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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： C Vijayalakshmi V. Kalist G. Charulatha J Giri M O Sabri Ulaganathan.M. S Department of Computer Science and Engineering B S Abdur Rahman Crescent Institute of Science and Technology Chennai Department of ECE Sathyabama Institute of Science and Technology Chennai Department of Biomedical Engineering Selvam College of Technology Namakkal Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Business Information Technology Department Faculty of Economic Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia Department of Computer and Communication Engineering Sri Sai Ram Institute of Technology Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Obesity and overweight are significant public health concerns worldwide, contributing to a range of chronic diseases, including cardiovascular disease, diabetes, and certain cancers. This study presents an advanced predictive model for obesity and overweight detection using an Intensive Neural Network (INN) integrated with Support Vector Machine (SVM). The model is designed to address the complex, non-linear relationships between various factors contributing to obesity, such as age, gender, height, weight, BMI, physical activity levels, dietary habits, and genetic factors. A dataset consisting of 28,765 manually collected data points was used to train and validate the model. Through meticulous data preprocessing, including handling missing values, noise reduction, feature scaling, and feature selection, the data was prepared for optimal model performance. The proposed INN + SVM model achieved an outstanding accuracy of 94.76%, outperforming nine existing models, including Convolutional Neural Network (90.47%) and Deep Neural Network (89.12%). Additional metrics such as Precision (93.22%), Recall (92.88%), and F1-Score (93.05%) further validate the model's superiority. The integration of SVM with INN enhances the model's ability to classify difficult cases, resulting in a robust and reliable tool for predicting obesity and overweight conditions. The results suggest that this hybrid approach holds significant potential for improving early detection and intervention strategies in healthcare.

关键词： Support vector machines Measurement Obesity Noise reduction Predictive models Genetics Feature extraction Data models Reliability Public healthcare

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A Broker Approach in Elevation of Potential Service Provider for Consumer Preferences with Utility Function in Cloud Environment 3

A Broker Approach in Elevation of Potential Service Provider...

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3rd International Conference on Inventive research in Computing Applications, ICIRCA 2021

作者： Kiranbabu, M.N.V. Devaraj, A. Francis Saviour Garapati, Yugandhar Sandeep, Kotte Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering Vaddeswaram Guntur India Kalasalingam Academy of Research and Education Department of Computer Science Engineering Gitam Deemed to Be University Department of Computer Science Engineering Telangana Hyderabad India Dhanekula Institute of Engineering and Technology Department of Computer Science Engineering AndhraPradesh India

ISBN: (纸本)9780738146270

Cloud Computing is a looming and auspicious outcome which offers optimal resource utilization on demand. Elasticity is the feature available in cloud computing which compliments to the Consumer side requests dynamically. The tendency of elasticity exhibits the characteristics of provisioning and de provisioning of resource utilities in an autonomic manner. An SLA with identified SLO's must have elasticity which is really one of the qualitative terms for the recent analysts. The delegate responsible role of broker utilized in selection of suitable SaaS provider with a Distinctive utility algorithm where this utility algorithm endeavors performance compared to the currently prevailing utility driven algorithm. The outcome dataset of this SaaS provider selection algorithm exhibits minimum variance, in the order of Service provider selection, towards customer QoS Preferences. © 2021 IEEE.

关键词： Elasticity

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EventPS: Real-Time Photometric Stereo Using an Event Camera

EventPS: Real-Time Photometric Stereo Using an Event Camera

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Bohan Yu Jieji Ren Jin Han Feishi Wang Jinxiu Liang Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University School of Mechanical Engineering Shanghai Jiao Tong University Graduate School of Information Science and Technology The University of Tokyo National Institute of Informatics

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhancing data efficiency. EventPS seamlessly integrates with both optimization-based and deep-learning-based photo-metric stereo techniques to offer a robust solution for non-Lambertian surfaces. Extensive experiments validate the effectiveness and efficiency of EventPS compared to frame-based counterparts. Our algorithm runs at over 30 fps in real-world scenarios, unleashing the potential of EventPS in time-sensitive and high-speed downstream applications. 1 1 Code available: https://***/ybh1998/EventPS

关键词： computer vision Image resolution Lighting Bandwidth Cameras Real-time systems Pattern recognition

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EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning

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Journal of Neuroscience Methods 2024年 409卷 110215页

作者： Kumari, Annu Edla, Damodar Reddy Reddy, R. Ravinder Jannu, Srikanth Vidyarthi, Ankit Alkhayyat, Ahmed de Marin, Mirtha Silvana Garat Department of Computer Science and Engineering National Institute of Technology Goa Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Vaagdevi Engineering College Department of CSE&IT Jaypee Institute of Information Technology The Islamic University Engineering Research & Innovation Group Universidad Europea del Atlántico Department of Project Management Universidad Internacional Iberoamericana Department of Project Management Universidade Internacional do Cuanza

Brain–computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model's overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments © 2024 Elsevier B.V.

