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Optimizing Production and Inventory Management for Defective Items in Manufacturing systems 3

Optimizing Production and Inventory Management for Defective...

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3rd international conference on Intelligent systems. Advanced computing, and Communication, ISACC 2025

作者： Ahmed, Khandakar Rabbi Hossain, Arafat Asif, MD Amaddus Bepary Mohammad, Mahathir Rahaman, Mustafizur Dewan, MD Arafat Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Central Michigan University Dept. of Business Data Analytics Mt Pleasant United States Southern California State University Dept. of Business Administration and Management Los AngelesCA United States Southern California State University Dept. of Business Administration and Management United States Westcliff University Dept. of Cloud Engineering IrvineCA United States Los Angeles City College Dept. of Computer Science Los Angeles United States

ISBN: (纸本)9798331523893

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems. © 2025 IEEE.

关键词： Industry 4.0

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3rd international IFIP/GI conference on Trends in distributed systems. USM 2000

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3rd international IFIP/GI conference on Trends in distributed systems. USM 2000

ISBN: (纸本)9783540410249

The proceedings contain 40 papers. The special focus in this conference is on Electronic Auctions, Trading, Internet-Based Service Markets, Quality of Service and Middleware Architectures. The topics include: distributed processing for integrated fixed and mobile communications;quality of service and service provisioning on a competitive market;agents for automating the bandwidth commerce;integrating trading and load balancing for efficient management of services in distributed systems.a scheme for component based service deployment;performance modeling of a service provisioning design;programming internet quality of service;monitoring quality of service across organizational boundaries;an infrastructure for seamless mobile systems.integration;a mobile distributed telemedical system for application in the neuropsychological therapy;loadable smart proxies and native-code shipping for CORBA;an information model for communication services;specification of a service management architecture to run distributed and networked systems.context notification in mobile environment to find the right person in time;automated adaption for mobile computing based on mobile agents;a scalable location aware service platform for mobile applications based on java RMI;design-aspects for the integration of CORBA-based value added services and intelligent networks;experiences building a service execution node for distributed IN systems.and virtual malls for web commerce.

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Voice Command Fingerprinting: An in-Depth Examination of Challenges and Issues 3

Voice Command Fingerprinting: An in-Depth Examination of Cha...

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3rd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Singh, Paras Nath Mathews, Ann Antu, Anisha Suryanarayana, Ch.V.S. GMR Institute of Technology Department of CSE Rajam532127 India CMR Institute of Technology Department of CSE Bengaluru560037 India Federal Institute of Sc. & Technolgy Department of CSE Kerala Angamaly683577 India Mallareddy Engg. College Maisammaguda Department of CSE-AIML Secundrabad500100 India

ISBN: (纸本)9798331533205

Voice assistant systems.have gained widespread adoption across diverse sectors, including electronics, healthcare, automotive, and retail. Spoken Language Understanding is essential for voice-text interaction systems. Security researchers have already identified vulnerabilities in encrypted network traffic, particularly in the context of traffic analysis, enabling the network-level adversaries to infer the websites visited by users. This security threat, common in traditional web communication, extends to Website Fingerprinting attacks in voice query communication, giving rise to the concept of Voice Command Traffic Fingerprinting. This paper analyses the features, challenges and linguistic properties of VCF(Voice Command Fingerprinting) considering various features. A detailed analysis reveals that the proposed models for voice command categories and factors are more capable to handle the issues. The results and methods in this paper pave the way for further research on the secured models in voice command processing in. The Python tools like Tensor-flow, Scikit-learn and specially Pyttsx3 have been used for generating and filtering datasets. An CTC-Attention-based hybrid model recurrent unit which effectively combine the two models. The proposed model achieves over 99.7% accuracy. © 2025 IEEE.

关键词： distributed computer systems

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XMeta-OS: A Conceptual Framework for Unified Resource Management in distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd international conference on Advances in computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems.like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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Resilient 3D Object Recognition using GR-Net in Sparse Point Clouds 3

Resilient 3D Object Recognition using GR-Net in Sparse Point...

