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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Malsoru, Vankudothu Patil, Amareshwari Kusuma, S.M. Shivakanth, Gandla Samanvita, N. Cmr Technical Campus Department of It Hyderabad India Pda College of Engineering Department of Computer Science & Engineering Kalaburagi India Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bengaluru India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India

ISBN: (纸本)9798350398465

Cloud calculating is a computing prototype that offers inexpensive, scalable computing resources like CPU, storage, and network bandwidth. It provides a pay-as-you-go model for gaining instantaneous online access to a shared pool of resources. This strategy is proposed to cut down on the required amount of computing time. In the most recent paper, authors employ a hybrid approach to resource allocation by combining two powerful optimization algorithms: the Shuffled Frog Leaping Algorithm (SFLA) and the Cuckoo Search (CS) Algorithm. With this algorithm, we can get around the computational time constraints of the HABCCS algorithm and the krill herd algorithm. It's easily scalable, so even niche uses can benefit. The SFLA has the potential to generate a global optimization solution and comes with the added benefits of faster convergence and simpler implementation. Algorithms from the field of computer science are used in difficult situations because of their advantageous simplicity in evaluation. The knapsack problems that normally crop up during the phase of allocating resources are also addressed. The efficacy of the third strategy is evaluated. The investigational consequences demonstrate the greater presentation of the projected method equated to the state-of-the-art methods. © 2023 IEEE.

关键词： Cloud computing

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Neurocognitive assessment under various human-robot teaming environments 46

Neurocognitive assessment under various human-robot teaming ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Packy, Anna L. Jayahankar, Jayesh Teymourlouei, Arya Stone, Joshua Oh, Hyuk Katz, Garrett E. Reggia, James A. Gentili, Rodolphe J. University of Maryland Neuroscience and Cognitive Science Program College Park United States University of Maryland Department of Computer Science College Park United States University of Maryland Department of Electrical & Computer Engineering College Park United States University of Maryland Department of Kinesiology College Park United States Syracuse University Department of Electrical Engineering & Computer Science Syracuse United States

ISBN: (纸本)9798350371499

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams. © 2024 IEEE.

关键词： Man machine systems

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Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study 13

Pain Classification Using Statistical Feature Extraction Usi...

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13th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2023

作者： Sudimanto Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia

ISBN: (纸本)9798350323184

Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people's psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from ***. The *** data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from *** will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%. © 2023 IEEE.

关键词： Extraction

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Entropy and Relative Entropy in the Composition of Commodities 26

Entropy and Relative Entropy in the Composition of Commoditi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Rakib, Mahmudul Islam Akter, Mausumi Milu, Sharmin Akter Nobi, Ashadun Noakhali Science and Technology University Department of Computer Science and Telecommunication Engineering Noakhali3814 Bangladesh Noakhali Govt. Mohila College Department of Information and Communication Technology Noakhali3800 Bangladesh

ISBN: (纸本)9798350359015

This study uses statistical and information-theoretic measures to analyze the dynamics of the five top trading countries' positions in exporting eight commodities and their trade dependency on the GDP of corresponding importers from 1995 to 2017. Results from the Shannon entropy show that China has emerged as a trade giant in the 21st century, while the USA and Germany have dominated steadily. The relationship between trade and GDP is investigated from the relative entropy of VAR-predicted and empirical time series, revealing a higher divergence during financial crises. This implies relatively less market predictability during crises due to risk, reduced production and supply, and less market orientation of exporters. We observe that the USA and Germany are market-oriented exporters, China has become trade-reliant in this century, and Japan and France show relatively less production and supply. The Machinery and transport equipment market is found to be useful in identifying financial crises as it is more sensitive to financial risk and can be considered an important financial branch to tackle crises. The remarks of this research can be useful for traders and policymakers of the international trade market in portfolio investment and risk management. © 2023 IEEE.

关键词： Entropy Financial Crises Relative Entropy Trade Market VAR

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A Novel MIMO Antenna for 6G Applications 1st

A Novel MIMO Antenna for 6G Applications

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Fasal, Umor Hasan, Md. Kamrul Ayubali Bhuiyan, Md. Emdadul Hoque Imran, Abu Zafar Md. Ahmed, Md. Razu Khan, Ferose Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Kumira Chattogram4318 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Kumira Chattogram4318 Bangladesh

ISBN: (纸本)9783031346217

This paper proposes a novel Antenna with many inputs and outputs 6G MIMO (sixth generation) applications at a frequency of 300 GHz. The antenna performance enhancement is studied by inserting different widths (very slight, slightly noticeable, and hardly noticeable) of a decoupling structure between two closely installed radiating elements with a very close together edges of 0.13 mm. With 0.09 mm width decoupling structure, the highest performance (S21 9.99 dB) is attained. Therefore, MIMO antenna design is suggested can be a good candidate for 6G technologies. © 2023, ICST Institute for computer sciences, Social Informatics and telecommunications engineering.

