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Reconstruction probability-based anomaly detection using variational auto-encoders

International Journal of computers and Applications 2023年第3期45卷 231-237页

作者： Iqbal, Touseef Qureshi, Shaima Department of Computer Science and Engineering National Institute of Technology Jammu and Kashmir Srinagar India

Anomaly detection is a method of categorizing unexpected data points or events in a dataset. Variational Auto-Encoders (VAEs) have proved to handle complex problems in a variety of disciplines. We propose a technique for detecting anomalies based on the reconstruction probability of VAEs. The proposed method trains VAEs on three different datasets. The reconstruction probability is a much more principled and realistic anomaly score than the reconstruction error utilized by auto-encoders and other data compression methods because of the theoretical background and by including the concept of variability. The paper describes recent deep learning models for anomaly detection, as well as a comparison to other methodologies. Variational auto-encoders are trained on three different datasets, in an unsupervised setup to classify the anomalies, based on reconstruction probability. Further, the in-depth study of anomaly detection techniques is presented in this paper. The data are reconstructed using the VAEs generative characteristics to investigate the root cause of the anomalies. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Anomaly detection

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Machine Learning for Cloud Cover Detection Using Multispectral Satellite Images

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Annals of Data science 2023年第6期10卷 1543-1557页

作者： Verma, Preeti Patil, Sunil Department of Computer Science Engineering Vedica Institute of Technology RKDF University Bhopal India

Reliable cloud detection in satellite based remote sensing applications is a vital pre-processing step. It can be viewed as a classification technique by partitioning objective pixels into cloud or non-cloud shadow classes. However, certain cloud pixels, particularly narrow pixels in the cloud, can be regarded as a blend of cloud reflection and land-based objects. Hence, it is difficult to classify them using a single classifier. In the present study, a technique based on an ensemble classifier is proposed to identify these pixels. The proposed ensemble classifier uses the Extreme Learning Machine, Naïve Byes, and K-Nearest Neighbour classifier. The model is trained and evaluated using the Spatial Procedures for Automated Removal of Cloud and Shadow datasets. The findings of the analysis indicate that the proposed classifier provides better classification accuracy. The performance of the model other than RGB band is also analyze and results shows that adding images from different bands significantly improves the results. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Machine learning Cloud cover detection Remote sensing Satellite images

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Integrating Bi-LSTM with Deep Learning for Emotion Analysis from Brain Wave Patterns

Integrating Bi-LSTM with Deep Learning for Emotion Analysis ...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Jyothi, G.N. Nagesh, H.R. Canara Engineering College Department of Computer Science and Engineering Mangaluru India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517892

Emotion detection and classification are becoming new frontiers in the capability of brain machine interface in the present times. Several studies have demonstrated that audio, video, as well as EEG data are helpful in the automated detection of affective states. EEG based affect detection is an important aspect of psychiatric evaluation for persons. In case sensor EEG data is gathered from various experimental sessions or participants, the signals obtained tend to be non-stationary. Building an intelligent emotion recognition system, which performs fair in terms of accuracy remains a problem due to the presence of numerous noisy, non-stationary, and nonlinear EEG signals. Some researchers have provided evidence that specific EEG brain waves reflect certain thoughts or feelings. This research proposes a completely new system responsible for automatically recognizing the emotions of users using deep learning techniques of computer network EEG signals acquired during playing computer games. EEG data from 28 participants were acquired on 14 channels Emotive Epoc + portable and wearable devices. Each participant played four distinct emotional games for five minutes each, yielding a total of 20 minutes of EEG data per volunteer. It is also shown that this framework is capable of identifying four fundamental emotion types in real time during games. The findings indicate that the suggested model-based emotion identification framework is a useful way for detecting emotions from EEG data. The network achieves 99.32% accuracy while using less computation time. © 2024 IEEE.

