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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Butler, Kurt Waxman, Daniel Djurić, Petar M. Department of Electrical and Computer Engineering Stony Brook University United States

Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have been proposed to study time series that are generated by dynamical systems, where traditional approaches like Granger causality are unreliable. However, CCM often yields inaccurate results depending upon the quality of the data. We propose the Tangent Space Causal Inference (TSCI) method for detecting causalities in dynamical systems. TSCI works by considering vector fields as explicit representations of the systems' dynamics and checks for the degree of synchronization between the learned vector fields. The TSCI approach is model-agnostic and can be used as a drop-in replacement for CCM and its generalizations. We first present a basic version of the TSCI algorithm, which is shown to be more effective than the basic CCM algorithm with very little additional computation. We additionally present augmented versions of TSCI that leverage the expressive power of latent variable models and deep learning. We validate our theory on standard systems, and we demonstrate improved causal inference performance across a number of benchmark tasks. © 2024 Neural information processing systems foundation. All rights reserved.

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Speech Recording Analysis for Parkinson’s Detection Using Machine Learning Approach 8th

Speech Recording Analysis for Parkinson’s Detection Using M...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Deepika, G. Vinothiyalakshmi, P. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Chennai India

ISBN: (纸本)9783031736162

Parkinson’s Disease (PD) is a neuro-degenerative disorder that affects the motor skills of a person when the production of dopamine is reduced and eventually motor dysfunction. Dopamine is a chemical produced by brain cells, which is responsible for controlling movements. Thus, if Parkinson’s disease is detected earlier, then the progression and the effects of its symptoms can be controlled. The proposed Parkinson’s Disease Detection (PDD) model used for the detection of Parkinson’s Disease using various Machine Learning (ML) algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Voting Classifier, and Random Forest (RF). The algorithms are applied on balanced data scales and on features selected using LDA and PCA from the speech recording dataset. On experimental analysis, the SVM algorithm is found to be more efficient on the balanced dataset and PCA extracted features, giving an accuracy of 96.73% and 95.42% respectively as compared to the other classification algorithms. While the Random Forest algorithm gives the best results on LDA applied dataset giving an accuracy of 95.85%. © IFIP International Federation for Information Processing 2025.

关键词： Random forests

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Probabilistic Feamework for Modeling and Analyzing Complex Networks 15

Probabilistic Feamework for Modeling and Analyzing Complex N...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nair, Lekshmi S Cheriyan, Jo Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India SAINTGITS College of Engineering Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350370249

Complex networks, characterised by intricate structures arising from the relationships and interactions among their constituent elements, play a pivotal role in various domains such as social networks, biological systems, and technological infrastructures. The evolution and dynamics of complex networks are analysed for various applications such as link prediction, information diffusion, and recommendation systems. Recent research has focused on the study of structural motifs and the probabilistic analysis of motifs to capture the dynamics of the network. The motifs, considered building blocks of the larger network structure, are recurrent patterns in the network. This study focuses on the different probabilistic models built on triads, which are sets of three interconnected nodes. We investigate six different probability distributions, including Poisson, Gamma, Exponential, Power Law, Gaussian, Lorentzian, and Gaussian Mixture Models (GMMs) that can be applied in the complex network to capture the insight of the connectivity pattern. The applications of this research extend to refining network models, improving simulations, and enhancing our understanding of the likelihood of observing distinct patterns within complex social networks. © 2024 IEEE.

关键词： Poisson distribution

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Open Radio Access Network RIC Empowered Reconfigurable Intelligent Surface: A Physical-Layer Security Perspective

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 615-625页

作者： Lien, Shao-Yu Tseng, Chih-Cheng Hung, Wei-Cheng Tsai, Cheng-You Liu, Ting-Yu Deng, Der-Jiunn Lin, Yuan-Chun Lin, Shih-Cheng Chang, Chia-Chan Chang, Sheng-Fuh Institute of Intelligent Systems National Yang Ming Chiao Tung University Tainan711010 Taiwan Department of Electrical Engineering National Ilan University Ilan260007 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua50074 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621301 Taiwan

The reconfigurable intelligent surface (RIS) steering reflective beam directions toward a target mobile user equipment (UE) has been a promising technology for coverage enhancement and physical-layer (PHY) security to avoid signal eavesdroppers and jammers. However, tracking and predicting the moving trajectory of UE demand significant computing burdens unaffordable to the RIS. Performing beam steering computations on the radio access network (RAN) intelligent controller in the open RAN (O-RAN) architecture therefore emerges as a revolutionary paradigm. In this paper, we propose a PHY-secured beam steering design based on a pre-processing-based Kalman filter algorithm. Through a pre-processing phase to derive the radiation feature, the proposed design accurately tracks the position of the mobile UE and quickly recovers the position search while position tracking fails. Through practical field-try experiments, we justify the capability of O-RAN RIC-empowered RIS for PHY security. © 2013 IEEE.

