how state-of-the-art machine learning techniques could fulfil the needs of medical diagnosis. The approach we used in this study was based on Faster Region-based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process was applied to apply this technique to the microscopic detection of blood cells. Our experimental results demonstrated that fast and efficient analysis of blood cells via automated microscopic imaging can achieve much better accuracy and faster speed than the conventionally applied methods, implying a promising future of this technology to be applied to the microfluidic point-of-care medical devices.

关键词： Microfluidics

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Research on Intrusion Detection and Target Recognition System Based on Deep learning 3

Research on Intrusion Detection and Target Recognition Syste...

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3rd International conference on Artificial Intelligence Applications and Technologies (AIAAT)

作者： Hu, Xianwei Li, Tie Wu, Zongzhi Gao, Xuan Univ Sci & Technol Beijing Sch Civil & Resource Engn Beijing 100083 Peoples R China State Adm Work Safety Beijing 100713 Peoples R China China Natl Offshore Oil Corp Ltd Res Inst Beijing 100034 Peoples R China China Natl Offshore Oil Corp Res Inst Beijing 100028 Peoples R China

Intrusion target detection and recognition are of great significance to security protection of oil and gas fields. An intrusion detection system is built with the integration of infrared image acquisition module, infrared image processing module, moving target detection module and recognition module. Traditional target recognition algorithm highly relies on manual design feature extraction algorithm, which requires designer to have adequate prior knowledge, and cannot avoid the influence of subjective factors of people. Intrusion detection and target recognition system are proposed based on deep learning, which uses neural network algorithm. Deep learning model is built through feature extraction and training of acquired images of intrusion objects, and thus subsequent invasion objects are detected and recognized. Intrusion detection is achieved through simulation of human brain, which boasts of more intelligent recognition process and more accurate recognition results compared with traditional recognition method. According to applications in real scenario, the system proposed has better detection and recognition results and great practical value.

关键词： Feature extraction

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intelligent optimization of Computer Image Processing Technology Analysis 3rd

Intelligent Optimization of Computer Image Processing Techno...

引用

3rd EAI International conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wei, Huayong Anhui Communications Vocational and Technical College Hefei China

ISBN: (纸本)9783031631382

intelligent optimization algorithm is an advanced computing technology, which simulates the biological evolution process in nature or the logical thinking of human beings to find a solution to the problem. In computer image processing, intelligent optimization algorithms are widely used, mainly in image enhancement, image restoration, image segmentation, feature extraction, image recognition and so on. intelligent optimization algorithms have developed rapidly, and many excellent algorithms with different characteristics have emerged, which have achieved good results in practical applications. Image analysis is the basis for realizing machine vision, including image enhancement, image fusion, image recognition, image tracking, image retrieval and many other technologies. It has a great demand in medicine, transportation, military, aerospace and other fields. In particular, the development of many industries and fields such as intelligent robots, smart medicine, and smart cities has brought many optimization challenges to image analysis, At present, image analysis based on swarm intelligence optimization algorithm has become an important research hotspot. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Image enhancement

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Student Profile in E-learning Environment Based on Two-Dimensional Ontologies 3rd

Student Profile in E-learning Environment Based on Two-Dimen...

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3rd International conference on Advanced Machine learning Technologies and Applications (AMLTA)

作者： Al-Mamoori, Heba A. A. Shehab, Mohamed Elemam El Fakharany, Essam Arab Acad Sci Technol & Maritime Transport Informat Syst Dept Cairo Egypt Arab Acad Sci Technol & Maritime Transport Cairo Egypt Arab Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol Cairo Egypt

ISBN: (纸本)9783319746906;9783319746890

A huge relevance has been observed in ontologies as a developing technology within the E-learning. Its groundwork paved the way for ontologies improvement and systems that use ontologies in different areas;E-learning is one of these systems. In This article, a recommendation data model is proposed, which takes the student activities and behavior of the social networks to enhance his/her account. The proposed model identifies both the student activities and behavior based on the development of two-dimensional ontologies. The first dimension is the social networks and the second one is the student portal, in anther word learning management system (LMS). The proposed model utilizes data from student engagement with the E-learning management system (Build activities) and the social network (Build behavior), Facebook. The model helps students identifies students' profile enhances the learning progress and improves learning quality. The model also provides vital information for educators, equipping them with a better understanding of each student's personality. A new technique for improves user profile based on students' behavior to social media and students' activates in LMS.

