the proceedings contain 36 papers. the special focus in this conference is on softcomputing: theories and Applications. the topics include: Detection of Duplicate Question Pairs by Applying Proposed BoW, TF-IDF, and ...
ISBN:
(纸本)9789819720309
the proceedings contain 36 papers. the special focus in this conference is on softcomputing: theories and Applications. the topics include: Detection of Duplicate Question Pairs by Applying Proposed BoW, TF-IDF, and USE Approach;the Hand Glove Enabling Voice and Text Communication;advancing Rheumatoid Arthritis Care: Exploring Technological Breakthroughs and Future Directions;twitter Trolling Detection Using machinelearning;HMHDTML: Human Mental Health Detection Using Text and machinelearning Model;analyzing the Growth Profile of Brain Tumor with Caputo Fractional Operator via Sumudu Transform;multi-labelled Topic Classification of Research Articles Using machinelearning;security in Mobile Ad Hoc Networks: Impact of Attacks and Countermeasure Approaches;optimization of Vibrational Frequencies for Orthotropic Parallelogram Plates With Circular Variations in Tapering at Simply Supported Boundary;drowsiness Detection Using Adaboost Method and Haar Cascade Classifier to Improve Safety of Drivers;early Detection of Colorectal Cancer from Polyps Images Using Deep learning;motion Control of Underactuated Cart-Double-Pendulum System Via Fractional-Order Sliding Mode Controller;a Comparative Study of Pedestrian Detection Techniques Over the Last Decade;approximation Properties of Modified-Bernstein Operators Having Szász Weight Functions;fourier-Laguerre Expansion of Signals by Composite Summable Technique;a Multiple Linear Regression Model to Estimate Global, Direct and Diffuse Irradiance in Gurugram, India, Using Python;supervised machinelearning Approaches for Customer Reviews Sentiment Analysis;stock Price Prediction on Indian Share Market Using machinelearning;transparent Price Forecasting for Basic Food Commodities in a Developing Economy;Parallel Deep Convolution Neural Network (P-DCNN) Prediction of Paddy Crop Disease;Convolutional-LSTM Network for Emotion Recognition Using EEG Data in Valence-Arousal Dimension;Analysis of Multiply-Accumulate (MAC) Unit Using C
the proceedings contain 15 papers. the special focus in this conference is on Robotics and Networks. the topics include: KineFormer: Solving the Inverse Modeling Problem of soft Robots Using Transformers;multiple Node...
ISBN:
(纸本)9783031644948
the proceedings contain 15 papers. the special focus in this conference is on Robotics and Networks. the topics include: KineFormer: Solving the Inverse Modeling Problem of soft Robots Using Transformers;multiple Node Localization in Cognitive Radio-Based Wireless Sensor Networks Using Grid Search;systematic Review of Network Slicing Resource Management in 5G;comparatively Studying Modern Optimizers Capability for Fitting Vision Transformers;Comparing LSTM and Transformer for Video Depth Estimation;style Transfer to Enhance Data Augmentation for Facial Action Unit Detection;best Image Processing for Higher Face Detection Rate Using Haar Cascades;prediction of Road Traffic Accident Severity Using machinelearning Techniques in the Case of Addis Ababa;detection of Misinformation Related to Pandemic Diseases Using machinelearning;Test the Capability of Arduino TinyML for machinelearning;Comparison of Different Methods for Estimation of Arterial Blood Pressure Using PPG Signals;Explainable AI for Discovering Disease Biomarkers: A Survey;analytical Approach for Mitigating Phishing Attempts.
With a comprehensive review of relevant literature, data, and theories, this study collects post-pandemic data from the Shanghai Stock Exchange 50 Index. Utilizing the Sparse Inverse Covariance Estimation method (Grap...
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Federated learning has emerged as a promising approach for collaborative machinelearning while preserving data privacy in distributed settings. Despite recent advancements, challenges such as privacy preservation and...
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ISBN:
(数字)9783031518263
ISBN:
(纸本)9783031518256;9783031518263
Federated learning has emerged as a promising approach for collaborative machinelearning while preserving data privacy in distributed settings. Despite recent advancements, challenges such as privacy preservation and communication overhead persist, limiting its practical utility. this work proposes a novel model - RuCIL - Resource utilization and Computational Impact metric-based model for Edge learningthat synergizes federated learning with edge computing, leveraging the computational capabilities of latest edge devices. By doing so, it optimizes privacy-preserving mechanisms and communication overhead of the model. this work not only addresses the limitations of federated learning but also paves the way for more efficient and privacy-conscious machinelearning applications in distributed environments.
Although depth completion has achieved remarkable performance relying on deep learning in recent years, these models tend to suffer a performance degradation when exposed to new environments. Online adaptation, where ...
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Personality and behavior are great factors when considering students' learning strategies. Students tend to utilize their best capacity when the environment around them is comforting and the situation go hand in h...
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Finding the stage of melanoma cancer is important for cancer research. Because of the lack of awareness of the signs, the number of mortalities has dramatically increased, making early detection of skin cancer essenti...
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Recently, several studies have proposed frameworks for Quantum Federated learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and TensorFlow Federated (TFF) libraries have been deployed for realizing QFL....
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ISBN:
(纸本)9798400707551
Recently, several studies have proposed frameworks for Quantum Federated learning (QFL). For instance, the Google TensorFlow Quantum (TFQ) and TensorFlow Federated (TFF) libraries have been deployed for realizing QFL. However, developers, in the main, are not as yet familiar with Quantum computing (QC) libraries and frameworks. A Domain-Specific Modeling Language (DSML) that provides an abstraction layer over the underlying QC and Federated learning (FL) libraries would be beneficial. this could enable practitioners to carry out software development and data science tasks efficiently while deploying the state of the art in Quantum machinelearning (QML). In this position paper, we propose extending existing domain-specific Model-Driven Engineering (MDE) tools for machinelearning (ML) enabled systems, such as MontiAnna, ML-Quadrat, and GreyCat, to support QFL.
this study examines the Boosted Tree Motif Classifier (BTMC), which leverages gradient boosting for the precise classification of intricate motion patterns in quadrupedal robots, with a particular emphasis on dynamic ...
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this study examines the Boosted Tree Motif Classifier (BTMC), which leverages gradient boosting for the precise classification of intricate motion patterns in quadrupedal robots, with a particular emphasis on dynamic actions like jumping. Building upon our previous conference paper [C. Allred, J. Pusey and M. Harper, Detecting ballistic motions in quadruped robots: A BTMC for understanding reinforcement learning, in 7th IEEE Int. Conf. Robotic computing (IEEE, 2023)], this journal extension expands the study by incorporating multivariate motif analysis, additional machinelearning models, and a more comprehensive evaluation of reinforcement learning dynamics. BTMC achieves 96% precision, effectively identifying complex motion patterns during training. through experiments at multiple levels of from individual motors to full robotic actuation, BTMC consistently outperforms traditional techniques in accuracy. this study lays the foundation for future research on whole-body reward development, providing clearer insights into the intricate relationship between rewards and learned skills.
the primary goal of our design is to reduce stress among IT professionals by combining graphical technology literacy and image processing techniques. Our system is a continued improvement over the old stress discovery...
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