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Vibrating Particles System Algorithm for Solving Classification Problems

computer Systems science & engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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Reliability of Electronic Connections in Fast Chargers of Electric Vehicle 1

Reliability of Electronic Connections in Fast Chargers of El...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Semwal, Priyanka Raturi Ekatpure, Shubham Rajendra Sharma, Merlyn Singh, Neelima Kumar, Anil Pandey, Bhumika Tula's Institute Dehradun Department of Electrical & Electronics Engineering Dehradun India Global Supply Chain in Semiconductor Manufacturing Kulicke and Soffa PA United States IIMT College of Engineering Department of ASH UP Grater Noida India Uttaranchal University Department of Computer Science and Engineering Dehradun India Physics Bharati Vidyapeeth's College of Engineering Dept. of Applied Science New Delhi India Bipin Tripathi Kumaon Institute of Technology Department of ECE Almora Dwarahat India

ISBN: (纸本)9798331518578

Global mobility is undergoing a dramatic change as cars fuelled by fossil fuels give way to those with zero or extremely low tailpipe emissions. Every one of the parts in big battery packs normally don't have similar properties and may corrupt essentially in view of assembling fluctuation and different elements. The performance and economics of the entire battery pack are impacted by the increased power waste from degraded battery modules. Additionally, the influence changes depending on the trip pattern. A variety of important factors, such as the age of the car, the internal resistance of the lithium-ion battery pack, awareness of grid energy pricing, and the distance driven, affect how much an electric vehicle (EV) costs to drive every day. It has used the grid's energy price data to optimise the cost of the typical driving cycle. Accurately forecasting battery behaviour reliability for the daily driving cycle is the aim of this project. The driver will be able to determine the best charging schedules, driving energy consumption, and excess battery energy with the aid of the reliability battery behaviour forecast. The driver can reduce the cost of travel both daily and over the battery's lifespan by having a previous understanding of the battery's behaviour, the grid's energy pricing, and the trip's duration. © 2025 IEEE.

关键词： Lithium-ion batteries

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Secure Computation Meets Distributed Universal Optimality

Secure Computation Meets Distributed Universal Optimality

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Annual IEEE Symposium on Foundations of computer science

作者： Merav Parter Department of Computer Science and Applied Math Weizmann Institute Rehovot Israel

We present a new algorithmic approach to distributed secure algorithms that is based on combining two independent lines of research: secure computation and distributed universal optimality. Our end result provides round-efficient distributed algorithms that protect the privacy of the graph vertices against a (possibly large) coalition of semi-honest *** Computation: The notion of perfect privacy dates back to Yao [FOCS ’82], and has been extensively addressed by the Cryptographic community over the years. Most of the prior work considers the Multi-Party-Communication (MPC) model, in which the parties are fully connected. Considerably less is known on the (round) complexity of secure algorithms for general graphs, especially under the classical message passing models, such as the CONGEST model. For any biconnected D-diameter graph, a recent line of works [Parter and Yogev, SODA 19, ICALP 19, PODC 19] presented a simulation result that protects against a single semi-honest corruption, by paying an overhead of $D \cdot \mathsf{poly}(\Delta)$ CONGEST rounds, where $\Delta$ is the maximum degree. Due to an inherent structural barrier, the generalization of the current framework to handling f corruptions, provably leads to an overhead of $O(\Delta D)^{\Theta(f)}$ rounds. This can also be shown to be tight for the class of store-and-forward algorithms 1 . 1 In which nodes can only propagate messages as atomic units, without the ability to mix multiple messages *** Computation & Universal Optimality. We present an improved framework for secure computation which bypasses the current exponential in f barrier. For every graph $G=(V, E)$ with vertex-connectivity $\widetilde{\Omega}(f)$, our simulation provides a round overhead of $\mathsf{poly}(\Delta) \cdot \widehat{O}(\mathbf{S Q}(G))$. 2 The graph measure $\mathbf{S Q}(G)$ (Shortcut Quality) captures the universal optimal complexity of many network optimization tasks in the (non-secure) CONGEST mo

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Analysis of Renewable Energy Harvesting Sources with Interaction of Piezoelectric Materials 2

Analysis of Renewable Energy Harvesting Sources with Interac...