关键词： Emotion Recognition

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Out-of-Distribution Detection for Contrastive Models Using Angular Distance Measures 23

Out-of-Distribution Detection for Contrastive Models Using A...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Hossain, M Shifat Jha, Sumit Kumar Walker, Chase Ewetz, Rickard University of Central Florida Department of Electrical and Computer Engineering OrlandoFL32816 United States Knights Foundation School Florida International University Computer Science Department MiamiFL33199 United States University of Florida Department of Electrical and Computer Engineering GainsvilleFL32611 United States

ISBN: (纸本)9798350374889

Vision-language models have demonstrated extraordinary zero-shot image classification capabilities. Out-of-distribution (OOD) detection is the problem of determining if a model is operating within its knowledge limits. While distance-based detection algorithms have emerged as a promising approach to OOD detection, we observe that there is a disparity between the distance measures used for OOD detection and model training. Recent studies have attempted to mitigate this shortcoming by modifying the contrastive learning process, which is highly undesirable for foundation models. In this paper, we propose an Angular distance-based out-of-distribution detection method for Contrastive models (AEC), an OOD detection framework for foundational contrastive models based on an angular distance measure. The angular distance-based score is compliant with the standard training process and circumvents the need to modify the training process of the model. We also formulate a distance transformation and solve an optimization problem to determine an OOD score threshold value for in-distribution and out-of-distribution data. The experimental evaluation demonstrates that AEC outperforms state-of-the-art OOD detection models in terms of AUROC, FPR@95TPR, accuracy, and correct ID metrics. We obtained an overall AUROC and FPR@95TPR of 70.42 and 83.98 from the proposed algorithm, which is significantly better compared to the SOTA OOD detection algorithms. © 2024 IEEE.

关键词： Contrastive Learning

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Real Time Slumbering Detection Using DLIB 2

Real Time Slumbering Detection Using DLIB

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Agrawal, Harsh Saxena, Pallavit Tiwari, Shiva Awasthi, Sandhya Agarwal, Kadambri Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350383867

Worldwide, driving when fatigued is still a major factor in traffic accidents. Our solution to this problem is a complete slumbering detection system that uses real-time monitoring, machine learning algorithms, and computer vision techniques to identify indicators of driver sleepiness. To recognize signs like eye closure, yawning, and head movement, our system makes use of many libraries, including dlib for facial landmark identification and OpenCV for face detection. Furthermore, the driver' s past history of sleepiness is also stored in the database. To prevent tiredness the next time, the database viewer might take proactive steps to enhance his or her sleep. The likelihood of feeling drowsy increases significantly at night compared to the day. It should thus be possible to have a system that operates differently during the day and at night. We may tighten our system at night so that we are warned if someone yawns once or twice, and we can be alerted if someone yawns three or four times during the day. Head movement follows similar rules: during the day, we may ignore some head movement, while at night, we can pay close attention to even the tiniest movement. © 2024 IEEE.

关键词： Face recognition

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Nutrigrow Using Gradient Boosting Regressor 1

Nutrigrow Using Gradient Boosting Regressor

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Ashwini, A. Loganathan, V. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307795

In modern agriculture, the quest for enhanced crop yield and nutrient efficiency is paramount. Leveraging the Gradient Boosting Regressor algorithm, this research proposes a novel method to address this challenge. By analyzing historical rainfall data and soil fertility levels, the system predicts crop nutrient requirements, aiding farmers in making informed decisions on fertilizer application. Moreover, real-time weather forecasts are integrated to issue timely alerts, mitigating risks associated with adverse weather conditions. This holistic approach not only enhances crop yields but also minimizes nutrient wastage, contributing to environmental sustainability and economic viability. Through the synergy of advanced algorithms and agricultural data, this work offers a promising avenue for optimizing crop production and resource management in modern farming practices. © 2024 IEEE.

关键词： Potassium

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The Role of Metaverse Technology in Education: A Framework for Implementation and Future Research

The Role of Metaverse Technology in Education: A Framework f...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Sineka, P. Jothikumar, Karthikeyan Nivetha, V.M. Luckshika, M.K. Priyadharshini, P. Janani Ratthna, S.G. National Engineering College Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350374711

In this article, our study aims to pioneer a transformative approach to online education by creating a Metaverse college environment. In this immersive 3D space, avatars fluidly represent both teachers and students, fundamentally altering the dynamics of virtual learning. Lectures transcend traditional boundaries through VR presentations, enriching engagement and understanding. Interactive quizzes inject a gamified element into assessments, fostering a dynamic and enjoyable learning journey. One of the standout features is the use of VR glasses for attendance tracking, ensuring precise reflection of real-world participation. This innovative fusion of cutting-edge technology and traditional teaching methodologies heralds a paradigm shift in remote education. Our platform is designed to provide students with an authentic college experience within the digital realm, fundamentally reshaping the landscape of virtual learning. By seamlessly integrating advanced technologies with tried-and-true educational practices, we aim to create an environment where students thrive and actively engage with course materials. This Metaverse college experience promises not only to enhance learning outcomes but also to redefine the possibilities of online education. Join us on this journey as we revolutionize the way we learn and teach in the digital age. © 2024 IEEE.

