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Comparative Analysis of Spatial Human Mobility Parameters in 15 Most Populous U.S. Metropolitan Statistical Areas

IAENG International Journal of Applied Mathematics 2024年第6期54卷 997-1012页

作者： Matloub, Zaid Kostanic, Ivica Senior Data Engineer at Better World Analytics MelbourneFL United States Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL United States

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a

关键词： Big data

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Analysis on unit maximum capacity of orthogonal multiple watermarking for multimedia signals in B5G wireless communications

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Digital Communications and Networks 2024年第1期10卷 38-44页

作者： Mianjie Li Senfeng Lai Jiao Wang Zhihong Tian Nadra Guizani Xiaojiang Du Chun Shan School of Electronics and Information Guangdong Polytechnic Normal UniversityGuangzhou510665China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China Schoolof Electrical Engineering and Computer Science Washington State UniversityPullmanWA99164USA Department of Computer and Information Science Temple UniversityPhiladelphiaPA19122USA

Beyond-5G(B5G)aims to meet the growing demands of mobile traffic and expand the communication *** that intelligent applications to B5G wireless communications will involve security issues regarding user data and operational data,this paper analyzes the maximum capacity of the multi-watermarking method for multimedia signal hiding as a means of alleviating the information security problem of *** multiwatermarking process employs spread transform dither *** the watermarking procedure,Gram-Schmidt orthogonalization is used to obtain the multiple spreading ***,multiple watermarks can be simultaneously embedded into the same position of a multimedia ***,the multiple watermarks can be extracted without affecting one another during the extraction *** analyze the effect of the size of the spreading vector on the unit maximum capacity,and consequently derive the theoretical relationship between the size of the spreading vector and the unit maximum capacity.A number of experiments are conducted to determine the optimal parameter values for maximum robustness on the premise of high capacity and good imperceptibility.

关键词： B5G Multimedia information security Spread transform dither modulation Spreading vector measurement Unit maximum capacity

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Integrating GIS-Remote Sensing: A Comprehensive Approach to Predict Oceanographic Health and Coastal Dynamics

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Remote Sensing in Earth Systems Sciences 2025年第1期8卷 200-212页

作者： Krishnamoorthy, R. Tanaka, Kazuaki Begum, M. Amina Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of ECE Sri Venkateswaraa College of Technology Tamilnadu Chennai India

The monitoring of oceanographic and coastal dynamics is essential for understanding the effects of climate change, predicting natural disasters, and managing coastal resources. Remote sensing technology, particularly through satellite imagery, has revolutionized the ability to collect vast amounts of environmental data over large spatial and temporal scales. However, the complexity of these datasets, which often include intricate spatial patterns and long-term temporal trends, requires advanced machine learning models capable of handling both dimensions effectively. In this study, we present a Hybrid GIS-Transformer Network for analyzing oceanographic and coastal dynamics using remote sensing data. The proposed model integrates the spatial feature extraction capabilities of the GIS network with the temporal analysis strengths of the transformer model. This approach enables a comprehensive understanding of both spatial and temporal patterns in high-resolution satellite imagery, allowing for more accurate detection and prediction of environmental changes. Our methodology was tested across multiple tasks, including coastal erosion detection, sea surface temperature (SST) prediction, wave height prediction, and ocean currents detection. The Hybrid GIS-Transformer model consistently outperformed existing models such as CNN, ResNet50, MobileNet, EfficientNet, and LSTM. For coastal erosion detection, the hybrid model achieved an accuracy of 96.12%, while for SST prediction, the accuracy was 95.43%. In wave height prediction, the model achieved an accuracy of 93.02%, and for ocean currents detection, an accuracy of 91.88% was recorded. These results demonstrate the model’s superiority in capturing both fine spatial details and long-term temporal dynamics compared with individual models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Coastal dynamics Deep learning Environmental monitoring Hybrid GIS-Transformer Machine learning Oceanographic analysis Remote sensing Sea surface temperature prediction Spatial feature extraction Temporal analysis Wave height prediction

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Voice Analysis for Detecting Artificial Speech and Predicting Gender and Age: A Literature Survey 7

Voice Analysis for Detecting Artificial Speech and Predictin...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bharadwaj, Rakhi Dugad, Raj Gile, Akshata Patil, Gyaneshwari Hatyalikar, Shrinivas Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798350304268

This article surveys the literature on voice analysis. The present invention relates to a method and system for analyzing voice data to detect artificial speech then demodulate it to original speech and predict the gender and age of the speaker if the input speech is not fake. The system includes a voice analysis module that processes the voice data and extracts features from the voice signals. The extracted features are then compared with pre-stored reference features to determine the authenticity of the voice signal and the gender and age of the speaker. © 2023 IEEE.

