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WOA-XGBoost based financial statement fraud detection 3

WOA-XGBoost based financial statement fraud detection

引用

3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Dou, Xinfeng Liu, Rong Yin, Shengpeng Nanjing Institute of Technology No.1 Hongjing Avenue Jiangning District Jiangsu Province Nanjing City211167 China Xinjiang Shihezi Vocational Technical College 185 Beiwu Road Xinjiang Uygur Autonomous Region Shihezi City832000 China

ISBN: (纸本)9781510680531

Financial statement fraud detection is a crucial topic in maintaining financial security. To optimize parameter tuning and improve model performance, we innovatively combined Whale Optimization Algorithm (WOA) with Extreme Gradient Boosting Algorithms (XGBoost) to construct the WOA-XGBoost model. We first construct a XGBoost based classification model based on XGBoost, and adopt the WOA algorithm to adaptively optimize the XGBoost parameters. Compared to manually adjusting the model training parameters, WOA-XGBoost adaptively searches for the optimal parameters. The experimental results presents a noticeable improvement in F1-Score for WOA-XGBoost. Meanwhile, WOA-XGBoost achieved the highest accuracy among the algorithms participating in the comparative experiment, which demonstrating the superior performance of the proposed method. © 2024 SPIE.

关键词： Machine learning

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ETL Based data Integration Scheduling 3

ETL Based Data Integration Scheduling

引用

3rd international conference on intelligent Computing and Human-Computer Interaction, ICHCI 2022

作者： Zheng, Qianqian College of Artificial Intelligence Southwest University Chongqing China

ISBN: (纸本)9781510661301

As the open source ETL tool with the largest number of users, Kettle's powerful and concise functions are welcomed by the majority of ETL practitioners. ETL is the process of extracting, converting and loading data from the data source to the target. Based on the enterprise data model, ETL builds a reasonable data storage mode, establishes an enterprise data exchange platform, meets the data exchange needs between various application systems, provides all-round data services, and meets the data support needs of enterprise decision-making [1]. In addition to formulating the corresponding extraction, transformation and loading tasks, ETL tools are also particularly important for the formulation of scheduling monitoring. For industrial economic data, benefit data and production data are generated periodically (such as daily, weekly and daily). Through the scheduler, users can more easily realize ETL task scheduling management, task execution performance and other data monitoring and analysis. However, Kettle's scheduling and monitoring function is very weak. This paper will use the scheduling system to monitor and schedule tasks to improve the efficiency of data integration. © 2023 SPIE.

关键词： data integration

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An Analysis and Comparison Of Deep-learning Techniques and Hybrid Model for Sentiment Analysis for Movie Review 3

An Analysis and Comparison Of Deep-Learning Techniques and H...

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Sinha, Swapnil Jayan, Abishek Kumar, Rishabh Government Engineering College Barton Hill. Electronics and Communication Trivandrum India Mar Baselios College of Engineering Civil Engineering Trivandrum India Manipal University Jaipur Department of Information Technology Jaipur India

ISBN: (纸本)9781665494991

In today's world, where each day 2.5 quintillion bytes of data is generated, the safe storage and secure processing of such data is a fundamental task. The vastness of the web has brought trillions of data as comments, reviews, blog posts etc. For a service provider the analysis of these are very essential and also finding out the trends in which sentiment analysis plays a huge role. This differs from typical topic-based text classification as it is done by classifying text based on the sentiment it conveys. User generated text is the basis from which opinion and subject knowledge is drawn for sentiment analysis. In this paper, we deal with the sentiment analysis of the IMDB dataset of movie reviews. We explore the effectiveness of different deep learning techniques such as CNN, LSTM, LSTM-CNN, GRU, BERT, BERT-CNN, BERT-LSTM for the sentiment analysis of the IMDB movie review dataset. data cleaning and the techniques used are explained and the performance of these various algorithms used are measured in terms of recall, precision and accuracy. In this we hope to find the best machine learning model amongst the ones tested for future research. © 2022 IEEE.

关键词： Sentiment analysis

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A Hybrid learning Approach for Automatic data Labelling and Anomaly Detection in IoT Networks 3

A Hybrid Learning Approach for Automatic Data Labelling and ...

