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Communications and Information Technologies (GCCIT), Global Conference on

作者： Udhayakumar Pasupathi A. Gopi Saminathan A. Manikandan M. Subhashini C. Gowrishankar T. S. Poornappriya Department of Computer Science Srimad Andavan Arts and Science College (Autonomous) (Affiliated to Bharathidasan University Tiruchirappalli) Tamilnadu India NPR College of Engineering and Technology Dindigul ECE SRM Institute of Science and Technology Chennai Department of Computer Science Srimad Andavan Arts and Science College (Autonomous) Trichy-5 Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Tiruchirappalli Module Lead Data and Analytics Aspire Systems Pvt Ltd Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Wireless sensor networks (WSNs) are an innovative technology that can be used in critical situations such as battlefields as well as commercial applications including buildings, traffic monitoring, smart homes, habitat monitoring, and more. A wireless sensor network (WSN) comprises small nodes capable of wireless sensing, processing, and communication. One of the most pressing concerns plaguing WSNs today is security. This paper suggests and investigates an energy-efficient cluster head (CH) selection approach using the Whale Optimization Algorithm (WOA) (called WOA-Clustering (WOA-C)) and the Geographic and Energy Aware Routing Algorithm (GEAR). As a result, the suggested method simplifies the decision-making process of energy-aware CHs by utilizing the fitness function that assesses node residual energy and the total energy of neighbouring nodes. Compared to the GWO-C, GABEEC, and PSOMSB protocols, the proposed WOA-GEAR protocol consumes 28%, 22%, and 7.5% less power. The suggested algorithm's network lifetime, power efficiency, buffer occupancy, throughput, and end-to-end latency were evaluated.

关键词： Wireless communication Wireless sensor networks Gears Query processing Clustering algorithms Energy efficiency Whale optimization algorithms Routing protocols Security Monitoring

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Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction

arXiv

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arXiv 2024年

作者： Pain, Cameron Dennis George, Yasmeen Fornito, Alex Egan, Gary F. Chen, Zhaolin Monash Biomedical Imaging Monash University Melbourne Australia The Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia The Department of Data Science and AI Monash University Melbourne Australia Turner Institute for Brain and Mental Health Monash University MelbourneVIC Australia

Low-dose positron emission tomography (PET) image reconstruction methods have potential to significantly improve PET as an imaging modality. Deep learning provides a promising means of incorporating prior information into the image reconstruction problem to produce quantitatively accurate images from compromised signal. Deep learning-based methods for low-dose PET are generally poorly conditioned and perform unreliably on images with features not present in the training distribution. We present a method which explicitly models deep latent space features using a robust kernel representation, providing robust performance on previously unseen dose reduction factors. Additional constraints on the information content of deep latent features allow for tuning in-distribution accuracy and generalisability. Tests with out-of-distribution dose reduction factors ranging from ×10 to ×1000 and with both paired and unpaired MR, demonstrate significantly improved performance relative to conventional deep-learning methods trained using the same data. Code: https://***/cameronPain © 2024, CC BY.

关键词： Magnetic resonance imaging

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Facial Expression Recognition in the Wild using Artificial Rabbits Optimizer based Residual Neural Network

Facial Expression Recognition in the Wild using Artificial R...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： P S Metkewar N Uma Rajesh Kumar Dhanaraj Balasubramanian Prabhu Kavin Malathy Sathyamoorthy Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India New Horizon College of Engineering Bengaluru Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Information Technology KPR Institute of Engineering and Technology Coimbatore India

In affective computing, emotion acknowledgement in the wild is a much-studied area. Although there have been advancements, the difficulty of emotion acknowledgement in the wild due to head movement, face deformation, illumination fluctuation, etc. remains an open subject. To improve the model's simplification ability and, by extension, its performance across a variety of learning tasks, it is crucial that a wide variety of features be extracted by the deep neural network. Interest in facial expression detection in natural settings has grown in recent years despite the difficulty of obtaining discriminative and informative characteristics from partially obscured photos. In the first stage of this paper's pre-processing, the noisy pictures are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE). After that, Residual Neural Network (ResNet) features extraction is used, and the same method serves as an emotion classifier. An iterative technique called stochastic gradient descent (SGD) is utilized to refine the ResNet model's objective function. Finally, the Artificial Rabbits Optimization Algorithm (ARO) is used to choose the best value for, thereby enhancing the reliability of the categorization. The suggested method is effective, as evidenced by experimental findings on three popular facial expression recognition in-the-wild datasets: AffectNet, AFEW dataset, and RAF-DB, where it accomplishes state-of-the-art presentation with 96% accuracy and improves upon existing models by roughly 5% to 8%.

