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检索条件"任意字段=International Conference on Measurement and Modeling of Computer Systems"

共 90984 条记录，以下是271-280 订阅

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Enhancing Academic Feedback systems with Deep Learning and Ensemble Methods

Enhancing Academic Feedback Systems with Deep Learning and E...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Hemant Sharma Hiren Joshi Dept. Computer Science Gujarat University Ahmedabad India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems.

关键词： Deep learning measurement Accuracy Computational modeling Multilayer perceptrons Data models Ensemble learning Hybrid learning Convolutional neural networks Reliability

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Exploit Innovative computer Architectures with Molecular Dynamics 4th

Exploit Innovative Computer Architectures with Molecular Dyn...

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4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Marchetti, Filippo Gregori, Daniele E4 Computer Engineering via Martiri della Libertà 66 Scandiano Italy INSTM Via G. Giusti 9 Firenze Italy

ISBN: (纸本)9783031812460

Atomistic simulations are a powerful tool to analyse the structure and the behavior of various biological molecules and are widely used in biochemistry studies. Currently, there are several software packages available which provide the algorithms for the numerical resolution of the physical equation necessary for the simulations. Those softwares are demanding and need to handle a large amounts of resources, therefore the hardware in use has to be efficient in terms of performance and costs. Here we tested the Molecular Dynamics toolkit Gromacs on different platforms: Intel, AMD and ARM clusters are considered, also with the support of NVIDIA GPUs. During the tests energy consumption is measured in both idle and working case obtaining the energy to solution for every machine. While the highest performances are obtained with Intel we found that AMD and ARM servers are valid option if energy savings is considered alongside performance, the use of GPU gives a remarkable contribution to reduce consumption. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graphics processing unit

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Adaptive Queue Dynamics (AQD): A Novel Scheduling Algorithm for Response Time Minimization in Baseband Unit (BBU) systems 2nd

Adaptive Queue Dynamics (AQD): A Novel Scheduling Algorithm ...

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2nd international conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Malviya, Abhishek Pandey, Sudhakar Tiwari, Amit Kumar Department of Information Technology NIT-Raipur Chattisgarh Raipur492010 India Department of Computer Science and Engineering United Institute of Technology Uttar Pradesh Prayagraj211010 India

ISBN: (纸本)9789819776023

In the complex world of modern telecommunication networks, Baseband Unit (BBU) pools play a crucial role in optimizing data processing and minimizing response time. This paper presents the Adaptive Queue Dynamics (AQD) scheduling algorithm, a cutting-edge approach specifically tailored for BBU systems. By combining traditional queuing principles with a feedback control mechanism, AQD allows for real-time adaptation to changing network conditions. Through constant monitoring of important system metrics and the use of a real-time feedback loop, AQD is able to intelligently adapt queuing parameters for optimal scheduling. With its ability to dynamically tune and continuously adapt parameters, AQD stands out by enabling the system to swiftly respond to unexpected events or sudden changes, ensuring minimal response time. Mathematical modeling and extensive simulations confirm the effectiveness of AQD in achieving this objective when compared to traditional methods. The AQD approach represents a significant leap forward in telecommunication network efficiency, providing a strong and adaptable solution designed for the demands of modern BBU systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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33rd international conference on Organization and Technology of Maintenance, OTO 2024

33rd International Conference on Organization and Technology...

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33rd international conference on Organization and Technology of Maintenance, OTO 2024

ISBN: (纸本)9783031805967

The proceedings contain 40 papers. The special focus in this conference is on Organization and Technology of Maintenance. The topics include: Thermal Model of a Soldering Iron;periodic Calibration of Temperature measurement Equipment;Dataset Ratio Influence on kNN Classification Results;High-Voltage SF6 Circuit Breaker Maintenance;Life Cycle Management of Operational Technology – OT;list of Priorities for Replacement, Reconstruction and Revitalization of the Observed Units Using the New Methodology;utilizing Faculty computers for Cryptocurrency Validation and Mining During Idle Time;using Stable Diffusion Model for Text-Driven Generation in Augmented Reality Applications;current and Voltage Calculations of the 400 kV Transmission Line Using Laboratory measurements and computer Simulation;improving Concrete Plant Operations and Maintenance with Digital Twin Technology;influence of Axial Stress on the Distribution of Main Normal Stresses in a Plate with an Elliptical Opening;Comparison of RAWEC and AROMAN Methods in Material Selection for Manufacturing or Maintenance;maintenance of Mechanical Equipment in Potentially Explosive Atmospheres;Study and Optimization of DEG-1 Content in Epoxy Paint for Product Quality Maintenance;safety Procedures Implementation in Steel Industry During Machine Inspection and Maintenance;analysis of Downtime of the Technical systems of the Lime Production Plant for Increasing the Efficiency of the Maintenance Service;Applying the DEAR Method to Optimize Multi-objective Process on a Conventional Lathe with New Cutting Tools;wear Behavior of Hypereutectic Al-18Si Composites Under Linear Reciprocating Sliding;the Influence of Combine Harvester Design on Noise Levels in the Operator’s Cabin;multi-criteria Approach to Vehicle, Machines and Equipment Maintenance Impact.

