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检索条件"任意字段=7th International Conference on the Art, Science, and Engineering of Programming, Programming 2023"

共 1138 条记录，以下是1071-1080 订阅

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Feature Selection on Epistatic Problems Using Genetic Algorithms with Nested Classifiers 26th

Feature Selection on Epistatic Problems Using Genetic Algor...

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26th international conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Carvalho, Pedro Ribeiro, Bruno Rodrigues, Nuno M. Batista, João E. Vanneschi, Leonardo Silva, Sara University of Coimbra Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering Coimbra Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate LAboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal LASIGE Department of Informatics Faculty of Sciences University of Lisbon Lisbon Portugal Universidade Nova de Lisboa Campus de Campolide Lisboa1070-312 Portugal

ISBN: (纸本)9783031302282

Feature selection is becoming an essential part of machine learning pipelines, including the ones generated by recent AutoML tools. In case of datasets with epistatic interactions between the features, like many datasets from the bioinformatics domain, feature selection may even become crucial. A recent method called SLUG has outperformed the state-of-the-art algorithms for feature selection on a large set of epistatic noisy datasets. SLUG uses genetic programming (GP) as a classifier (learner), nested inside a genetic algorithm (GA) that performs feature selection (wrapper). In this work, we pair GA with different learners, in an attempt to match the results of SLUG with less computational effort. We also propose a new feedback mechanism between the learner and the wrapper to improve the convergence towards the key features. Although we do not match the results of SLUG, we demonstrate the positive effect of the feedback mechanism, motivating additional research in this area to further improve SLUG and other existing feature selection methods. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Comparative Assessment of State-of-the-art Image Fusion Techniques for Fusion of Multi-modality Medical Images 7th

Comparative Assessment of State-of-the-art Image Fusion Tec...

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7th international conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Singh, Vineeta Kaushik, Vandana Dixit Department of Computer Science and Engineering Harcourt Butler Technical University East Campus Nawabganj Uttar Pradesh Kanpur208002 India

ISBN: (纸本)9789811988646

Clinical imaging is a vital part in clinical field for analysis of illnesses, for example, growth recognition, malignant growth identification and a lot more where inside organ infections are required to have been analyzed. Clinical instruments are exorbitant and accompanied impediment. For instance, CT images give better insights concerning hard designs in a human body, while MRI portrays better insights concerning delicate tissues. Be that as it may, there might be sure cases where both of the subtleties are expected by a clinical specialist to productively analyze. Here, machine vision, human insight, PC vision, clinical imaging, minute imaging and a lot more are such encouraging fields where clinical images combination plays out a vital job. In this examination paper, creators have assessed various images amalgamation strategies on clinical picture dataset for exhibition of best performing techniques through specific execution measurements. Trial results outlined that pulse coupled neural network has performed better when contrasted with non-sub sampled contourlet transform, sparse representation as well as NSCT+SR methodologies including execution evaluators: average gradient, standard deviation and spatial frequency with a best performing values of 10.4, 82.2 and 8.1 separately. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical imaging

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Interval T-S Fuzzy Modeling Based on Minimizing 1-norm on Approximation Error

Interval T-S Fuzzy Modeling Based on Minimizing 1-norm on Ap...

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CENet 2017-the 7th international conference on Computer engineering and Networks

作者： Xiaoyong Liu Shufang Zhou Zhonggang Xiong College of engineering and technology Zunyi Normal College Affiliated Hospital of Zunyi Medical College

As the obtained data in many practical applications tend to be uncertain or inaccurate,the conventional modeling methods characterized by the deterministic model for this type of data have become *** linear programming,the T-S fuzzy model and some ideas from1-norm minimization into consideration,a novel method identifying interval fuzzy model（INFUMO） consisted of the upper and lower T-S fuzzy model（referred to as f and f）has been studied in this *** order to solve the INFUMO,optimization problems based on minimizing 1-norm with respect to the approximation error corresponding to f and f are ***,the optimization problems are solved by the linear programming and INFUMO is thus *** demonstrate its effectiveness,the proposed method is applied to identify the interval T-S model of static and dynamic nonlinear model with *** proposed method can not only deal with uncertain data to be usually modeled as the deterministic model,but also has better robustness.

