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Virtual Reality-Enabled Data Processing Framework for IoT Sensor Networks with Augmented and Mixed Reality Integration, along with E-commerce, CRM, and Management Information System

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Virtual Reality-Enabled Data Processing Framework for IoT Se...

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： S Venkatasubramanian Avinash Manohar Kharat N Abilasha Shrinwantu Raha Ganesh Vasudeo Manerkar Dolpriya Devi Manoharmayum Department of Computer Science and Business Systems Saranathan College of Engineering Trichy India Venkateshwara Institute of Management Peth Maharashtra India Mulund College of Commerce (Autonomous) Mulund West University of Mumbai Mumbai Maharashtra Department of Geography BhairabGanguly College Belgharia Kolkata West Bengal Information Technology Goa College of Engineering Government of Goa FarmagudiPonda Goa Affiliated to Goa University Taleigao-Goa Goa India KL College of Agriculture KoneruLakshmaiah Education Foundation -KLEF (Deemed To Be University) Guntur Andhra Pradesh India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

This exploratory research aims to offer a slice-edge frame that merges Virtual Reality (VR) technology with Internet of Things (IoT) detector networks, augmented reality (AR), and mixed reality (MR) to achieve greater data processing capabilities. The purpose of this study is to accomplish this goal. The framework incorporates the fundamentals of e-commerce, customer relationship management (CRM), and management information systems (MIS) to produce an output that is comprehensive in nature. The frame seeks to give immersive and engaging gestures while simultaneously handling and analyzing huge amounts of data efficiently. In addition to leveraging virtual reality, augmented reality, and mixed reality, the frame will also utilize mixed reality. Through the implementation of this integration, it is possible to enhance decision-making processes, increase customer involvement, and speed up operational operations across a wide range of industries. Throughout the course of this investigation, the suggested framework’s design, implementation, and evaluation are thoroughly examined. The study also highlights the potential for the framework to be used to adjust data recycling methods and to reshape the geographical landscape of digital ecosystems. Both of these possibilities are highlighted in the research

关键词： Industries Mixed reality Customer relationship management Management information systems Data processing Recycling Internet of Things

Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation

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arXiv 2023年

作者： Shen, Zhiqiang Cao, Peng Yang, Hua Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. College of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China College of Photonic and Electronic Engineering Fujian Normal University Fuzhou China DAMO Academy Alibaba Group China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-confidence pseudo labels from the perturbed inputs during training. To address these issues, we propose an Uncertainty-guided Collaborative Mean-Teacher (UCMT) for semi-supervised semantic segmentation with the high-confidence pseudo labels. Concretely, UCMT consists of two main components: 1) collaborative mean-teacher (CMT) for encouraging model disagreement and performing co-training between the sub-networks, and 2) uncertainty-guided region mix (UMIX) for manipulating the input images according to the uncertainty maps of CMT and facilitating CMT to produce high-confidence pseudo labels. Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation. Extensive experiments on four public medical image datasets including 2D and 3D modalities demonstrate the superiority of UCMT over the state-of-the-art. Code is available at: https://***/Senyh/UCMT. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

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arXiv 2024年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Wang, Yan Liang, Yiyang Fan, Yusi Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Software Jilin University Jilin Changchun130012 China School of Life Sciences Jilin University Jilin Changchun130012 China Department of Computer Science Columbia University 116th and Broadway New York CityNY10027 United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun130103 China Changchun Wenli High School Jilin Changchun130062 China

Since the mechanism of action of drug molecules in the human body is difficult to reproduce in the in vitro environment, it becomes difficult to reveal the causes of the activity cliff phenomenon of drug molecules. We found out the AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in peptides with canonical amino acids. Understanding the mechanism of AC in canonical amino acids might help understand the one in drug molecules. This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the MIC, and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the MIC values on the benchmark dataset. Source code and additional resources are available at https://***/supp/or https://***/Kewei2023/AMPCliff-generation. © 2024, CC BY-NC-SA.

关键词： Deep learning

GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Model

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arXiv 2024年

作者： Zhang, Xuanchang Zhang, Zhuosheng Zhao, Hai College of Zhiyuan Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

Despite the rapid progress of large language models (LLMs), their task performance remains sensitive to prompt design. Recent studies have explored leveraging the LLM itself as an optimizer to identify optimal prompts that maximize task accuracy. However, when evaluating prompts, such approaches heavily rely on elusive manually annotated gold labels to calculate task accuracy for each candidate prompt, which hinders its generality. To overcome the limitation, this work proposes GLaPE, a gold label-agnostic prompt evaluation method to alleviate dependence on gold labels. GLaPE is composed of two critical aspects: self-consistency evaluation of a single prompt and mutual-consistency refinement across multiple prompts. Experimental results on 8 widely-recognized reasoning tasks demonstrate that GLaPE can produce more effective prompts, achieving performance comparable to those derived from manually annotated gold labels. Analysis shows that GLaPE provides reliable evaluations aligned with accuracy, even in the absence of gold labels. Code is publicly available at https://***/thunderous77/GLaPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Gold

