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International Conference on Contemporary Computing and Informatics (IC3I)

作者： A. Senthil Kumar Ritika Mehra Prashant Kumar S. Hemelatha S. Arul Antran Vijay P.R. Rege Sri Vishnu Engineering College for Women Andhra Pradesh India School of Engineering and Computer Science Dev Bhoomi Uttarakhand University Uttarakhand India Department of ECE TMU Teerthankar Mahaveer University Moradabad India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Vishwakarma Institute of Information Technology Pune Maharashtra India

Low-risk learning approaches for classifying images encourage learning via little data. In order to facilitate packed semantics segmentation of pictures, we expand those methods. We particular instruct a neural network which generates factors for the completely Convolutional The network (FCN) from just a few sets of analyzed with annotations. From an experiment picture to known as newly introduced semantics the classroom, we execute packed sensor-level prediction using that FCN. Our system is a minimum of three times quicker and exhibits an approximate mean IoU enhancement of 25% over the top-performing baseline techniques for person-shot division on courses that have not yet been observed within the PASCAL volatile organic compounds 2012 information set.

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Adaptive Ant Selection for Pheromone Update in Ant Colony Optimization

Adaptive Ant Selection for Pheromone Update in Ant Colony Op...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Biao Wang Dan-Ting Duan Qiang Yang Xiao-Yan Zhao Tao Li Dong Liu Jun Zhang College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China Key Laboratory of Media Audio & Video Ministry of Education Communication University of China Beijing China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China College of Artificial Intelligence Nankai University Tianjin China Department of Electrical and Electronic Engineering Hanyang University Ansan South Korea

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Ant selection for updating the pheromone is one most crucial operation in ant colony optimization (ACO). In this direction, this paper designs an adaptive ant selection strategy (AAS) to adaptively and dynamically select ants to update the pheromone for ACO. Therefore, a new ACO, called AAS-ACO is developed. Specifically, AAS-ACO first assigns a non-linear selection probability to each ant based on its path ranking. As a result, better ants preserve exponentially higher selection probabilities. Then, based on the selection probabilities, ants are adaptively selected for the pheromone update. By this means, on the one hand, the number of ants involved in the pheromone update is uncertain; on the other hand, relatively better ants instead of absolutely better ones are adaptively selected to update the pheromone, leading to the promotion of search diversity. Subsequently, a dynamic weighting strategy is designed to adjust the amount of the pheromone deposited by the best ant in the current iteration to enhance the search convergence. With the two schemes, AAS-ACO is expected to maintain a suitable compromise between search diversity and search convergence to seek the optimal solutions to TSP. Experiments on 10 classical TSP instances varying from 100 to 1000 cities have proven the significant superiority of AAS-ACO to 5 classic ACOs, especially on high-dimensional TSP problems.

关键词： Hands Ant colony optimization Heuristic algorithms Urban areas Optimization Cybernetics Convergence

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A New Cloud Resource Manufacturing System Based on the Internet of Things Using Optimized Deep Neural Network

A New Cloud Resource Manufacturing System Based on the Inter...

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Cognitive Computing and engineering education (ICCCEE), International Conference on

作者： Supriya Menon M K. Spurthi V. Elanangai D. Nagaraju C. Ashokkumar Ajith Sundaram Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur AP India Computer Science and Engineering Swarna Bharathi Institute of Science and Technology Khammam Department of Electrical and Electronics Engineering St. Peter's Institute of Higher Education and Research AVADI Chennai Department of CSE Sri Venkatesa Perumal College of Engineering and Technology Puttur Andhra Pradesh India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Amrita School of Business Amrita Vishwa Vidyapeetham Kochi Kerala India