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3rd international conference on Intelligent systems. Advanced computing, and Communication, ISACC 2025

作者： Ghadekar, Premanand Dhame, Pratik Dixit, Soham Patil, Arpit Sanjekar, Rushikesh Shinde, Siddhesh Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India

ISBN: (纸本)9798331523893

Recognizing three-dimensional (3 Dimensonal) objects is crucial for several uses for computer vision like service robots, self-driving cars and surveillance drones to navigate effectively in complex environments. However, existing classification techniques struggle with challenges such as varying resolutions, noisy data, and diverse object poses. Previous studies have highlighted the limitations of point cloud-based methods in handling sparsity, rotation, and positional variance. In this study, we concurrently address these difficulties with a unique technique to 3D object categorization. Our method leverages the graph structure of point clouds and employs a to develop a strong latent representation of 3D objects using a neural network. This representation achieves invariance to rotation, positional shift, and scaling while remaining resilient to point sparsity. Our technique outperforms existing approaches, as evidenced by experimental findings on the ModelNet40 dataset, which show a 45.0% and 38.1% gain in classification accuracy when employing sparse point clouds, respectively, over existing models. © 2025 IEEE.

关键词： Object detection

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719457

The proceedings contain 35 papers. The special focus in this conference is on Computational Electronics for Wireless Communications. The topics include: Design of a Four-Port MIMO Antenna for C-, X-, and Ku-Band Applications;Exploration of Diverse Techniques for Design and Optimization of QCA-Based Multiplexer Circuits;melanoma Detection Via Deep Convolutional Neural Network;Pattern Diversity mm-Wave Rectangular Ring Shaped MIMO Antenna for 5G Communication and Internet of Things Applications;Flexible Two/Eight-Port MIMO Antenna Configuration for LTE B41/WLAN/Wi-Fi 6E and C-Band Applications;Improvement in Machining Ability of Tungsten Carbide Tool Insert Through Microwave Hybrid Heating During Turning of EN 24 Steel;optimizing Airfare Pricing: A Data-Driven Approach for Affordable Travel Planning;advancements in Emotion Recognition: Systematic Review and Research Roadmap;epoc-Based Electroencephalography Signals Analysis of Different Stress Levels;Two-Element Tilted Rectangular MIMO UWB Antenna for Underwater Communications;deep Learning-Based Maize Crop Disease Detection and Remedial Recommendation System;design of Splash Plate Dielectric Hat Feed for Ka-Band Tracking Radar Applications;soft computing Paradigms for Load Balancing in Cloud computing;strategies for Effective Network Congestion Control: Insights from Parameter-Based Analysis;BICC: Optimizing Sensor Network Performance Via an Efficient Bioinspired Iterative Approach with Congestion Control;Leakage Power Reduction and Stability Analysis of 5 nm Node GAA CNTFET SRAMs;bandwidth Enhancement and Improvement of Directivity of a Composite Conical Structure in the Presence of a Circular Loop Antenna;development of Compact Dual Polarized Corrugated Feed Horn Antenna with Low Cross-Polarization for Doppler Weather Radar Applications in C-Band;A Review on Recent Breakthroughs and Accomplishments in the Development of FeFET.

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719426

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A Novel 3D-CNN with DAG Framework for Enhanced Alzheimer's Disease Diagnosis Using Structural MRI 3

A Novel 3D-CNN with DAG Framework for Enhanced Alzheimer's D...

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3rd international conference on Intelligent systems. Advanced computing, and Communication, ISACC 2025

作者： Prasad, D. Jayanthi, K. Tilakan, Pradeep Puducherry Technological University Department of Electronics and Communication Engineering Puducherry605014 India Pondicherry Institute of Medical Sciences Department of Psychiatry Puducherry605014 India

