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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Akhilesh Latoria S. K. Joshi A. Priya M. Kalyan Chakravarthi Radha Raman Chandan Bhasker Pant School of Information Technology Auro University Surat Gujarat Uttaranchal University Dehradun Uttarakhand India School of Architecture KoneruLakshmaiah Educational Foundation Guntur Andhra Pradesh India School of Electronics Engineering VIT-AP University Amaravathi India Department of Computer Science School of Management Sciences (SMS) Khushipur Bachhaon Varanasi UP Department of CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

Due to the rising use of the internet for communication, commercial financial transactions, and other purposes, cloud computing is described as a crucial feature that has risen lately. Through network systems, this entails providing a flexible growth of different services. The fog is often used as a concept to describe different *** often trust cloud service providers to store enormous amounts of data and information with the intention of accessing it later. This paper is mainly concerned with doing a critical examination of the usage of supervised learning and deep learning in cloud applications, as well as their effects on production *** businesses are concentrating on using technology to increase returns on investments, assemble vast quantities of data, and archive it securely so that it may be utilised for organisational decision-making. Notwithstanding, they are more concerned with the cost and security of storing the information. so in order to accommodate the customers, the cloud services have used a wide range of approaches, including human brains, supervised learning, advanced analytics, and others. This helps to increase the users’ credibility and help them use cloud storage to achieve their *** order to understand the overall efficacy of employing neural networks and advanced learning processes connected to cloud technology, this research is capable of using both secondary and primary information. The efficacy will be assessed by the researchers using correlation matrix and other non-parametric approaches. The conclusion is reached after thorough data examination and interpretation.

关键词： Deep learning Cloud computing Correlation Statistical analysis Supervised learning Production Syntactics

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A Machine Vision-Based Person Detection Under Low-Illuminance Conditions Using High Dynamic Range Imagery for Visual Surveillance System

A Machine Vision-Based Person Detection Under Low-Illuminanc...

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Smart Technologies in Computing, Electrical and electronics (ICSTCEE), International Conference on

作者： Julie Ann B. Susa Sammy V. Militante Aimee G. Acoba Ryan Soriente Evangelista Luisito Lolong Lacatan Charlemagne G. Laviňa Bartolome T. Tanguilig Dept. of Computer Engineering Southern Luzon State University Lucban Quezon Philippines College of Engineering and Architecture University of Antique Antique Philippines Electronics Eng'g Technology Department Technological University of the Philippines Manila Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Computing and Engineering Pamantasan ng Cabuyao Laguna Philippines

The most difficult component of any computer vision application objects recognition and tracking. Video surveillance is a major study subject in computer vision in a dynamic environment, especially for security purposes. Video surveillance technology is critical in preventing crime, terrorism, and other threats. Several existing technologies can accurately monitor a person's mobility in interior environments. As well as regulated outdoor settings, object recognition and tracking at night, on the other hand, remain significant challenges for visual surveillance. The objects are usually farther away, inconspicuous, and have low brightness against the background. In this paper, detecting objects specifically people under low lighting conditions with the help of deep learning algorithms was used. For applications like in-car cameras and surveillance systems that operate in low light, these algorithms might improve picture identification performance.

关键词： Deep learning Training Visualization computer vision Heuristic algorithms Computational modeling Lighting

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InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis

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Biomedical Signal Processing and Control 2025年 110卷

作者： Ishak Pacal Omneya Attallah Department of Computer Engineering Faculty of Engineering Igdir University 76000 Igdir Turkey Department of Electronics and Information Technologies Faculty of Architecture and Engineering Nakhchivan State University Nakhchivan AZ 7012 Azerbaijan Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Alexandria 21937 Egypt Wearables Biosensing and Biosignal Processing Laboratory Arab Academy for Science Technology and Maritime Transport Alexandria 21937 Egypt