关键词： Radio access networks

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Intelligent Underwater Sound Surveillance for Intrusion Detection and Emergency Alerting 15

Intelligent Underwater Sound Surveillance for Intrusion Dete...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohite, Vaishnavi Shivaji Patra, Krishna Gayatri Sankar, Ch Hari Srinivas, M. Ullas, S. Amrita School of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

With the demand of protective necessity of underwater environment and infrastructures, it is high time for the technological advancement towards intelligently monitoring and ensuring responsive security mechanism. A novel approach uses modern signal analysis and machine learning methods for the differentiation and subsequent analysis of underwater sounds based on the specific source, focusing on interfering objects like submarines, ships, or other noise caused by humans. The introduced system entails the use of the real-time monitoring system so as to allow constant assessment of the acoustic data collected beneath the water. In case of exposure to any abnormal sounds, an alarm system is activated meant to inform a monitoring and control centre. Its versatility for various underwater conditions is addressed by exercising of the system that considers the fluctuations of water temperature, pressure, and acoustic background. The state centralized monitoring of the system makes it easy for the system to respond to the potential threats, faster than using the graphical user interface. The following sections of this paper describe both the general system design and the architecture and feature extraction of the proposed machine learning algorithms, specific experimental test results and where appropriate demonstrations of the specific features and an assessment of the envisioned role of this system in underwater security and emergency alerting. © 2024 IEEE.

关键词： Emergency Alerting Machine Learning for Underwater Surveillance Underwater Acoustic Audio Processing

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Advancements in Heart Disease Classification: Leveraging Deep Learning Techniques for ECG Analysis 15

Advancements in Heart Disease Classification: Leveraging Dee...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Aatarsh, K.M. Adharshini, G. Gokul, P. Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350370249

Heart disease, a global health burden, demands early and accurate detection. While vast medical datasets exist, extracting crucial diagnostic patterns remains a challenge. This study investigates the potential of deep learning architectures to improve heart disease diagnosis accuracy. While traditional machine learning approaches have shown promise, this research delves deeper. It explores Convolutional Neural Networks (CNNs) and pre-trained models like VGG19, ResNet50, and DenseNet201 to identify patterns within medical data. The proposed framework leverages deep learning models to analyze diverse medical records, potentially including electrocardiograms (ECGs) and imaging data. These models automatically extract significant features, which subsequently build a classification model for heart disease detection. The study aims to compare the performance of deep learning models against traditional methods using metrics like accuracy, sensitivity, and specificity. By harnessing the power of deep learning for both feature extraction and classification, this research strives to contribute to the development of more robust and accurate diagnostic tools for heart disease. © 2024 IEEE.

关键词： Deep learning Densely Connected Convolutional Networks Heart disease classification Residual Neural Networks Visual Geometry Group

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VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LSTM for LOS Blockage Prediction 2024

VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LST...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Bajaj, Sagar Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, Shabbir N. School of Engineering and Applied Science Ahmedabad University Ahmedabad India Department of Computer Science and Engineering SVNIT Surat India Department of Electrical Engineering IIT Bombay Mumbai India

ISBN: (纸本)9798400709722

The mmWave and sub-THz signals rely on Line-of-Sight (LOS) links for higher throughput. Blocking these links can lead to a sudden drop in the received SNR and increase the latency of the communication network. We propose a CNN-LSTM pipeline designed to enhance the reliability of 6G wireless networks by predicting LOS blockages in advance using a sequential stream of RGB images. The proposed solution's efficiency is demonstrated by comparing key performance indicators between the baseline (YOLO-GRU) and the developed pipelines (ResNet-LSTM, ResNeXt-LSTM). The results are validated on the first real-world 6G communication dataset, DeepSense 6G, which contains diverse data modalities captured at different locations and times of the day. The Top-1 accuracy for future 1, 5, and 10 instances of scenario 22 yields an average increase of 10.63% and 12.60% with the Resnet-LSTM and ResNeXt-LSTM pipelines respectively compared to the baseline YOLO-GRU model. The F1 score of scenarios 17-22 for future-1 time window prediction increased by an average of 7.58% and 8.13% for both pipelines respectively. © 2024 Copyright held by the owner/author(s).