关键词： Audio Processing deep learning gender voice prediction neural networks Voice Detection

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Efficient Dynamic Clustering Mechanism

Efficient Dynamic Clustering Mechanism

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jacob, T. Prem Gopika, G.S. Pravin, A. Sreekrishna, M. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350305708

Generally, Data Mining or Knowledge Discovery is the procedure of analyzing information from various viewpoints and summary the data for further information Clustering is an unsupervised learning process where it generates an object group based on their similarity in such a way that the objects fit into another group are same and those belonging to other are dissimilar. Initialization of cluster size and iteration is the major problem of data clustering traditional clustering mechanisms like fuzzy C means algorithm and K-Means algorithm. To overcome the problems in data clustering a novel clustering mechanism is proposed in this research named Efficient Dynamic Clustering Mechanism. Our proposed mechanism computes the centroid value using Gaussian distribution and this system computes the new clusters depending on the distance between the data points. Finally, we conduct a set of experiments to prove our proposed Efficient Dynamic Clustering Mechanism is rather than other approaches. © 2023 IEEE.

关键词： Clustering Quality Data Clustering Data Mining K-means Clustering

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Improved Coronary Artery Disease (CAD) Prediction using Machine Learning Method with Ensemble Technique 4

Improved Coronary Artery Disease (CAD) Prediction using Mach...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Mehmood, Waqas Khaliq, Abdul Qamar, Zoha Qudsia, Mammona Raza, Syed Saqib Capital University of Science & Technology Computer Science Department Islamabad Pakistan Capital University of Science & Technology Electrical Engineering Department Islamabad Pakistan Institute of Space Technology Electrical Engineering Department Islamabad Pakistan National University of Science & Technology Computer Science Department Islamabad Pakistan

ISBN: (纸本)9798350391862

Predicting Coronary Artery Disease (CAD) presents a critical and intricate challenge within medical science. Late-stage detection of CAD can gravely affect cardiac and vascular health, often leading to obstructions in the veins and perilous conditions. The application of Machine Learning (ML) in assessing CAD risks is pivotal for the early identification and mitigation of these severe consequences. Our study introduces a voting classifier that integrates eight distinct classification algorithms, employing an ensemble method to refine the accuracy of CAD diagnosis. This approach incorporates supervised learning algorithms such as Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees, Bagging, Boosting, with multi model approach to construct a more generalized and robust predictive framework. A comparative evaluation with existing methodologies was performed to ascertain effectiveness of our proposed model. The experimental findings reveals that our proposed technique markedly enhances diagnostic precision, achieving an accuracy rate of 99% by leveraging the benefits of multi model aggregation in ensemble learning. The high accuracy of our ensemble and more generalized model not only improves diagnostic precision but also holds potential for better patient outcomes by facilitating earlier intervention and treatment. © 2024 IEEE.

关键词： Risk assessment

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Federated Learning Using Multi-institutional Data for Generalizable Chest X-ray Diagnosis

Federated Learning Using Multi-institutional Data for Genera...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Chowdari, Dabbara Keshava Radhasyam, Nunna Pal, Anabik Paul, Angshuman Department of Computer Science and Engineering Indian Institute of Technology Rajasthan Jodhpur India Department of Computer Science and Engineering SRM University Andhra Pradesh India

ISBN: (纸本)9781510660359

Deep learning models have achieved great success for the automated analysis of chest x-rays.9 However, many such models lack generalizability, i.e., a model trained in one dataset often performs poorly in a different dataset. One possible reason of such performance drop is the difference in the distribution of data from different institutions. In this context, utilization of data from multiple institutions to train a deep learning model may be helpful towards including a wider variety of data during training. This can improve the generalizability of the trained model. However, such an approach do not to preserve data privacy. To deal with the aforementioned limitation, federated learning may be useful. Federated learning allows multiple institutions to develop a machine learning model utilizing data from all institutions without sharing the data. Thus, federated learning approaches help in preserving data privacy. Although there has been a significant advancement in federated learning,8 such methods are rare in the context of chest x-ray diagnosis.7,10 Furthermore, most of such models do not utilize chest x-ray datasets from multiple institutions. In this work, we design a federated learning framework for chest x-ray diagnosis using datasets from multiple institutions. Our model shows improved generalizability in chest x-ray diagnosis across several publicly available large-scale chest x-ray datasets. © 2023 SPIE.