Cloud Computing (CC) and the Internet of Things (IoT) are important in the modern era for providing new techniques of connection through intelligence perception, M2M, sharing efficient resources, and on-demand use. In a cloud environment, the manufacturing frame has numerous action tasks to perform or manufacture. Various Cloud Manufacturing system (CMfgs) resources influence some difficulties and attacks in manufacturing resources (work units, resource pools, workstations, and so on) and system modeling. To address these issues and meet the deadline, CMfgs proposed a novel Hybrid Grey Wolf-Ant Lion Regression Frame (HGW-ALRF) deep learning technique. In this case, the fitness function of the Ant Lion and Grey Wolf in the recurrent frame is used to effectively schedule cloud resources. During testing, the presented attacks (malware injection, DDoS, bottleneck attacks) can be monitored by the designed IoT application. Finally, the proposed model's obtained results are described, including computation time, accuracy, energy consumption, delay, and resource utilization.

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Signal Quality Evaluation and Processing for QRS Detection in ECG based Smart Healthcare Systems

Signal Quality Evaluation and Processing for QRS Detection i...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Devendra Singh ShaikVaseem Akram T Sivakumar U. Prakash J Loyola Jasmine Chaithra K N Uttaranchal University Dehradun India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The next-generation wearable electrocardiogram (ECG) equipment requires signal processing with low battery usage in order to transmit signals when harmful rhythms are recognized and to capture signals when anomalous rhythms are discovered. The visual deflections that are frequently visible on an ECG make up the QRS complex. This research suggests a real-time QRS recognition and R point detection system that is extremely accurate and simple. The recommended ECG signal modification eliminates baseline wandering while also enhancing QRS intervals and controlling P and $T$ waves. In this work, the peaks and valleys of the converted signal were used to calculate the fiducial point for the QR. The R point could then be determined using four QRS waveform templates, and the initial categorization of cardiac rhythms could be completed simultaneously. On two benchmark datasets, the proposed method's effectiveness is shown. Positive prediction (+P) and detected sensitivity (Se) values for QRS are 99.82 and 99.81 percent, respectively, according to the standard. The outcome demonstrates that the method has a low processing complexity and that real-time software may be successfully executed on both an embedded system and a mobile device.

关键词： Visualization Heart beat Medical services Electrocardiography Real-time systems Software Internet of Things

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Design and Development of a Low-Power Wireless MEMS Lead-Free Piezoelectric Accelerometer System

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IEEE Transactions on Instrumentation and Measurement 2023年 72卷

作者： Li, Cheng-Ying Chen, Ze-Hui Kao, Hsueh-Yu Chang, Sheng-Kai Hsiao, Po-Yu Huang, Yen-Hsiang Huang, Yu-Chieh Chu, Sheng-Yuan Wei, Chia-Ling Liu, Yun-Hui Cheng, Kuang-Wei Tsai, Cheng-Che National Cheng Kung University Department of Electrical Engineering Tainan701 Taiwan National Cheng Kung University Nano-Integrated-Circuit Engineering Program Tainan701 Taiwan Southern Taiwan University of Science and Technology Department of Mechanical Engineering Tainan71005 Taiwan National Cheng Kung University College of Electrical Engineering & Computer Science Department of Electrical Engineering Tainan701 Taiwan Tung Fang Design University School of Application Design Department of Digital Game and Animation Design Kaohsiung82941 Taiwan

In this article, a complete 3-D integrated sensing system intended for practical applications was presented on a printed circuit board (PCB) board, starting with lead-free piezoelectric films deposited using an RF sputtering system, an MEMS structure design/simulation, device fabrication, and a signal processing chip design, including charge- to-voltage transformation, signal amplification, 60-Hz noise rejection, and low-power RF wireless transmission chip for remote monitoring. Both the signal processing chips and the RF wireless transmitter chips were manufactured using the Taiwan Semiconductor Manufacturing Company (TSMC) CMOS process. The piezoelectric coefficient d33 was measured as 27.21 pm/V of the proposed ZnO:Li doped Mg (MLZO) films. To meet the frequency specifications of industrial applications, the ring-structure devices with a 7730-Hz resonance frequency were designed using ANSYS simulation software. There was only a 0.39% deviation compared to the simulation results with the actual experimental data. The measurement results of the proposed system showed that the acceleration detection range was approximately 137 mV/ g (p-p). The voltage-controlled oscillator (VCO) adopted in the wireless transmitter chip had a four-leaf clover structure inductor with two symmetrical axes resulting in better suppression of magnetic field radiation. Finally, this system was applied to industrial spindles, and the normal and abnormal operation information of spindles was successfully and wirelessly monitored. © 1963-2012 IEEE.

