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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Mondal, Rishita Mandal, Sourab Dutta, Paramartha Pal, Arindrajit Visva-Bharati Department of Computer & System Sciences WB Santiniketan India Academy of Technology Department of Computer Science and Engineering Adisaptagram WB India

ISBN: (纸本)9798350389128

In response to the escalating demand for machine learning techniques capable of handling real-time data streams, particularly in applications like stock markets, this research dives deep into the domain of stream regression. The objective is to adeptly navigate challenges posed by evolving data over time, differentiating it from conventional static regression. Existing models, such as Fast and Incremental Model Trees with Drift Detection (FIMT-DD), k-Nearest Neighbours (KNN), and Adaptive Linear Regression (ALR) and some other studies, though competent, exhibit certain limitations like learning based on certain portions of data, using a significantly large memory and not capable of handling each data in every timestamp it arrives. We propose the Dynamically Adjustable Linear Regression Model (DALR) to address these shortcomings. DALR introduces an adaptive regression model that dynamically evolves based on historical knowledge and newly acquired data, providing an innovative solution to the intricacies of dynamic data streams. This endeavor has also highlighted that the DALR algorithm consistently demonstrates strong performance across various datasets according to the RMSE metric, surpassing other state-of -the-art algorithms in most instances. © 2024 IEEE.

关键词： Financial markets

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Human arm motion capture system 9

Human arm motion capture system

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9th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2024

作者： Olar, Marius Leonard Leba, Monica Ionica, Andreea University of Petrosani Department of System Control and Computer Engineering Petrosani Romania University of Petrosani Department of Management and Industrial Engineering Petrosani Romania

ISBN: (纸本)9798350377224

The implementation of robotics and human support technologies has opened up new possibilities for recovering the mobility impaired and increasing human productivity in the last few decades. Exoskeletons have been developed as a major component of this progress in order to capture any form of arm motion be it passive or active. Motion capture systems, employing different sensors like IMU sensors, EMG sensors, motion sensors, fiber optic sensors are used to capture arm movements. These sensors provide information on position, movement, muscle contraction, and movement intent of the arm. The use of multiple sensor types allows for redundancy and accurate outcomes. Potentiometer position sensor can be combined with other types of sensors to measure acceleration, orientation, force exerted and arm bendability. The combinations improve accuracy and adaptability for arm motion capture during rehabilitation. Various combinations of adapted sensors can be used for each joint of the hand depending on the peculiarities of application. Sensors enable precise and natural control over recovery devices or exoskeletons which is especially important in terms of rehabilitation process improvement. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Enhancement Methods for Chiral Optical Signals by Tailoring Optical Fields and Nanostructures

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engineering 2025年第2期45卷 25-43页

作者： Hanqing Cai Liangliang Gu Haifeng Hu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Zhangjiang Laboratory Shanghai 201204China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

The unique property of chirality is widely used in various *** the past few decades,a great deal of research has been conducted on the interactions between light and matter,resulting in significant technical advancements in the precise manipulation of light field *** this review,which focuses on current chiral optics research,we introduce the fundamental theory of chirality and highlight the latest achievements in enhancing chiral signals through artificial nano-manufacturing technology,with a particular focus on mechanisms such as light scattering and Mie resonance used to amplify chiral *** providing an overview of enhanced chiral signals,this review aims to provide researchers with an indepth understanding of chiral phenomena and its versatile applications in various domains.

关键词： Mie scattering Optical chirality Circular dichroism Orbital angular momentum Bound states in the continuum Nonlinear optics

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Salp Swarm Algorithm Based Optimal Network Reconfiguration with Distributed Generation for Power Loss Minimization and Voltage Profile Improvement

Salp Swarm Algorithm Based Optimal Network Reconfiguration w...