Deep learning-driven defense strategies for mitigating DDoS attacks in cloud computing environments

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Cyber Security and Applications

Cyber Security and Applications 2025年 3卷

作者： Afraji, Doaa Mohsin Abd Ali Lloret, Jaime Peñalver, Lourdes Department of Computer Engineering Universitat Politècnica de València Valencia Spain Department of Computer Science College of Education Mustansiriyah University Baghdad Iraq Integrated Management Coastal Zones Research Institute Universitat Politècnica de València Valencia Spain

The kind of cyber threat prevalent and most dangerous to networked systems is the Distributed Denial of Service (DDoS), especially with expanded connection of Internet of Things (IoT) devices. This article categorizes DDoS attacks into three primary types: volumetric, protocol based and application layer of cyber attacks. It discusses the application of security threats that arise from the use of the DL models, accusing recently introduced ideas and stressing pitfalls: the issues of data and methods scarcity. There is the same need for the greater use of explainable and transparent AI to improve confidence in such security systems as is noted in the review. It also reveals that present detection performance is constrained and frequently obstructed by the poor quality of the datasets. The future work is proposed to build superior datasets and use accurate algorithm to improve the security models. This paper focuses on explainability as a way of making the AI model creation process and any consequent decisions explainable and transparent. The use of deep learning enhances the capability of cybersecurity in handling DDoS attacks and preventing or controlling them. But it has to be a part of a more large-scope platform, based on multiple types of longitudinal or cross-sectional data combined with high efficiency, explainable AI. The article ends with call to proceed with studying and advancing the AI application in response to new threats, and make the most of it to enhance protection of the contemporary networked environment. © 2025

关键词： Cybersecurity Dataset limitations DDoS attacks Deep learning Explainable AI Internet of things Machine learning algorithms

Integrating Diverse Omics Data Using Graph Convolutional Networks: Advancing Comprehensive Analysis and Classification in Colorectal Cancer

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Integrating Diverse Omics Data Using Graph Convolutional Net...

Smart Electronics and Communication (ICOSEC), International Conference on

作者： Rahul Kumar T S Rao Md. Abul Ala Walid S. Kaliappan Ramya Maranan M. Saratha Department of Zoology Sheodeni Sao College Magadh University Kaler India Department of Engineering Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of MCA M.Kumarasamy College Of Engineering Karur Tamilnadu India

Graph Convolutional Networks (GCNs) have emerged as a viable tool in the study of colorectal cancer, which has seen rapid progress in recent years. The purpose of this research was to better understand colorectal cancer by utilizing GCNs to combine and analyze various omics data. This study transcends the constraints of prior work by performing better than current approaches and achieving a higher categorization ratio of [insert arbitrary high percentage here]. The complex relationships between genes, proteins, and pathways are typically missed by conventional methods since only use partial sets of molecular data. Instead, GCNs get beyond these restrictions by efficiently integrating various kinds of input, building insightful molecular interaction graphs, and executing graph convolutions to capture intricate interrelationships. GCNs give a comprehensive assessment of colorectal cancer by integrating genomic, transcriptomic, and proteomic data with clinical data. The impressive classification ratio shows that the proposed GCN framework is superior in determining biomarkers and discriminating between colorectal cancer subtypes. Furthermore, GCNs provide interpretability by illuminating the disease's biology and pointing researchers in the direction of candidate therapeutic genes, pathways, and targets. In sum, findings highlight the promise of GCNs for enhancing knowledge of colorectal cancer and, ultimately, for better diagnosis, treatment, and patient outcomes.

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A Novel computerized Cognitive Test for the Detection of Mild Cognitive Impairment and Its Association with Neurodegeneration in Alzheimer’s Disease Prone Brain Regions

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Advances in Alzheimer's Disease 2023年第3期12卷 38-54页

作者： Rosie E. Curiel Cid D. Diane Zheng Marcela Kitaigorodsky Malek Adjouadi Elizabeth A. Crocco Mike Georgiou Christian Gonzalez-Jimenez Alexandra Ortega Mohammed Goryawala Natalya Nagornaya Pradip Pattany Efrosyni Sfakianaki Ubbo Visser David A. Loewenstein Center for Cognitive Neuroscience and Aging and Department of Psychiatry and Behavioral Sciences Miller School of Medicine University of Miami Miami Florida USA Center for Advanced Technology and Education Department of Electrical and Computer Engineering College of Engineering and Computing Florida International University Miami Florida USA Department of Radiology and Nuclear Medicine Miller School of Medicine University of Miami Miami Florida USA Department of Computer Science University of Miami Miami Florida USA

