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7th international conference on soft computing: theories and Applications, SoCTA 2023

ISBN: (纸本)9789819720309

the proceedings contain 36 papers. the special focus in this conference is on soft computing: 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 machine learning;HMHDTML: Human Mental Health Detection Using Text and machine learning Model;analyzing the Growth Profile of Brain Tumor with Caputo Fractional Operator via Sumudu Transform;multi-labelled Topic Classification of Research Articles Using machine learning;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 machine learning Approaches for Customer Reviews Sentiment Analysis;stock Price Prediction on Indian Share Market Using machine learning;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

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7th EAI international conference on Robotics and Networks, ROSENET 2023

7th EAI International Conference on Robotics and Networks, R...

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7th EAI international conference on Robotics and Networks, ROSENET 2023

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 machine learning Techniques in the Case of Addis Ababa;detection of Misinformation Related to Pandemic Diseases Using machine learning;Test the Capability of Arduino TinyML for machine learning;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.

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Visualization Research on the Market Structure of China's SSE 50 Index Based on the Affinity Propagation machine learning Algorithm 24

Visualization Research on the Market Structure of China's SS...

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8th international conference on machine learning and soft computing, icmlsc 2024

作者： Yu, Fuxiang Wang, Xiaowen Wang, Yakun Business School Hangzhou City University Hangzhou China College of Digital Economics Nanning University Nanning China

ISBN: (纸本)9798400716546

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 (GraphicalLassoCV), the research computes the conditional correlation among stocks. Additionally, an unsupervised learning method, the Affinity Propagation algorithm from machine learning, is employed to further analyze and explore the market structure of the Shanghai Stock Exchange 50 Index in the post-pandemic period. the results reveal that the constituent stocks of SSE 50 are divided into 11 clusters, and specific clusters exhibit industry cohesion, reflecting similar intraday volatility patterns within sectors. Clusters 5 and 6 encompass various industries, implying shared price fluctuations across different sectors. Clusters 8 and 9, predominantly composed of financial listed companies, emerge as the largest clusters with the highest total market capitalization, signifying significant influence in the market. Furthermore, Cluster 5 exhibits noteworthy market capitalization, representing diverse manufacturing stocks. this underscores the potential risk of common fluctuations among stocks from the cluser. © 2024 ACM.

关键词： Inverse problems

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RuCIL: Enabling Privacy-Enhanced Edge computing for Federated learning 1

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7th international conference on Cognitive computing (ICCC) Held as Part of the Services conference Federation Meeting (SCF)

作者： Nimsarkar, Sahil Ashish Gupta, Ruchir Raj Ingle, Rajesh Balliram Dr Shyama Prasad Mukherjee Int Inst Informat Tech Naya Raipur India

ISBN: (数字)9783031518263

ISBN: (纸本)9783031518256;9783031518263

Federated learning has emerged as a promising approach for collaborative machine learning 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 learning that 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 machine learning applications in distributed environments.

关键词： edge computing federated learning privacy management context-awareness communication overhead computation

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Self-supervised Depth Completion with Adaptive Online Adaptation 23

Self-supervised Depth Completion with Adaptive Online Adapta...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Chen, Yang Tan, Yang Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781450398633

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 the model is trained in a self-supervised manner during testing, seems a promising technique to alleviate the drop. However, continuous online adaptation may cause the model to over-adapt and miss the optimal parameters, resulting in oscillation or even degradation of the model performance, in addition to wasting computational resources. therefore, this paper proposes an adaptive online adaptation framework to make model adaptively trigger online adaptation when encountering novel environments and stop adaptation when model has adapted to the current environment. In detail, we design a trigger to detect the familiarity of model to the current scenario based on image similarity and then launch online adaptation when the scenario is novel. Besides, we elaborate a stopper to monitor the error between prediction and depth input and convert online adaptation to inference when online adaptation does not bring improvement for model. Experimental results demonstrate that our method improves the accuracy of model prediction and increases average running speed of the model on each frame in online adaptation. © 2023 ACM.

关键词： Deep learning

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learning Analytics in Blended Mode During Covid-19 Pandemic 7

Learning Analytics in Blended Mode During Covid-19 Pandemic

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Tejal, R.K. Kanchana, V. Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (纸本)9798350304268

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 hand with their personality. learning analytics is a study based on theories and until recently, was not a topic any researcher would show interest in. During 2019- 2020, a severe change was caused not only to students but the entire education system when the Covid-19 pandemic was caused. Exploring the factors which affected students' performance during and after the pandemic is a topic yet to be researched. this entire study is based on students' feedback on their performance during and after the pandemic has nearly ended © 2023 IEEE.

关键词： Anova blended learning data mining learning analytics SVM-Support vector machine

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machine learning Approach in Melanoma Cancer Stage Detection 7

Machine Learning Approach in Melanoma Cancer Stage Detection

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Chitte, Aniruddha Pawar, Aniket Nale, Mukund JSPM's Rscoe Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798350304268

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 essential. Due to its capacity to learn from data and create predictions based on that learning, the machine learning techniques that use deep learning algorithms such as Convolutional Neural Network (CNN) have been widely used in this sector. © 2023 IEEE.

关键词： CNN Melanoma Stage 1 Stage 2

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Model-Driven Quantum Federated learning (QFL) 23

Model-Driven Quantum Federated Learning (QFL)

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7th international conference on the Art, Science, and Engineering of Programming (Programming)

作者： Moin, Armin Badii, Atta Challenger, Moharram Tech Univ Munich Munich Germany Univ Antwerp Antwerp Belgium Univ Reading Reading Berks England Flanders Make Lommel Belgium

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 machine learning (QML). In this position paper, we propose extending existing domain-specific Model-Driven Engineering (MDE) tools for machine learning (ML) enabled systems, such as MontiAnna, ML-Quadrat, and GreyCat, to support QFL.

关键词： model-driven engineering quantum computing federated machine learning

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Cultivating Quadrupedal Robotic Agility: Unveiling Reinforcement learning Dynamics with the Boosted Tree Motif Classifier

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international JOURNAL OF SEMANTIC computing 2025年

作者： Allred, Christopher Pusey, Jason Harper, Mario Utah State Univ USU Dept Comp Sci Logan UT 84322 USA Army Res Lab ARL Adelphi MD USA

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 machine learning 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.

关键词： Reward Modeling Boosted Tree Motif Classifier Quadruped Robots

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Detection of Stress by machine learning in IT Industry 7

Detection of Stress by Machine Learning in IT Industry

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Mohana Priya, D. Hari Haran, S. Budugarla Rohith, B. Hariprasath, M. Jagadeesh Kumar, P. Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9781665464086

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 systems that tried to prevent live discovery and specific comforting, but this system includes periodic worker analysis and live invention, which helps it recognize workers who really are enduring both physical and compressive load difficulties and to propose them appropriate stress-management strategies by outfitting periodic check forms. Our approach places a significant emphasis on stress management, creating a work environment that is stress-free for the employees, and relieving their tension while they are at work. © 2023 IEEE.

关键词： machine learning

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