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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Setia, Sonia Ravulakollu, Kiran Kumar Verma, Kimmi Garg, Setu Mishra, Sunil Kumar Sharan, Bhagwati Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Woxsen University School of Technology Hyderabad India Delhi Technical Campus Greater Noida India Its Engineering College Ece Department Greater Noida India Accurate Institute of Management and Technology Greater Noida India Srm University School of Engineering and Sciences Department of Computer Science and Engineering AP Guntur India

ISBN: (纸本)9789380544519

A software's most crucial component is its quality. Software Defect Prediction has gained a lot of traction in recent years and has the potential to directly impact quality. Software quality is greatly impacted by defective software modules, which may result in budget overruns, missed deadlines, and significantly increased maintenance costs. There are diverse phases executed to predict the defect in software such as to employ the data for input, pre-process it, extract the attributes and classify the defect. This research work presents numerous algorithms, namely Gaussian naive bayes (GNB), Bernoulli NB, random forest (RF) and multi-layer perceptron (MLP), for predicting the software defect. This work also focuses on developing an ensemble algorithm to enhance the efficacy of predicting the defects. This ensemble consisted of a Principal Component Analysis (PCA) algorithm with class balancing. Diverse parameters such as accuracy, precision and recall are employed for analyzing the results. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Bernoulli Naive Bayes Class Balancing Gaussian Naive Bayes PCA Random Forest Software Defect

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STC: Student-Teacher Collaborative Model for Multi-Target Domain Adaptation 21

STC: Student-Teacher Collaborative Model for Multi-Target Do...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Jiayi, Xu Jiaqi, Yu Yixin, Wang Shijia, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9798331519254

Cross-domain object detection poses significant challenges due to the susceptibility of object detection models to data variance, particularly the domain shifts that can occur between different domains. To address the limitation, we draw inspiration from knowledge distillation, proposing a collaborative learning framework. Our method employs CycleGAN to generate target-style images, and during pretraining, an unsupervised domain adaptation teacher model is trained for each source-target pair. In the distillation process, our proposed algorithm implements an out-of-distribution estimation strategy to select samples that best align with the current model, thereby enhancing the cross-domain distillation process. Furthermore, each expert model is encouraged to collaborate by designating the student model as a bridge between different target domains, facilitated by the Exponential Moving Average (EMA) algorithm. Experiments show that the proposed method leverages structured information, not only does it perform well across various target domains, but it also yields favorable results compared to state-of-the-art unsupervised methods that are specifically trained on single source-target pair. © 2024 IEEE.

关键词： Contrastive Learning

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Detection of Plant Disease Using Machine Learning and Deep Learning Algorithms 8

Detection of Plant Disease Using Machine Learning and Deep L...

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Gollapudi, Kalyani Varigonda, Preethi Jancy, S. Pushpalatha, S. Marya, Viji Amutha Helen, Suji Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Simats Department of Data Analytics Chennai602105 India

ISBN: (纸本)9798350347791

Agriculture performs an critical position in India's economic system. Early detection of plant illnesses is critical to save you crop damage and similarly spread of diseases. Most plants, along with apple, tomato, cherry, grape, show symptoms of leaf ailment. These visible patterns may be found to correctly predict the disorder and take early movement to save you it. This can be triumph over with system getting to know and deep getting to know algorithms. We therefore recommend a method that determines tomato plant disease from pix of leaves. This method is performed with aid vector device (SVM), random woodland gadget studying algorithm, and look at algorithms Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and ResNet that is one of the switch learning techniques. Snoring. After the facts set is processed by way of the algorithms, the accuracy of the algorithms is compared and the snapshots are categorized. © 2023 IEEE.

关键词： Recurrent neural networks

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Energy-efficient computation offloading assisted by RIS-based UAV

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The Journal of China Universities of Posts and Telecommunications 2024年第1期31卷 37-48页

作者： Li Linpei Zhao Chuan Su Yu Huo Jiahao Huang Yao Li Haojin School of Intelligence Science and Technology University of Science and Technology BeijingBeijing 100083China Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China Shunde Innovation School University of Science and Technology BeijingFoshan 528399China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Research Department of 5G and Drone China Mobile(Chengdu)Institute of Research and DevelopmentChengdu 610200China College of Communication Engineering Chengdu University of Information TechnologyChengdu 610225China Research and Development Center Sony(China)LimitedBeijing 100027China

The new applications surge with the rapid evolution of the mobile *** explosive growth of the data traffic aroused by the new applications has posed great computing pressure on the local *** is essential to innovate the computation offloading methods to alleviate the local computing burden and improve the offloading *** edge computing(MEC)assisted by reflecting intelligent surfaces(RIS)-based unmanned aerial vehicle(UAV)is a promising method to assist the users in executing the computation tasks in proximity at low *** this paper,we propose an energy-efficient MEC system assisted by RIS-based UAV,where the UAV with RIS mounted relays the computation tasks to the MEC *** energy efficiency maximization problem is formulated by jointly optimizing the UAV's trajectory,the transmission power of all users,and the phase shifts of the reflecting elements placed on the *** that the optimization problem is non-convex,we propose a deep deterministic policy gradient(DDPG)-based *** combining the DDPG algorithm with the energy efficiency maximization problem,the optimization problem can be ***,the numerical results are illustrated to show the performance of the system and the superiority compared with the benchmark schemes.

