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Early Detection of Influenza Using machine learning Techniques 4th

Early Detection of Influenza Using Machine Learning Techniqu...

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4th international conference on Recent Innovations in computing, ICRIC 2021

作者： Maheshwari, Sajal Sharma, Anushka Kumar, Ranjan Pratyush Department of Computer Science Aryabhatta College University of Delhi Delhi India

ISBN: (纸本)9789811688911

Influenza is an infectious disease that rapidly affects all living beings and the effect of this outbreak can be seen as infection in thousands of people yearly across the world. For example, the Spanish Flu is detected as one of the most devastating influenza outbreak in 1918. Recently, the usage of machine learning techniques has been increased in medical research fields which include early diagnosis of a disease, pathology, and classification of various diseases. In this study, the dataset is extracted Human Surveillance records from the Influenza Research Database. Initially, 15,651 records extracted over the period of 2006–2017 for data pre-processing and then 9548 records have been selected for further analysis. The next step follows the classification and analysis of data based on the four different machine learning methods, support vector machines, K-nearest neighbors, artificial neural networks, and random forest. Their performances are evaluated in terms of sensitivity, specificity, and accuracy. It is evident from the experiments that random forest is one of the best machine learning techniques for early detection of the influenza. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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A Survey on Identification of Hate Speech on Social Media Post 3

A Survey on Identification of Hate Speech on Social Media Po...

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3rd international conference on computing, Analytics and Networks, ICAN 2022

作者： Jain, Archika Sharma, Sandhya Suresh Gyan Vihar University Department of Computer Engineering Jaipur India Suresh Gyan Vihar University Department of Electronics & Communication Jaipur India

ISBN: (纸本)9781665499446

In the age of social media and mobile internet, the development of autonomous methods for online detection of hate speech or abusive language is vital for societal and community empowerment because social media is accessible worldwide via the internet. Anybody may simply attack someone or a group who adheres to a different culture or ideology on social media. While everyone has the freedom to express their own opinions, it should not be destructive, and everyone has the right to be free to say anything. To learn about hate speech, a review process include a large number of research articles which were published in the period of year 2017 to year 2022. After an exhaustive review process, we have found the common findings, strengths, weaknesses, gaps and solution approaches & their results. © 2022 IEEE.

关键词： machine learning

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Optimum Particle's Initial Values Impact on PSO Optimizer of Arrays Pattern 21

Optimum Particle's Initial Values Impact on PSO Optimizer of...

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5th international conference on machine learning and soft computing, icmlsc 2021

作者： Zhang, Shuchun Liu, Yu Chou, Yongbin AVIC International Simulation Technology and Service CO. Ltd Shanghai China

ISBN: (纸本)9781450387613

Abstract: When particle swarm optimization (PSO) is adopted to optimize antenna array pattern, the optimizer's convergence can be greatly improved by the efficient estimations of the optimum particle's initial values, and some global optimal solutions better than usual can be obtained. By using the array's Taylor weighting to estimate the optimum particle's initial values, a new method is presented to improve PSO optimizer's convergence. Comparisons are made between three methods of estimations, which are random weights, Taylor weights and analytic weights respectively, it can be said that Taylor weights and analytic weights are both effective to improve the PSO optimizer convergence relative to the random weights, and they also have similar performance. As the analytic weights only can be solved by performing large-scale matrix's generalized inversion which is usually difficult and hardly afforded by common PC, while Taylor weights are relatively simple and easy to be calculated, the practicability of the latter is far better than the former. © 2021 ACM.

关键词： Particle swarm optimization (PSO)

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Sequence Labeling Approach to the Task of Sentence Boundary Detection 2020

Sequence Labeling Approach to the Task of Sentence Boundary ...

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4th international conference on machine learning and soft computing (icmlsc)

作者： The Anh Le Moscow Inst Phys & Technol Neural Networks & Deep Learning Lab Moscow Russia Vietnam Maritime Univ Fac Informat Technol Hai Phong Vietnam

ISBN: (纸本)9781450376310

One of the keys to enable chatbots to communicate with human in a more natural way is the ability to handle long and complex user's utterances. In order to achieve this goal, we propose to integrate the Sentence Boundary Detection (SBD) module into the chatbot architecture, whose role is to take as input a user's utterance from an automatic speech recognition device, in which sentence boundaries are not available, and output the corresponding list of punctuated sentences for downstream modules such as Intent Detection, Topic Classification, Sentiment Analysis, Named Entity Recognition, as well as Coreference Recognition. To address the SBD task, we reformulate it as a sequence labeling task. In this way, both deep neural network models (e.g., Bi-directional Long Short-Term Memory, Convolutional Neural Network) and structured prediction models (e.g., Hidden Markov Model, Maximum Entropy Model, Conditional Random Field) can be leveraged. After reformulating the SBD task, we built a hybrid deep neural network model and achieved good performance on both CornellMovie-Dialog and DailyDialog datasets.