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2nd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2023

作者： Yadav, Mohit Kumar, Surendra Yadav, Rajkumar Yadav, Dinesh Garg, Ramesh Kumar Kaushik, Ashish Ahlawat, Akash Chhabra, Deepak Maharshi Dayanand University Department of Applied Sciences Mathematics Uiet Rohtak124001 India University Institute of Engineering and Technology M.D. University Department of Applied Sciences Mathematics Rohtak124001 India University Institute of Engineering and Technology M.D. University Department of Computer Science & Engineering Rohtak124001 India Deenbandhu Chhotu Ram University of Science and Technology Department of Mechanical Engineering Sonipat131039 India Uiet Maharshi Dayanand University Department of Mechanical Engineering Rohtak124001 India

ISBN: (纸本)9798350326666

The results of enormous climate change and other industrial carbon emissions have been driven by the rise of nonrenewable traditional energy sources. The scientific community is looking at unconventional renewable techniques that can harvest energy in the external atmosphere. This research article provides a critical analysis of the growth in the field of piezoelectric energy harvesting. Recent advances in bio-inspired piezo-smart equipment are critically analyzed. A comprehensive overview of harvesting power from mechanical vibrations and its renewable sources, such as human body movements, flowing fluid, and circuitry arrangements of structures with piezoelectric materials have been discussed. Capturing and converting mechanical energy into useful electric power is the easiest and most prevalent way to harness the massive amounts of ambient energy around us. With the help of piezoelectric materials, vibrations induced by mechanical sources can convert into electrical energy. Various industries, including transportation, construction, aeronautical, smart systems, micro-fluids, biomedical devices, and implantable electronics, employ piezoelectric energy generating at the micro level. Piezoelectric energy generating technique's applications along with their achievements, and future possibilities are critically analyzed. This overview concisely presents the extensive range of piezoelectric materials for wireless electronic devices that use eco-friendly, renewable, and sustainable energy harvesting sources. © 2023 IEEE.

关键词： Energy harvesting

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Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1308-1322页

作者： Erxin Xie Na Chen Jiangtao Peng Weiwei Sun Qian Du Xinge You Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Recently,transformer‐based networks have been introduced for the classification of hyperspectral image(HSI).Although transformer‐based methods can well capture spectral sequence information,their ability to fuse different types of information contained in HSI is still *** exploit rich spectral,spatial and semantic information in HSI,a novel semantic and spatial‐spectral feature fusion transformer(S3FFT)network is proposed in this *** the proposed S3FFT method,spatial attention and efficient channel attention(ECA)modules are employed for the extraction of shallow spatialspectral ***,a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature ***,the semantic,spatial and spectral features are combined by the transformer for *** with traditional deep learning methods and recently transformer‐based methods,the proposed S3FFT shows relatively better results on three HSI datasets.

关键词： image classification machine learning

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A Novel Perspective on Brain Tumor Classification Using Hybrid Algorithm

A Novel Perspective on Brain Tumor Classification Using Hybr...

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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Jayanthi, R. Christinal, A. Hepzibah Hephzibah, R. Shekinah, T. Bajaj, Chandrajit Chandy, D. Abraham Karunya University Department of Mathematics Coimbatore India Loyola - Icam College of Engineering and Technology Department of Computer Science and Engineering Chennai India University of Texas Computational Applied Mathematics Chair in Visualization Austin United States Karunya University Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350338003

Brain tumor classification is a critical task in medical imaging, where the early and accurate detection of brain tumors is crucial for patient treatment and survival. In recent years, Machine Learning techniques have emerged as a promising approach for automated brain tumor classification. In this paper, we investigate the performance of three ML classifiers - Support Vector Machine, Naïve Bayes, and an ensemble of SVM and Naïve Bayes - for the two-class classification of brain tumor data into a tumor and no tumor classes. Our experiments demonstrate that the ensemble of SVM and Naïve Bayes classifier achieved an accuracy of 97% outperforming the other classifiers. These results indicate that our ensemble classifier can be a powerful tool for the accurate and efficient classification of brain tumor data, potentially leading to improved patient outcomes. © 2023 IEEE.

关键词： Tumors

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Fault Tolerance in the Joint EDF-RMS Algorithm: A Comparative Simulation Study

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computers, Materials & Continua 2022年第9期72卷 5197-5213页

作者： Rashmi Sharma Nitin Nitin Deepak Dahiya University of Petroleum&Energy Studies(UPES) School of Computer ScienceEnergy Acres BuildingBidholiDehradun248007UttarakhandIndia Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of Cincinnati2600 Clifton AveCincinnatiOH 45221United States College of Computer and Information Sciences(CCIS) Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia

Failure is a systemic error that affects overall system performance and may eventually crash across the entire *** Real-Time Systems(RTS),deadline is the key to successful completion of the *** tasks effectively meet the deadline,it means the system is working in pristine ***,missing the deadline means a systemic fault due to which the system can crash(hard RTS)or degrade inclusive performance(soft RTS).To fine-tune the RTS,tolerance is the critical issue and must be handled with extreme *** article explains the context of fault tolerance with improvised Joint EDF-RMS algorithm in *** backup method has been derived to prevent the system from being recursively migrating the same *** any task migrates three times,this migrated task will get shifted to the backup *** backup queue assigns the task to a backup processor and is destined for final *** performance evaluation purposes,a relative graph between fault and failure rates,failure and total processor utilization along with other averages have been ***,these archived results are compared with fault-tolerant Earliest Deadline First(EDF)and Rate Monotonic Scheduling(RMS)algorithms independently in relatively similar *** comparisons show better performance against overloading conditions.