Manual breast cancer diagnosis is time-consuming, error-prone, and requires skilled professionals, while deep learning-based computer-Aided Diagnosis (CAD) systems offer greater performance. Yet existing CAD systems often focus on single imaging modalities, leading to reduced accuracy when applied to different image types. Furthermore, the challenges faced by current deep learning approaches in balancing local feature extraction and global context understanding hinder the development of comprehensive diagnostic tools. To address these issues, we introduce the InceptionNext-Transformer, a novel hybrid deep learning architecture designed for multi-modal breast cancer image analysis. This innovative approach combines Convolutional Neural Networks and Vision Transformers in a unique four-stage design: two initial stages with InceptionNext blocks for multi-scale feature extraction, a third stage employing self-attention to capture global dependencies, and a final stage integrating local and global data for enhanced classification. The study presents a computationally efficient CAD for breast cancer detection applicable to resource-constrained clinical environments. The model exhibited outstanding performance when assessed on seven datasets encompassing histopathology, mammography, and ultrasound imaging modalities. In binary classification using the BreakHis histopathology dataset, it attained 100% accuracy. Using the same dataset, the method achieved 98.25% accuracy for multi-class classification. In the analysis of mammography utilizing the INbreast, MIAS, and DDSM datasets, it achieved an accuracy of 99.97%. On BUSI and BLUID ultrasound datasets, the model attained an accuracy of 92.86%. These findings validate the InceptionNeXt-Transformer’s enhanced capacity for generalization across various imaging modalities. It demonstrates superior diagnostic reliability and computational efficiency, rendering it suitable for resource-limited clinical environments.

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A Novel Method for Wind Power Plants’ Repowering Based on Multi-Criteria Decision-Making

SSRN

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SSRN 2023年

作者： Gil García, Isabel C. Fernández-Guillamón, Ana García-Cascales, M. Socorro Molina-García, Ángel c/ Coruña km 38500 Collado Villalba Madrid28400 Spain Dept. of Applied Mechanics and Projects Engineering - E.T.S. de Ingenieros Industriales & Renewable Energy Research Institute Universidad de Castilla-La Mancha C/ De la Investigación S/N Albacete02006 Spain Department of Electronics Computer Architecture and Project Engineering Department of Automatics Electrical Engineering and Electronic Technology Universidad Politécnica de Cartagena Campus Muralla del Mar Murcia Cartagena30202 Spain

Due to the climate change, over the last decades there have been changes in the rhythm of nature. However, there is an increasing social consciousness regarding decarbonization, and governments include new policies and economic support measures for renewables. The participation of the onshore and offshore wind sectors play a central role in this scenario, expecting that more than 6000 GW will be globally installed in 2050. However, the useful life of many wind power plants is coming to their end, and thus, some countries will face in the coming years the decision of repowering, decommissioning, installing new turbines, or a combination of them. In general, such areas with the greatest wind potential are already occupied by relatively old wind turbines. Subsequently, a complex group of factors influences on the best solution decision-making: climatic, technological, environmental, social or economic. This paper proposes an optimized methodology based on multi-criteria decision-making to estimate the most appropriate solution for extending the useful-life of wind power plants. The methodology is divided into three stages: (i) data, (ii) alternatives and (iii) optimization. It was assessed with a Spanish onshore wind power plant connected to the grid. From the initial data, 11 alternatives were designed based on 10 selection factors. The results provide an optimal solution, combining both repowering and installation of new wind turbines. It would allow us to reduce the LCOE of the new wind plants installed in the European Union by more than 50%. © 2023, The Authors. All rights reserved.

关键词： Wind turbines

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Cerebral micro-bleeding identification based on a nine-layer convolutional neural network with stochastic pooling

Cerebral micro-bleeding identification based on a nine-layer...