关键词： Sentence boundary detection sequence labeling voice-enabled chatbot

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OSE-IDS: Optimized Stacked Ensemble Intrusion Detection System Using Automated machine learning Approach

OSE-IDS: Optimized Stacked Ensemble Intrusion Detection Syst...

引用

Mobile, Intelligent, and Ubiquitous computing conference (MIUCC), international

作者： Mohamed A. Salama Radwa M. Tawfeek Sara Hamdy Omar Salim Electrical Engineering Department Benha Faculty of Engineering Benha University Benha Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

As cybercrimes are increasingly evolving, the existence of an intelligent Network intrusion detection system (NIDS) is indispensable in the network infrastructure. In addition, there are many challenges face NIDS design based on Artificial Intelligence - technology such as irrelevant features in network traffic, rare examples of malicious traffic, and the efforts for machine learning model selection and models' hypermeters finetuning. This study proposes efficient NIDS concerned with these challenges to accurately detect malicious behaviors. First, A parallel hybrid feature selection approach filters the most important features. Second, to address data imbalance, we integrated a combined Random Under-sampling Strategy and Synthetic Minority Oversampling Technique-Edited Nearest Neighbors technique to ensure the balanced representation of minority attacks. Finally, the stacked ensemble classifier, comprising the four best base models selected through the Automated machine learning approach. Using the CICIDS2017 dataset, a comprehensive benchmark for intrusion detection research, our approach achieves an impressive detection rate of 99.76%, effectively identifying both majority and minority classes.

关键词： Intelligent networks Accuracy Filters Network intrusion detection Telecommunication traffic Feature extraction Ubiquitous computing Hyperparameter optimization Data models Faces

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Network Intrusion Detection Using Weighted Voting Ensemble Deep learning Model

Network Intrusion Detection Using Weighted Voting Ensemble D...

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Electrical, Electronic and computing Engineering (IcETRAN), international conference on

作者： Marija Milosevic Vladimir Ciric Ivan Milentijevic Faculty of Electronic Engineering Nis University of Nis Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

System and network security have become one of the main considerations for the system or network design, implementation and maintenance. The increased volume of traffic and the ever-changing form of internet communications has led to a steep rise in network attacks, hence the need to protect both sides of the communication is essential. Over the last couple of years, an upsurge in the use of machine learning and deep learning techniques in detecting network attacks can be noticed. Alongside with individual machine and deep learning techniques, various ensemble techniques have been implemented in order to combine multiple models working on the same problem. However, ensemble techniques mostly focus on machine learning models, disregarding the fact that deep learning techniques have been proven to be efficient when handling large amounts of data. In this paper, a network intrusion detection system is implemented using a weighted voting ensemble deep learning model. Fifteen various Deep Neural Network models have been trained and evaluated on the imbalanced multiclass CICIDS-2017 dataset. Weighted voting system has been implemented for combining decisions of various models in two ways. The evaluation results for multiple combinations are given. The proposed system has the ability to use heterogeneous models for the decision.

关键词： Deep learning Computational modeling Network intrusion detection Artificial neural networks Network security Data models Maintenance

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XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI 43

XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated ...

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43rd Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Zhou, Yirong Qian, Chen Guo, Yi Wang, Zi Wang, Jian Qu, Biao Guo, Di You, Yongfu Qu, Xiaobo Xiamen Univ Natl Inst Data Sci Hlth & Med Dept Elect Sci Biomed Intelligent Cloud R&D Ctr Xiamen 361005 Peoples R China Xiamen Univ Dept Instrumental & Elect Engn Xiamen 361005 Peoples R China Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China China Mobile Grp Xiamen 361005 Peoples R China Xiamen Univ Sch Elect Sci & Engn Biomed Intelligent Cloud R&D Ctr Xiamen 361005 Peoples R China

ISBN: (纸本)9781728111797

machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI). Cloud computing technologies have great advantages in building an easily accessible platform to deploy advanced algorithms. In this work, we develop an open-access, easy-to-use and high-performance medical intelligence cloud computing platform (XCloud-pFISTA) to reconstruct MRI images from undersampled k-space data. Two state-of-the-art approaches of the Projected Fast Iterative soft-Thresholding Algorithm (pFISTA) family have been successfully implemented on the cloud. This work can be considered as a good example of cloud-based medical image reconstruction and may benefit the future development of integrated reconstruction and online diagnosis system.