关键词： Fault tolerance joint edf-rms algorithm real-time systems(RTS) distributed systems migration

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DeepONet: Learning the sound propagation in 1D with parameterized sources using deep learning for approximating the wave equation operators 24

DeepONet: Learning the sound propagation in 1D with paramete...

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24th International Congress on Acoustics, ICA 2022

作者： Borrel-Jensen, Nikolas Engsig-Karup, Allan P. Jeong, Cheol-Ho Technical University of Denmark Department of Electrical and Photonics Engineering Denmark Technical University of Denmark Department of Applied Mathematics and Computer Science Denmark

Realistic acoustics for virtual environments – such as computer games and mixed reality – is essential to achieve an immersive user experience and is getting more attention these years. Numerical methods have traditionally been used for calculating the sound propagation off-line;however, when solving for moving sources, these methods quickly get intractable both from a calculation time and storage viewpoint. Therefore, we propose a novel approach, where the wave equation operators (contrary to functions) are learned for arbitrary source inputs. The DeepONet architecture is used, as it is a general framework having already demonstrated its capability to approximate operators in other applications. As a result, an efficient surrogate model handling impedance boundaries in 1-D is learned. We present results of a model showing errors below 2%, executes in milliseconds and can be trained in less than 5 minutes, leaving optimistic expectations for extending the method to more complex 3-D scenes. © 2022 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： computer games

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Epidemiological Image Analysis: Insights, Perspectives, and Challenges 1

Epidemiological Image Analysis: Insights, Perspectives, and ...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Ivanovska, Tatyana Kruger, Markus Daboul, Amro East Bavarian Technical University of Applied Sciences Amberg-Weiden Department of Electrical Engineering Media and Computer Science Amberg Germany University Medicine Greifswald Department of Prosthodontics Gerodontology and Biomaterials Greifswald Germany

ISBN: (纸本)9798350328042

The spotlight on large population-based studies is growing within the research community. Epidemiological studies, amassing extensive data through questionnaires and check-ups, often include imaging data like magnetic resonance imaging (MRI) or ultrasonography from numerous participants. This article offers an exploration of several recent epidemiological studies conducted in Germany, addressing not only the intriguing research tasks but also the multifaceted perspectives and challenges of analyzing image data. This includes potential enhancements in imaging technologies, machine learning applications for data analysis, and collaboration between different research disciplines. Key insights into the methodologies, algorithms, and techniques used for processing and interpreting complex imaging data are detailed. The potential of epidemiological image analysis to guide clinical practices and contribute to personalized medicine is also discussed. © 2023 IEEE.

关键词： Magnetic resonance imaging

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IoT based Soil Nutrients Analysis and Monitoring System for Smart Agriculture 3

IoT based Soil Nutrients Analysis and Monitoring System for ...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Pyingkodi, M. Thenmozhi, K. Karthikeyan, M. Kalpana, T. Palarimath, Suresh Kumar, G. Bala Ajith Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences Department of It Salalah Oman

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Soil fertility is an important factor in determining soil quality as it reflects how well the soil can support plant growth in agriculture. Soil sensor and Arduino can be used to quickly determine the nutrient content of the soil. Nitrogen, phosphorus, and potassium are all considered as important nutrient source components. These components should be measured in order to determine how much extra nutrient content should be added to the soil in order to increase the crop fertility. Soil fertility can be detected by using NPK sensors. Soil nutrient concentration data can help us to determine whether the soil used to support plant production is nutrition deficient or abundant. The nutrient content of the soil samples can be obtained in various ways by using sensing element or mass spectrogram. However, the spectral analysis method is inconvenient, where the records are only 60-70% accurate. By comparing the spectrum analysis method with classic wet chemistry methods, the accuracy of the products needs to be fully resolved due to a scarcity of data. Hence, to detect soil nitrogen, phosphorous, and potassium, a soil NPK sensor should be used. By utilizing a soil NPK sensor, which is of limited cost, fast and easy, elevated, and transportable. Its advantage over a standard detection approach is that it provides extremely fast measurements with accurate data. This paper analyzes and compares different nutrient levels in soil by using kernel density estimation algorithm and machine learning. © 2022 IEEE.

关键词： Soils

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