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Federated learning on Non-Independent and Identically Distributed data 3

Federated Learning on Non-Independent and Identically Distri...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Li, Haowei Luo, Like Wang, Haolong School of Foreign Languages Yangtze University Nanhuan Road Jingzhou China Software College Nanchang University 235 Nanjing East Road Qingshan Lake District Nanchang China Software College Northestern University 195 Chuangxin Road Hunnan District Shenyang China

ISBN: (纸本)9781510664814

Federated Average algorithm (FEDAVG) is the preferred algorithm for federated learning (FL) because of its simplicity and low communication cost However, if all clients's local data aren't independent and equally distributed (that is, nonindependent and identically distributed), FedAvg will have the phenomenon of customer drift, which will lead to the slow convergence speed of the model, and then the efficient cooperative learning to realize the cooperative training of multiple clients will face the challenge of data heterogeneity and vulnerability to attack. This paper systematically summarizes three aspects: local model training improvement, server-side aggregation optimization, and personalized federated learning. The local model can be improved by adjusting the loss function and control variables. Servers can be optimized through asynchronous aggregation, hierarchical aggregation. Personalized federated learning can improve global model performance through both data-based and model-based approaches. This paper puts forward the future research direction of federated learning from all above aspects, and provides reference for the further research of non-IID in federated learning, so as to provide investigation and help for researchers in related fields. © 2023 SPIE.

关键词： learning systems

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A Self-play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-learning in a Crowdsourcing Scenario : Extended Abstract 39

A Self-play and Sentiment-Emphasized Comment Integration Fra...

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39th IEEE international conference on Data engineering, ICDE 2023

作者： Rong, Huan Sheng, Victor S. Ma, Tinghuai Zhou, Yang Al-Rodhaan, Mznah Nanjing University of Information Science & Technology School of Artificial Intelligence Nanjing China Texas Tech University Department of Computer Science Lubbock United States Nanjing University of Information Science & Technology School of Computer & Software Nanjing China Auburn University Department of Computer Science & Software Engineering Auburn United States King Saud University College of Computer & Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350322279

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. The most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1]. However, the same instance may have inconsistent class labels, in the eyes of various annotators. Therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. The future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2]. Consequently, we evolve toward another challenging problem of comment integration. That is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels. © 2023 IEEE.

关键词： Crowdsourcing

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Code Smell Detection using Hybrid machine learning Algorithms 5

Code Smell Detection using Hybrid Machine Learning Algorithm...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Mahalakshmi, D. Kasinathan, Prabakaran Elangovan, D. Rohith Bhat, C. Balamurugan, M. Sivakumar, S. Panimalar Engineering College Dept. of Information Technology India School of Computing Veltech Rangarajan Dr. Sagunthula R & D Institute of Science and Technology Dept. of Cse India Panimalar Engineering College Dept. of Cse India Chennai India Sri Sairam Engineering College Affiliated to Anna University Dept. of Cse India Lakshmi Bangaru Arts and Science College Dept. of Computer Applications melmaruvathur India

ISBN: (纸本)9798350321425

Code smells frequently leads to the discovery of decreased code quality, drains on application resources, or even critical security vulnerabilities embedded within the application's code. While code smells may not always indicate a particularly serious problem, it do often lead to the discovery of these issues. software's structural characteristics lead to a design issue that makes it challenging to manage and maintain code refactoring. The goal of the current research is to create methods for identifying code smells. The machine learning algorithm is a reliable method for individualized smell detection, but there aren't many studies on how well it works for different developers. In this proposed work used two different deep learning algorithms and five different machine learning ensembles to detect suspicious code. Investigation of the Data class, God class, Feature-envy, and Long-method datasets revealed that each contained various levels of code smells. Although there is room for improvement, the outcomes of prior publications' applications of machine learning and stacking ensemble learning methods to this dataset were satisfactory. A class balancing method (SMOTE) was implemented to address the problem of class imbalance within the datasets. While the Feature- envy dataset with the selected dozen metrics produced the lowest accuracy (91.45%) for the Max voting method, the Long-method dataset with the various chosen metrics produced the highest accuracy (100%) for all five methods. © 2023 IEEE.

