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Audio spectrogram analysis in IoT paradigm for the classification of psychological-emotional characteristics

International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Kumar, Ankit Singh, Sushil Kumar Bhardwaj, Indu Singh, Prakash Kumar Khanna, Ashish Brahma, Biswajit Dr. APJ Abdul Kalam Technical University Lucknow India Department of Computer Engineering Marwadi University Rajkot India Department of Electronics and Communication Engineering Galgotias University U.P. Greater Noida India Rajkiya Engineering College U.P Mainpuri India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology (GGSIPU) New Delhi India Senior Data and Visualization Engineer McKesson Corporation USA 32559 Lake Bridgeport St Fremont CA United States

Psychological activities have various dimensions in which they correlate with their respective behavior generated by the human body. Understanding the relationship of psychological events from external action units is one of the research subjects to explore various human behavior and their dependencies. The study of psychological analysis in the medical field is very time-consuming and costly. It requires constant monitoring of the patient for some time and various interrogation sessions to finalize the emotional severity of an individual. The challenges in exploring human emotions propel the requirement of computer vision techniques in this field. The proposed study explicitly evaluates the recognition of psychological-emotional activities with the help of an audio spectrogram of people fetched through an IoT (Internet of Things) device comprising a microphone to investigate its correlation with psychological events. The audio samples are collected in an asymmetric environment where the chances of the noise are random. Noise cancellation, low power consumption, and sensitivity controls are some of the prominent features of the microphone IoT that have been used to extract raw audio samples. The proposed system follows the extraction of features such as mel-frequency cepstral coefficients (MFCC), harmonic to noise rate (HNR), zero crossing rate (ZCR), and Generative Adversarial networks (GAN) from the audio spectrogram. The study uses a deep learning-based model containing a convolutional neural network model to recognize and classify different psychological-emotional stages including happiness, anger, disgust, surprise, fear, and sadness from audio spectrogram features. The average accuracy of the classification model for the recognition of all emotions is found to be 99.42% in a maximum of 312 iterations. The model is found to be robust for various applications such as preventing suicidal cases, improving decision-making in the diagnosis of depression patients, im

关键词： Audio spectrogram Convolutional neural network (CNN) Emotion recognition Generative adversarial networks (GAN) Harmonic to noise rate (HNR) Mel-frequency cepstral coefficient (MFCC) Zero crossing rate (ZCR)

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Exploration of an Attention based Two-Stream Dual-Path networks for Speech Enhancement

Exploration of an Attention based Two-Stream Dual-Path Netwo...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Xiangkun Tong Xiaoyan Ye Xiaoying Ye Guangdong Computer Science Department Neusoft Institute Foshan China Department of Big Data and Network Security Longhua Vocational and Technical School Shenzhen China Scientific Research Department Guangdong Neusoft Institute Foshan China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Deep neural networks are incapable of tackling inter-frame correlation information within speech sequences, which is important for speech enhancement task. Based on this consideration, attention mechanisms are designed to learn long-term temporal dependencies information from input features. This paper proposes an attention based Two-Stream Dual-Path Conformer networks (TSDPCNet) for speech enhancement work, which can effectively extract both amplitude and phase information from speech signals. In this network, self-attention mechanism is employed to focus on the global information of the speech sequence, while the dual-path attention enables simultaneous extraction of sequence information from both the time and frequency domains. Experimental results on the VoiceBank + DEMAND datasets shows that the proposed TSDPCNet outperforms the conventional speech enhancement methods.

关键词： Deep learning Time-frequency analysis Attention mechanisms Correlation Computational modeling Speech enhancement Feature extraction data mining Noise measurement Streams

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Quantum Variational Based Support Vector Machine for Early Detection of Sepsis 4

Quantum Variational Based Support Vector Machine for Early D...

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4th IEEE International Conference on Mobile networks and Wireless Communications, ICMNWC 2024

作者： Rana Veer Samara Sihman Bharattej, R. Al-Fatlawy, Ramy Riad Sundaram, S. Meenakshi Rathnakumari, E. Annie Sudha, M.N. Saint Leo University Department of Computer Science FL United States College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial intelligence and Data Science Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Physics Chennai India Government College of Engineering Department of Information Technology Erode India

ISBN: (纸本)9798350352931

The sepsis is a very toxic disorder with associated clinical appearance which is difficult to identify and treat. The early analysis and proper treatment are challenging to minimize the death and encourage subsistence in the suspected cases which enhance the detection result. The screening prediction systems that estimating and evaluating the early detection of patient weakness because the efficacy is imperfect at the distinct level. To overcome these challenges, the Machine Learning (ML) techniques is used for predicting sepsis in early phases. In this research the Quantum Variational based Support Vector Machine (QV-SVM) is proposed for early detection of sepsis. It improves scalability and efficiency through leveraging quantum speedups, permitting quick procedure of large datasets. The min-max normalization approaches scale the information to an explicit range, enhancing the performance through confirming features contribute equally. The proposed QV-SVM achieves accuracy of 98.25%, recall of 96.58%, precision of 97.59% and F1-score of 97.08 % thorough Medical Information Mart for Intensive Care (MIMIC) III dataset as compared to exiting methods Extreme Gradient Boosting (XG Boost). © 2024 IEEE.

