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SIEM integration with SOAR

SIEM integration with SOAR

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international conference on Futuristic Technologies (INCOFT)

作者： Anish Sridharan V Kanchana Department of Computer Science Amrita School of Arts and Sciences Amrita Vishwa Vidyapeetham Mysuru Campus Mysuru India

The reliance on basic perimeter controls is coming to an end, as it is no longer reliable to rely solely on the firewall, intrusion detection system, or antivirus. Sophisticated cyber threats have emerged as a significant adversary in the cyber space. These advanced cyber threats include ”zero-day,” social engineering tactics, or advanced evasion techniques to avoid detection. As of now, today’s technology has accumulated a large amount of data, which necessitates the use of an effective tool for ”control, monitoring, and defense against potential *** a result, enterprises have resorted to building SOCs to combat these advanced threats, and as an add-on to the SOC, enterprises implement SOAR as well, as it has its own benefit of reducing SOC Alert volume by identifying duplicate cases. The SOAR playbook aims to reduce the amount of manual work required, potentially reducing threat and vulnerability response times.

关键词： Automation Firewalls (computing) Intrusion detection Manuals Market research pattern recognition Time factors

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing soft computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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international conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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Acoustic pattern recognition Technology Based on the Viola-Jones Approach for VR and AR Systems 11th

Acoustic Pattern Recognition Technology Based on the Viola-J...

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11th Annual international conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020

作者： Alyushin, Alexander M. Dvoryankin, Sergey V. Kashirskoe shosse 31 Moscow115409 Russia Plekhanov Russian University of Economics st. Hook 43 Moscow117997 Russia Financial University under the Government of the Russian Federation Leningradsky prospect 49 Moscow125993 Russia

ISBN: (纸本)9783030655952

The ability to solve problems of graphic images recognition in virtual reality (VR), augmented reality (AR), mixed reality (MR) and cross reality (XR) systems is highlighted as one of the most important. The urgency of solving problems of recognition and classification of acoustic images has been substantiated, which will bring the quality of VR, AR, MR and XR systems closer to real reality (RR). Independent solution of graphic and acoustic patterns recognition problems using heterogeneous algorithmic and software tools is attributed to the disadvantages of modern systems. The study proposes an approach that allows the use of unified methodological and software tools for the simultaneous solution of graphic and acoustic patterns recognition problems. The proposed approach is based on converting acoustic information into graphic information using 2D-images of dynamic sonograms. This allows the recognition of acoustic patterns using unified algorithmic and software tools. It is proposed to use the Viola-Jones technology as such a unified tool. It is shown that the implementation of a two-stage determination of similarity measures of primitives and areas of the original image makes it possible to increase the speed of algorithms. For this purpose, at the first iteration, it is proposed to use not the graphic primitives themselves, but their coordinate projections. In the study, by analogy with Haar’s features, parametrizable acoustic primitives were developed, presented in the classical graphical version, as well as in the form of coordinate projections. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mixed reality

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Facial Expression recognition Using Improved Adaptive Local Ternary pattern 3rd

Facial Expression Recognition Using Improved Adaptive Local ...

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3rd international conference on Computer Vision and Image Processing (CVIP)

作者： Saurav, Sumeet Singh, Sanjay Saini, Ravi Yadav, Madhulika Acad Sci & Innovat Res AcSIR Chennai Tamil Nadu India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India Banasthali Vidyapith Dept Elect Vidyapith Rajasthan India

ISBN: (纸本)9789813292918;9789813292901

Recently, there has been a huge demand for assistive technology for industrial, commercial, automobile, and societal applications. In some of these applications, there is a requirement of an efficient and accurate system for automatic facial expression recognition (FER). Therefore, FER has gained enormous interest among computer vision researchers. Although there has been a plethora of work available in the literature, an automatic FER system has not yet reached the desired level of robustness and performance. In most of these works, there has been the dominance of appearance-based methods primarily consisting of local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP). Keeping in view the popularity of appearance-based methods, in this paper, we have proposed an appearance-based descriptor called Improved Adaptive Local Ternary pattern (IALTP) for automatic FER. This new descriptor is an improved version of ALTP, which has been proved to be effective in face recognition. We have investigated ALTP in more details and have proposed some improvements like the use of uniform patterns and dimensionality reduction via principal component analysis (PCA). The reduced features are then classified using kernel extreme learning machine (K-ELM) classifier. In order to validate the performance of the proposed method, experiments have been conducted on three different FER datasets using well-known evaluation measures such as accuracy, precision, recall, and F1-Score. The proposed approach has also been compared with some of the state-of-the-art works in literature and found to be more accurate and efficient.