关键词： learning systems

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The Role of machine learning Algorithms in Developing Android App and to do Malware Detection 3

The Role of Machine Learning Algorithms in Developing Androi...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Sudhamsu, Gadug Bhatt, Rahul Jakka, Geethamanikanta Jweeg, Muhsin J. Alfurhood, Badria Sulaiman Tripathi, Vikas Department of Computer Science and Engineering Bangalore India Dev Bhoomi Uttarakhand University Department of CSE Uttarakhand Dehradun India University of the Cumberlands Data Analytics Information Technology United States Al-Farahidi University College of Technical Engineering Baghdad Iraq Princess Νourah Hint Ahdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department CSE India

ISBN: (纸本)9798350399264

Because there are so many issues that have had suitable solutions, cybersecurity is now the subject that worries computer scientists and system administrators the most. The rapid development of information technology and its inherent integration into every aspect of our lives have made it possible to analyze and identify a broad variety of harmful software packages and the targets of these programmes. Malware ***, Android malware has gotten some notice. It seems to attract a lot of attention on the internet. Android is one of the most well-liked operating systems that dominates the operating system market. In 2020, 85% of smartphones will run on the Android operating system, according to statistics from the system. Mobile virus attacks have increased in frequency over time. Known to infect a phone system with applications that seem harmless but really contain malware is one of the most popular approaches. Malware on cellphones has already been detected using machine learning. Deep learning and supervised learning techniques have been used to achieve this. Drawing on consumer rights, this study evaluates the effectiveness of multiple categorization approaches to find virus in Android apps. © 2023 IEEE.

关键词： Android (operating system)

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Personal Loan Default Prediction and Impact Analysis of Debt-to-Income Ratio 8

Personal Loan Default Prediction and Impact Analysis of Debt...

引用

8th international conference on Information Technology Research, ICITR 2023

作者： Rodrigo, K.L.S. Sandanayake, T.C. Silva, A.T.P. University of Moratuwa Faculty of Information Technology Sri Lanka

ISBN: (纸本)9798350359503

Loan defaults affect the financial sector, particularly impacting banks and lending institutions, resulting in a rise of non-performing assets and financial strain. To counteract this trend, traditional credit assessments use methods like credit scores and exploitation of socio-demographic composition of the customers. However, customers may possess numerous debt obligations that credit bureaus uncover, which can help to measure their repayment ability. This study proposed a comparative methodology that leverages five machine learning algorithms to predict personal loan defaults using debt-to-income ratio apart from the credit scoring models that prevail at banks. It analyzed the impact of debt payments on loan defaults and applied ensemble clustering to categorize customers' risk levels based on their debt-to-income ratio. Experimental results indicated that ensemble clustering has enhanced the prediction power compared to conventional classification models to predict loan defaults. © 2023 IEEE.

关键词： machine learning

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Graph neural networks for protein-protein interactions 3

Graph neural networks for protein-protein interactions

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Chang, Sheng Wang, Yifan Zhang, Xinhong Zhang, Fan School of Computer and Information Engineering Henan University Kaifeng475004 China School of Software Henan University Kaifeng475004 China

ISBN: (纸本)9781510668522

Protein-protein interaction (PPI) is the process by which two or more protein molecules form a protein complex through non-covalent bonds. The interactions between protein and the protein complexes formed by these interactions are the primary complements of various essential cellular functions and carry out almost all vital life activities. Most PPI research today focuses on the use of machine learning methods, of which the use of graph neural networks (GNN) for prediction is a current hot direction. This paper highlights the main advances in the application of GNN to proteinprotein interactions. The first part reviews protein sequence-based methods for PPI prediction. And the second part focuses on structure-based graph neural network PPI prediction methods. Finally, we discuss the shortcomings of GNNs in this area and future directions in PPI prediction. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

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SmartCrop: An Intelligent IoT System for Automated Weed Detection and Precision Pesticide Spray 4

SmartCrop: An Intelligent IoT System for Automated Weed Dete...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Chandaragi, Pooja V Mallidu, Jayashree Kallimani, Padmaja Itagi, Anupama R Kle Technological University Department of Electrical and Electronics Engineering Hubballi India

ISBN: (纸本)9798350300888

The rapid advancements in IoT, image processing, and machine learning (ML) have presented numerous opportunities for diverse applications. One such application is the classification of images for day-to-day agricultural tasks. Effective weed monitoring and control are crucial in agriculture, necessitating the use of pesticides. This paper introduces a novel hardware-based implementation using Raspberry Pi, presenting the design of a robot that automates the process of weed detection and pesticide application. The proposed work utilizes the *** software tool to label input images and assign corresponding names, while leveraging the Yolov5 architecture, a Deep learning (DL) based object detector, for image segregation in our system. The obtained results demonstrate efficient weed detection and control, contributing to farmer safety by reducing pesticide exposure, saving time, and minimizing manual labor. This research significantly advances agricultural practices, offering a solution that enhances productivity and promotes a safer and more sustainable farming approach. © 2023 IEEE.