关键词： Support vector machines

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A Novel Development of Artificial Intelligence Enabled Learning Methodology for Human Depression Prediction Scheme

A Novel Development of Artificial Intelligence Enabled Learn...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ramkumar, G. Priyadharshini, R. Selvi, K. Kalaiselvi, K. Rathika, S. Kanagavalli, N. Saveetha University Department of Ece Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Human Depression Prediction is essential for several reasons, primarily centered around improving mental health outcomes and providing timely interventions. Firstly, early detection of depression allows for prompt and targeted interventions, enabling individuals to receive appropriate support and treatment before their condition worsens. The Human Depression Prediction Scheme (HDPS) introduces an innovative method for predicting depression by synergizing the capabilities of deep learning through the LeNet architecture with the optimization prowess of the Grey Wolf Optimization (GWO) algorithm. The proposed scheme achieves a commendable accuracy of 95%, attesting to its effectiveness in discerning depressive tendencies. Notably, the HDPS is implemented in Google Colab, emphasizing its accessibility and ease of use. This integration of advanced technologies and optimization techniques positions HDPS as a promising tool for early detection of depression, offering both high accuracy and practical implementation in a widely accessible computing environment. © 2024 IEEE.

关键词： Forecasting

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Socioeconomic Changes Based Climate Training for Agricultural Application Using Deep Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 399-410页

作者： Sunitha, M. Durairaj, M. Rajalingam, A. Yusoff, Siti Khalidah Mohd Prasad, S. Hari Chandra Malluvalasa, S. N. Lakshmi Kiran, Ajmeera Department of CSE Vasavi College of Engineering Hyderabad India Department of ECE Saveetha School of Engineering SIMATS Chennai India Department of Engineering University of Technology and Applied Sciences-Shinas and Saveetha Institute of Medical and Technical Sciences Chennai India Management and Science University Selangor Shah Alam Malaysia Department of Electronics & amp Communication Engineering Aditya University Surampalem India Department of Artificial Intelligence & amp Data Science Koneru Lakshmaiah Education Foundation Guntur Dist. Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Telangana Hyderabad 500043 India

In socioeconomic factor studies, the use of the logit and probit models has the disadvantage of representing random variation in preference for unobserved components and time-linked errors. The agricultural sector in the coastal nations of South-East Asia is facing mounting strain due to the adverse effects of climate change. Anticipating and controlling climate variables is challenging and necessitates expensive fixes. Based on social and economic shifts and the use of a deep learning model to detect climate change, the two main facets of agricultural application have been examined in this study. The suggested model focusses on these two factors because the economics and climate are crucial components of agriculture. Initially, financial flow–based data analysis employing recurrent LSTM spatial neural networks for text data analysis was used to assess the social economic analysis for those countries with minimal agricultural development. The study of crop growth data based on climate change is then done using Boltzmann Markov quantile regression neural networks. In terms of training accuracy, sensitivity specificity, and ROC, a performance study is conducted for crop growth data based on climatic change and agricultural development based on economic data. The proposed approach in this instance produced training accuracy of 79%, specificity of 83%, ROC of 84%, and sensitivity of 80% for economic data–based agricultural development;the suggested method achieved 75% training accuracy, 73% specificity, 80% ROC, and 84% SENSITIVITY for climate change–based crop growth data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Agricultural application Climate change detection Deep learning model Quantile regression Social economic changes

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Malicious Traffic Classification in WSN using Deep Learning Approaches 2

Malicious Traffic Classification in WSN using Deep Learning ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Gupta, Chhote Lal Prasad Rajassekharan, Dinesh Sharma, Dilip Kumar Elangovan, Mohanraj Myilsamy, Varatharaj Upreti, Kamal Bansal Institute of Engineering and Technology Dept. of Computer Science and Engineering Lucknow India Peninsula College Faculty of Computer Science Peninsula Malaysia Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruchengode637215 India V.S.B College of Engineering Technical Campus Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350369960

Classifying malicious traffic in Wireless Sensor networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. © 2023 IEEE.