关键词： BCI Deep learning Electroencephalography Optimization

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DViTPolyp: Small Polyp Detection in Colonoscopy Videos based on Deep Vision Transformer

DViTPolyp: Small Polyp Detection in Colonoscopy Videos based...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Ming Ma Likun Xia Hyunseok Seo Dept. of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University New York USA College of Information Engineering Capital Normal University Beijing China Biomedical Research Institute Korea Institute of Science and Technology Seoul South Korea

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Early detection of colorectal polyps and removal play a crucial role in the prevention of colon cancer. computer-aided detection (CAD) methods can be employed to help endoscopists reduce the polyp miss-rates. Numerous automatic polyp detection methods have been explored. However, it is still challenging to automatically detect colon polyps in small size in colonoscopy videos. This paper presents a method termed DViTPolyp for detecting small colorectal polyp in colonoscopy videos. The proposed method leverages the power of the deep vision transformer as the detection head in the network to aggregate rich global features, and uses the re-attention mechanism to regenerate attention maps to capture the features from the backbone model and DViT block. Also, we propose to use a power regularized Intersection Over Union (IoU) as the loss function to further enhance the accuracy of detection. Experiments are conducted on a mix of several public polyp datasets. Comparative results show that our proposed method has a better performance than other methods.

关键词： computer vision Solid modeling Head Design automation Aggregates Prevention and mitigation Colonoscopy Transformers Feature extraction Videos

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Hybrid-Sarcasm: An Approach for Sarcasm Detection and Classification Using Hybrid Machine Learning Methods 6th

Hybrid-Sarcasm: An Approach for Sarcasm Detection and Classi...

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6th International Conference on Advanced Informatics for Computing research, ICAICR 2023

作者： Rajani, B. Saxena, Sameer Kumar, B. Suresh Geetika Narang AMITY Institute of Information Technology Amity University Rajasthan Jaipur India School of Computer Science and Engineering Sanjay Ghodawat University Kolhapur India Trinity College of Engineering and Research Maharashtra Pune India

ISBN: (纸本)9783031840586

Emotions serve as a means of perceiving the affective states of others, but the comprehension of sarcasm requires additional cognitive exertion. Sarcasm, a kind of linguistic irony, involves the use of words or phrases that diverge from their literal meaning. Academic researchers continue to dedicate their efforts towards the development of an algorithm capable of accurately detecting and identifying instances of sarcasm. The challenge of teaching a computer to perceive sarcasm is not a straightforward undertaking, since people even frequently struggle to comprehend it. The demand for Hybrid Machine Learning (HML) is seeing a significant increase in the context of detection and classification tasks. This study examines three distinct categories of characteristics: lexical, sarcastic, and contextual factors. The aforementioned feature sets are used in the task of classifying tweets into two categories: sarcastic and non-sarcastic. This study introduces a feature set characterized by a sardonic tone, along with a hybrid machine learning technique that demonstrates enhanced accuracy. The experimental findings demonstrate that the suggested hybrid machine learning approach attains a 95.30% accuracy rate when applied to sarcastic feature sets. This performance surpasses that of established machine learning methods such as k-nearest neighbor, random forest, support vector machine, and decision tree in terms of feature selection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Multi-resolution Neural Network Compression Based on Variational Bayesian Inference

Multi-resolution Neural Network Compression Based on Variati...

