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DynamicDeepFlow: An Approach for Identifying Changes in Network Traffic Flow Using Unsupervised Clustering 1

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4th international conference on machine learning for Networking (MLN)

作者： Shen, Sheng Kiran, Mariam Mohammed, Bashir Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Tula Technol San Jose CA 95131 USA

ISBN: (数字)9783030989781

ISBN: (纸本)9783030989781;9783030989774

Understanding flow changes in network traffic has great importance in designing and building robust networking infrastructure. Recent efforts from industry and academia have led to the development of monitoring tools that are capable of collecting real-time flow data, predicting future traffic patterns, and mirroring packet headers. these monitoring tools, however, require offline analysis of the data to understand the big versus small flows and recognize congestion hot spots in the network, which is still an unfilled gap in research. In this study, we proposed an innovative unsupervised clustering approach, DynamicDeep-Flow, for network traffic pattern clustering. the DynamicDeepFlow can recognize unseen network traffic patterns based on the analysis of the rapid flow changes from the historical data. the proposed method consists of a deep learning model, variational autoencoder, and a shallow learning model, k-means++. the variational autoencoder is used to compress and extract the most useful features from the flow inputs. the compressed and extracted features then serve as input-output pairs to k-means++. the k-means++ explores the structure hidden in these features and then uses them to cluster the network traffic patterns. To the best of our knowledge, this is one of the first attempts to apply a real-time network clustering approach to monitor network operations. the real-world network flow data from Energy Sciences Network (a network serving the U.S. Department of Energy to support U.S. scientific research) was utilized to verify the performance of the proposed approach in network traffic pattern clustering. the verification results show that the proposed method is able to distinguish anomalous network traffic patterns from normal patterns, and thereby trigger an anomaly flag.

关键词： Network traffic pattern clustering Unsupervised learning Flow recognition Wide area network

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Design Methodology for Single-Channel CNN-Based FER Systems 6

Design Methodology for Single-Channel CNN-Based FER Systems

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6th international conference on Information and Computer Technologies, ICICT 2023

作者： Parra, Dorfell Camargo, Carlos Universidad Nacional Department of Electric and Electronic Engineering Bogota Colombia

ISBN: (纸本)9798350300956

Facial Expression recognition (FER) systems classify emotions by using geometrical approaches or machine learning (ML) algorithms such as Convolutional Neural Networks (CNNs). Due to their complexity, these FER systems need to be implemented on high-performance hardware, which makes them unsuitable for embedded devices. To address this challenge, we propose a methodology for the design of low-complexity, CNN-based FER systems. Our methodology includes data preprocessing, Local Binary pattern (LBP) implementation, data Augmentation (DA), and CNN design. Here, we also introduce the Model M6, a single-channel CNN that reaches an accuracy of 94% in less than 30 epochs. M6 has 306,182 parameters that correspond to 1.17 MB of memory. therefore, our methodology and M6 model are feasible for implementation onto embedded systems capable of computing floating point operations. We validated our methodology and M6 model using 66 tests with 6 CNN models and 4 training parameters (batch size, learning rate, number of epochs, optimizer). this validation was performed using the Japanese Female Facial Expression (JAFFE) dataset and TensorFlow. In each test, the relationship between parameters, layers, overfitting, and underfitting was studied. Moreover, we present a step-by-step guideline on how to design the single-channel CNN and provide open-source code for readers interested in reproducing our work. © 2023 IEEE.

关键词： Local binary pattern

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ML Algorithm-Based Detection of Leaf Diseases 4

ML Algorithm-Based Detection of Leaf Diseases

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Kathiravan, M. Priya, K. Hari Sreesubha, S. Irumporai, A. Kumar, V. Sukesh Reddy, Vishnu Vardhan Hindustan Institute of Technology and Science Deemed to be University Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9781665401180

In day-to-day life, while the plant is important for every human being to get rich vitamins of vegetables, the plant disease rate is increasing proportionally. the disease on the plant causes severe suffering to farmers economically. So, the plant disease is either to be controlled or predicted before it leads to big damages. To solve this issue, machine learning algorithms have been proposed to analyze the root causes of the disease and to prescribe suitable pesticides to cure the plant. though many methods have been encountered to detect the disease of a crop, the existing methods failed to give accurate results. While many existing methods failed to apply image processing techniques and databases to store the crop data, the proposed CNN algorithm adopted these features by creating web applications to know the different types of disease that have occurred on the plant. the result witnesses that the proposed algorithms have shown improved accuracy (93.3%) which helps the farmer to raise the productivity of a crop. the developed app also helps young farmers to read and select suitable image options to improve the good yielding of their agriculture. © 2022 IEEE

关键词： pattern recognition

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AD-ResNet50: An Ensemble Deep Transfer learning and SMOTE Model for Classification of Alzheimer’s Disease 6th

AD-ResNet50: An Ensemble Deep Transfer Learning and SMOTE Mo...