关键词： Wireless sensor networks

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Innovative approache for the nonlinear atangana conformable Klein-Gordon equation unveiling traveling wave patterns

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Partial Differential Equations in Applied Mathematics 2024年 12卷

作者： Rezazadeh, Hadi Hosseinzadeh, Mohammad Ali Zaidan, Lahib Ibrahim Awad, Fatima SD. Batool, Fiza Salahshour, Soheil Faculty of Engineering Modern Technologies Amol University of Special Modern Technologies Amol Iran Education College University of Babylon Babil Iraq Department of Mathematics College of Education for Pure Sciences University of Kerbela Iraq School of Mathematics Minhaj University Lahore 54590 Pakistan Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The aim of current work is to establish novel traveling wave solutions of the nonlinear Atangana conformable Klein - Gordon equation using a new extended direct algebraic technique. The Klein - Gordon equation is the relativistic state of the Schrödinger equation with a second - order time derivative and zero spin. Complex wave variable transformation is used to convert Atangana conformable nonlinear differential equation into an ordinary differential equation. Using the proposed technique based on Maple software structure, various types of solutions, such as, generalized trigonometric, generalized hyperbolic, and exponential functions, are established. When special parameteric values are considered for this method, solitary wave solutions can be obtained through other methods, such as the ([Formula presented])-expansion method, the modified Kudryashov method, the sub-equation method, and so forth. A physical explanation is provided for the solutions under consideration to enhance comprehension of the physical phenomena resulting from the obtained solutions, provided that the physical parameters are set appropriately using 3D, 2D, and contour simulations. The results demonstrated that the new extended direct algebraic method provides a more potent mathematical tool for solving numerous more nonlinear partial differential equations with the aid of symbolic computation. © 2024

关键词： Atangana conformable derivative New extended direct algebraic technique Nonlinear Klein-Gordon equation Traveling wave solution

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The Impact of Artificial Intelligence as Advanced Technologies on Bitcoin Industries

The Impact of Artificial Intelligence as Advanced Technologi...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Srinivas D Bhagyashree Ambore Sumit Chauhan Abhijit Tarawade Melanie Lourens Mohit Tiwari School of Business SR University Warangal Telangana Information Science and Engineering RNS Institute of technology Management Education & Research Institute New Delhi Inha university in Tashkent Tashkent Uzbekistan Faculty of Management Sciences Durban University of Technology South Africa Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi

Cryptocurrencies have become a leading kind of digital currency in recent years, and the financial system is also becoming a key factor. Some Artificial Intelligence techniques are needed to lower investing risk and anticipate price, trend, portfolio construction, and fraud detection. The most well-known cryptocurrency, Bitcoin, is discussed along with recent research on AI approaches for cryptocurrencies. The goal of this study is to examine how well machine learning (ML) and artificial intelligence (AI) techniques forecast the value of cryptocurrencies like bitcoin. With regard to Bitcoin, the largest decentralized digital currency in terms of market capitalization, we investigate the possibility of offering unusual risk-changed rewards using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) assembled techniques. The outcomes demonstrate the way that trader can utilize SVM to create moderate profits from a risk changed premise, even subsequent to thinking about exchange costs. The exploration likewise suggests that ANN can outflank even buy and-hold procedures during delayed bull patterns by using transient educational shortcomings to create abnormal benefits.

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A Radiation Performance Improvement Method of Extremely Low Frequency Acoustics Promoted Antenna

A Radiation Performance Improvement Method of Extremely Low ...