关键词： Virtual environments

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Amplification/Reflection Attack Suppression Using Victim Separation

IEEE Networking Letters

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IEEE Networking Letters 2023年第2期5卷 140-143页

作者： Gurjar, Hawa Singh Somani, Gaurav Central University of Rajasthan Department of Computer Science and Engineering Ajmer305817 India

Amplification and reflection attacks represent flavors of DDoS attacks which use servers such as DNS, NTP, SNMP, SSDP and CoAP as amplifier to launch the attacks. In this letter, we describe a method to minimize the effects of reflection and amplification DDoS attacks by separating the victim service under the attack. We propose three approaches involving container level separation, packet limiting, and network hardware level separation. Our detailed experimental results on DNS based amplification attacks show a 'near-no-attack' performance with an improvement of up to 88% in the test completion time while using the proposed approaches. © 2019 IEEE.

关键词： Cybersecurity

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Topology-adaptive Bayesian optimization for deep ring echo state networks in speech emotion recognition

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Neural Computing and Applications 2025年第1期37卷 399-416页

作者： Soltani, Rebh Benmohamed, Emna Ltifi, Hela University of Sfax BP 1173 Sfax3038 Tunisia Department of computer science Onaizah college of Engineering and Information Technology Oniazah colleges Saudi Arabia Computer Science and Mathematics Department Faculty of Sciences and Techniques of Sidi Bouzid University of Kairouan Kairouan Tunisia

Speech emotion recognition is a difficult task that is gaining attention in a variety of domains, including psychology, human–computer interaction, and speech processing. To recognize speech emotions, machine learning techniques such as convolutional neural networks and long short-term memory networks have been successfully used. However, these models often fail to capture the long-term temporal dynamics of emotional speech. In this work, data augmentation was performed on all datasets. The generated result was used to extract the discriminant features. Then we apply a deep echo state network with a ring topology. The Bayesian optimization (BO) algorithm was applied to the spectral radius of the reservoir matrix. Our approach reveals term-weighted and unweighted accuracies of (75.45%, 74.79%), (86.89%, 86.14%), (98.99%, 97.62%), and (62%, 61.24%) on EMO-DB, SAVEE, RAVDESS, and TESS datasets. The findings demonstrate that by using BO, we are able to determine an optimal spectral radius. Therefore, this approach balances the minimal complexity of reservoir design and high emotion recognition accuracy. Notably, we demonstrate that a simple, deterministically constructed cycle reservoir within DRESN is comparable to other methodologies. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Bayesian networks

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Patient Health Diagnostic and Fitness Recommendation Application 1

Patient Health Diagnostic and Fitness Recommendation Applica...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Nidhin, R. Syamraj, B.S. Philip, Diya Mariam Abraham, Joel P. Nair, Rohith R. John, Shalin Jeena Providence College of Engineering Department of Computer Science and Engineering Chengannur India

ISBN: (纸本)9798350307795

With the busy lifestyle and lack of time, people are looking for a way for the proactive management of their well-being. A health monitoring app helps users obtain insights about their health and take necessary steps to improve their health. The app uses advanced machine learning techniques like Bayesian Ridge Regression and Lasso regression and Ridge classifier for prediction of health factors such as blood pressure, cholesterol and blood sugar respectively. The evaluation of the app produced favorable results concerning both user satisfaction and model accuracy. The accuracy and usability of prediction models are quite satisfactory, with each model trained using the best algorithm for the specified prediction task achieving the best results. Users can actively manage and enhance their well-being through the system's tailored food recommendations, which is possible using rule-based filtering. This app makes it easier to closely examine one's own health patterns and obtain useful suggestions for leading healthy lives. © 2024 IEEE.

关键词： Forecasting

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