关键词： Interval T-S Fuzzy Modeling Based on Minimizing 1-norm on Approximation Error Fuzzy

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Automated Detection of Anomalous Events in Surveillance Video Scenes Using Deep Learning Techniques

Automated Detection of Anomalous Events in Surveillance Vide...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Abhishek S. Rao Karthik Pai B H Sathvik R Rao Samarth Prathap M Anjani Prabhu K Rakshitha K Dept. of Information Science & Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte India

the identification of anomalous events in surveillance camera situations is a critical issue in public safety and security. Traditional surveillance systems, reliant on human operators, are labor-intensive and prone to errors. To address this challenge, we propose a deep learning approach for the automated detection of unusual events in surveillance video scenes. Utilizing the remarkable performance of deep learning techniques in computer vision, we extract spatiotemporal information from video frames and classify them as normal or unusual using a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. the objective of this project is to develop a deep learning approach that overcomes the limitations of traditional video surveillance systems by automatically detecting and classifying unusual events. the proposed approach combines CNNs to extract spatial features and LSTMs to model temporal dependencies between frames, effectively capturing both spatial and temporal characteristics of video scenes. Experiments were conducted on two public datasets for abnormal event detection, and the proposed approach outperformed state-of-the-art methods, achieving an accuracy of 80%. the significant implications of this approach for public safety and security lie in its ability to improve the accuracy and efficiency of video surveillance systems while reducing the workload of human operators. By automating the detection of unusual events, this deep learning approach can enhance overall surveillance capabilities and help prevent critical security issues.

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A Hybrid Data Acquisition Model for Precision Agriculture using IoT, engineering Nanomaterials and artificial Intelligence

A Hybrid Data Acquisition Model for Precision Agriculture us...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： K.Sankara Moorthy Ms. Peddinti Neeraia P. Pavitra R. Arivumalar K. Veeranjaneyulu S.Mohana Murali College of Management SRM Institute of Science and Technology Kattankulathur Department of Computer Applications School of Computing Mohan Babu University Tirupati Department of Basic science and humanities Dadi Institute of Engineering and Technology visakhapatnam Anakapalli Sri Sairam Engineering College West Tambaram Chennai-44 CVR College of Engineering Rangareddy Telangana Dept of commerce Govt Degree College Andhra Pradesh India

Two key ideas for wireless sensor network (WSN) optimization in precision farming serve as the guidelines for this study. According to its definition, a wireless sensor network is a collection of sensor nodes that link to one another and/or with a node outside of their coverage area via wireless communication. these nodes frequently have one or more sensors for evaluating internal or external elements as well as environmental characteristics. A CPU then controls the sensors, compiling, storing, processing, and, if requested, transmitting the data. In some systems, the sensor nodes' processing power is constrained to make them more energy-efficient. In this application, the sensor nodes transfer the collected data to a remote node with more powerful processing capabilities and greater storage in specified time intervals or condition-based events storage. Because of its ability to monitor processes at a small scale, nanotechnology is regarded as a leading technology for controlling agriculture. So, it paves the way for crucial advantages such as improving food quality and quantity, lowering the input needed for agricultural production, fully utilizing soil nutrients, etc. the availability of natural resources, detecting the right nutrients from the soil for crop-specific production, and crop cultivation are issues in these models. In order to boost crop yield by assessing the nutrients in the soil, various nano-sensors are used in this research study. the type of crop that can be employed for cultivation or irrigation depends on the accuracy of acquisition and detection. Real-time nanosensors are used to collect the soil constituents that are necessary for crop productivity. In comparison to state-of-the-art models, simulation results utilizing a deep learning detector based on input obtained from nanosensors, which demonstrate increased productivity

关键词： Productivity Wireless sensor networks Protocols Nanosensors Crops Process control Soil

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Model-free Resilient Controller Design based on Incentive Feedback Stackelberg Game and Q-learning

Model-free Resilient Controller Design based on Incentive Fe...

