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A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: a biometric application

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Multimedia Tools and Applications 2024年第33期83卷 80179-80200页

作者： Habib, Zeeshan Mughal, Muhammad Ali Khan, Muhammad Attique Hamza, Ameer Alturki, Nazik Jamel, Leila Department of Electrical Engineering HITEC University Taxila Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia

Parkinson’s disease (PD) disorder is caused by the imbalance of inhibitory dopamine and excitatory acetylcholine neurotransmitters, which causes hindrance in locomotion. Freezing of gait (FOG), tremors, and bradykinesia are the most debilitating and disconcerting symptoms of Parkinson’s Disease (PD). FOG episodes occur due to neurological control disorder and motorized impairments, which sternly hinder voluntary movement and forward locomotion. The computational intelligence techniques can effectively predict PD from the sensor data. To detect the FOG episodes often experienced by PD patients, this paper utilizes a 5G spectrum that operates at 4.8 GHz. The main idea of the proposed system is to extract the wireless channel characteristics information (containing amplitude variances) that can be integrated into a 5G communication system. This system collects data through wireless devices such as dipole antennas, network interface cards, and RF signal generators. Five human activities were performed: fast walking, slow walking, sitting on a chair, FOG episodes, and voluntary stopping. We proposed a novel deep dual self-attention and Bi-LSTM fusion architecture for PD prediction to train the collected dataset. Two architectures have been proposed, namely Deep Dual Attention Neural Network (D2ANN) and BiLSTM Neural Network (BNN), for the classification of PD using Chanel State Information (CSI) data. After that, the most prominent information is extracted using self-attention activation. A newly informed weighted fusion approach is proposed, and tree growth optimization is employed for the best feature selection. Finally, the selected features are classified using a multilayer full SoftMax neural network. The experimental process was conducted on FOG wireless sensor network data and obtained the highest accuracy of 98.66%. Compared to the results with state-of-the-art techniques, the proposed framework shows improved accuracy, recall, and precision rates. © The Author(s

关键词： Network architecture

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CTRL: Clustering Training Losses for Label Error Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4121-4135页

作者： Yue, Chang Jha, Niraj K. Princeton University Department of Electrical and Computer Engineering PrincetonNJ08540 United States

In supervised machine learning, use of correct labels is extremely important to ensure high accuracy. Unfortunately, most datasets contain corrupted labels. Machine learning models trained on such datasets do not generalize well. Thus, detecting the label errors can significantly increase their efficacy. We propose a novel framework, called CTRL11CTRL is open-source: https://***/chang-yue/ctrl. (Clustering TRaining Losses for label error detection), to detect label errors in multiclass datasets. It detects label errors in two steps based on the observation that models learn clean and noisy labels in different ways. First, we train a neural network (NN) using the noisy training dataset and obtain the loss curve for each sample. Then, we apply clustering algorithms to the training losses to group samples into two categories: cleanly labeled and noisily labeled. After label error detection, we remove samples with noisy labels and retrain the model. Our experimental results demonstrate state-of-the-art error detection accuracy on both image and tabular datasets under labeling noise. We also use a theoretical analysis to provide insights into why CTRL performs so well. © 2020 IEEE.

关键词： Neural networks

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A novel trusted hardware-based scalable security framework for IoT edge devices

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Discover Internet of Things 2024年第1期4卷 4页

作者： Khan, Mohd Hatami, Mohsen Zhao, Wenfeng Chen, Yu Department of Electrical and Computer Engineering Binghamton University BinghamtonNY13902 United States

The Internet of Things (IoT) devices are pervasively deployed and embedded into our daily lives. Over several years, the massive assimilation of IoT devices has given rise to smart cities, smart factories, smart farms, etc. While IoTs can collect immense amounts of user and environmental information, the lack of adequate security measures is among the major concerns about information security and privacy. With the ever-increasing presence of IoTs, information flow between IoT devices in a single or multi-layered network is becoming increasingly vulnerable to attacks. Although several software-based solutions have been developed over the years that improve the security of resource-constrained IoT devices, there are limited efforts on hardware-oriented IoT security solutions. This paper proposes a Trusted HArdware-based Scalable Secure Architecture (THASSA) for IoT Edge systems. Leveraging standard cryptographic algorithms, THASSA provides a secure mechanism for data transfer and off-loads cryptography operations to trusted hardware. The modularized THASSA architecture allows devices to be added or removed without user intervention. Also, THASSA could be scaled to multiple subsystems using multiple trusted hardware to form IoT clusters with secure data path(s) for mission-critical applications. An experimental study has verified the correctness and effectiveness of THASSA. © The Author(s) 2024.