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Likhita, M. Kumar, Kethe Manoj Sasank, Nerella Sai Abhinaya, Mallareddy Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India

ISBN: (纸本)9789819940707

Today, one of the emerging challenges faced by neurologists is to categorize Alzheimer's disease (AD). It is a type of neurodegenerative disorder and leads to progressive mental loss and is known as Alzheimer's disease (AD) (Tanveer et al. in Commun Appl 16:1–35, 2020). An immediate diagnosis of Alzheimer's disease is one of the requirements and developing an effective treatment strategy and stopping the disease’s progression. Resonance magnetic imaging (MRI) and CT scans can enable local changes in brain structure and quantify disease-related damage. the standard machine learning algorithms are designed to detect AD to have poor performance because they were trained using insufficient sample data. In comparison with traditional machine learning algorithms, deep learning models have shown superior performance in most of the research studies stated specific to diagnosis of AD. One of the elegant DL method is the convolutional neural network (CNN) and has helped to assist the early diagnosis of AD (Sethi et al. in BioMed Research international, 2022;Islam and Zhang in Proceedings IEEE/CVF 841 conference computing vision pattern recognition workshops (CVPRW), pp 1881–1883, 2018). However, in recent days advanced DL methods have also attempted for classification of AD, especially in MRI images (Tiwari et al. in Int J Nanomed 14:5541, 2019). the purpose of this paper is to propose a ResNet50 model for Alzheimer's disease, namely AD-ResNet50 for MRI images that incorporates two extensions known as transfer learning and SMOTE. this research uses the proposed method and compares it with the standard deep models VGG19, InceptionResNet V2, and DenseNet169 with transfer learning and SMOTE (Chawla et al. in J Artif Intell Res 16:(1)321–357, 2002). the results demonstrate the efficiency of the proposed method, which outperforms the other three models tested. When compared with baseline deep learning models, the proposed model outperformed them in terms of accuracy and ROC values

关键词： Computerized tomography

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Substation switching device identification method based on deep learning 4

Substation switching device identification method based on d...

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4th international conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Wang, Liang Kou, Qilong Zeng, Qinggai Ji, Zhonghao Zhou, Leiyue Zhou, Shuai Status Grid Luoyang Electric Power Supply Company Communication & Information Center Luoyang China Zhengzhou University College of Electrical Engineering Zhengzhou China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In order to realize the real-time and efficient detection of substation switching device status, and to solve some existing problems of substation inspection system, such as the limited degree of automation and the need for human intervention to solve the problem. this paper proposes a status recognition method of substation switching device based on deep learning. the method obtains image data by the optical camera of an inspection robot, and uses the deep learning technology to analyze and detect the data. In this method, firstly, the image data of substation switching device is selected and marked as the data set of model training, then the Yolov3 target detection network is used to build an automatic status recognition model of substation switch device. Experimental results show that this method can automatically identify the substation switching device status, and the recognition accuracy reached 97%. © 2022 IEEE.

关键词： pattern recognition

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4th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2022

4th EAI International Conference on Multimedia Technology an...

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4th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2022

ISBN: (纸本)9783031181221

the proceedings contain 59 papers. the special focus in this conference is on Multimedia Technology and Enhanced learning. the topics include: Fuzzy Evaluation Model of Teaching Quality of Physical Education Course Based on Deep Reinforcement learning;design of Online Teaching Mode recognition System for Ideological and Political Curriculum Based on Hash Algorithm;design of Cross Language Education Resource Sharing Platform Based on Hadoop Framework;design of Interactive Language Education Assistant System Based on data Classification;basketball Posture recognition Method in Physical Education Based on machine Vision;image recognition Method of Educational Scene Based on machine learning;human Motion recognition Method in Physical Education Based on Wearable Perception;acquiring the Cultural Characteristics of English Online Courses Based on Wavelet Transformation;sentiment Analysis of Opinions over Time Toward Saudi Women’s Sports;research on Fault Monitoring Device of Highway Bridge Expansion Joint Based on Internet of things Technology;"I Show You How I Solved It!": Empowering Novice University Students to Learn Programming and Mathematics through Self-produced Videos to Potentially Teach to their Peers;design of Enterprise Economic Dynamic Management System Based on Spark Technology;design of Enterprise Financial and Economic data Accurate Classification Management System Based on Random Forest;marketing Risk Assessment Method of Industrial and Commercial Enterprises Based on Convolutional Neural Network;evaluation Method of Enterprise Economic Management Model Effectiveness Based on Deep data Mining;Research on the Whole Process Quality Control Method of Water Conservancy and Hydropower Construction Based on BP Neural Network;real-Time Detection and recognition of License Plates for Traffic Monitoring.

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Multimodal IR and RF Based Sensor System for Real-Time Human Target Detection, Identification, and Geolocation 4th

Multimodal IR and RF Based Sensor System for Real-Time Human...