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Industrial Cyber-Physical Systems (ICPS)

作者： Jiajun Shen the Fengjun Li Morteza Hashemi Huazhen Fang The Department of Mechanical Engineering The University of Kansas Lawrence KS USA The Department of Electrical Engineering and Computer Science The University of Kansas Lawrence KS USA

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

In the swift evolution of Cyber-Physical Systems (CPSs) within intelligent environments, especially in the industrial domain shaped by Industry 4.0, the surge in development brings forth unprecedented security challenges. this paper explores the intricate security issues of Industrial CPSs (ICPSs), with a specific focus on the unique threats presented by intelligent attackers capable of directly compromising the controller, thereby posing a direct risk to physical security. Within the framework of hierarchical control and incentive feedback Stackelberg game, we design a resilient leading controller (leader) that is adaptive to a compromised following controller (follower) such that the compromised follower acts cooperatively with the leader, aligning its strategies with the leader’s objective to achieve a team-optimal solution. First, we provide sufficient conditions for the existence of an incentive Stackelberg solution when system dynamics are known. then, we propose a Q-learning-based Approximate Dynamic programming (ADP) approach for the online resolution of the incentive Stackelberg solution without requiring prior knowledge of system dynamics. Last but not least, we prove the convergence of our approach to the optimum.

关键词： Sufficient conditions Q-learning System dynamics Games Cyber-physical systems Dynamic programming Fourth Industrial Revolution

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Data-Centric Publish-Subscribe Approach for Distributed Complex Event Processing Deployment in Smart Grid Internet of things

Data-Centric Publish-Subscribe Approach for Distributed Comp...

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2016 IEEE 7th international conference on Software engineering and Service science

作者： Xiangrong Zu Yan Bai Xu Yao Department of Control ＆ Computer Engineering North China Electric Power University

Smart grid is an important application of Internet Of things（IOT）.Monitoring data in large-scale smart grid are massive,real-time and dynamic that collected by a lot of sensors,services components *** are many challenges in deploying and managing distributed real time monitoring *** paper adopted data-centric approach in deploying Distributed Complex Event Processing（DCEP） monitoring system based on OMG Data Distribution Service（DDS） middleware,to bring about a more robust and scalable distributed monitoring system,and that easier to maintain and enhance over *** DDS data-centric publish-subscribe programming model with support for a number of QoS properties gives many advantages for supporting DCEP components communications,that reliably transports events from data producers to Event Processing Agents（EPAs）,between internal DCEP Processing Node（PN）,and from the EPAs to the event *** paper analyzed DCEP requirements in IoT,and its PN configuration and deployment flow,designed a DDS/DCEP integration architecture for smart grid IoT monitoring system,and gave a detail design about DCEP development and configuration method based on DDS data-centric programming model,that can simplify the DCEP complex deployment *** work has beneficial for DECP large scale development ＆ deployment research.

关键词： data-centric approach data distribution service distributed complex event processing internet of things publish-subscribe modle smart grid

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CovDNet: A Hybrid Multi - Level Densely Connected Neural Network for Improvised Analysis and Classification of COVID-19 Using Chest CT-Scans

CovDNet: A Hybrid Multi - Level Densely Connected Neural Net...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Deepa S Praveenkumar G D R Suguna Vivek Duraivelu C Jamunadevi S G Sundhar Department of Computer Technology-PG Kongu Engineering College Perundurai Erode Tamilnadu Department of computer science Vet institute of Arts and Science (Co-Education) College Erode Tamilnadu Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana Department of Computer Technology PG Kongu Engineering College Perundurai Erode Tamilnadu Final M.Sc(Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