关键词： data routing deep learning LSTM malicious transmission neural network Traffic data transmission WSN

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SYSTEMATIC CONSTRUCTION OF CONTINUOUS-TIME NEURAL networks FOR LINEAR DYNAMICAL SYSTEMS

arXiv

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arXiv 2024年

作者： datar, Chinmay datar, Adwait Dietrich, Felix Schilders, Wil Institute of Advanced Study Technical University of Munich Germany Institute of Data-Science Foundations Technische Universität Hamburg Germany TUM School of Computation Information and Technology Germany Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Discovering a suitable neural network architecture for modeling complex dynamical systems poses a formidable challenge, often involving extensive trial and error and navigation through a high-dimensional hyper-parameter space. In this paper, we discuss a systematic approach to constructing neural architectures for modeling a subclass of dynamical systems, namely, Linear Time-Invariant (LTI) systems. We use a variant of continuous-time neural networks in which the output of each neuron evolves continuously as a solution of a first-order or second-order Ordinary Differential Equation (ODE). Instead of deriving the network architecture and parameters from data, we propose a gradient-free algorithm to compute sparse architecture and network parameters directly from the given LTI system, leveraging its properties. We bring forth a novel neural architecture paradigm featuring horizontal hidden layers and provide insights into why employing conventional neural architectures with vertical hidden layers may not be favorable. We also provide an upper bound on the numerical errors of our neural networks. Finally, we demonstrate the high accuracy of our constructed networks on three numerical *** Codes 93B17, 65L70, 68T07 Copyright © 2024, The Authors. All rights reserved.

关键词： Linear time-invariant system

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Detecting Malicious Domain Names from Domain Generation Algorithms using Bi-directional LSTM Network

Detecting Malicious Domain Names from Domain Generation Algo...

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2022 International Conference on Signal Processing and Communication Security, ICSPCS 2022

作者： Luo, Suliang Han, Gang Li, An Peng, Jialiang School of Data Science and Technology Heilongjiang University Harbin China Heilongjiang Branch National Computer Network Emergency Response Technical Team Coordination Center of China China Institute for Cryptology and Network Security Heilongjiang University Harbin China

ISBN: (纸本)9781510660151

DNS (Domain Name System/DNS) is one of the most important infrastructures of Internet. People can easily access the rich network resources worldwide using the DNS technology. However, the Domain Generation Algorithm (DGA) is also accompanied by the DNS technology, which is used to generate malicious domain names. To detect DGA malicious domains, the previous studies often used unreal small DNS domain name datasets to train the detection models that always overlooked real user data traffic. These models generally did not have good generalization performance. In this paper, we propose a new DGA malicious domain name detection model based on Bi-directional LSTM network. We also propose a new evaluation metric to evaluate the real unlabeled DNS traffic data. Compared with LSTM model, the detection effect of our proposed model is improved effectively. The experimental results show that the precision of the model and the value of AUC reach 98.4% and 0.9079, respectively. © 2022 SPIE.

关键词： Long short-term memory

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Active Learning with the nnUNet and Sample Selection with Uncertainty-Aware Submodular Mutual Information Measure 7

Active Learning with the nnUNet and Sample Selection with Un...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Föllmer, Bernhard Schulze, Kenrick Wald, Christian Stober, Sebastian Samek, Wojciech Dewey, Marc Department of Radiology Charité-Universitätsmedizin Berlin Freie Universität Berlin Humboldt-Universität zu Berlin Berlin10117 Germany Institute of Mathematics Technical University of Berlin Berlin Germany Artificial Intelligence Lab. Otto-von-Guericke-Universität Magdeburg Germany Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany partner site Berlin Berlin Germany

Annotating medical images for segmentation tasks is a time-consuming process that requires expert knowledge. Active learning can reduce this annotation cost and achieve optimal model performance by selecting only the most informative samples for annotation. However, the effectiveness of active learning sample selection strategies depends on the model architecture and training procedure used. The nnUNet has achieved impressive results in various automated medical image segmentation tasks due to its self-configuring pipeline for automated model design and training. This raises the question of whether the nnUNet is applicable in an active learning setting to avoid cumbersome manual configuration of the training process and improve accessibility for non-experts in deep learning-based segmentation. This paper compares various sample selection strategies in an active learning setting in which the self-configuring nnUNet is used as the segmentation model. Additionally, we propose a new sample selection strategy for UNet-like architectures: USIM - Uncertainty-Aware Submodular Mutual Information Measure. The method combines uncertainty and submodular mutual information to select batches of uncertain, diverse, and representative samples. We evaluate the performance gain and labeling costs on three medical image segmentation tasks with different segmentation challenges. Our findings demonstrate that utilizing nnUNet as the segmentation model in an active learning setting is feasible, and most sampling strategies outperform random sampling. Furthermore, we demonstrate that our proposed method yields a significant improvement compared to existing baseline methods. © 2024 CC-BY 4.0, B. Föllmer, K. Schulze, C. Wald, S. Stober, W. Samek & M. Dewey.

关键词： Active learning

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