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Development of deep learning models for microglia analyses in brain tissue using deepathology^TM STUDIO

JOURNAL OF NEUROSCIENCE METHODS 2021年第0期364卷 109371-109371页

作者： Mohle, Luisa Bascunana, Pablo Brackhan, Mirjam Pahnke, Jens Univ Oslo UiO Dept Pathol Translat Neurodegenerat Res & Neuropathol Lab Oslo Norway Oslo Univ Hosp OUS Oslo Norway Univ Lubeck LIED Lubeck Germany Univ Latvia Fac Med Dept Pharmacol Riga Latvia

Background: Interest in artificial intelligence-driven analysis of medical images has seen a steep increase in recent years. Thus, our paper aims to promote and facilitate the use of this state-of-the-art technology to fellow researchers and clinicians. New method: We present custom deep learning models generated in deepathologyTM STUDIO without the need for background knowledge in deep learning and computer science underlined by practical suggestions. Results: We describe the general workflow in this commercially available software and present three real-world examples how to detect microglia on IBA1-stained mouse brain sections including their differences, validation results and analysis of a sample slide. Comparison with existing methods: deep-learning assisted analysis of histological images is faster than classical analysis methods, and offers a wide variety of detection possibilities that are not available using methods based on staining intensity. Conclusions: Reduced researcher bias, increased speed and extended possibilities make deep-learning assisted analysis of histological images superior to traditional analysis methods for histological images.

关键词： Artificial intelligence deep learning algorithm Histology Image analysis Machine learning Microglia Alzheimer

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Ensembling of Attention-based Recurrent Units for Detection and Mitigation of Multiple Attacks in Cloud

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第10期14卷 86-92页

作者： Kalaivani, M. Padmavathi, G. Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore India Avinashilingam Inst Home Sci & Higher Educ Women Sch Phys Sci & Computat Sci Coimbatore India Avinashilingam Inst Home Sci & Higher Educ Women Dept Comp Sci Coimbatore India

the recent years, number of threats to network security increases exponentially as the Internet users which poses serious threat in cloud storage application. Detection and defending against the multiple threats are currently a hot topic in industry and considered as one of the challenging research in academia. Many methodologies and algorithms devised to predict the different attacks. Still, most of the methods cannot simultaneously achieve high performance of prediction with a small number of false alarm rates. In this scenario, deep learning (DL) algorithms are appropriate and intelligent to categorize the multiple attacks. Still, most of the existing DL techniques are computationally inefficient that may degrade the performance in predicting the both normal and attack information. To overcome this aforementioned problem, this paper proposes the hybrid combination of attention maps with deep recurrent networks to mitigate the multiple attacks with low computational overhead. Initially, the pre-processing step is proposed to the inputs in a specified range. Later on, input data are fed into the Attention Enabled Gated Recurrent Networks (AEGRN) which is used to remove the redundant features and select the optimal features that aids for the better classification. Further to enhance the faster response, deep feed forward layers are proposed to replace the traditional deep neural networks. Numerous metrics for performance, including accuracy, precision, recall, specificity, and F1-score, are examined and analyzed as part of the thorough experimentation utilizing multiple datasets, including NSL-KDD-99, UNSW -2019, and CIDC-001. Comparisons of performance between the method that is suggested and existing models developed with DL are used to demonstrate the proposed algorithm's supremacy. The suggested framework surpasses the other DL models and has the best accuracy in predicting with little computational overhead, according to an investigation.

关键词： Multiple threats deep learning algorithm attention enabled gated recurrent networks NSL-KDD UNSW CIDC-001

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Application of Depth learning algorithm in Automatic Processing and Analysis of Sports Images

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Computer Systems Science & Engineering 2023年第10期47卷 317-332页

作者： Kai Yang North China University of Water Resources and Electric Power Henan450000China

With the rapid development of sports,the number of sports images has increased *** and automatic processing and analysis of moving images are significant,which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports *** this paper,a method of table tennis identification and positioning based on a convolutional neural network is proposed,which solves the problem that the identification and positioning method based on color features and contour features is not adaptable in various *** the same time,the learning methods and techniques of table tennis detection,positioning,and trajectory prediction are studied.A deep learning framework for recognition learning of rotating flying table tennis is put *** mechanism and methods of positioning,trajectory prediction,and intelligent automatic processing of moving images are studied,and the self-built data sets are trained and verified.