关键词： Microwave antennas

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Dynamic mobility and handover management in software-defined networking-based fifth-generation heterogeneous networks

Dynamic mobility and handover management in software-defined...

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作者： Khan, Adil Ahmad, Shabeer Ali, Ihsan Hayat, Babar Tian, Yanan Liu, Weixing School of Information Engineering Xi'an Eurasia University Xian China School of Electronic Engineering Beijing University of Posts and Telecommunication Beijing China Department of Computer Science Southeast Missouri State University Cape GirardeauMO United States

The adoption and application of mobile communication technology have rapidly escalated, leading to a significant upsurge in the demand of data traffic. Ultra-densification stands as one of the network solutions within the realm of 5G and beyond technologies, aimed to enhance data rates and network capacity. Heterogeneous networks (HetNets) are deployed with different types of small cells (SCs) in mobile networks to provide high capacity, data rate, throughput, and low latency communication. HetNet solves the problem of network densification at the expense of mobility management problems such as ping-pong handover, unnecessary handovers, handover delay, and cell load. This paper introduces an enhanced optimal cell selection technique employing software-defined networking (SDN) to tackle the challenges of handover and mobility management in 5G and beyond 5G (B5G) HetNet. The proposed SDN-based cell selection scheme leverages linear programming (LP) to manage the mobility of users dynamically, facilitating the selection of the optimal cell for user equipment (UE) handover. This selection is based on multi-attribute decision-making criteria, which include user direction, received signal strength (RSS) value, cell load, and dwell time. By applying LP, computational overhead during cell selection is significantly reduced. The results indicate that the proposed scheme leads to a 39% reduction in number of handovers. This reduction signifies a substantial advancement in mitigating issues associated with frequent and unnecessary handovers, ultimately leading to minimized signaling overhead between UE and cells. Moreover, the proposed solution outperformed the existing scheme in terms of system's throughput and selects an optimal target cell with a lower cell load. © 2024 The Authors. International Journal of Network Management published by John Wiley & Sons Ltd.

关键词： Linear programming

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A Shoe Shopping System based on Convolutional Neural Network Image Recognition 5

A Shoe Shopping System based on Convolutional Neural Network...

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5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Chen, Chen-Sheng Chen, Jian-An Yeh, Shih-Hsien Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Program in Engineering Science Hsinchu Taiwan Chang Gung University Department of Computer Science and Information Engineering Taoyuan Taiwan Chung Hua University Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

In this article, we present an innovative approach to enhance the online shoe shopping experience. The convolutional neural network (CNN) image recognition technology was used to enhance shoe classification and recommendations. By training the CNN model on an extensive dataset, unique shoe features and styles were learned. Integrated into a user-friendly online platform, the system offers real-time image recognition, allowing users to snap a photo of a desired shoe for instant identification, including brand, price, and availability details. Moreover, the CNN-based recommendation engine provides personalized suggestions based on style, color, and customer preferences, enriching the shopping experience. Evaluation results confirmed the system's feasibility, and user feedback highlighted its effectiveness in simplifying the shopping process and enhancing satisfaction. This innovative system presents a significant leap in merging AI and e-commerce and shows the potential of image recognition to transform online marketplaces, benefiting consumers, offering valuable insights for retailers, and ultimately reshaping the future of online shoe shopping. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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A Comprehensive Ensemble-Based Approach for Heart Disease Prediction with Feature Selection and Model Analysis

A Comprehensive Ensemble-Based Approach for Heart Disease Pr...

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Advanced Research in Computing (ICARC), International Conference on

作者： Mst. Sazia Tahosin Ismail Hossain Pranto Mohamamd Muid Uddin Chowdhury Sonet Barua Emon Md Ashraful Alam Chonchal Khan Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering American International University-Bangladesh Dhaka Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science Colorado State University Colorado USA

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Heart disease is a leading global cause of mortality, emphasizing the urgent need for early and accurate diagnostic methods. However, traditional diagnostic tools often lack the scalability and precision to meet diverse healthcare demands. We propose a robust ensemble-based machine learning framework for heart disease prediction that combines advanced data preprocessing, feature selection, and model optimization techniques to address this. This study utilized a consolidated dataset of 1,888 records from five heart health databases. First, systematic preprocessing addressed missing values and standardized features. Next, a hybrid feature selection strategy, integrating Univariate Selection, Correlation Coefficient Analysis, and Mutual Information, reduced the feature set from 14 to 11 key indicators. Subsequently, we evaluated 12 machine learning models on all and reduced feature sets. Finally, our ensemble model, combining Random Forest, Decision Trees, XGBoost, CatBoost, and Extra Trees, achieved an outstanding accuracy of 98.12±1.20. Thus, our results demonstrate that integrating multiple classifiers with optimized feature selection significantly enhances predictive accuracy and model reliability. This scalable framework can transform early heart disease detection, improving outcomes across diverse healthcare settings.

关键词： Heart Analytical models Accuracy Systematics Medical services Transforms Predictive models Feature extraction Random forests Diseases

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