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作者： Wang, Shuihua Sun, Junding Mehmood, Irfan Pan, Chichun Chen, Yi Zhang, Yu-Dong School of Computer Science and Technology Henan Polytechnic University Jiaozuo China School of Architecture Building and Civil Engineering Loughborough University Loughborough United Kingdom Department of Informatics University of Leicester Leicester United Kingdom Department of Computer Science and Engineering College of Electronics and Information Engineering Sejong University Seoul Korea Republic of School of Computer Science and Technology Nanjing Normal University Nanjing China School of Business Nanjing Normal University Nanjing China Jiangsu Laboratory of Lake Environment Remote Sensing Technologies Huaiyin Institute of Technology Huaian China

Cerebral micro-bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six-convolution layer and three fully-connected layer CNN, nine-layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state-of-the-art methods. © 2019 John Wiley & Sons, Ltd.

关键词： Convolution

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Multi-Objective Squirrel Search Algorithm for EEG Feature Selection

SSRN

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SSRN 2022年

作者： Wang, Chao Li, Songjie Shi, Miao Zhao, Jie Acharya, U. Rajendra Xie, Neng-Gang Cheong, Kang Hao College of Civil Engineering and Architecture Anhui Polytechnic University Anhui Wuhu241000 China Anhui Province Key Laboratory of Multidisciplinary Management and Control of Complex Systems Anhui Ma’anshan 243002 China Department of Management science and Engineering Anhui University of Technology Anhui Maanshan243002 China 8 Somapah Road Singapore487372 Singapore Department of Mechanical Engineering Anhui University of Technology Maanshan243002 China Department of Electronics and Computer Engineering Ngee Ann Polytechnic S599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Feature selection plays a critical role in the application of Brain computer Interface (BCI) systems. Many methods have been used to solve the feature selection problem, but they model it as a single-objective problem, considering only classification accuracy or number of features. To close this critical gap, we improve the squirrel search algorithm by combining it with the grid method, and propose a Multi-Objective Squirrel Search Algorithm (MOSSA) to solve the feature selection problem in BCI. We conduct experiments on three publicly available motion imagery datasets, and the experimental results reveal the best classification results of the method on dataset 1. The average classification accuracy of dataset 2 is 96.71%, with the number of selected features reduced to 18 on average. The highest classification accuracy of dataset 3 is 83.57% on the training set and 82.86% on the test set. In addition, we compare MOSSA with other algorithms and the results show the superiority of our proposed method in solving the feature selection problem. Finally, we combine MOSSA with an online application of BCI, where subjects visualise controlling the robot to perform the corresponding actions by the left and right hand movements. The average recognition rate of the three subjects is approximately 70%. In summary, the MOSSA is an effective method for solving the feature selection problem and is useful for the development of online © 2022, The Authors. All rights reserved.

关键词： Brain computer interface

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Cacao-X: Contour Assisted Convolutional Neural Networks with Image Explainability and Inference on the Edge

SSRN

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SSRN 2022年

作者： Daher, Ali W. Muselli, Marco Caviglia, Daniele D. Ferrari, Enrico Verda, Damiano Chible, Hussein Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genoa Via Opera Pia 11 Liguria Genoa16145 Italy Ph.D. School for Sciences and Technology Lebanese University Beirut6573/14 Lebanon Institute of Electronics Computer and Telecommunication Engineering Consiglio Nazionale delle Ricerche Via De Marini 6 Genoa Liguria16149 Italy Rulex Innovation Labs Rulex inc. Via Felice Romani 9 Liguria Genoa16122 Italy

Image classification and interpretability is a basic procedure in modern AI systems as well as being employed in the fields of security, instrumentation, and image classification. Explainable Artificial Intelligence also plays a crucial role in providing machine learning techniques with a white-box output that allows the practitioner to follow or interpret the rules. Therefore, we present CACAO-X which is a framework for ML forecasts that utilizes an algorithm which converts images into time-series and employs CNN, LSTM, and MLP on the original images and converted data in a mixed input approach achieving accurate forecasts on multiple applications. Furthermore, Shapley values are employed on the proposed ML models to extract the local explainable features of such images. These features are again used with the same conversion algorithm to generate time-series and application specific landmarks using the Logic Learning Machine (LLM) to determine the explainable rules. In addition to being generated automatically, explicative labelling is adopted in the dataset which give insight to the operator to which landmarks, positions and angles contribute to the classification of each record. The system is robust as it achieves benchmark results in the gender classification application while remaining competitive in all others where it is used as a reference model for global image explainability. Additionally, the ML inference for every application is performed according to the edge-computing paradigm, by deploying the models on a limited resources Raspberry Pi platform through SSH access. Moreover, a novel callback function has been developed which is able to stop the training of the CACAO-NET model at a stable state based on the level of oscillation around the most recent mean accuracy. © 2022, The Authors. All rights reserved.