关键词： Internet of things

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Quantum Circuits for Stabilizer Error Correcting Codes: A Tutorial

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IEEE Circuits and Systems Magazine 2024年第1期24卷 33-51页

作者： Mondal, Arijit Parhi, Keshab K. University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN55455 United States

Quantum computers have the potential to provide exponential speedups over their classical counterparts. Quantum principles are being applied to fields such as communications, information processing, and artificial intelligence to achieve quantum advantage. However, quantum bits are extremely noisy and prone to decoherence. Thus, keeping the qubits error free is extremely important toward reliable quantum computing. Quantum error correcting codes have been studied for several decades and methods have been proposed to import classical error correcting codes to the quantum domain. Along with the exploration into novel and more efficient quantum error correction codes, it is also essential to design circuits for practical realization of these codes. This article serves as a tutorial on designing and simulating quantum encoder and decoder circuits for stabilizer codes. We first describe Shor's 9-qubit code which was the first quantum error correcting code. We discuss the stabilizer formalism along with the design of encoding and decoding circuits for stabilizer codes such as the five-qubit code and Steane code. We also design nearest neighbor compliant circuits for the above codes. The circuits were simulated and verified using IBM Qiskit. © 2001-2012 IEEE.

关键词： Timing circuits

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Graph isomorphism—Characterization and efficient algorithms

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High-Confidence Computing 2024年第4期4卷 48-55页

作者： Jian Ren Tongtong Li Department of Electrical and Computer Engineering Michigan State UniversityEast Lansing 48824USA

The Graph isomorphism problem involves determining whether two graphs are isomorphic and the computational complexity required for this *** general,the problem is not known to be solvable in polynomial time,nor to be *** this paper,by analyzing the algebraic properties of the adjacency matrices of the undirected graph,we first established the connection between graph isomorphism and matrix row and column interchanging ***,we prove that for undirected graphs,the complexity in determining whether two graphs are isomorphic is at most O(n^(3)).

关键词： Undirected graph Characterization Isomorphism Algorithm Polynomial time complexity

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Liberty: An Economy, Three Fold Collaboration, and Virtualization-aware Resource Management Approach for Multiverse Based Application Execution Over 6G Enhanced Networks

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1804-1844页

作者： Chowdhury, Mahfuzulhoq Computer Science and Engineering Department Chittagong University of Engineering and Technology Chittagong4349 Bangladesh

Metaverse-based virtual worlds can provide users with an immersive digital experience by utilizing extended reality, IoT, 6G communication, and computing technology. Unlike the multiverse, in which users can access one virtual environment-based activity at a time, metaverse applications allow users to engage in different virtual environment-based activities simultaneously. Previous research did not look at three-fold collaboration, time-criticality, economy, energy, and network function virtualization (NFV)-aware resource management for multiverse, metaverse, and non-metaverse applications concurrently. To conquer these issues, this article proposes a three-fold collaboration (usage of host, neighbor user, and service provider-owned resources), time-criticality, NFV, energy, economy, and service delivery delay-aware resource management scheme for multiverse, metaverse, and non-metaverse application execution over 6G networks, taking into account heterogeneous physical and digital workers, NFV, communication resources, application arrival, and deadline. Experimental results show that the proposed liberty scheme outperforms existing methods by at least 22.67% economic cost, 31.28% energy expenditure cost, 44.11% service provider profit, and 8.83% task completion ratio gain. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network function virtualization

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Image description using tags latent concepts in convolutional neural networks

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Multimedia Tools and Applications 2024年 1-24页

作者： Barati, Alireza Farsi, Hassan Mohamadzadeh, Sajad Department of Electrical and Computer Engineering University of Birjand Shahid Avini Highway Birjand Iran