COVID-19, caused by the new coronavirus SARS-Co V-2, has turned into a worldwide health emergency, needing speedy and precise diagnostic techniques. this abstract provides a thorough evaluation of research works focusing on COVID-19 identification using DenseNet, a cutting-edge convolutional neural network architecture. DenseNet is notable for its innovative design, which fosters feature reuse while relieving the vanishing gradient problem and enhancing network information flow. Researchers have created novel ways for COVID-19 identification using medical imaging, such as chest X-rays and CT scans, by harnessing the capabilities of DenseNet. the analyzed studies demonstrate DenseNet's efficiency in recognizing COVID-19-specific patterns and distinguishing them from other lung diseases. these investigations have shown remarkable accuracy, sensitivity, and precision, exceeding established machine learning approaches and even professional radiologists. this research study also highlights the difficulties and constraints experienced while using DenseNet to identify COVID-19. Issues such as dataset quantity, class imbalance, and model decision-making process interpretability are addressed. Furthermore, future research paths and prospective enhancements, such as multi-modal data integration and the development of explainable AI systems, are investigated. the experimental results indicate that the proposed approach achieves high accuracy, sensitivity, and precision in the identification of COVID - 19. the proposed methodology achieved an accuracy of 89.74%, sensitivity of 87.25 % , and precision of 79.56%, which outperforms the existing state-of-the-art methods. the proposed approach is robust and can effectively differentiate between COVID-19 positive and negative cases, which is essential for early detection and prompt treatment.

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A Brief Overview of Deep Learning based Techniques for the Detection of Wheat Leaf Disease: A Recent Study

A Brief Overview of Deep Learning based Techniques for the D...

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international conference on ent Computing and Control Systems (ICICCS)

作者： Protyush Protim Neog Salil Batra Sudhir Saraswat Emani Likith Sharma P. Pavan Kumar Ankit Kumar Pandey Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab

Wheat is an important cereal crop and is the second most consumed cereal after rice globally. It is a staple food for more than one-third of the world’s population. the production of wheat depends on various factors, such as climate, temperature, soil, pests, bacteria, and other biotic and abiotic factors. However, diseases can have a significant impact on wheat production. Various wheat diseases can affect crop yields,including leaf rust, leaf spot, spike infection, virus, bacterial back chaff, bacterial spike blight, and aphids. Leaf rust, in particular, is known to cause severe damage to wheat leaves. To combat these diseases, researchers have been investigating the use of advanced technologies such as deep learning and image-processing approaches for plant disease recognition. the process of disease detection involves several steps, including image preprocessing, segmentation, feature extraction, and classification. the accuracy of these steps directly affects the reliability and accuracy of the classification algorithms used to identify plant diseases. Recent studies have reviewed the state-of-the-art techniques used in this context and evaluated their effectiveness in real life applications. Overall, the use of advanced technologies such as deep learning and image processing for disease detection in wheat crops holds great promise in improving crop yields and reducing losses due to diseases. By identifying diseases at an early stage, farmers can take appropriate measures to control the spread of the disease and prevent significant crop damage. this research work presents a comparative analysis of the state-of-the-art work that has been carried out in this context and the effectiveness of the techniques in real-time.

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A New Diagnostic Approach for the Detection of Wheat Leaf Disease Using Deep Transfer and Ensemble Learning Based Models

A New Diagnostic Approach for the Detection of Wheat Leaf Di...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Sudhir Saraswat Salil Batra Protyush P Neog Emani Likith Sharma P. Pavan Kumar Ankit Kumar Pandey Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab

More than one third of the global population consume wheat as a staple food. However, the production of this vital crop is hampered by various challenges, with diseases being a chief concern. Various factors, spanning from climate and soil to pests and pathogens, influence wheat health. Among the numerous diseases affecting wheat, leaf rust stands out as a severe disease hampering both the quality and quantity of wheat produced. In order to combat these diseases, research scholars are utilizing cutting-edge tools like deep learning and image processing to identify and categorize wheat plant diseases, thereby increasing crop yields and reducing losses. Recent studies have closely studied the practical viability of these advanced techniques in the context of wheat plant disease detection and classification. this research introduces a state-of-art framework for wheat disease detection. By harnessing the power of transfer-learning and ensemble-learning, the methodology achieves an outstanding testing accuracy of 99.56%. It performs remarkably better than popular deep learning models like VGG19, ResNet50, and DenseNet121. Importantly, the experimental results highlight not only the method's higher performance in key measures including precision, recall but also its elevated accuracy. this research's breakthrough holds promise in addressing the pressing issue of disease detection in wheat cultivation, offering a brighter outlook for effective crop management and increased production.

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