关键词： Phase locked loops

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An Eco-Friendly Approach for Reducing Carbon Emissions in Cloud Data Centers

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computers, Materials & Continua 2022年第8期72卷 3175-3193页

作者： Mohammad Aldossary Hatem A.Alharbi Department of Computer Science College of Arts and SciencePrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Engineering College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia

Based on the Saudi Green initiative,which aims to improve the Kingdom’s environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve netzero carbon by 2060,NEOM city has been proposed to be the“Saudi hub”for green energy,since NEOM is estimated to generate up to 120 Gigawatts(GW)of renewable energy by ***,the Information and Communication Technology(ICT)sector is considered a key contributor to global energy consumption and carbon *** data centers are estimated to consume about 13%of the overall global electricity demand by ***,reducing the total carbon emissions of the ICT sector plays a vital factor in achieving the Saudi plan to minimize global carbon ***,this paper aims to propose an eco-friendly approach using a Mixed-Integer Linear Programming(MILP)model to reduce the carbon emissions associated with ICT infrastructure in Saudi *** approach considers the Saudi National Fiber Network(SNFN)as the backbone of Saudi Internet ***,we compare two different scenarios of data center *** first scenario considers a traditional cloud data center located in Jeddah and Riyadh,whereas the second scenario considers NEOM as a potential cloud data center new location to take advantage of its green energy ***,we calculate the energy consumption and carbon emissions of cloud data centers and their associated energy *** that,we optimize the energy efficiency of different cloud data centers’locations(in the SNFN)to reduce the associated carbon emissions and energy *** results show that the proposed approach can save up to 94%of the carbon emissions and 62%of the energy cost compared to the current cloud physical *** savings are achieved due to the shifting of cloud data centers from cities that have conventional energy sources to a city that has rich in renewable energy ***,we

关键词： Cloud computing carbon emissions energy efficiency energy consumption energy costs eco-friendly data center

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Secure speech coding using chaotic shift keying for encryption combined with error recovery

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Multimedia Tools and Applications 2025年第7期84卷 3893-3927页

作者： Boumaraf, Messaouda Merazka, Fatiha LISIC Laboratory Telecommunications Department Electronic and Computer Engineering Faculty USTHB University Algiers Algeria LISIC Laboratory Telecommunications Department Electrical Engineering Faculty USTHB University Algiers Algeria

This paper presents a speech encryption algorithm utilizing chaotic shift keying alongside error recovery techniques for the G.722.2 codec. The algorithm employs chaotic data interleaving on speech inter-frames to replace continuous packet loss with isolated losses, thus restoring missing data packets. Codec parameters undergo encryption through the combination of multiple chaotic maps applied to intra sub-frames of speech, facilitating confusion and diffusion processes. Enhanced security is achieved through the utilization of multiple chaotic maps, including chaotic shift keying generators applied to codec sub-frame parameters. Specifically, the chaotic shift keying mechanism is employed using four chaotic maps: a Henon map for xn and yn, a logistic map for zn, a tent map for wn, and a cubic map for un. The keys derived from these maps are utilized for chaotic data interleaving on both inter-frames and intra sub-frames of the speech file to shuffle frame parameters (confusion) and scramble sub-frame parameters (diffusion). Additionally, the proposed system enhances speech quality by mitigating degradation caused by packet loss through a waveform substitution-based recovery method, which replaces lost packets with previously received copies. A series of experiments, including waveform representation, spectrogram analysis, Enhanced Modified Bark Spectral Distortion (EMBSD) test, WideBand Perceptual Evaluation of Speech Quality (WB-PESQ) test, MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) score, Mean Opinion Score (MOS) score, correlation test, entropy test, Number of Samples Change Rate (NSCR) test, Unified Average Modified Intensity (UACI) test, key space analysis, and sensitivity test, are conducted to evaluate the security and speech quality of the proposed system. The results validate the system’s security and confirm its ability to maintain good audio quality even under packet loss rates ranging from 5% to 20%. © The Author(s), under exclusive licen

关键词： Cryptography

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An Empirical Survey on the Classification Strategies for Automated Alzheimer’s Disease

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SN computer science 2024年第8期5卷 1140页

作者： Mandawkar, Umakant Diwan, Tausif Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India Department of Computer Engineering SVKM’s Institute of Technology Maharashtra Dhule India

The early Alzheimer’s disease (AD) classification is highly important as this neuro-degenerative disease causes severe problems particularly, loss of memory among the patients. In addition, classifying Normal Control (NC), Mild Cognitive Impairment (MCI) and AD in time assists the patient to undergo treatments in the initial period of the disease. Hence, it is necessary to develop a trust worthy categorization model to classify the patients with or without AD. One of principal modalities commonly used for AD classification is the magnetic resonance imaging (MRI), the analysis of which achieved improved classification performance in categorizing the stages of AD. Hence, this review paper presents the detailed survey of 55 research articles showing different strategies to classify AD. The two important categories of the research papers are the methods using machine learning strategies, and the methods using deep learning strategies. The papers are classified based on various modalities, datasets, performance indices, and the software tools used for implementation. In addition to this, various research gaps and the challenges associated with the existing works of AD classification are discussed. In addition, this review provides the future scope for researchers with the analysis of research issues seen in the literary works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer’s disease Deep learning and machine learning Magnetic resonance imaging Mild cognitive impairment

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