关键词： Ontology E-learning LMS Ontology matching Ontology merging

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An Architecture for Knowledge-Based Educational Administration System

An Architecture for Knowledge-Based Educational Administrati...

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IEEE Region 10 Colloquium/3rd International conference on Industrial and Information Systems

作者： Kumar, Suneel Natarajan, B. R. BITS Comp Sci & Informat Syst Grp Pilani Rajasthan India Birla Inst Technol & Sci Distance Learning Programmes Div Pilani Rajasthan India

ISBN: (纸本)9781424428052

Developing an intelligent system for educational administration involving knowledge-intensive activities is a challenging task Hardly adequate architectures exist which allow integration and sharing of multiple knowledge bases inherently distributed logically and physically. The intelligent systems that have been proposed in the contemporary research specifically related to educational domain, intend to cater to the needs of the front-office operations only such as E-learning, Online Course Management Systems, intelligent Tutoring Systems, etc. To offer a more comprehensive solution, the present research paper proposes an architecture for a Knowledge-Based Educational Administration System (KBEAS) to facilitate the workability of back-office operations that span a wide spectrum from routine procedures to highly judgmental issues. Further, an overall architecture of the system is illustrated by considering a case of Work-Integrated learning-Division of a leading Indian institution to demonstrate the validity and benefits of the knowledge-based technique.

关键词： Back-office operations Knowledge-Based System Educational Administration System Multi Agent System

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learning DEEP FEATURES FOR EFFICIENT FACE DETECTION 3

LEARNING DEEP FEATURES FOR EFFICIENT FACE DETECTION

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3rd International conference on intelligent Computing in Data Sciences (ICDS)

作者： Hbali, Youssef Ballihi, Lahoucine Ed-doughmi, Younes Sadgal, Mohammed Cadi Ayyad Univ Fac Sci Semlalia Comp Syst Engn Lab Marrakech Morocco Mohammed V Univ LRIT CNRST URAC 29 Fac Sci Rabat Morocco Sultan Moulay Slimane Univ Fac Tech Sci Dept Comp Sci3 Beni Mellal Morocco

ISBN: (纸本)9781728100036

Over the last decade, large companies have heavily invested in the collection, storage and the extraction of structured and unstructured data related to their clients. This trend has lead to a rise of a large data volumes considered as mine of knowledge. With the appearance of more powerful computers and the application of the graphics processing unit (GPU) computing, Deep learning has appeared as a practical solution for large data sets processing and offers an alternative strategy to learning characteristics. In this paper, we consider learning a collection of high-level facial representation characteristics, referred to as Deep hidden face characteristics, through deep learning. We present a deep learning model (DeepDetect) trained on the Annotated Facial Landmarks in the Wild (AFLW) dataset witch offers large visual variations that allows learning very discriminative face representation. We compare the performance of our DeepDetect model with different variants of Boosting LBP features, and we show that DeepDetect outperforms the hand-crafted features based approach. A detection accuracy of 98.17% is achieved on the fusion of Feret data-set and negative images extracted from the wild. The depiction of an picture is no longer a heavy hand-engineered operation with our suggested strategy, but a learning operation that leads to an effective solution to accurately differentiate faces from backgrounds.

关键词： Features learning Deep learning Face detection Computer vision

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3rd International conference on "Emerging Trends and Technologies on intelligent Systems" (ETTIS-2023)

3rd International Conference on "Emerging Trends and Technol...

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3rd International conference on "Emerging Trends and Technologies on intelligent Systems" (ETTIS-2023)