ISBN: (纸本)9798331523893

Alzheimer's is the most common and neurodegenerative kind of dementia impacting the elderly. It is noteworthy that a limited number of older individuals with Alzheimer's disease are timely and effectively diagnosed in the early stages. This is an essential precondition, since the illness is more controllable and therapy is more successful when diagnosed at the beginning or early mild cognitive impairment stage. In recent years, several researchers have focused on enhancing prediction and diagnostic accuracy using an AI-driven image categorization methodology. This research proposes a novel 3D-CNN with Directed Acyclic Graph architecture aimed at precise and efficient diagnosis of Alzheimer's disease in its early stages, utilizing segmented left and right hippocampus regions, grey matter, cerebrospinal fluid, and white matter extracted from structural MRI images of the brain. The structural MRI images undergo pre-processing using 3D Slicer and SPM12. This study utilizes the ADNI, NACC, and KAGGLE datasets. The findings are persuasive, since the suggested 3D-CNN with Directed Acyclic Graph demonstrates superior performance achieving an accuracy of 96.86% relative to other current studies. © 2025 IEEE.

关键词： Neurodegenerative diseases

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3rd international Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

3rd International Workshop on Applications of Medical Artifi...

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3rd international Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031820069

The proceedings contain 24 papers. The special focus in this conference is on Applications of Medical Artificial Intelligence. The topics include: SP-NAS: Surgical Phase Recognition-Based Navigation Adjustment System for Distal Gastrectomy;transforming Multimodal Models into Action Models for Radiotherapy;enhanced Interpretability in Histopathological Images via Combined Tissue and Cell-Level Graph Analysis;targeted Visual Prompting for Medical Visual Question Answering;deep Learning for Resolving 3D Microstructural Changes in the Fibrotic Liver;predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework;Optimizing ICU Readmission Prediction: A Comparative Evaluation of AI Tools;source Matters: Source Dataset Impact on Model Robustness in Medical Imaging;evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence systems.in Low-Resource Settings: Semi-automated Classification and Detection of Community Acquired Pneumonia;incremental Augmentation Strategies for Personalised Continual Learning in Digital Pathology Contexts;assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears;automated Feedback System for Surgical Skill Improvement in Endoscopic Sinus Surgery;quantifying Knee Cartilage Shape and Lesion: From Image to Metrics;RadImageGAN – A Multi-modal Dataset-Scale Generative AI for Medical Imaging;Ensemble-KAN: Leveraging Kolmogorov Arnold Networks to Discriminate Individuals with Psychiatric Disorders from Controls;SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury;EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models;evaluating the Impact of Pulse Oximetry Bias in Machine Learning Under Counterfactual Thinking;normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification;one-Shot Medical

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Scalable Real-Time Anomaly Detection in Financial Markets Using a Novel Machine Learning Model 3rd

Scalable Real-Time Anomaly Detection in Financial Markets Us...

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3rd international conference on Intelligent systems.and Sustainable computing, ICISSC 2023

作者： Andi, Hari Krishnan Bommisetti, Ravi Kumar Subbarao, Buggavarapu V. S. S. Sonthi, Vijaya Krishna Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor Bahru81750 Malaysia Department of Business Administration and Dean-Research and Development Maris Stella College Vijayawada520008 India Department of MBA Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

Stakeholder objectives can be put at risk if anomalies are not detected sufficiently promptly in the constantly evolving financial systems. This paper recommends an innovative Machine Learning (ML) method, the Hybrid Temporal-Feature Embedding Model (HTEM), dedicated to real-time Anomaly Detection (AD). Fundamental to this framework is the use of a distributed system to store and process real-time data that has been collected through a strong Data Ingestion Layer. The HTEM framework is a vital component of this layout. The temporal flow of data collected from LSTM networks is precisely combined with the significance of individual features that are analyzed by autoencoders in this technique. By applying this dual embedding, a complete model of financial data sequences is accomplished. By applying a fully connected layer to provide this combined representation, the research affects an accurate anomaly value for each data point. If the overall rating is higher than a particular threshold, the data point is classified as an anomaly. Training this model frequently on known anomalies enables it to be fine-tuned to a higher level. The timely, accurate, and efficient finding of anomalies can be ensured through the incorporation of the HTEM within the model being proposed, protecting the interests of stakeholders in the financial market environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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