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Self-Healing Email Systems: Autonomous Recovery from Cyber Attacks

Self-Healing Email Systems: Autonomous Recovery from Cyber A...

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Knowledge Engineering and Communication Systems (ICKECS), international conference on

作者： Harsh Kumar Singh H Suhas Prabhu Bedadala Mohaneeshwar Reddy Shinu M. Rajagopal Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Email systems are critical for communication and collaboration, yet cyber attacks are a major threat. Traditional approaches to security heavily depend upon human intervention, which can be slow and ineffective against advanced attacks. This paper proposes self-healing email systems as a novel solution for autonomous recovery from cyber attacks. These systems can use machine learning and artificial intelligence to automatically detect, diagnose, and recover from attacks, minimizing downtime and disruption. Key components of self-healing email systems, including anomaly detection, fault localization, recovery mechanisms, and adaptive learning, have been discussed. The challenges and opportunities associated with this technology have been explored.

关键词： Location awareness Machine learning System recovery Threat assessment Real-time systems Safety pattern recognition

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Comparison of pattern recognition Techniques for Classification of the Acoustics of Loose Gravel 7

Comparison of Pattern Recognition Techniques for Classificat...

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7th international conference on soft computing and Machine Intelligence (ISCMI)

作者： Saeed, Nausheen Alam, Moudud Nyberg, Roger G. Dougherty, Mark Jomaa, Diala Rehreyend, Pascal Dalarna Univ Fac Data & Informat Sci Borlange Sweden Halmstad Univ Sch Informat Technol Halmstad Sweden

ISBN: (纸本)9781728175591

Road condition evaluation is a critical part of gravel road maintenance. One of the parameters that are assessed is loose Gravel. An expert does this evaluation by subjectively looking at images taken and written text for deciding on the road condition. This method is labor-intensive and subjected to an error of judgment;therefore, it is not reliable. Road management agencies are looking for more efficient and automated objective measurement methods. In this study, acoustic data of gravel hitting the bottom of the car is used, and the relation between these acoustics and the condition of loose gravel on gravel roads is seen. A novel acoustic classification method based on Ensemble bagged tree (EBT) algorithm is proposed in this study for the classification of loose gravel sounds. The accuracy of the EBT algorithm for Gravel and Nongravel sound classification is found to be 973. The detection of the negative classes, i.e., non- gravel detection, is preeminent, which is considerably higher than Boosted Trees, RUSBoosted Tree, Support vector machines (SVM), and decision trees.

关键词： gravel roads loose gravel ensemble bagged trees acoustics classification road maintenance

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Comparative analysis of histograms of oriented gradients and local binary pattern coefficients for facial emotion recognition 8

Comparative analysis of histograms of oriented gradients and...

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Subudhiray, Swapna Palo, Hemanta Kumar Das, Niva Mohanty, Sachi Nandan Bhubaneswar India College of Engineering Pune Dept. of Computer Engineering Pune India

ISBN: (纸本)9789380544434

This paper examines the human expressive states dependent on facial pictures utilizing a few viable component extraction methods. It reproduces the K-Nearest Neighbor (k-NN) classifier to approve the adequacy of successful capabilities separated from the Local Binary pattern (LBP) and Histograms of Oriented Gradients (HOG) for the said task. An examination of the strategies has been made dependent on the normal acknowledgment precision of the classifiers utilizing the calculation unpredictability as a compromise. The component extraction methods have been approved for their discriminative force under various preparations for testing information division proportions, Kappa Coefficient, and order time. The LBP has outperformed the HOG include extraction strategy with a normal precision of 79.6% yet remains computationally costly. On the contrary, the HOG method has furnished a lower characterization time with a normal precision of 59.3 % as uncovered from our outcomes. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Emotion recognition

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A Multimodal Diagnostic Framework for Autism Spectrum Disorder Using Deep Learning: An In-Depth Exploration

A Multimodal Diagnostic Framework for Autism Spectrum Disord...

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international conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Tamizhmalar D Sankari Subbiah R. Premkumar Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Information Technology Sri Sai Ram Engineering College Chennai India Department of Electronics and Instrumentation Engineering Sri Sai Ram Engineering College Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Autism Spectrum Disorder (ASD) represents a multifaceted neurodevelopmental condition, characterised by persistent and pervasive challenges in social communication, behavioural patterns, and sensory processing. The early and accurate diagnosis of ASD is imperative for timely intervention and support. This case study delineates a sophisticated deep learning-based multimodal diagnostic framework designed to augment the detection and analysis of ASD. By amalgamating various data modalities—encompassing neuro imaging, facial expressions, and speech patterns—this framework harnesses the capabilities of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance diagnostic precision. Furthermore, we delve into the intricacies of the challenges encountered, the preprocessing methodologies employed, and the comparative performance of the model vis-à-vis traditional diagnostic approaches.

关键词： Neuroimaging Autism Accuracy Recurrent neural networks soft sensors Process control Imaging Speech enhancement Genetics pattern recognition

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2nd international conference on Applied Intelligence and Informatics, AII 2022

2nd International Conference on Applied Intelligence and Inf...