关键词： mobile edge computing unmanned aerial vehicle reflecting intelligent surface

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On the Adversarial Robustness of Hierarchical Classification

On the Adversarial Robustness of Hierarchical Classification

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Ran Zeng, Simeng Wu, Wenhui Jia, Yuheng Ng, Wing W. Y. Wang, Xizhao School of Mathematical Sciences Shenzhen University Shenzhen518060 China College of Electronic and Information Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering South China University of Technology Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665410205

Deep neural networks (DNNs) have demonstrated remarkable success on various learning problems, but they face a formidable challenge in the form of adversarial attacks. Especially, when dealing with complex classification tasks for numerous classes with a hierarchical structure, the adversarial robustness of a DNN model may drop seriously. In this paper, we investigate the adversarial robustness of DNN models on such complex classification tasks. In response, we propose a two-stage hierarchical classification framework, which is composed of a coarse-grained classifier and a series of fine-grained classifiers. A data correction sampling module is designed between the two stages, in order to mitigate the influence of misclassification caused by the coarse-grained classifier;and a discriminative filter learning module is employed in the fine-grained classification, in order to gain better distinguish abilities among fine-grained categories. Experiments on the well-known dataset CIFAR-100 and a newly-constructed hierarchical dataset mini-ImageNet76 demonstrate that employing a hierarchical framework can effectively improve the model robustness on such complex classification tasks. © 2024 IEEE.

关键词： Generative adversarial networks

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MHANDTI: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Multi-Hop Attention Networks

MHANDTI: Drug-Target Interaction Prediction Model Based on H...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Chen Sun, Jiaqi Xing, Linlin Zhang, Longbo Cai, Hongzhen Guo, Maozu Shandong University of Technology School of Computer Science and Technology Zibo China Shandong University of Technology College of Agricultural Engineering and Food Science Zibo China Beijing University of Civil Engineering and Architecture College of Electrical and Information Engineering Beijing China

ISBN: (纸本)9798350386226

Predicting drug-target interactions (DTI) has become an important step in the drug discovery and drug repositioning process. The biological identification of DTI incurs significant financial and temporal costs, and the deployment of computational methodologies for DTI prediction can substantially curtail both the duration and economic expenditure of drug discovery or repositioning. Drawing from disparate data sources can offer a comprehensive overview and diverse perspectives for predicting drug-target interactions. However, existing methods for processing drug and target information are unable to capture long-range dependency across heterogeneous graphs. To capture more comprehensive drug and target features for DTI prediction, this article proposes a drug-target interaction prediction model based on heterogeneous graph multi-hop attention networks, called MHANDTI. Concretely, the word-level deep convolutional neural network is used to obtain the structural features of drugs and targets as attributes of drug and target entity nodes in heterogeneous graphs. For the processing of heterogeneous graphs, MHANDTI designs a multi-hop attention diffusion layer (MHADL) to establish a connection between the non-direct connection nodes, and summarize the characteristics of the adjacent nodes of the center nodes in several hops. A series of results indicate that the performance of this method is superior to the latest existing frameworks. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring the Significance and Paradoxical Nature of the Zero Value in Mathematics Using Artificial Intelligence 4

Exploring the Significance and Paradoxical Nature of the Zer...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ishwarya, K. Saranya, G. Appathurai, K. Surendran, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Kg College of Arts and Science Department of Computer Technology Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300888

One of the greatest paradoxes in the human mind is the odd number zero. Both everything and nothing are meant by it. It performs an important function in mathematics as the additive identity of integers, real numbers, and a variety of other algebraic structures. Without zero, it would have been difficult for all branches of science - not just mathematics - to come up with definitions that were more exact. The digit 0 serves as a stand-in for other digits in place systems. Here, it creates an interactive learning session to show the existence and importance of '0' to a supernatural that does not utilize zeros in their number system. Three key concepts are used in this study to explain what zero is and what it means. The first idea is based on Apple logic, a fundamental approach to logical reasoning. Understanding gravity includes exploring how the second principle is used in this context. The usage of contemporary technology is the last idea. Without the usage of zero, the logic in all of these concepts will be completely erroneous. These ideas interactively applied artificial intelligence algorithms. © 2023 IEEE.