引用

3rd IEEE international conference on Artificial Intelligence, ICAI 2023

作者： Kanwal, Rimsha Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Computer Science Islamabad Pakistan College of Engineering Technology Management Economics and Policy Program Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798350322125

Internet of Things (IoT) is an environment in which digital equipment is augmented with sensors to share and receive data through network. When devices share data it can be effected by anomalies or any attack due to corrupted data or by any other uncertainty and ambiguity in data. The data can also be corrupted through a damage in device. These attacks or anomalies damage the working of the IoT networks. The anomalous data can be detected through detection techniques however most anomaly detection techniques depend upon labelled data but for IoT datasets, usually class labels are not available. Labeling is performed by a manual process which is time consuming and also costly. As data in IoT increases day by day so there is a need to label and classify data for future unseen data. In this paper a hybrid algorithm is proposed in which both clustering and classification techniques are applied for automatic labeling and classifying on IoT dataset. The model contains two function. In the first phase k-means clustering is employed for labelling dataset instances as normal or anomalous. In the second phase labelled dataset is used to train Random Forest model to detect anomalies in IoT networks. The results show that the proposed model is detecting anomalies in IoT networks with an accuracy 98%, precision 98 %, recall 98%, and F-meausre 0.98%. © 2023 IEEE.

关键词： Classification (of information)

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IoMT Enabled Prototype Edge Computing Healthcare System for Isolated Patients 3

IoMT Enabled Prototype Edge Computing Healthcare System for ...

引用

3rd international conference on intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Shamna, P.A. Mohan, Priyanka C Kmct College of Engineering Dept. of Ece Kerala Kozhikode India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Continuous patient care and the use of multiple medical machines are two challenges facing today's healthcare sector in terms of patient's healthcare. During the pandemic situation, many people isolated in their home, such as covid-19 positive patients, elderly people living away from their families, bedridden patients, etc., need regular health checks and controls, but during this pandemic is lacking. Recent advances in the Internet of Medical Things (IoMT) has been able to give good results in collecting health data of patients at home environment. Deep learning (DL) applications can able to run on edge nodes, it locally processes, computes and analyzes data from IOMT devices to make inferences on patient health information. This ensures the privacy and security of the patient's physiological information and also and allows patient health information to remain at the patient's side. Send all this information to healthcare professionals and relatives of patients. This framework will provide safety for isolated patients and a health support systemas a whole. © 2022 IEEE.

关键词： Software prototyping

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Employee Attrition Prediction Using Machine learning Algorithms 3

Employee Attrition Prediction Using Machine Learning Algorit...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ankita Mittal, Sonam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Artificial intelligence spreading its wings all over the world. This technology helps to make predictions and forecast future predictions. Employees are the pillar of an organization. An efficient and productive employee may help to take the organization to the next step of growth and success. Employee attrition prediction can help an organization understand its employees and win their trust, finally providing them with a better working environment. Employee attrition can also cause a loss of economic growth and time. In this paper, we will discuss the linear regression model and its various types. Linear regression helps to predict employee attrition depending on various factors from the given data bank. Linear regression algorithms are very good at solving problems in a very authentic and useful way. It uses the best-fit line and then finds the best results by differentiating the variables. Different algorithms of linear regression, such as Support Vector Machine, NU-Support Vector Machine, and K-Nearest Neighbour classification are implemented and compared to predict employee attrition. This paper discusses the process of Attrition of employees why employees are leaving the organization what are the reasons behind it, and all data of employees, its pros and cons are discussed. © 2023 IEEE.

关键词： Artificial Intelligence Employment Linear Regression Machine learning NuSVM and KNN Social devleopment Support Vector Machine

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Proposed Convolution Architecture for Monkeypox Detection using Dermoscopy Images 3

Proposed Convolution Architecture for Monkeypox Detection us...

引用

3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Rahul Sharma, Avinash Gupta, Sheifali Anand, Vatsala Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dept. of CSE Haryana Mullana India

ISBN: (纸本)9781665494007

Monkeypox is a skin disease that spreadsfrom animals to people and then people to people, the class of the monkeypox is zoonotic and its genus are othopoxvirus. There is no special treatment for monkeypox but the monkeypox and smallpox symptoms are almost similar, so the antiviral drug developed for prevent from smallpox virus may be used for monkeypox Infected person, the Prevention of monkeypox is just like COVID-19 proper hand wash, Smallpox vaccine, keep away from infected person, used PPE kits. In this paper Deep learning is use for detection of monkeypox with the help of CNN model, The Original Images contains a total number of 228 images, 102 belongs to the Monkeypox class and the remaining 126 represents the normal. But in deep learning greater amount of data required, data augmentation is also applied on it after this the total number of images are 3192. A variety of optimizers have been used to find out the best result in this paper, a comparison is usedbased on Loss, Accuracy, AUC, F1 score, Validation loss, Validation accuracy, validation AUC, Validation F1 score of each optimizer. after comparing alloptimizer, the Adam optimizer gives the best result its total testing accuracy is 92.21%, total number of epochs used for testing is 100. With the help of deep learning model Doctors are easily detect the monkeypox virus with the single image of infected person. © 2023 IEEE.