引用

4th international conference on Dynamic data Driven Applications Systems, DDDAS 2022

作者： Cheng, Peng Lin, Xinping Zhang, Yunqi Blasch, Erik Chen, Genshe Intelligent Fusion Technology Inc GermantownMD20874 United States Air Force Research Laboratory RomeNY United States

ISBN: (纸本)9783031526695

the Dynamic data Driven Applications System (DDDAS) paradigm incorporates forward estimation with inverse modeling, augmented with contextual information. For cooperative infrared (IR) and radio-frequency (RF) based automatic target detection and recognition (ATR) systems, advantages of multimodal sensing and machine learning (ML) enhance real-time object detection and geolocation from an unmanned aerial vehicle (UAV). Using an RF subsystem, including the linear frequency modulated continuous wave (LFMCW) ranging radar and the smart antenna, line-of-sight (LOS) and non-line-of-sight (NLOS) friendly objects are detected and located. the IR subsystem detects and locates all human objects in a LOS scenario providing safety alerts to humans entering hazardous locations. By applying a ML-based object detection algorithm, i.e., the YOLO detector, which was specifically trained with IR images, the subsystem could detect humans that are 100 m away. Additionally, the DDDAS-inspired multimodal IR and RF (MIRRF) system discriminates LOS friendly and non-friendly objects. the whole MIRRF sensor system meets the size, weight, power, and cost (SWaP-C) requirement of being installed on the UAVs. Results of ground testing integrated with an all-terrain robot, the MIRRF sensor system demonstrated the capability of fast detection of humans, discrimination of friendly and non-friendly objects, and continuously tracked and geo-located the objects of interest. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning

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DeepD_DrugC: Deep and distributed workflow to predict drug- candidates 4

DeepD_DrugC: Deep and distributed workflow to predict drug- ...

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4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

作者： Sid, Karima Zertal, Soumia Mezioud, Chaker University of Oum El Bouaghi Department of Mathematics & Computer Science Algeria Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

the applications of computational tools at various stages of drug discovery is one of the most active axes of research. Virtual Screening (VS) is a very common application, which aims to screen and analyze large chemical libraries using algorithms and models to extract drug-candidates that can bind to therapeutic targets. machine learning (ML) techniques are widely applied as a tool to analyze the chemical libraries in ligand-based virtual screening (LBVS). Deep learning (DL) is a novel mode of machine learning that provides several new architectures primarily based on classical Artificial Neural Network algorithms, but with many hidden layers to learn features with multiple levels of abstraction. Recently, chemical libraries are identified as Big data, due to their huge size, the variety of data, and the speed at which they are created, streamed and aggregated. In this context, we need advanced tools to handle and treat this type of data. Apache Spark is the most widely used engine for big data processing, with many improvements that make it more suitable for virtual screening analysis. In this work, we propose a novel workflow named DeepD_ DrugC based on Spark and Deep Neural Network model implemented with Deeplearning4j (DL4J) to improve the prediction results in LBVS. To evaluate the workflow, we suggest a process to create training datasets using the PubChem Bioassay database for cancer disease. the evaluation results show a good precision more than 93%, with acceptable scaling behavior. © 2022 IEEE.

关键词： Big data

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Ctransnet: A Modern Deep learning Model to Estimate Tropical Cyclone Intensity

Ctransnet: A Modern Deep Learning Model to Estimate Tropical...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xingchen Shan Jiagen Li Kaijun Ren College of Computer Science University of National Defense Technology ChangSha China College of Meteorology and Oceanography University of National Defense Technology ChangSha China

Tropical cyclone (TC) intensity estimation is a crucial factor in disaster protection. Fast and accurate intensity estimation can provide data support for TC forecasts. With the popularization and development of deep learning networks, massive meteorological and oceanic data, such as remote sensing satellite images and multidimensional reanalysis data, have been fully utilized, providing new methods and ideas for estimating TC intensity. However, challenges still exist in the current state of deep learning in TC estimation: the research area is mainly in the Northwest Pacific Ocean, and there is a lack of universal deep learning models for the global region. In this study, we combine the CBAM module’s adaptive attention mechanism with the Swin Transformer’s excellent global modeling ability in computer vision tasks and propose a new deep learning network named CtransNet. this network consists of two main branches: feature extraction net, fine-grained intensity classification and numerical estimation net. the first branch is used to extract high-order semantic vectors from remote sensing images, the second is to output categorical intensity probability distribution and intensity estimation. Massive experiments were carried out on the TCIR dataset, which includes multi-channel remote sensing satellite images of TC in the Northwest Pacific Ocean, the Eastern Pacific Ocean (including the Central Pacific Ocean), the Indian Ocean, the Atlantic Ocean and the Southern Hemisphere. the root mean squared error (RMSE) in the intensity estimation task reached 11.85kt. Compared with the traditional method, the performance is improved by about 31.5%.

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022) 1

1st International Virtual Conference on Visual Pattern Extra...

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Amelio, Alessia Montelpare, Sergio Ursino, Domenico InGeo University "G. d’Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DII Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy

this document introduces the 1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022), a premier forum for presenting the state-of-the-art, new research, ongoing work, academic and project reports in advanced statistics and machine learning, 3D modelling and simulation, knowledge representation, intelligent systems, information retrieval and software engineering, for visual pattern extraction, analysis and recognition to preserve the cultural heritage. © 2022 Copyright for this paper by its authors.

关键词： 3D modeling

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