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2nd international conference on Applied Intelligence and Informatics, AII 2022

ISBN: (纸本)9783031248009

The proceedings contain 36 papers. The special focus in this conference is on Applied Intelligence and Informatics. The topics include: ML-Based Radiomics Analysis for Breast Cancer Classification in DCE-MRI;Decoding Motor Preparation Through a Deep Learning Approach Based on EEG Time-Frequency Maps;A Novel Fuzzy Semi-supervised Learning Approach for the Classification of Colorectal Cancer (FSSL-CRCC);Machine Learning Models to Analyze the Effect of Drugs on Neonatal-ICU Length of Stay;iConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligence;ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease;identification of Crown and Rump in First-Trimester Ultrasound Images Using Deep Convolutional Neural Network;A Pyramidal Approach for Emotion recognition from EEG Signals;Evaluation of Galvanic Skin Response (GSR) Signals Features for Emotion recognition;innovative soft computing Techniques for the Evaluation of the Mechanical Stress State of Steel Plates;a Privacy-Preserving Federated-MobileNet for Facial Expression Detection from Images;Television Programs Classification via Deep Learning Approach Using SSMI-CNN;a Hybrid Deep Learning System to Detect Face-Mask and Monitor Social Distance;handwriting and Drawing for Depression Detection: A Preliminary Study;identifying Synthetic Voices’ Qualities for Conversational Agents;identifying Colluding Actors in Social Communities by Reputation Measures;weakly Supervised Transfer Learning for Multi-label Appliance Classification;toward an Interoperable Catalogue of Multimodal Depression-Related Data;explainable Deep Learning Classification of Respiratory Sound for Telemedicine Applications;nonexclusive Classification of Household Appliances by Fuzzy Deep Neural Networks;an Indirect Approach to Forecast Produced Power on Photovoltaic Plants Under Uneven Shading Conditions;Applied Enhanced Q-NAS for COVID-19 Detection in CT Images;beware the Sirens: Prototyping an Emer

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15th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2020

15th International Conference on Soft Computing Models in In...

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15th international conference on soft computing Models in Industrial and Environmental Applications, SOCO 2020

ISBN: (纸本)9783030578015

The proceedings contain 83 papers. The special focus in this conference is on soft computing Models in Industrial and Environmental Applications. The topics include: A Smart Crutch Tip for Monitoring the Activities of Daily Living Based on a Novel Neural-Network Intelligent Classifier;Hourly Air Quality Index (AQI) Forecasting Using Machine Learning Methods;interpretable Deep Learning with Hybrid Autoencoders to Predict Electric Energy Consumption;on the Performance of Deep Learning Models for Time Series Classification in Streaming;An Approach to Forecasting and Filtering Noise in Dynamic Systems Using LSTM Architectures;novel Approach for Person Detection Based on Image Segmentation Neural Network;an Adaptive Cognitive Model to Integrate Machine Learning and Visual Streaming Data;smart Song Equalization Based on the Classification of Musical Genres;machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution;A Preliminary Study for Automatic Activity Labelling on an Elder People ADL Dataset;software Sensors for the Monitoring of Bioprocesses;RGB Images Driven recognition of Grapevine Varieties;discovering Spatio-Temporal patterns in Precision Agriculture Based on Triclustering;counting Livestock with Image Segmentation Neural Network;smart, Precision or Digital Agriculture and Farming - Current State of Technology;an Automated Platform for Microrobot Manipulation;growth Models of Female Dairy Cattle;a Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops;blocks of Jobs for Solving Two-Machine Flow Shop Problem with Normal Distributed Processing Times;soft computing Analysis of Pressure Decay Leak Test Detection.

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Multi-feature gait pattern recognition system on account of genetic algorithm

Multi-feature gait pattern recognition system on account of ...

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The international conference on Forthcoming Networks and Sustainability (FoNeS 2022)

作者： Y. Yao School of computing Guangdong Business and Technology University Zhaoqing 526020 Guangdong People's Republic of China

Gait recognition technology belongs to a new recognition technology applied in the biological category. This technology collects people's walking, so as to carry out data analysis and realize the functions of identity recognition, physiology, pathology and psychology. This technology has many advantages, such as remote video images, and recognition in the case of poor line of sight. In recent years, stations, banks, airports and other public locations have realized multi-angle customer service network systems, so experts in the field must improve the intelligence of remote scenes. A significant feature of gait recognition is that it can intercept the biometric features of human body or object from a long distance. So gait recognition technology in multiple scenarios, multiple applications is a very popular and practical technology. The test shows that the multi-feature gait pattern recognition system based on genetic algorithm has high performance in multi-feature gait pattern recognition.

关键词： biometrics (access control) customer services data analysis feature extraction gait analysis genetic algorithms image recognition legged locomotion pattern recognition

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19th international conference on Hybrid Intelligent Systems, HIS 2019, and the 14th international conference on Information Assurance and Security, IAS 2019

19th International Conference on Hybrid Intelligent Systems,...