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30th international conference on Cooperative Information systems, CoopIS 2024

30th International Conference on Cooperative Information Sys...

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30th international conference on Cooperative Information systems, CoopIS 2024

ISBN: (纸本)9783031813740

The proceedings contain 28 papers. The special focus in this conference is on Cooperative Information systems. The topics include: Using Eye-Tracking to Detect Search and Inference During Process Model Comprehension;conversationally Actionable Process Model Creation;event Log Extraction for Process Mining Using Large Language Models;All Optimal k-Bounded Alignments Using the FM-Index;unsupervised Anomaly Detection of Prefixes in Event Streams Using Online Autoencoders;autoencoder-Based Detection of Delays, Handovers and Workloads over High-Level Events;swiftMend: An Approach to Detect and Repair Activity Label Quality Issues in Process Event Streams;towards Fairness-Aware Predictive Process Monitoring: Evaluating Bias Mitigation Techniques;a User-Driven Hybrid Neuro-Symbolic Approach for Knowledge Graph Creation from Relational Data;assisted Data Annotation for Business Process Information Extraction from Textual Documents;fleX: Interpreting Graph Neural Networks with Subgraph Extraction and Flexible Objective Estimation;handling Catastrophic Forgetting: Online Continual Learning for Next Activity Prediction;A Decomposed Hybrid Approach to Business Process modeling with LLMs;self-organising Approach to Anomaly Mitigation in the Cloud-to-Edge Continuum;TALOS: Task Level Autoscaler for Apache Flink;automating Pathway Extraction from Clinical Guidelines: A Conceptual Model, Datasets and Initial Experiments;IML4DQ: Interactive Machine Learning for Data Quality with Applications in Credit Risk;optimizing B-Trees for Memory-Constrained Flash Embedded Devices;predictive Process Approach for Email Response Recommendations;achieving Fairness in Predictive Process Analytics via Adversarial Learning;enhancing Temporal Knowledge Graph Reasoning with Contrastive Learning and Self-attention Mechanisms;graph Convolution Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs;collaboration Miner: Discovering Collaboration Petri Nets;discovering Order-Inducing Features i

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13th international conference on Biomimetic and Biohybrid systems, Living Machines 2024

13th International Conference on Biomimetic and Biohybrid Sy...

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13th international conference on Biomimetic and Biohybrid systems, Living Machines 2024

ISBN: (纸本)9783031725968

The proceedings contain 31 papers. The special focus in this conference is on Biomimetic and Biohybrid systems. The topics include: FlyWheel: A Robotic Platform for modeling Fly Visual Behavior;a Coupled-Oscillator Model of Human Attachment Dynamics Evaluated in a Robot Dyadic Interaction;creating an Artificial No-Fly Zone with Sensory Disruptions;maximizing Robotic Limb Rigidity and Strain Sensing Capabilities Through Localized Kevlar Fiber Reinforcement;Flexible Strain Gauge Sensors as Real-Time Stretch Receptors for Use in Biomimetic BPA Muscle Applications;sensory Feedback Cancellation: Developing Resonator Networks to Mimic A. leptorhynchu’s Cerebellar Processing of Sensory Feedback;binocular Vision and Vector-Summation Based Integration of Bilateral Innate and Learned Visual Cues in Insect Navigation;bioinspired Magnetic Navigation for Exploring Celestial Bodies;bioinspired Navigation Based on Distributed Sensing in the Leech Using Dynamic Neural Fields;a Comparative Study of Reinforcement Learning and Insect-Inspired Visual Navigation Methods;erodium Awn’s Water Transport Insights for Controlled Swelling Agent Rearrangement in Anisotropic Structures;simulated Control of an Aquatic Serpentine Robot with Stable Heteroclinic Channels;a Comparison of Model-Free Controllers for Trajectory Tracking in a Plant-Inspired Soft Arm;pulse Modulation in Braided Pneumatic Actuators Mimics Contractile Behavior of Biological Muscles;design of a Rat Robotic Forelimb;cellular Plasticity Model for Bottom-Up Robotic Design;vertical Closure Constraint for Self-replicating Machines;mechanical Design of a Feline Robot for Dynamic Scaling Testing;moving Inward with Front Legs Improves Tripod Gaits for Crab-Like Robot Walking in Sand;encoding 3D Leg Kinematics Using Spatially-Distributed, Population Coded Network Model;analysis Pipeline for High-Dimensional Neuromechanical Model Improvement;sequence Generator Network for Neuromechanical Control of Rat Hindlimbs.

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Emotion Recognition Using EEG Signals in Human Brain Waves and Deep Learning Approaches 10

Emotion Recognition Using EEG Signals in Human Brain Waves a...