关键词： Facial expression recognition (FER) Adaptive local ternary pattern (ALTP) Principal component analysis (PCA) Kernel extreme learning machine (K-ELM)

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A compact and cost-effective pattern recognition based myoelectric control system for robotic prosthetic hands

A compact and cost-effective pattern recognition based myoel...

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IEEE/ASME international conference on Advanced Intelligent Mechatronics (AIM)

作者： Zhou, Hao Alici, Gursel Univ Wollongong ARC Ctr Excellence Electromat Sci Wollongong NSW 2522 Australia Univ Wollongong Sch Mech Mat & Mechatron Engn Wollongong NSW 2522 Australia

ISBN: (纸本)9781728167947

In this study, we propose, implement and test a compact pattern recognition (PR) based myoelectric control system to operate robotic prosthetic hands. Instead of using a bulky sensor configuration (8 or more sensors on the forearm) as most commercial or academic PR systems use, only two sEMG-IMU sensors are deployed on the forearm, consistent with the existing myoelectric hands' electrode configuration, to bring the ease of implementation and consequently improve its practical application. For maintaining the recognition performance as much as possible, two additional sEMG-IMU sensors are attached to the upper arm, which aims to collect more information of muscular activities for the benefits of classification but not affecting the existing forearm sockets of the prosthetic hand users. The offline analysis by using the data from 10 intact subjects and 10 transradial (i.e. below-elbow) amputees shows the comparable recognition performance using the proposed sensor configuration (2 + 2 sensors) and classification technique (Random Forest classifier) to that using the full pack of sensing system (10 sensors on the forearm + 2 sensors on the upper arm). In the online tests, the proposed PR system was employed to control the UoW/ACES soft robotic prosthetic hand to successfully and reliably perform seven common wrist/thumb movements for the activities of daily life (ADL).

关键词： Robotics

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Discriminative BULBPH Descriptor with KDA for Palmprint recognition 3rd

Discriminative BULBPH Descriptor with KDA for Palmprint Reco...

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3rd international conference on Computer Vision and Image Processing (CVIP)

作者： Tamrakar, Deepti Khanna, Pritee Jabalpur Engn Coll Jabalpur India PDPM Indian Inst Informat Technol Design & Mfg Jabalpur India

ISBN: (纸本)9789813290884;9789813290877

This work proposes Block-wise uniform local binary pattern histogram (BULBPH) followed by kernel discrimination analysis (KDA) as descriptor for palmprint recognition. BULBPH provides distribution of uniform patterns (such as line and wrinkles) in local region and can be better used as palmprint features. KDA is applied on BULBPH to reduce dimension and enhance discriminative capability using chi-RBF kernel. The experiments are conducted on four palmprint databases and performance is compared with related descriptors. It is observed that KDA on BULBPH descriptor achieves more than 99% accuracy with 4.04 decidability index on four palmprint databases.

关键词： Palmprint Blockwise uniform local binary pattern Kernel discriminant analysis

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Cut the peaches: image segmentation for utility pattern mining in food processing

Cut the peaches: image segmentation for utility pattern mini...

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IEEE international conference on Data Mining Workshops (ICDM Workshops)

作者： Diletta Chiaro Edoardo Prezioso Stefano Izzo Fabio Giampaolo Salvatore Cuomo Francesco Piccialli Department of Mathematics and Applications “R. Caccioppoli” University of Naples Federico II Napoli Italy

The progress achieved in the field of information and communication technologies, particularly in computer science, and the growing capacity of new types of computational systems (cloud/edge computing) significantly contributed to the cyber-physical systems, networks where cooperating computational entities are intensively linked to the surrounding physical en-vironment and its on-going operations. All that has increased the possibility of undertaking tasks hitherto considered to be an exclusively human concern automatically: hence the gradual yet progressive tendency of many companies to adopt artificial intelligence (AI) and machine learning (ML) technologies to automate human activities. This papers falls within the context of deep learning (DL) for utility pattern mining applied to Industry 4.0. Starting from images supplied by a multinational company operating in the food processing industry, we provide a DL framework for real-time pattern recognition applied in the automation of peach pitters. To this aim, we perform transfer learning (TL) for image segmentation by embedding seven pre-trained encoders into multiple segmentation architectures and evaluate and compare segmentation performance in terms of met-rics and inference speed on our data. Furthermore, we propose an attention mechanism to improve multiscale feature learning in the FPN through attention-guided feature aggregation.