关键词： Image classification

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CACAO-X: Contour Assisted Convolutional Neural Networks with Image Explainability and Inference on the Edge

SSRN

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SSRN 2022年

作者： Daher, Ali W. Ferrari, Enrico Verda, Damiano Chible, Hussein Musellic, Marco Caviglia, Daniele D. Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genoa Via Opera Pia 11 Liguria Genoa16145 Italy Ph.D. School for Sciences and Technology Lebanese University Beirut6573/14 Lebanon Institute of Electronics Computer and Telecommunication Engineering Consiglio Nazionale delle Ricerche Via De Marini 6 Genoa Liguria16149 Italy Rulex Innovation Labs Rulex inc. Via Felice Romani 9 Liguria Genoa16122 Italy

关键词： Image classification

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ZETA: a library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems

arXiv

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arXiv 2025年

作者： Rego, Brenner S. Scott, Joseph K. Raimondo, Davide M. Terra, Marco H. Raffo, Guilherme V. Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Engineering and Architecture University of Trieste Trieste34127 Italy Department of Electronics Engineering Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper introduces ZETA, a new MATLAB library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems. It features user-friendly implementations of set representations based on zonotopes, namely zonotopes, constrained zonotopes, and line zonotopes, in addition to a basic implementation of interval arithmetic. This library has capabilities starting from the basic set operations with these sets, including propagations through nonlinear functions using various approximation methods. The features of ZETA allow for reachability analysis and state estimation of discrete-time linear, nonlinear, and descriptor systems, in addition to active fault diagnosis of linear systems. Efficient order reduction methods are also implemented for the respective set representations. Some examples are presented in order to illustrate the functionalities of the new library. © 2025, CC BY.

关键词： Digital arithmetic

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States-Based Energy Management System for an EV Hybrid Charging Station with Z-Source Converters

States-Based Energy Management System for an EV Hybrid Charg...

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Power Tech Conference

作者： Enrique González-Rivera Pablo García-Triviño Luis M. Fernández-Ramirez Raúl Sarrias-Mena Juan P. Torreglosa Francisco Jurado Department of Electrical Engineering Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP-023) University of Cadiz Algeciras Spain Department of Engineering in Automation Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP-023) Electronics and Computer Architecture & Networks University of Cadiz Algeciras Spain Electrical Engineering Department University of Huelva Palos de la Frontera Spain Department of Electrical Engineering Research Group in Research and Electrical Technology (PAIDI- TEP-152) University of Jaen Linares Spain

ISBN: (数字)9781665435970

ISBN: (纸本)9781665411738

This work presents a new energy management system (EMS) based on states for a hybrid charging station of electric vehicles (EV). The hybrid charging station is composed of a photovoltaic (PV) system, a complete hydrogen system based on fuel cell (FC), electrolyzer (EZ) and tank as energy storage system (ESS), a battery energy storage system (BES), a grid connection, and six fast charging units, all of them connected to a common MVDC bus through Z-sources converters (ZSC). The states-based EMS is designed to control the power flow among the energy sources of the hybrid charging station. The viability of the EMS is proved under a long-term simulation of 25 years in Simulink by using an EV load profile taken from the Strategy technology Energy Plan of the EU and real data for the sun irradiance.

关键词： Photovoltaic systems Renewable energy sources Costs Software packages Hydrogen Charging stations Hybrid power systems

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