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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Leveraging Machine Learning to Predict IFC Project Success and Failure

Leveraging Machine Learning to Predict IFC Project Success a...

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Women in Computing (InCoWoCo), International Conference for

作者： S. Jayanthi JeyaGanesan J Ismatha Begum E Ramesh Ravikumar Balaraman Sunkam Gunasree Department of AI&DS Faculty of Science and Technology The ICFAI Foundation for Higher Education Telangana India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Information Technology Guru Nanak Institute of Technology Ibrahimpatnam Telangana India Department of Finance and Accounting ICFAI Business School The ICFAI Foundation for Higher Education Telangana India Department of Information Technology Teegala Krishna Reddy Engineering College Telangana India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

This research expounds on the efficacy and impact of a machine learning-based approach in managing the investment services projects of the International Finance Corporation (IFC). It is achieved by focusing on the impact of financial interventions and project completion. We used a detailed dataset to assess trends in project status over time. The details about the growth of completed and active projects and the fluctuations in pending stages, like signing, disbursement, and approval are available in the dataset. We experimented with the ML models, including Random Forest, Gradient Boosting, Logistic Regression, SVC, and Decision Tree, to predict project outcomes. These models are evaluated with the metrics such as, accuracy, precision, recall, F1-score, and ROC AUC. After rigorous experiments, we observed that Gradient Boosting achieved the highest accuracy (90.76 % ) and Random Forest closely followed (90.43%). We identified significant trends that include a high success rate in project completion, an incremental growth in active projects, and a low occurrence of hold and pending approval statuses. We also performed statistical analysis and observed that loans dominate IFC's financial support and risk management and guarantees see less use. Additionally, correlations between total investments and other financial products highlight a diversified investment strategy. The findings emphasize opportunities for improving project approval efficiency, managing bottlenecks, and implementing data-driven decision-making to optimize investment strategies and enhance sustainable development outcomes. This research offers actionable insights for policymakers, demonstrating how ML can improve project management processes and maximize the impact of IFC investments in developing economies.

关键词： Accuracy Biological system modeling Computational modeling Decision making Finance Predictive models Boosting Market research Random forests Investment

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiang Liu Mahdi Soleymani Hessam Mahdavifar National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements.

关键词： Image coding Signal processing algorithms Signal processing Acoustics Sensors Decoding Sparse matrices Speech processing Compressed sensing

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A novel approach to real-time environmental monitoring and automated air quality enhancement using smart nanostructured sensors with adaptive data processing

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Nanotechnology for Environmental engineering 2025年第3期10卷 1-27页

作者： Gowda, V. Dankan Suneel, Sajja Prasad, V. Nuthan Jagtap, Madan Mohanrao Prasad, K. D. V. Ramaswamy, Shekhar Hariram, Venkatesan Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore India Department of CSE (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Electronic and Communication M S R I T Bangalore India Symbiosis Institute of Operations Management Nashik Campus Constituent of Symbiosis International (Deemed University) Pune India Department of Research Symbiosis Institute of Business Management Hyderabad India Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Alliance University Bangalore India Department of Practice of Medicine Vinayaka Mission’s Homoeopathic Medical College and Hospital A Constituent College of Vinayaka Mission’s Research Foundation Deemed to be University Salem India

This paper defines the development of an enhanced and accurate real-time environment monitoring and Air quality control system utilizing smart Nano-sensors. The sensors were given improved nanomaterials for instance, graphene and more metal oxides for a better response when identifying various gaseous pollutants as well as gases in the air including Nitrogen Dioxide (NO2), Carbon Dioxide (CO2) and Volatile Organic Compounds (VOC’s). These sensors were incorporated to a system in form of an air pollution monitoring and control system that could immediately analyze data and respond to it. Before the outcome was obtained, field tests were carried out in many cities, and all of them recorded low pollution levels better air quality. Also, there is an environmental and sustainability perspective which proved that this idea can be used as the further strategy of regulating air quality and would meet the requirements of the law. Thus, the conclusions that can be drawn from the analysis of the results demonstrate the advantage of this approach in terms of systematic environmental management and predictive air quality control.