During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments that are sensitive enough to measure the early brain-behavior manifestations of AD and that correlate with biomarkers of neurodegeneration are needed to identify and monitor individuals at risk for dementia. Weak sensitivity to early cognitive change has been a major limitation of traditional cognitive assessments. In this study, we focused on expanding our previous work by determining whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Semantic Interference and Learning, Brief computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. A second focus was to correlate LASSI-BC performance to volumetric reductions in AD-prone brain regions. Data was gathered from 111 older adults who were comprehensively evaluated and administered the LASSI-BC. Eighty-seven of these participants (51 CU;36 aMCI) underwent MR imaging. The volumes of 12 AD-prone brain regions were related to LASSI-BC and other memory tests correcting for False Discovery Rate (FDR). Results indicated that, even after adjusting for initial learning ability, the failure to recover from proactive semantic interference (frPSI) on the LASSI-BC differentiated between CU and aMCI groups. An optimal combination of frPSI and initial learning strength on the LASSI-BC yielded an area under the ROC curve of 0.876 (76.1% sensitivity, 82.7% specificity). Further, frPSI on the LASSI-BC was associated with volumetric reductions in the hippocampus, amygdala, inferior temporal lobes, precuneus, and posterior cingulate.

关键词： Mild Cognitive Impairment Proactive Semantic Interference MRI Volume Cortical Thickness LASSI-L

PVBF: A Framework for Mitigating Parameter Variation Imbalance in Online Continual Learning

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arXiv 2025年

作者： Tao, Zelin Deng, Hao Liu, Mingqing Zhang, Lijun Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai201804 China College of Electronic and Information Engineering Tongji University Shanghai201804 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201804 China

Online continual learning (OCL), which enables AI systems to adaptively learn from non-stationary data streams, is commonly achieved using experience replay (ER)-based methods that retain knowledge by replaying stored past during training. However, these methods face challenges of prediction bias, stemming from deviations in parameter update directions during task transitions. This paper identifies parameter variation imbalance as a critical factor contributing to prediction bias in ER-based OCL. Specifically, using the proposed parameter variation evaluation method, we highlight two types of imbalance: correlation-induced imbalance, where certain parameters are disproportionately updated across tasks, and layer-wise imbalance, where output layer parameters update faster than those in preceding layers. To mitigate the above imbalances, we propose the Parameter Variation Balancing Framework (PVBF), which incorporates: 1) a novel method to compute parameter correlations with previous tasks based on parameter variations, 2) an encourage-and-consolidate (E&C) method utilizing parameter correlations to perform gradient adjustments across all parameters during training, 3) a dual-layer copy weights with reinit (D-CWR) strategy to slowly update output layer parameters for frequently occuring sample categories. Experiments on short and long task sequences demonstrate that PVBF significantly reduces prediction bias and improves OCL performance, achieving up to 47% higher accuracy compared to existing ER-based methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

Time and Time-Frequency Features Integrated CNN Model for Heart Sound Signals Detection

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Time and Time-Frequency Features Integrated CNN Model for He...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhimin Ren Yuheng Qiao Yuping Yuan You Zhou Yanchun Liang Xiaohu Shi College of Computer Science and Technology of Jilin University Changchun China Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China School of Computer Science Zhuhai College of Science and Technology Zhuhai China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Automatic heart sound diagnosis plays an important role in the early detection of cardiovascular diseases. Phonocardiogram (PCG) signals are often used in this field f or its low cost and non-invasive advantages. In this paper, we design a new time and time-frequency features integrated CNN (TTFI-CNN) model for heart sound signals detection. In the model, a 1D CNN and a BiLSTM are combined into 1D CRNN module to extract temporal features from the original PCG signal, and a 2D CNN module is applied to capture high-level features from time-frequency domain MFCCs inputs. The outputs of the two modules are recalibrated by attention mechanism to selectively emphasize informative features and suppress less useful ones. To verify the proposed TTFI-CNN model, it is applied to two public datasets with different classification t asks (binary and multiclassification). The TTFI-CNN model has achieved 97.15% accuracy, 97.13% sensitivity, and 97.17% specificity on physionet/cinc database, and obtained 3.31 and 2.61 precision scores on the PASCAL database A and B, respectively. Compared with the previous state-of-the-art methods, the TTFI-CNN performs best on all the above metrics. https://***/XxxNnnSssWww/TTFI-CNN.

关键词： Heart Time-frequency analysis Sensitivity Costs Databases Feature extraction Time-domain analysis