关键词： deep learning algorithm convolutional neural network moving image trajectory intelligent processing

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Semi-Supervised False Data Detection Using Gated Recurrent Units and Threshold Scoring algorithm

Semi-Supervised False Data Detection Using Gated Recurrent U...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Parizad, Ali Hatziadoniu, Constantine Southern Illinois Univ Dept Elect & Comp Engn Carbondale IL 62901 USA

ISBN: (纸本)9781665405072

In recent years, cyber attackers are targeting the power system and imposing different damages to the national economy and public safety. False Data Injection Attack (FDIA) is one of the main types of Cyber-Physical attacks that adversaries can manipulate power system measurements and modify system data. Consequently, it may result in incorrect decision-making and control operations and lead to devastating effects. In this paper, we propose a two-stage detection method. In the first step, Gated Recurrent Unit (GRU), as a deep learning algorithm, is employed to forecast the data for the future horizon. Meanwhile, hyperparameter optimization is implemented to find the optimum parameters (i.e., number of layers, epoch, batch size, beta(1), beta(2), etc.) in the supervised learning process. In the second step, an unsupervised scoring algorithm is employed to find the sequences of false data. Furthermore, two penalty factors are defined to prevent the objective function from greedy behavior. We assess the capability of the proposed false data detection method through simulation studies on a real-world data set (ComEd. dataset, Northern Illinois, USA). The results demonstrate that the proposed method can detect different types of attacks, i.e., scaling, simple ramp, professional ramp, and random attacks, with good performance metrics (i.e., recall, precision, F1 Score). Furthermore, the proposed deep learning method can mitigate false data with the estimated true values.

关键词： Cyber Security Attack Templates False Data Injection Attack deep learning algorithm Gated Recurrent Unit (GRU) Supervised/Unsupervised learning Detection Method

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deep learning application in diverse fields with plant weed detection as a case study 21

Deep learning application in diverse fields with plant weed ...

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Proceedings of the International Conference on Artificial Intelligence and its Applications

作者： Uchechi F. Ukaegbu Lagouge K. Tartibu Modestus O. Okwu Isaac O. Olayode Department of Mechanical & Industrial Engineering University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9781450385756

Machine learning applications have gained popularity over the years as more advanced algorithms like the deep learning (DL) algorithm are being employed in signal identification, classification and detection of cracks or faults in structures. The DL algorithm has broader applications compared to other machine learning systems and it is a creative algorithm capable of processing data, creating pattern, interpreting information due to its high level of accuracy in pattern recognition under stochastic conditions. This research gives an exposition of DL in diverse areas of operations with a focus on plant weed detection which is inspired by the need to treat a specific class of weed with a particular herbicide. A Convolutional Neural Network (CNN) model was trained through transfer learning on a pre-trained ResNet50 model and the performance was evaluated using a random forest (RF) classifier, the trained model was deployed on a raspberry pi for prediction of the test data. Training accuracies of 99% and 93% were obtained for the CNN and RF classifier respectively. Some recommendations have been proffered to improve inference time such as the use of better embedded systems such as the Nvidia Jetson TX2, synchronizing DL hardware accelerators with appropriate optimization techniques. A prospect of this work would be to incorporate an embedded system, deployed with DL algorithms, on an unmanned aerial vehicle or ground vehicle. Overall, it is revealed from this study that DL is highly efficient in every sector and can improve the accuracy on automatic detection of systems in especially in this era of Industry 4.0.