Automatic image annotation systems are employed to describe the visual content of images via tag assignment. Most of these image description systems use deep convolutional neural networks as feature extractors or multi-label classification systems. However, the majority of these systems do not optimally employ latent variables and image concepts within the side information. The idea of this study is to add a latent variable to the CNN-based annotation problem. A Bayesian modeling framework is used for this purpose. High-Level Semantic Concepts of tags are used as an instance of a latent variable to implement the proposed model. The Bayesian modeling result interpretation leads to localizing the entire image annotation process and breaks it down into smaller problems. Thus, a CNN-based sub-network is trained independently for each sub-problem and converted to an expert in annotation for a particular high-level semantic concept. To estimate tags in the test phase, the tags obtained from all sub-networks are combined in weighted form. The weight of each sub-net is proportional to the relation of its high-level semantic concept with the test image content. These weights are obtained from a separate general CNN as a classifier of concept. At the end, a tag refinement method is used to remove noisy tags. The obtained results (F1 values are 40.3, 35.2, and 38.1 for Corel5k, ESPGame, and IAPRTC12 datasets, respectively) confirm the effectiveness of the proposed method and can be comparable with the state-of-the-art annotation systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image annotation

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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The consequences of eye tracking on brain and heart coherence

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Multimedia Tools and Applications 2024年第38期83卷 86025-86035页

作者： Attar, Eyad Talal Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

When the eye uses the brain and heart, the cardiovascular and nervous systems integrate and interact. Because changes in retinal microcirculation are independent predictors of cardiovascular events, the eye serves as a "display" to the cardiovascular system and brain. The eye, which has two circulatory systems and a rich vascular supply, is a prime candidate for this study and benefits from early damage to the target organ. Eye movements performed during the visual search pose a challenge in identifying critical points in the eye scene. Because it uses different brain pathways and relates to the cardiac cycle, humans’ ability to spot anomalies under challenging circumstances means they are always needed for visual search. ECG (electrocardiogram), electroencephalogram (EEG), and eye tracking can improve visual search training and attention-tracking performance. EEG data can also be analyzed in real time using eye-tracking technology. Previous work has discussed the EEG or ECG concerning attraction during visual search. The eyeball’s movement combined with the ECG in the previous investigation and introduced large electroencephalographic (EEG) artifacts. This assessment aims to (a) identify brain–heart coherent features influenced by the visual search task and (b) discover the behavior of EEG frequency bands and heart rate variability (HRV) features. EEG and ECG were used to analyze and predict inattention in individuals during a visual search task. The EEG determines human brain function and considers to detect the variability in the EEG frequency band. The work proposed a visual search task with EEG and ECG analysis. Five participants recorded EEG and ECG recordings in three different scenarios: rest, gaze tracking, and normal. Statistical evaluation was used to compare EEG and ECG characteristics and Pearson’s correlation was employed for statistical analysis. Statistical ANOVA analysis revealed statistically significant (p > 0.05) differences between theta (F3) an

关键词： Electrocardiograms

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Reinforcement Learning Enhanced Controller Synthesis for Autonomous Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-12页

作者： Dong, Yanqi Dong, Wei Yang, Dong Tang, Ying Xu, Rongjia College of Computer Science National University of Defense Technology Changsha China Department of Electrical and Computer Engineering Rowan University Glassboro NJ USA

Developing control programs for autonomous vehicles is a challenging task, mainly due to factors such as complex and dynamic environments, intricacy of tasks, and uncertain sensor information. To tackle the challenge, this paper harnesses the potential of formal methods and deep reinforcement learning (DRL) for a more comprehensive solution that integrates Generalized Reactivity(1) (GR(1)) synthesis with DRL. The GR(1) synthesis module takes care of high-level task planning, ensuring a vehicle follows a correct-by-construction and verifiable plan for its mission. On the other hand, the DRL model operates as the low-level motion controller, allowing the vehicle to learn from experience and adjust its actions based on real-time sensor feedback. Therefore, the resulting controller for autonomous vehicles is not only guaranteed to finish its designated tasks but also intelligent to handle complex environments. Through comparative experimental studies, we demonstrate that the control program generated by the proposed approach outperforms the ones generated independently utilizing GR(1) reactive synthesis and DRL. IEEE

关键词： Reinforcement learning

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