关键词： Numbering systems

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Biomaterials and Their Applications

Biomedical Materials and Devices

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Biomedical Materials and Devices 2025年 1-16页

作者： Menagadevi, M. Nirmala, M. Thiyagarajan, D. Somasundram, D. Department of Computer Science and Engineering School of Engineering Malla Reddy University Hyderabad India Department of Biomedical Engineering Dr. N G P Institute of Technology Coimbatore India Department of Artificial Intelligence and Machine Learning School of Engineering Malla Reddy University Hyderabad India Department of Micro & Nanoelectronics School of Electronics Engineering Vellore Institute of Technology Vellore India

A biomaterial is a biocompatible substance engineered to interact with biological systems for therapeutic and diagnostic, such as pacemakers, dental implants, vascular stents, artificial joints, drug delivery systems, tissue engineering, biosensors, image contrast agents in computer Tomography, Magnetic Resonance Imaging, optical imaging, and ultrasound. Biomaterials are also used as assays in point-of-care testing devices. Based on the organs’ biological properties, the biomaterials are mainly classified into metals, polymers, ceramics, and composite biomaterials. The success rate of the implant depends on the physical, chemical, and mechanical properties of the selected material. Each biomaterials have unique properties, so it is used in both soft and hard tissue replacements. In this review, various properties and applications of biomaterials are discussed. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Applications Biocompatibility Biomaterials Properties Types

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Degree reduction of ball Said-Ball surfaces using an enhanced coot optimization algorithm

Degree reduction of ball Said-Ball surfaces using an enhance...

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作者： Hu, Gang Dou, Wanting Du, Bo Department of Applied Mathematics Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China

Degree reduction of ball Said-Ball (BSB) surface is a complex and unsolved problem in computer-aided design (CAD) and computer graphics (CG), which has potential application prospects in many engineering fields of geometric modeling. In this paper, the degree reduction of BSB surface is transformed into the optimization problems of center surface and radius function, and the improved coot optimization algorithm is used to solve it. An effective method to solve the degree reduction of BSB surface by intelligent optimization algorithm is proposed. Firstly, the degree reduction of BSB surface is decomposed into center surface reduction and radius function reduction, and the objective function and constraint conditions are determined according to the optimization idea, then the degree reduction optimization model of center surface and radius function is established. Secondly, an enhanced coot (ECOOT for short) algorithm is proposed, which combines chain strategy, dispersed foraging strategy, and quadratic interpolation strategy. Compared with the original algorithm, ECOOT algorithm has better convergence performance and higher calculation accuracy. Finally, the proposed ECOOT algorithm is used to solve the degree reduction optimization model, and the optimal center surface and minimum distance radius function of the BSB surface after degree reduction are obtained. The experimental results show that the ECOOT algorithm can effectively solve the problem of degree reduction for BSB surface, the degree reduction error is reduced by about 20% compared to the original COOT algorithm, and is superior to other intelligent optimization algorithms in accuracy, robustness, and convergence. © 2023 John Wiley & Sons, Ltd.

关键词： Optimization

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SAM-LSTM: Prediction of the Sunspot Cycles Based on LSTM-Attention Model with Soft Attention Mechanism 9

SAM-LSTM: Prediction of the Sunspot Cycles Based on LSTM-Att...

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9th International Symposium on computer and Information Processing technology, ISCIPT 2024

作者： Miao, Xinhua Li, Binhong South China University of Technology School of Computer Science and Engineering China South China University of Technology School of Electronics and Information Guangzhou China

ISBN: (纸本)9798350388404

This paper aims to develop a reliable mathematical model for predicting sunspot characteristics and their impact on various fields. Previous studies have used methods such as time series analysis, spectral analysis and neural networks to predict sunspots, but the results are not ideal. This study proposes a new mathematical model designed to more accurately predict sunspot cycles and explain their reliability. We will use publicly available observational data to provide improved methods and results for sunspot predictions. We collected the required data on the number of sunspots, magnetic field strength, etc. from relevant websites. In the data preprocessing stage, we filled missing values, handled outliers, and performed spectral analysis on the data through exploratory data analysis methods such as scatter plot visualization and trend exploration. We use Fourier transforms to explore periodicity and integrate relevant annual, monthly, and daily data into period-based data. Because of the relatively poor performance of ARIMA, we introduce the LSTM-Attention model based on soft attention mechanism to fit and predict the original and the integrated data. By comparing the results with NAOC (National Astronomical Observatories, CAS) and NASA (National Aeronautics and Space Administration), we achieved relatively accurate predictions of the start and duration of the current and next sunspot cycles. © 2024 IEEE.

关键词： NASA

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