关键词： Random forest fourth industrial revolution deep learning algorithm Agriculture Convolutional neural network

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Application of dynamic time warping optimization algorithm in speech recognition of machine translation

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HELIYON 2023年第11期9卷 e21625页

作者： Jiang, Shaohua Chen, Zheng Fujian Univ Technol Sch Humanities Fuzhou 350118 Peoples R China Krirk Univ Bangkok 10220 Thailand Fujian Normal Univ Concord Univ Coll Fuzhou Fujian Peoples R China

Speech recognition is the foundation of human-computer interaction technology and an important aspect of speech signal processing, with broad application prospects. Therefore, it is very necessary to recognize speech. At present, speech recognition has problems such as low recognition rate, slow recognition speed, and severe interference from other factors. This paper studied speech recognition based on dynamic time warping (DTW) algorithm. By introducing speech recognition, the specific steps of speech recognition were understood. Before performing speech recognition, the speech that needs to be recognized needs to be converted into a speech sequence using an acoustic model. Then, the DTW algorithm was used to preprocess speech recognition, mainly by sampling and windowing the speech. After preprocessing, speech feature extraction was carried out. After feature extraction was completed, speech recognition was carried out. Through experiments, it can be found that the recognition rate of speech recognition on the basis of DTW algorithm was very high. In a quiet environment, the recognition rate was above 93.85 %, and the average recognition rate of the 10 selected testers was 95.8 %. In a noisy environment, the recognition rate was above 91.4 %, and the average recognition rate of the 10 selected testers was 93 %. In addition to high recognition rate, DTW based speech recognition also had a very fast speed for vocabulary recognition. Based on the DTW algorithm, speech recognition not only has a high recognition rate, but also has a faster recognition speed.

关键词： Speech recognition Dynamic time warping algorithm Feature extraction deep learning algorithm

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Fully convolutional deep Stacked Denoising Sparse Auto encoder network for partial face reconstruction

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PATTERN RECOGNITION 2022年 130卷

作者： Dinesh, P. S. Manikandan, M. Anna Univ Madras Inst Technol Chennai India Anna Univ Dept Elect Engn Madras Inst Technol Chennai India

Face recognition is one of the most successful applications of image analysis. Since 1960s, automatic face recognition research has been carried out, but the problem is still unresolved. Therefore, in this manuscript, a novel Partial face reconstruction (PFR) algorithm called Self- motivated feature mapping (SMFM) combining a Fully Convolutional Network (FCN) and deep Stacked Denoising Sparse Autoencoders (DS-DSA) algorithm is proposed to overcome the challenges. The proposed approach focuses on the generation of feature maps from the Fully Convolutional Network and it is used deep Stacked Denoising Sparse Autoencoders to perform the partial face reconstruction. The spatial maps are generated by extracting the features from Fully Convolutional Network and it is supplied as the input for partial reconstruction and re-identification to the deep Stacked Denoising Sparse Autoencoders network. The main aim of the proposed work is "to enhance the accuracy during facial reconstruction". The proposed approach is implemented in MATLAB platform. The performance of the proposed approach attains 23.45% and 20.41% accuracy,25.93`% and 19.43% sensitivity, 22.21% and 24.41% precision and20.21% and 23.41% Specificity greater than the existing approaches, like Partial Face Reconstruction using generative adversarial networks (GANs), Partial Face Reconstruction using deep Recurrent neural network (DRNN). (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Partial face recognition deep learning algorithm Fully convolutional network Autoencoder

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Using Speech Acts to Elicit Positive Emotions for Complainants on Social Media

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JOURNAL OF INTERACTIVE MARKETING 2021年第1期55卷 67-80页

作者： Argyris, Young Anna Monu, Kafui Kim, Yongsuk Zhou, Yilu Wang, Zuhui Yin, Zhaozheng Michigan State Univ Coll Commun Arts & Sci Dept Media & Informat 404 Wilson Rd E Lansing MI 48824 USA Univ Northern British Columbia Sch Business Management Informat Syst Area Prince George BC Canada Sungkyunkwan Univ Sch Business Management Informat Syst Area Seoul South Korea Fordham Univ Gabelli Sch Business Informat Syst Area New York NY 10023 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA SUNY Stony Brook Artificial Intelligence Inst Dept Biomed Informat Dept Comp SciDept Appl Math & Stat Stony Brook NY 11794 USA