关键词： Representation learning Image segmentation Transfer learning Companies Logic gates Real-time systems Information and communication technology

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Characterization of Correlation in Stochastic computing Functions 2

Characterization of Correlation in Stochastic Computing Func...

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2nd international conference on Electrical, Communication and Computer Engineering, ICECCE 2020

作者： Abdellatef, Hamdan Khalil-Hani, Mohamed Shaikh-Husin, Nasir Ayat, Sayed Omid VeCAD Research Laboratory School of Electrical Engineering Universiti Teknologi Malaysia Faculty of Engineering Johor Bahru Johor Malaysia

ISBN: (纸本)9781728171166

Stochastic computing (SC) is an alternative computing paradigm that provides low-cost hardware implementation for complex operations. Many previous works adopted SC in several applications such as signal processing and pattern recognition, where these types of application possess a nature of successive computations. Successive SC computation affects correlation, which affects accuracy. This relationship is not studied well before. In this paper, we characterize correlation properties for SC functions to thoroughly understand the computation-correlation-accuracy relationship. We provide a measure ( delta{SCC}) to identify the significance of variation of correlation due to computation. We also offer a general method to determine the correlation characteristics of any designed SC function. © 2020 IEEE.

关键词： pattern recognition

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Language Detection Engine for Multilingual Texting on Mobile Devices 14

Language Detection Engine for Multilingual Texting on Mobile...

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14th IEEE international conference on Semantic computing (ICSC)

作者： Gothe, Sourabh Vasant Ghosh, Sourav Mani, Sharmila Bhanodai, Guggilla Agarwal, Ankur Sanchi, Chandramouli Samsung R&D Inst Bangalore 560037 Karnataka India

ISBN: (纸本)9781728163321

More than 2 billion mobile users worldwide type in multiple languages in the soft keyboard. On a monolingual keyboard, 38% of falsely auto-corrected words are valid in another language. This can be easily avoided by detecting the language of typed words and then validating it in its respective language. Language detection is a well-known problem in natural language processing. In this paper, we present a fast, light-weight and accurate Language Detection Engine (LDE) for multilingual typing that dynamically adapts to user intended language in real-time. We propose a novel approach where the fusion of character N-gram model [1] and logistic regression [2] based selector model is used to identify the language. Additionally, we present a unique method of reducing the inference time significantly by parameter reduction technique. We also discuss various optimizations fabricated across LDE to resolve ambiguity in input text among the languages with the same character pattern. Our method demonstrates an average accuracy of 94.5% for Indian languages in Latin script and that of 98% for European languages on the code-switched data. This model outperforms fastText [3] by 60.39% and ML-Kit(1) by 23.67% in Fl score [4] for European languages. LDE is faster on mobile device with an average inference time of 25.91 mu seconds.

关键词： Language detection multilingual character N-gram logistic regression parameter reduction mobile device Indian macaronic languages European languages soft-keyboard

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Artificial Intelligent and pattern recognition system based Novel Intelligent Algorithm for Scientific Measurements 2

Artificial Intelligent and Pattern Recognition system based ...

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2nd international conference on Inventive Research in computing Applications, ICIRCA 2020

作者： Pang, Yanhua Xiao, Shanshan Yan, Huaiyu Technology Centre of Dalian Customs 116001 China

ISBN: (纸本)9781728153742

Determination of different forms of Chromium in cosmetics by reversed-phase Ion-pair liquid Chromatography-inductively coupled plasma mass spectrometry with the artificially intelligent systems is analyzed in this paper. The pattern recognition system (PRS) that introduces these algorithms is intelligent. This so-called accuracy often has some considerable limitations, firstly statically, and then secondly unilaterally. Because each object of the theory of boundary is not clear, the core static membership function description is not reality, object and environment are not static. Hence, this paper proposes the novel intelligent algorithm is proposed for scientific measurement. The experiment is conducted through a massive database. The simulation results have then proven effectiveness. © 2020 IEEE.

关键词： Intelligent systems

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