关键词： Nitrogen oxides

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Network Learning with Directional Sign Patterns

Network Learning with Directional Sign Patterns

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IEEE Conference on Decision and Control

作者： Anqi Dong Can Chen Tryphon T. Georgiou Department of Mathematics Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mathematics School of Data Science and Society University of North Carolina Chapel Hill Chapel Hill NC USA Department of Mechanical and Aerospace Engineering University of California Irvine CA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Complex systems can be effectively modeled via graphs that encode networked interactions, where relations between entities or nodes are often quantified by signed edge weights, e.g., promotion/inhibition in gene regulatory networks, or encoding political of friendship differences in social networks. However, it is often the case that only an aggregate consequence of such edge weights that characterize relations may be directly observable, as in protein expression of in gene regulatory networks. Thus, learning edge weights poses a significant challenge that is further exacerbated for intricate and large-scale networks. In this article, we address a model problem to determine the strength of sign-indefinite relations that explain marginal distributions that constitute our data. To this end, we develop a paradigm akin to that of the Schrödinger bridge problem and an efficient Sinkhorn type algorithm (more properly, Schrödinger-Fortet-Sinkhorn algorithm) that allows fast convergence to parameters that minimize a relative entropy/likelihood criterion between the sought signed adjacency matrix and a prior. The formalism that we present represents a novel generalization of the earlier Schrödinger formalism in that marginal computations may incorporate weights that model directionality in underlying relations, and further, that it can be extended to high-order networks - the Schrödinger-Fortet-Sinkhorn algorithm that we derive is applicable all the same and allows geometric convergence to a sought sign-indefinite adjacency matrix or tensor, for high-order networks.

关键词： Proteins Maximum likelihood estimation Tensors Social networking (online) Computational modeling Encoding data models Computational complexity Complex systems Convergence

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Quantifying COVID-19 transmission risks based on human mobility data: A personalized PageRank approach for efficient contact-tracing

arXiv

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arXiv 2022年

作者： Zhou, Jiali Zhao, Zhan Zhou, Jiangping Department of Urban Planning and Design The University of Hong Kong 8/F Knowles Building Pokfulam Road Hong Kong Musketeers Foundation Institute of Data Science The University of Hong Kong 8/F Knowles Building Pokfulam Road Hong Kong

Given its wide-ranging and long-lasting impacts, COVID-19, especially its spatial spreading dynamics has received much attention. Knowledge of such dynamics helps public health professionals and city managers devise and deploy efficient contact-tracing and treatment measures. However, most existing studies focus on aggregate mobility flows and have rarely exploited the widely available disaggregate-level human mobility data. In this paper, we propose a Personalized PageRank (PPR) method to estimate COVID-19 transmission risks based on a bipartite network of people and locations. The method incorporates both individuals' mobility patterns and their spatiotemporal interactions. To validate the applicability and relevance of the proposed method, we examine the interplay between the spread of COVID-19 cases and intra-city mobility patterns in a small synthetic network and a real-world mobility network from Hong Kong, China based on transit smart card data. We compare the recall (sensitivity), accuracy, and Spearman's correlation coefficient between the estimated transmission risks and number of actual cases based on various mass tracing/testing strategies, including PPR-based, PageRank (PR)-based, location-based, route-based, and base case (no strategy). The results show that the PPR-based method achieves the highest efficiency, accuracy, and Spearman's correlation coefficient with the actual case number. This demonstrates the value of PPR for transmission risk estimation and the importance of incorporating individual mobility patterns for efficient contact-tracing and testing. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

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Tilted Least-Squares Parameter Estimation of Linear Regression Models in the Presence of Outliers

Tilted Least-Squares Parameter Estimation of Linear Regressi...

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IEEE Conference on Decision and Control

作者： Biqiang Mu Er-Wei Bai Wei Xing Zheng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Iowa Iowa City Iowa USA School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

The least squares estimator is the most popular identification method. In the absence of prior knowledge on the unknown noise, uniform weights on all samples are often as-sumed. In reality, potentially unknown contamination is always present and the uniform weights are not necessarily the best. Further, explicit information about the nature of contamination is usually absent. To this end, a relaxed-tilted least squares method is proposed here to assign unequal weights so that the effect of undesired noise contamination can be mitigated. The relaxed-tilted least squares method tilts the uniform prior on the samples so as to move the uniform distribution in a direction that enjoys the smallest estimation error in the neighborhood of the uniform distribution. Theoretical results are established including the ability of outlier removal and the guaranteed parameter convergence in the presence of outliers. Numerical algorithms are proposed and simulated, which support the theoretical derivations.

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