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IEEE International Conference on Communications (ICC)

作者： Chengyu Xia Haoyu Ma Huayan Guo Danny H.K. Tsang Vincent K. N. Lau Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Hong Kong University of Science and Technology Shenzhen Research Institute Shenzhen China Internet of Things Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, we investigate multi-resolution model compression for deep neural networks (DNNs) from a Bayesian perspective. By considering the DNN models with channel masks and proposing a resolution likelihood as well as a two-layer sparse prior for the channel masks, we formulate the multi-resolution model compression as a Bayesian inference problem. To solve this problem, we propose a partial update block variational Bayesian inference (PUB- VBI) algorithm which can infer an approximate posterior for the intractable true posterior. The variational posterior and the updating rules are carefully designed such that the proposed algorithm has a low complexity. Simulation results demonstrate that our proposed method can outperform the baselines on various neural network models and datasets.

关键词： Simulation Artificial neural networks Neural network compression Approximation algorithms Inference algorithms Bayes methods Complexity theory

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The Importance of Being Earnest: Multiple Heterogeneous Container Loading with a Simple Genetic Algorithm 28th

The Importance of Being Earnest: Multiple Heterogeneous Con...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Rusin, Francesco Fiala, Jan Sanker, Julian Bhattarai, Suyesh Ekárt, Anikó Department of Engineering and Architecture University of Trieste Trieste Italy Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Yale College Yale University New Haven United States Mechatherm International Limited Cradley Heath United Kingdom Aston Centre for Artificial Intelligence Research and Application Aston University Birmingham United Kingdom

ISBN: (纸本)9783031900617

In this study we address the complex practical problem of multiple heterogeneous container loading with a simple genetic algorithm. We demonstrate that with a well-chosen representation including a heuristic and a suitable fitness function the other aspects of the genetic algorithm do not need extensive work for good results. Following systematic study of our method on synthetically generated data, we visually showcase the solution for a company-based problem instance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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Enhancing WMMSE With EVM Feedback Through Bayesian Optimization

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IEEE Transactions on Vehicular technology 2025年

作者： Zhao, Zhuobing Guan, Xin Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai201804 China Tongji University College of Electronic and Information Engineering Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China

This paper presents an enhancement to the classical weighted minimum mean square error (WMMSE) approach which assumes ideal Gaussian-distributed transmitted symbols, addressing its performance decline for multi-user precoding in practice. By incorporating scaling coefficients that bridge the gap between theoretical communication rates and actual spectral efficiency based on the modulation and coding schemes (MCS) of each user, the proposed method maximizes real communication rates by optimizing precoding vectors and estimating scaling coefficients jointly. With fixed scaling coefficients, we first design an efficient algorithm for optimal precoders, which is surprisingly almost the same as the pure WMMSE. Due to the implicitness of the scaling coefficients, we then estimate these coefficients by minimizing the error vector magnitude (EVM) of all users, which is solved by Bayesian optimization due to the black box nature of the mapping between these coefficients and EVM. Simulation results demonstrate the superiority of the proposed method over existing benchmarks. © 1967-2012 IEEE.

关键词： Mean square error

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A Task-Resource Joint Management Model with Intelligent Control for Mission-Aware Dispersed Computing

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China Communications 2021年第10期18卷 214-232页

作者： Chengcheng Zhou Chao Gong Hongwen Hui Fuhong Lin Guangping Zeng School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083P.R.China Fifteenth Research Institute of China Electronic Science and Technology Group Corporation Beijing 100083P.R.China Shunde Graduate School of University of Science and Technology Beijing Foshan 528399P.R.China

Dispersed computing can link all devices with computing capabilities on a global scale to form a fully decentralized network,which can make full use of idle computing *** the overall resource allocation of the dispersed computing system is a significant *** detail,by jointly managing the task requests of external users and the resource allocation of the internal system to achieve dynamic balance,the efficient and stable operation of the system can be *** this paper,we first propose a task-resource joint management model,which quantifies the dynamic transformation relationship between the resources consumed by task requests and the resources occupied by the system in dispersed ***,to avoid downtime caused by an overload of resources,we introduce intelligent control into the task-resource joint management *** existence and stability of the positive periodic solution of the model can be obtained by theoretical analysis,which means that the stable operation of dispersed computing can be guaranteed through the intelligent feedback control ***,to improve the system utilization,the task-resource joint management model with bi-directional intelligent control is further *** control thresholds for the two resources not only reverse restrains the system resource overload,but also carries out positive incentive control when a large number of idle resources *** existence and stability of the positive periodic solution of the model are proved theoretically,that is,the model effectively avoids the two extreme cases and ensure the efficient and stable operation of the ***,numerical simulation verifies the correctness and validity of the theoretical results.

关键词： dispersed computing computing resource management intelligent control

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