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19th international conference on Hybrid Intelligent Systems, HIS 2019, and the 14th international conference on Information Assurance and Security, IAS 2019

ISBN: (纸本)9783030493356

The proceedings contain 43 papers. The special focus in this conference is on Hybrid Intelligent Systems. The topics include: Heterogeneous engineering in intelligent logistics;welcome message;dimension reduction with extraction methods (principal component analysis - Self organizing map - Isometric mapping) in indonesian language text documents clustering;extracting unknown repeated pattern in tiled images;convolutional deep learning network for handwritten arabic script recognition;diversity in recommendation system: A cluster based approach;contribution on arabic handwriting recognition using deep neural network;analyzing and enhancing processing speed of k-medoid algorithm using efficient large scale processing frameworks;multiple criteria fake reviews detection based on spammers’ indicators within the belief function theory;data clustering using environmental adaptation method;soft computing, data mining, and machine learning approaches in detection of heart disease: A review;a novel cad system for breast dce-mri based on textural analysis using several machine learning methods;an adversarial learning mechanism for dealing with the class-imbalance problem in land-cover classification;reducing data volume in instance based learning;an integrated fuzzy anp-topsis approach to rank and assess e-commerce web sites;implementation of block chain technology in public distribution system;chaotic salp swarm optimization using svm for class imbalance problems;three-layer security for password protection using rdh, aes and ecc;clothing classification using deep cnn architecture based on transfer learning;identification of botnet attacks using hybrid machine learning models;congestion control in vehicular ad-hoc networks (vanet’s): A review;advances in cyber security paradigm: A review;recursive tangent algorithm for path planning in autonomous systems.

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Comprehensive Framework for Facilitating the Deployment of Distributed On-Premise Analytics Applications in Resource-Constraint Environments

Comprehensive Framework for Facilitating the Deployment of D...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Nicolai Schoch Pascal Becker Virendra Ashiwal Andrew Habib ABB Corporate Research Center Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Data Analytics (DA) and Machine Learning (ML) algorithms today are inside many applications and systems. They provide state-of-the-art anomaly detection, pattern recognition, health monitoring, predictive maintenance, or help to optimize and calibrate parameter combinations. However, they usually require strong compute resources. It is therefore a challenge to deploy the corresponding algorithms on edge devices or in embedded systems, such as on cloud-decoupled drives and drive systems, as these usually have strict resource constraints. In this work, we look at the deployment of DA/ML algorithms on combinations of resource-constraint edge and device environments. We present our framework for the structured and guided definition, setup, distribution, and deployment of such DA/ML algorithms. A guided workflow leads a user through algorithm specification and configuration. While doing so, it extracts information, which is necessary for the distribution and deployment of the DA/ML algorithm to a suitable device-edge combination. With this solution, a DA/ML algorithm can be executed for sensor data processing and, in the ML context, for inference, on device-edge combinations without permanent cloud connection, e.g., on a drive/gateway combination on a cruise ship, so that it can be used for real-time anomaly detection or pattern analysis.

关键词： Cloud computing Machine learning algorithms Prediction algorithms Inference algorithms Real-time systems pattern analysis Marine vehicles Anomaly detection Monitoring Predictive maintenance

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The Method of Spatiotemporal-Frequency Multidimensional Feature Fusion Based on Emotional EEG Signals

The Method of Spatiotemporal-Frequency Multidimensional Feat...

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Data Intelligence and Innovative Application (DIIA), IEEE international conference on

作者： Weichao Guo Jie Liu Yihe Zhang Tinglei Huang Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Institute of Software Chinese Academy of Sciences Guilin University of Electronic Technology Beijing China

ISBN: (数字)9798331504434

ISBN: (纸本)9798331504441

Emotion recognition technology is an important research direction in the field of artificial intelligence and emotional computing, and it has been widely used in the fields of mental health, education and medical care. As a direct reflection of emotional states, electroencephalogram (EEG) signals have become an important data source for emotion recognition research. Due to the low signal-to-noise ratio and non-stationary nature of EEG signals, they are easily affected by external interference, thus requiring a rigorous preprocessing procedure. Traditional research on multi-channel EEG signal feature extraction often focuses on the time-domain and frequency-domain characteristics of the signal itself, while neglecting the correlation features between brain regions. This leads to a more limited and single-dimensional feature extraction process. To address the above issues, we propose a dual segmentation of multi-channel EEG signals across both frequency bands and brain regions, combined with cross-integration of various time-frequency features. This approach enables the fusion of spatiotemporal-frequency multidimensional features of EEG signals. On this basis, we improved the traditional MLP neural network by introducing a regularization strategy. Experimental results validate the effectiveness of the proposed spatiotemporal-frequency multidimensional feature fusion method. Based on the EEG emotion dataset (SEED), cross-subject emotion classification experiments were conducted. Compared to baseline methods, the proposed approach achieved the best recognition performance, with an accuracy of 87.41 %. This demonstrates that the method can significantly improve the precision of EEG-based emotion decoding, exhibiting both high accuracy and good generalization capabilities.

关键词： Emotion recognition Time-frequency analysis Accuracy soft sensors Face recognition Feature extraction Electroencephalography Spatiotemporal phenomena Middleware Biological neural networks

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