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2023 International Conference on Sustainable Technology and engineering, i-COSTE 2023

作者： Alende, Azmerawu Argawu School of Electrical and Computer Engineering Hawassa University Department of Power System and Energy Engineering Hawassa Ethiopia

ISBN: (纸本)9798350329711

In a distribution system, power loss and voltage deviation are the main concern for the customers and utility. The distribution system has a lower voltage level and higher amount of flowing current than the transmission system, so that, the percentage real power loss in the distribution system is higher. This study addresses power loss and voltage deviation concerns in distribution systems. It optimizes the Yirgalem-Ethiopia radial distribution network using the Salp Swarm Algorithm (SSA) and solar Distributed Generation (DG). SSA outperforms other algorithms in reducing real and reactive power losses and improving the voltage profile. The approach minimizes network costs and maintains voltage within acceptable limits. It proves cost-effective, demonstrating significant power loss reduction and improved voltage profiles. The resource feasibility of solar and wind power in Yirgalem city was analyzed and the outcomes showed that solar power generation is more desirable. For this reason, the solar-type Distributed Generation (DG) is used. The proposed SSA algorithm was compared with Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) in three various scenarios (only optimal network reconfiguration, only optimal DG size and site, and simultaneous optimal network reconfiguration and DG allocation) for the Aposto feeder. As stated, the SSA method performs better in terms of reducing both real and reactive power losses and improvement of voltage profile. The model has been formulated to minimize the total cost of the network by determining the optima of the substation locations and power, the load transfers between the demand centers, the feeder routes and the load flow in the network subject to a set of constraints. From the point of view of economic evaluations, the proposed approach is cost-effective. Generally, the simulation results show that the proposed technique is effective to maintain all buses voltage magnitude within the IEEE acceptable lim

关键词： Distributed power generation

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XGCN:a library for large-scale graph neural network recommendations

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Frontiers of computer Science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data 41

Two-timescale Derivative Free Optimization for Performative ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Haitong Li, Qiang Wai, Hoi-To Department of Computer Science ETH Zurich Switzerland Department of System Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

This paper studies the performative prediction problem where a learner aims to minimize the expected loss with a decision-dependent data distribution. Such setting is motivated when outcomes can be affected by the prediction model, e.g., strategic classification. We consider a state-dependent setting where the data distribution evolves according to a controlled Markov chain. We focus on stochastic derivative free optimization (DFO) where the learner is given access to a loss function evaluation oracle with the above Markovian data. We propose a two-timescale DFO(λ) algorithm that features (i) a sample accumulation mechanism that utilizes every observed sample to estimate the gradient of performative risk, (ii) a two-timescale diminishing step size that balances the rates of DFO updates and bias reduction. Under a non-convex optimization setting, we show that DFO(λ) requires O(1/Ε3) samples (up to a log factor) to attain a near-stationary solution with expected squared gradient norm less than Ε. Numerical experiments verify our analysis. Copyright 2024 by the author(s)

关键词： Prediction models

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A Hybrid CT-DEWCA Based Energy-Efficient Routing Protocol for Data and Storage Nodes in Underwater Acoustic Sensor Networks

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IEEE Access 2025年 13卷 91392-91408页

作者： Khan, Imran Rauf, Muhammad Jamil, Atif Dawood University of Engineering & Technology Department of Electronics Engineering Karachi 74800 Pakistan Dawood University of Engineering and Technology Department of Electronics Engineering Karachi 74800 Pakistan Dawood University of Engineering and Technology Department of Computer System Engineering Karachi 74800 Pakistan