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IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Junwei Qi Chenwei Zhang Yingsong Li Zhixiang Huang Tian Hong Loh Kwok L. Chung College of Information and Communications Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Middlesex UK School of Computer Science and Engineering Huizhou University P. R. China

In this paper, Q-value theory of conventional antennas is used to analyze reasons of low radiation performance of acoustics promoted antennas, where we modify the radiating structure of existing acoustics promoting antenna using the simulations to get the required improvement on radiation performance. The simulation experiments are carried out in the finite element simulation software Comsol Multiphysics to analyze the radiating performance.

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Multi-site MRI classification using Weighted federated learning based on Mixture of Experts domain adaptation

Multi-site MRI classification using Weighted federated learn...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Bai, Tian Zhang, Yingfang Wang, Yuzhao Qin, Yanguo Zhang, Fa Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China The Second Hospital of Jilin University The Department of Orthopaedics Changchun China Beijing Institute of Technology School of Medical Technology Beijing China

ISBN: (纸本)9781665468190

Deep learning often requires large amounts of data from different institutions. Federated learning, as a distributed training framework, enables multiple participants to collaboratively train models without collecting data together and hence protecting data privacy, but the datasets from different institutions usually bring the problem of domain shift, which affects the performance of the model. When addressing domain shift, previous works often use a single global model to share parameters. Therefore, we propose a novel method to train multiple public models with different structures under the federated framework to improve the reliability and robustness of the public models. And each participant keeps its own domain-tuned private model, the private model does not share parameters with other participants. We use Mixture of Experts (MoE) domain adaptation to dynamically combine different public models and private model, which utilizes the similarity between different datasets to update the parameters of the public models. We apply the proposed method to the multi-site Magnetic resonance imaging (MRI) end-to-end classification, and the experiments demonstrate its effectiveness. © 2022 IEEE.

关键词： Magnetic resonance imaging

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CamTran: An Intelligent Automatic System for the Pest Infestation Assessment

CamTran: An Intelligent Automatic System for the Pest Infest...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Saravana Kumar G Parthasarathy V Santhosh R Somnath Roy Bappaditya Kanrar Dept. of Electronics and Telecommunications Engineering Karpagam College of Engineering Coimbatore India Dept. of Computer Science and Engineering Karpagam Academy of Higher Education(Deemed to be University) Coimbatore India Entomology Department TOCKLAI Tea Research Institute Assam India Scientist and Quality Manager TLabs Tea Research Association Kolkata India

In the vast expanse of the tea industry, addressing pest infestations on tealeaves and improving vegetation indices has long been a challenge. While these issues may seem minor on the surface, in reality, they can lead to substantial financial strain. Beyond the immediate, monetary implications, the ripple effect of these challenges can profoundly impact local economies, disrupt trade, and jeopardize the livelihoods of those deeply entrenched in the world of tea cultivation. Convolutional Neural Networks (CNNs) are widely acknowledged for their capability to accurately identify and classify complex characteristics in images, especially those depicting disease or pest-afflicted tea leaves. The technology’s premise lies in autonomously extracting features from imagery, allowing for accurate and timely identification, which can potentially lead to early interventions. The process of capturing images of tealeaves in their natural habitat is fraught with complexities. Factors like fluctuating lighting conditions, owing to the time of day or changing weather patterns, can significantly alter an image’s clarity. Cultivating tea in the plantation, it has been observed that the holistic life cycle stages of several pests on tea leaves have been detrimentally affecting both the yield and quality of the tea leaves, demanding a comprehensive automated assessment strategy. Recognizing these shortcomings, the paper adopts a fundamental shift in approach and implements CamTran. The architecture, rooted in the Vision Transformer model, adopts a specialized attention mechanism to discern and prioritize salient features within localized image segments, ensuring efficient and relevant feature extraction. The Non-local Blocks, on the other hand, serve a strategic purpose by capturing and integrating long-range dependencies and spatial relationships in the image, providing a holistic understanding of the context. Additionally, the Atrous Spatial Pyramid Pooling (ASPP) module is integrat

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