关键词： Deep learning

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Few-Shot Learning based Anomaly Detection in Security Applications 23

Few-Shot Learning based Anomaly Detection in Security Applic...

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6th ACM India Joint International Conference on Data Science and Management of Data, CODS-COMAD 2023

作者： Sureshan, Shruti Das, Debasis Department of Computer Science and Engineering Indian Institute of Technology Rajasthan Jodhpur India

ISBN: (纸本)9781450397988

One of the most important areas of computer Science is network security. Due to the high volume of data being transferred, the network is now open to different security-related attacks. As a result, it is now of utmost importance to detect network based attacks. In this paper, we propose a simple, adaptable and general framework aiming to detect Anomaly in Security Applications using Relation Network based Few-Shot Learning (RNFSL) model, which is cheaper to compute and needs less data compared to the traditional Machine Learning (ML) and Deep Learning (DL) models that are data hungry. We perform extensive experiments on a publicly available dataset where RNFSL on 1% data resulted in a loss value of 0.032 and also outperformed traditional ML and DL models. © 2023 Owner/Author.

关键词： Anomaly detection

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Wireless Mesh Network for Smart Farming 2

Wireless Mesh Network for Smart Farming

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Gaikwad, Vijay Shewale, Sakshi Shikalkar, Asif Shilimkar, Avanish Solanke, Kuldip Pawar, Swapnil Vishwakarma Institute of Technology Department of ENTC Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350347982

Nearly every sector can be improved thanks to the Internet of Things (IoT). IoT in agriculture is fundamentally altering the way the world thinks about agriculture in addition to offering solutions to frequently timeconsuming and tiresome jobs. One of the foremost subjects in the area of communication engineering currently is the Internet of Things. But, when we talk about networking in the IoT world, wireless mesh networks are a network design that has been discussed for a long time but hasn't been widely adopted and can make a difference. For smart agriculture in this article, a mesh networks with NodeMCU ESP8266 has been employed. The built-in wifi module of the ESP8266 is utilized for mesh networking which facilitates simultaneous monitoring via multiple nodes in the network. The main goal of the proposed system is to implement a mesh network for simultaneous monitoring which facilitates smart farming. © 2023 IEEE.

关键词： Internet of things

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On Detecting Malicious Code Injection by Monitoring Multi-Level Container Activities 14

On Detecting Malicious Code Injection by Monitoring Multi-Le...

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14th International Conference on Cloud Computing and Services Science, CLOSER 2024

作者： Bhuiyan, Md. Olid Hasan Das, Souvik Majumder, Shafayat Hossain Majumdar, Suryadipta Hossain, Md. Shohrab Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh Concordia Institute for Information Systems Engineering Concordia University Canada

ISBN: (纸本)9789897587016

In recent years, cloud-native applications have been widely hosted and managed in containerized environments due to their unique benefits, such as being lightweight, portable, and cost-efficient. Their growing popularity makes them a common subject of cyberthreats, as evidenced by recent attacks. Many of those attacks take place due to malicious code injection to breach systems and steal sensitive data from a containerized environment. However, existing solutions fail to classify malicious code injection attacks that impact multiple levels (e.g., application and orchestrator). In this paper, we fill in this gap and propose a multi-level monitoring-based approach where we monitor container activities at both the system call level and the container orchestrator (e.g., Kubernetes) level. Specifically, our approach can distinguish between the expected and unexpected behavior of a container from various system call characteristics (e.g., sequence, frequency, etc.) along with the activities through event logs at the orchestrator level to detect malicious code injection attacks. We implement our approach for Kubernetes, a major container orchestrator, and evaluate it against various attack paths outlined by the Cloud Native Computing Foundation (CNCF), an open-source foundation for cloud native computing. © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Network security

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