关键词： Viruses

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Designing a Smart Healthcare framework based on disease prediction using Optimizer based AI 3

Designing a Smart Healthcare framework based on disease pred...

引用

3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Vyas, Garvit Kalshetty, Jagadevi N Pareek, Piyush Kumar Birla Institute of Technology and Science Department of Electrical and Electronics Engineering Pilani - Hyderabad Campus India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350340358

In light of the rapid shift towards an ageing population and the corresponding rise in healthcare spending, disease prediction and control through various healthcare devices and services have lately garnered considerable interest. The development of smart healthcare services is a direct result of the advancements in the Internet of Medical Things (IoMT), wearable sensors, and communication technologies that have occurred in this technological era. The Internet of Things (IoT) in healthcare also helps bring down the cost of many different diseases by analysing data quickly and thoroughly. One exciting new area of medical research is the use of machine learning to make illness predictions from patient symptoms. In order to collect signals, the framework makes use of (IoT) sensors, which then transmit them to a web server in the cloud. In order to ascertain the patients' conditions, these signals are further analysed by a deep learning network. By taking both spatial and temporal considerations into account, the given method shortens the time it takes to discover anomalies by minimising the vanishing gradient problem of Recurrent Neural Networks (RNN). Someone in the medical field has access to patients' medical records and the results of any processing that may be necessary in the event of an emergency. Heart Failure Clinical Records, a repository at UCI, provided the dataset utilised in this investigation. It has thirteen characteristics. The suggested model outperformed competing deep learning and ML models in the experiments, which yielded an accuracy value of 0.9417. © 2023 IEEE.

关键词： Recurrent neural networks

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Machine learning-Based Digital Twin for Monitoring Fruit Quality Evolution 3

Machine Learning-Based Digital Twin for Monitoring Fruit Qua...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Melesse, Tsega Y. Bollo, Matteo Di Pasquale, Valentina Centro, Francesco Riemma, Stefano Univ Salerno Dept Ind Engn Via G Paolo II Fisciano SA Italy SAP Italia SpA Via Amsterdam 125 I-00144 Rome Italy

A technological gap to monitor fruit quality evolution in the food supply chain is causing a huge waste of fruits. A digital twin is a promising tool to minimize fruit waste by monitoring and predicting the status of fresh produce throughout its life. In post-harvest engineering, the digital twin could be defined as a virtual representation of real produce. The objective of this work is to present a new approach to create a machine learning-based digital twin of banana fruit to monitor its quality changes throughout storage. The thermal camera has been used as a data acquisition tool due to its capability to detect the surface and physiological changes of fruits throughout the storage. In this study, after constructing the dataset of thermal data belonging to four classes, the training of the model has been performed using intelligent technologies from SAP. The solution has applied a deep convolutional neural network to monitor the fruit status based on the thermal information, and the training process has shown higher accuracy. Thus, 99% of prediction accuracy has been achieved which is proved to be a promising technique for the development of fruit digital twins. The application of thermal imaging techniques can be used as a data source to create a machine learning-based digital twin of fruit that can minimize waste in the food supply chain. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： digital twin thermal images machine learning image classification food waste

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intelligent Fuzzy Attack Vector Generation Framework Based on Deep Adversarial learning 3

Intelligent Fuzzy Attack Vector Generation Framework Based o...

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3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Tang, Linlin Zhang, Wenbing Wang, Gaozhou Yan, Li Wang, Cong Qu, Haipeng Zhang, Yikang State Grid Shandong Electric Power Company Information and Communication Company Jinan China Ocean University of China Faculty of Information Science and Engineering Qingdao China

ISBN: (纸本)9798350316674

With the advancement and proliferation of Internet of Things (IoT) technology, IoT has become intimately linked with various aspects of human life and production, especially in the realm of industrial control. And the security of industrial control protocols is imperative to ensure the security of industrial control systems. Fuzz testing has been applied to protocol testing, but traditional fuzzing methods require researchers to have a clear understanding of the specific protocol specifications and details of their implementations in order to conduct effective testing. This approach demands a considerable investment of time, resources, and effort. Therefore, we propose an industrial control protocol fuzz testing framework based on adversarial generative networks. The core module of this framework, the adversarial generation module, mainly employs an improved GANs network to train the model to learn the syntax of the protocol. The trained network generates fake but credible test protocol messages. This framework addresses the issue of high costs associated with traditional fuzz testing methods. Other modules will be introduced in Section 3. This paper tests a common protocol, Modbus. The experiments demonstrate that our framework can rapidly generate a large number of fake but credible test protocol messages, enabling efficient fuzz testing of the protocols. © 2023 IEEE.

关键词： component Fuzzing test GAN IOT Self-Attention

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