Underwater Wireless Sensor Networks (UWSNs) face significant communication and performance challenges due to their harsh and dynamic underwater environment. To address these issues, efficient and robust communication models are essential. This research proposes an energy-aware deployment approach and introduces the Controlled Transmission Enhanced by hybrid model scheme with Differential Evolution, Whale Optimization, and Chaotic Adaptation (CT-DEWCA) for relay selection. This model enhances node placement by implementing a "strategic dual transmission" approach, which employs CT for short-distance data transfer and utilizes the DEW-CA hybrid algorithm to select optimal relay nodes for long-distance communication. The strategy optimizes energy allocation between storage and regular nodes. Simulations show that the CT-DEWCA model outperforms conventional Vector-Based Forwarding (VBF), Hop-by-Hop Vector-Based Forwarding (HH-VBF), Depth-Based Routing (DBR), and, with recent work, Energy-Efficient Artificial Fish Swarm-based Clustering Cognitive Intelligence Protocol (EAFSCCIP). The results show an improvement by an average of 5.4% in packet delivery ratio, 20% reduction in cumulative energy consumption, 6% increase in throughput, and 12% lower packet‐loss rate on average compared to the other protocols. This research offers a robust solution for enhancing UWSN performance and longevity by addressing critical energy efficiency issues and reliable data transmission in underwater environments. The proposed approach demonstrates the utility and potential of bio-inspired optimization techniques to tackle the unique challenges of underwater communication networks. © 2013 IEEE.

关键词： Chaotic Adaptation Clustering Differential Evolution Energy Consumption Energy Optimization Hop-by-Hop Vector-based forwarding Storage Nodes Underwater Wireless Sensor Networks Vector-Based Forwarding Whale Optimization

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Android Based Arduino Learning Model by Implementing Multi Marker Augmented Reality

Android Based Arduino Learning Model by Implementing Multi M...

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2023 International Conference on Informatics engineering, Science and Technology, INCITEST 2023

作者： Adler, John Setiyadi, Dianto Br Ginting, Selvia Lorena Rinaldy Ginting, Yogie Wicaksono, Mochamad Fajar Universitas Komputer Indonesia Department Computer Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Computer System Bandung Indonesia Universitas Komputer Indonesia Department Computer System Bandung Indonesia Universitas Riau Pekanbaru Faculty of Engineering Department of Mechanical Engineering Indonesia

ISBN: (纸本)9798350317862

This research aims to develop an interactive learning system based on Arduino Uno basic material. To realize this system, Augmented Reality technology is used which will explain each Arduino Uno pin function along with an explanation of each example of the basic circuit provided. This started with the Arduino Uno learning process which often caused damage to the Arduino or the components that were connected to the Arduino itself during the process of making the circuit. This problem can occur due to a lack of basic understanding of theory. The use of Augmented Reality technology is able to display objects in the form of Arduinos in virtual 3D, so that 3D objects can appear, markers are used to mark each object, the introduction of the Arduino Uno itself can be simulated by providing a real picture using the multi marker method where there is a main marker containing 3D objects in the Arduino series and supporting markers containing explanations of the series. Thus, learning is not limited to conventional learning and minimizes errors in trials. The method used to develop the system is the waterfall method. The waterfall method itself is part of implementing a design with a good and reasonable target. Based on the tests obtained, the Arduino Uno learning application is able to run well using multiple markers, 3D or 2D objects can be run on a smartphone, and black box testing and marker reading testing are carried out with different lighting levels and reading from different sides. © 2023 IEEE.

关键词： Smartphones

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A Noise Shaping SAR ADC with kT/C Noise Cancellation and Mismatch Error Shaping

A Noise Shaping SAR ADC with kT/C Noise Cancellation and Mis...

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2024 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2024

作者： Yi, Tiange Li, Zhenbing Du, Bing Yang, Shiheng Liu, Jiaxin University of Electronic Science and Technology of China China National Computer System Engineering Research Institute of China China

ISBN: (纸本)9798331530709

The resolution of SAR ADCs is primarily limited by sampling noise, DAC mismatch, and comparator noise. This work presents a noise-shaping SAR ADC design that addresses all these error sources. Sampling noise is mitigated using a kT/C noise cancellation technique combined with resampling. DAC mismatch is managed through data-weighted averaging (DWA) and mismatch error shaping (MES), while comparator noise is reduced using a second-order passive noise-shaping (NS) technique. Designed in a 28nm CMOS process, the ADC achieves 86.8 dB SNDR across a 2.8 MHz bandwidth with a power consumption of 3.8 mW. © 2024 IEEE.

关键词： Comparators (optical)

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