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10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Emotion recognition using EEG signals has gained growing attention in recent years due to its promising applications in human-computer interaction, affective computing, and mental health assessment. This study investigates the performance of various deep learning architectures in classifying emotional states from EEG recordings. We utilize the Emotion EEG dataset as a benchmark and evaluate five prominent deep learning models. The classification task targets three emotion categories: neu-tral, positive, and negative. Experimental results reveal notable differences in model performance. Among the tested models, BiGRU achieves the highest classification accuracy of 97.70% (±0.69%), followed closely by BiLSTM with 97.37% (±0.17%). These bidirectional architectures significantly outperform the CNN model, which attains an accuracy of only 61.59% (±1.22%). The LSTM and GRU models yield intermediate results, nearing 92% accuracy. The findings highlight the effectiveness of bidirectional recurrent neural networks in modeling the temporal characteristics of EEG signals for emotion detection. This research contributes to advancing EEG-based emotion recognition by identifying the most suitable deep learning strategies, thus supporting the development of more intelligent and emotion-aware computing systems. © 2025 IEEE.

关键词： Recurrent neural networks

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A Vision-Based Behavioral measurement Using Facial Expression for Detecting Student Concentration in Classrooms

A Vision-Based Behavioral Measurement Using Facial Expressio...

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Smart systems for applications in Electrical Sciences (ICSSES), international conference on

作者： R Raghav Reddy Preethi S. H. Darius Koppala Rakshith Department of CSE CMR Institute of Technology Bengaluru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Effective teaching requires the facilitator to gauge the students' concentration levels during a lecture and accordingly elaborate on a particular topic or repeat a concept that students did not understand. The current methodology for in-classroom monitoring of concentration levels is by asking the students whether they have understood, or conducting a postassessment quiz. We propose a minimally intrusive computer vision-based approach to obtain facial expressions for behavioral measurements. Deep Learning models are used to detect emotions. A pairwise comparison and rank reciprocal is used for assigning weights for various emotions corresponding to behavioral engagement in the class. A sampling method is proposed to reduce computation time and calculate the aggregate concentration index over a time horizon. The results show that the facial expression of students during a lecture is a reasonably accurate metric in detecting the concentration levels of a class and may be implemented to provide real-time feedback.

关键词： Deep learning measurement Image resolution Head Computational modeling Smart systems Sampling methods Real-time systems Indexes Monitoring

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Noise-Mitigated Variational Quantum Eigensolver with Pre-training and Zero-Noise Extrapolation

Noise-Mitigated Variational Quantum Eigensolver with Pre-tra...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sun, Wanqi Xu, Jungang Duan, Chenghua School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

As a hybrid quantum-classical algorithm, the variational quantum eigensolver is widely applied in quantum chemistry simulations, especially in computing the electronic structure of complex molecular systems. However, on existing noisy intermediate-scale quantum devices, some factors such as quantum decoherence, measurement errors, and gate operation imprecisions are unavoidable. To overcome these challenges, this study proposes an efficient noise-mitigating variational quantum eigensolver for accurate computation of molecular ground state energies in noisy environments. We design the quantum circuit with reference to the structure of matrix product states and utilize it to pre-train the circuit parameters, which ensures circuit stability and mitigates fluctuations caused by initialization. We also employ zero-noise extrapolation to mitigate quantum noise and combine it with neural networks to improve the accuracy of the noise-fitting function, which significantly eliminates noise interference. Furthermore, we implement an intelligent grouping strategy for measuring Hamiltonian Pauli strings, which not only reduces measurement errors but also improves sampling efficiency. We perform numerical simulations to solve the ground state energy of the H4 molecule by using MindSpore Quantum framework, and the results demonstrate that our algorithm can constrain noise errors within the range of O(10-2) ∼ O(10-1), outperforming mainstream variational quantum eigensolvers. This work provides a new strategy for high-precision quantum chemistry calculations on near-term noisy quantum hardware. © 2025 IEEE.

关键词： Error mitigation Matrix product states Variational quantum eigensolver Zero-noise extrapolation

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Optimizing Drone Delivery Management: An Algorithmic Framework for Collision Detection and Avoidance with Resource Management 3

Optimizing Drone Delivery Management: An Algorithmic Framewo...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Sriharsha, Paramkusam Gowtham, Yelipe Chowdary, Geda Tejesh Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

The revolutionary impact of drones on delivery services encounters major challenges, such as collision with other obstacles and other drones when operating inside crowded areas. This research introduces a Collision Detection and Avoidance (CDA) algorithm which performs effective path identification together with altitude reservation to prevent mid-air collisions. The system improves operational safety together with generating effective delivery routes that deliver both operational safety and efficiency. The system optimizes resource allocation through its ability to arrange drones for the purpose of meeting delivery requirements without excessive congestion. Performance measurement facilitates continuous system improvement. The research implements Dijkstra, A∗ and Bellman Ford shortest path algorithms to the CDA system for evaluating their transport safety and productivity and resource utilization performance. The analysis checks appropriate drone usage in diverse drone applications. The system proves its capability in the incorporation of real-time data for improving drone operations that leads to effective resource use in challenging delivery settings. © 2025 IEEE.

关键词： Resource allocation

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