A carefully tailored tone in response to a complaint on social media can create positive emotions for an upset customer. However, very few studies have identified what response tones, based on an established theory, would be most effective for complaint management. This study conceptualizes a service agent's response tones based on Ballmer and Brennenstuhl's (1981) classification of speech acts and examines how an agent's use of speech acts elicit positive emotions for the complainant. Ballmer and Brennenstuhl classify speech acts within the dimensions of conventionality and dialogicality, and they suggest the two dimensions interact. Thus, we examine the impact of each dimension of speech acts and the interactions between the two dimensions on the elicitation of positive emotions for complainants. We collected over 100,000 tweets and classified firm agents' speech acts and complainants' emotions by designing deep learning architectures (i.e., bi-directional recurrent neural networks). Our fixed-effect regression results show that a low level of each speech act leads to the elicitation of customers' positive emotions but that the combination of the two erodes the individual advantages. This study expands Ballmer and Brennenstuhl's (1981) speech act classification from a speaker's perspectives to a listener's perspectives by contextualizing it in an analysis of service agents' tones and their roles in eliciting positive emotions among complainants. (c) 2021 Direct Marketing Educational Foundation, Inc. dba Marketing EDGE. All rights reserved.

关键词： Complaint management Social media deep learning algorithm Speech act theory

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Real-Time Behavioral Biometric Information Security System for Assessment Fraud Detection

Real-Time Behavioral Biometric Information Security System f...

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IEEE International Conference on Computing (ICOCO)

作者： Subash, Aditya Song, Insu James Cook Univ Coll Sci & Engn Singapore Singapore

ISBN: (纸本)9781665436892

Online education has become a major delivery method in education. Many schools have adopted online delivery of courses. This has exposed schools to greater information security risks, such as assessment cheating, identity theft, and loss of sensitive student information. To counter emerging new types of attacks, risks, and vulnerabilities, the protection measures must be able to adapt to the evolving behaviors of both user and attacker by learning new emerging vulnerabilities and behaviors of users. We propose a new Real-time Behavioral Biometric Information Security (RBBIS) architecture that non-invasively builds behavioral profiles on the fly using deep-learning approaches. The method learns behaviors of students to validate users and detect intrusion, identity theft, and assessment fraud. This greatly improves the current limitations of the existing user authentication approaches of online education platforms. RBBIS was evaluated using CNN deep-learning keystroke behavior biometric analysis and compared with various existing machine learning algorithms: j48, Naive Bayes, and Multi Layered Perceptron (MLP). The results show that our deep-learning method performed best with a Convolutional Neural Network (CNN) with 92.45% of accuracy, whereas Naive Bayes, j48 and MLP achieved accuracies of 68.87%, 73.38% and 77.11%, respectively.

关键词： Assessment fraud online education platforms sensitive student information keystroke analysis user validation and authentication real time behavioral biometric data deep learning algorithm

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A Survey of Sentiment Analysis of Internet Textual Data and Application to Pakistani YouTube User Comments

A Survey of Sentiment Analysis of Internet Textual Data and ...

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IEEE International Conference on Digital Futures and Transformative Technologies (ICoDT2)

作者： Rani, Mehwish Latif, Seemab Tahir, Muhaammad Ali Mumtaz, Rafia Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad Pakistan

ISBN: (纸本)9781665412858

With the advent of social media, e-commerce and video sharing websites, a large amount of textual data depicting people's opinions is being generated. This text has a great impact on other web users, product sellers and content creators. Text mining and emotion detection using text analysis has prospered a lot in recent years. Sentiment Analysis is the method of mining emotions, reviews and opinions of web users. In this paper, a survey of text Sentiment Analysis techniques is presented. Sentiment Analysis is applied to comments extracted from Pakistani news videos. Different machine learning algorithms previously surveyed in this paper have been applied to this dataset. Our results have shown that transfer learning can be effectively used for Sentiment Analysis.

关键词： Sentiment Analysis deep learning algorithm Natural Language Processing LSTM GRU and Transfer learning

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