关键词： Computers Cloud computing machine learning algorithms Magnetic resonance imaging Buildings machine learning Iterative algorithms

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learning Representations by Crystallized Back-Propagating Errors 1

引用

22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

作者： Grum, Marcus Potsdam University DE Potsdam14482 Germany

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

With larger artificial neural networks (ANN) and deeper neural architectures, common methods for training ANN, such as backpropagation, are key to learning success. Their role becomes particularly important when interpreting and controlling structures that evolve through machine learning. This work aims to extend previous research on backpropagation-based methods by presenting a modified, full-gradient version of the backpropagation learning algorithm that preserves (or rather crystallizes) selected neural weights while leaving other weights adaptable (or rather fluid). In a design-science-oriented manner, a prototype of a feedforward ANN is demonstrated and refined using the new learning method. The results show that the so-called crystallizing backpropagation increases the control possibilities of neural structures and interpretation chances, while learning can be carried out as usual. Since neural hierarchies are established because of the algorithm, ANN compartments start to function in terms of cognitive levels. This study shows the importance of dealing with ANN in hierarchies through backpropagation and brings in learning methods as novel ways of interacting with ANN. Practitioners will benefit from this interactive process because they can restrict neural learning to specific architectural components of ANN and can focus further development on specific areas of higher cognitive levels without the risk of destroying valuable ANN structures. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural networks

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Expand Object Detection Dataset Automatically via Deep learning Neural Networks

Expand Object Detection Dataset Automatically via Deep Learn...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Bing Liu Subramaniam Ganesan Department of Electrical and Computer Engineering Oakland University Rochester MI

Object detection is an important research field in image processing. The state-of-the-art machine learning models are trained with huge number of samples to solve this problem. Manually labeling objects on images is labor intensive, expensive, and time consuming. It is desirable to create an automatic labeling system to reduce the cost and the time needed. A deep learning neural networks based automatic labeling system is proposed in this paper to expand Microsoft Common Objects in Context 2017 (COCO 2017) dataset from 80 classes to 81 classes by adding labels for the new class, human faces, automatically. The system contains an Auto-Labeling Model (ALM) which is trained using Web Image Dataset for Event Recognition FACE (WIDER FACE) dataset. ALM detects faces in COCO 2017 dataset and provides bounding boxes automatically with very high accuracy. The generated new labels by ALM are merged to the existing labels in COCO 2017 dataset automatically. 4,110 hours human effort is saved using this system. The expanded COCO 2017 dataset is evaluated quantitatively and visually to ensure the efficiency of the proposed auto-labeling system.

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machine learning for Damage Classification, Risk Mitigation and Post-earthquake Management 7th

Machine Learning for Damage Classification, Risk Mitigation ...

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7th international conference on Earthquake Engineering and Seismology, ICEES 2023

作者： Di Michele, F. Giannopoulou, O. Stagnini, E. Pera, D. Rubino, B. Aloisio, R. Askan, A. Marcati, P. Milan Italy L’Aquila Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Departments of Civil Engineering and Earthquake Studies Middle East Technical University Ankara Turkey Assergi Italy

ISBN: (纸本)9783031573569

In recent years, we have witnessed a steady increase in the amount of data collected and made available to the scientific community. Simultaneously, Artificial Intelligence (AI) shows great potential in transforming data to knowledge offering increasingly more accurate tools to analyze, interpret and extract information from the data. In this context, data-driven approaches, in the fields of seismology, geophysics, and earthquake engineering show enormous promise. In this work a machine learning (ML) study is presented, based on a dataset containing around 3000 buildings damaged by the 2009 L’Aquila earthquake. This event was the first in a series of strong earthquakes that hit central Italy, resulting in many casualties, and having enormous economic and social impact. Each building in the dataset is described by 22 characteristics. Among them the damage level, divided, into six classes, from D0, corresponding to no damage, to D5, corresponding to heavy damage or collapse. We employ a Random Forest based algorithm to predict the level of damage accounting for different combinations of damage levels. As a case study we consider the following binary target variables-D0-D1-D2-D3 (no to medium damage) and D4-D5 (serious to heavy damage)-D0-D1 (no to light damage) and D2-D3-D4-D5 (moderate to heavy damage)-D0-D1-D02 (light to moderate damage) and D3-D4-D5 (medium to heavy damage). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning

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