ISBN: (纸本)9789819939626

The proceedings contain 56 papers. The special focus in this conference is on International conference on "Emerging Trends and Technologies on intelligent Systems". The topics include: Multi-step Online Hate Speech Detection and Classification Using Sentiment and Sarcasm Features;EEG-Based Real-Time Prediction of Cognitive State on Smartphone;comparative Study in Parkinson’s Disease Diagnosis Using Machine learning;ioT-Based Monitoring System for Hydroponics: Design and Implementation;prediction of Lattice Constant of Pyrochlore Compounds Using Optimized Machine learning Model;an Approach for Efficient Graph Mining from Big Data Using Spark;new Algorithm to Prevent Online Test Fraud Based on Cognitive Services and Input Devices Events;comparison of Sentinel and Landsat Data Sets over Lucknow Region Using Gradient Tree Boost Supervised Classifier;Evaluation Performance and Routing on VANET Architecture: A Narrative Review;WORK-PERF: An intelligent Predictive Model for Work Performance Rating;Touch Pointer Movement-Based PIN Entry in Smartphones to Assist Persons with Visual Impairments;A Novel Approach for Detection of Soybean Leaf Disease Using Bayesian Optimized-KNN Classifier with NCA-Based Feature Selection;Design and Analysis of a Dual-Band Quad-Ridge Horn Antenna Using 3D Printing and PLA for Wireless Communication Enhancement Performance;Identify the Need for Rainwater Harvesting System (RWHS) in India Using Rainfall Data Analysis;Solid Waste Identification and Classification Method Based on Feature Selection and Hybrid ResNet CNN Models in Smart Environment;machine learning Framework for Stress Identification of Humans;survey Paper on Detection of Water Bodies in Satellite Imagery;decentralized Market for Spices Using Hyperledger;analyzing the Involvement of Various Similarity Measures in Trust and Distrust-Based Recommender System;XAI-Based Light-Weight CNN-HAR Model Using Random Sampling.

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Online Dance Guidance intelligent System based on Online Video Action Recognition 3

Online Dance Guidance Intelligent System based on Online Vid...

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3rd International conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Shi, Yuanyuan Northwest Normal University Dance College Lanzhou730070 China

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

This paper proposes a dance training system based on motion capture and virtual reality technology. Inspired by the traditional learning model, this system imitates the actions of the teacher, and students can imitate the actions demonstrated by the virtual teacher projected on the screen. The system captures students' movements, analyzes the movements, and feeds the results back to the students. The dance teaching form based on motion capture technology is an interdisciplinary field of computer science, education science, and teaching psychology. It draws on new ideas in other fields. Theory, new technology and new methods to resolve the shortcomings of the traditional teaching model. © 2022 IEEE.

关键词： Students

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Research on auxiliary decision-making for sea striking of naval aviation based on deep reinforcement learning 3

Research on auxiliary decision-making for sea striking of na...

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3rd International conference on Image Processing and intelligent Control, IPIC 2023

作者： Wu, Minjie Yin, Dawei The unit 92728 of PLA Shanghai China

ISBN: (纸本)9781510668140

The situation of the future naval battlefield will become more and more complex, and it will become a trend to develop various military auxiliary decision-making systems based on artificial intelligence and big data technology. This paper sorts out the key technologies of the auxiliary decision-making system based on deep reinforcement learning. On this basis, it proposes the construction method of the naval aviation sea-striking agent model, and completes the construction of the training framework with the combat deduction system as the environment. Finally, it summarizes and prospects some of future work. © 2023 SPIE.

关键词： Decision making

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Improving 3rd Grade student's writing ability via asynchronous discussions: A case study

Improving 3rd Grade student's writing ability via asynchrono...

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International Workshop on Computational Intelligence in Networks and Systems

作者： Bratitsis, Tharrenos Kandroudi, Marina Early Childhood Education Department University of Western Macedonia Florina Greece

ISBN: (纸本)9780769542782

The aim of the research, presented in the current paper, was to examine the impact of asynchronous discussions' usage in students' writing ability. It is proposed that critical thinking skills could be cultivated through electronic discussions. In fact, through dialogues students have the opportunity to construct meanings, interact with others and thus acquire knowledge through meaningful communication. In support of this view, the present study was conducted with the participation of 27 3rd Grade students in a primary school in Athens, who participated in the creation of an electronic, interactive newspaper through online discussions. The activity was implemented using the DIAS system, an asynchronous discussion platform with integrated Interaction Analysis tools, exploited in this case to facilitate the researcher's analysis. The results indicate that students made significantly fewer mistakes in the online environment, both quantitatively and qualitatively. Additionally, they took initiatives, worked individually and were engaged into a purposeful interaction with others, therefore reinforcing autonomous learning. © 2010 IEEE.

关键词： Students

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