关键词： machine design

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What Is the Cost of AI Ethics? Initial Conceptual Framework and Empirical Insights 14th

What Is the Cost of AI Ethics? Initial Conceptual Framework ...

引用

14th international conference on software Business (ICSOB)

作者： Kemell, Kai-Kristian Vakkuri, Ville Univ Helsinki Dept Comp Sci Pietari Kalmin Katu 5 Helsinki 00560 Finland Univ Vaasa Sch Mkt & Commun Wolffintie 32 Vaasa PL 700 FI-65200 Vaasa Finland

ISBN: (纸本)9783031532269;9783031532276

AI ethics has become a common topic of discussion in both media and academic research. Companies are also increasingly interested in AI ethics, although there are still various challenges associated with bringing AI ethics into practice. Especially from a business point of view, AI ethics remains largely unexplored. The lack of established processes and practices for implementing AI ethics is an issue in this regard as well, as resource estimation is challenging if the process is fuzzy. In this paper, we begin tackling this issue by providing initial insights into the cost of AI ethics. Building on existing literature on software quality cost estimation, we draw parallels between the past state of quality in software engineering (SE) and the current state of AI ethics. Empirical examples are then utilized to showcase some elements of the cost of implementing AI ethics. While this paper provides an initial look into the cost of AI ethics and useful insights from comparisons to software quality, the practice of implementing AI ethics remains nascent, and, thus, a better empirical understanding of AI ethics is required going forward.

关键词： Ethics machine learning Cost estimation software engineering Artificial intelligence

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An Improved Voltage Regulation in DC Smart Grid Using machine learning

An Improved Voltage Regulation in DC Smart Grid Using Machin...

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2023 international conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Rubia Gandhi, R.R. Makanth, S. Abhi, V.P. Amritha, R. Harish, M. Sri Ramakrishna Engineering College Department of EEE Coimbatore India

ISBN: (纸本)9798350312126

The power generation from solar photovoltaic is variable in nature, and may contain unacceptable fluctuations so in current use the renewable energy is stored in the battery or super capacitors to use it later but it requires high maintenance charge. The main idea for the proposed work is to regulate the renewable power during the fluctuation to give uninterrupted power supply. The continuous monitoring of power generation from solar is carried out by machine learning model using KNN algorithm and simultaneously the load consuming values are again sent to machine learned model to classify the switching status of regulator circuit. Using Thonny software, the dataset is fed to the model and all these AI process is done in Python Platform. Hardware implementation for the same is used with 1200 W solar panel and for the load variation, three LED's are used. Panel when exposed to sunlight, solar power is utilized using BUCK converter and when no light falls on the panel, the SMPS supply the LED switching from solar power to DC rectifier. © 2023 IEEE.

关键词： Solar energy

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Hybrid Feature-Based machine learning Method for Phishing URL Detection 3

Hybrid Feature-Based Machine Learning Method for Phishing UR...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Kumar, Joney Meerut Institute of Engineering & Technology Information Technology Meerut India

ISBN: (纸本)9798350300710

The Internet's exponential growth has led to an increase in e-commerce usage, but it has also attracted hackers who seek to steal personal information online. One of the most prevalent methods used by cybercriminals is the phishing scheme, where they trick individuals into divulging sensitive information through fake URLs. Due to the semantics-based attack strategy used in phishing, it is challenging to differentiate between legitimate and phishing URLs, taking advantage of computer users' vulnerabilities. software companies provide anti-phishing systems that utilize blacklists, heuristics, visuals, and machine learning, but they cannot prevent all phishing attempts. Therefore, this research proposes five classification methods that use hybrid features, including natural language processing (NLP) and principal component analysis (PCA), to address this issue. The study finds that the Random Forest algorithm utilizing NLP and word vector features surpasses its competitors, with a 99.75% accuracy rate in classifying phishing URLs. © 2023 IEEE.

关键词： Principal component analysis

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