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Adverse Drug Reaction Prediction and Feature Importance Mining Based on SIDER Dataset 3

Adverse Drug Reaction Prediction and Feature Importance Mini...

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

作者： Chen, Tianqi Liu, Chun Huang, Mingzhe Cheng, Xiang Zhou, Lixian Department of Electrical and Computer Engineering University of Western Ontario LondonON Canada Department of Software Engineering GuangZhou Xinhua University GuangZhou China Department of Computer Science Michigan State University Michigan United States Department of Physics and Astronomy University College London London United Kingdom ChangJun High School International Department Changsha China

ISBN: (纸本)9781510664814

Adverse Drug Reaction (ADR) refer to harmful and irrelevant reactions that occur when normal dosage drugs are used to prevent, diagnose, treat diseases or regulate physiological functions. This definition excludes reactions caused by intentional or accidental overdose and inappropriate medication. In this paper, several models were measured and compared. The results demonstrated that base learners such as LR, SVM, RF, Adaboost, XGBoost may perform exceptionally well in some specific situations. On the other hand, if the precision of the outputs is emphasized, applying Stacking or even Multi-layer Stacking will be the most efficient tool. © 2023 SPIE.

关键词： Adaptive boosting

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machine learning modeling on acoustic emission leak detection of metal-sealed pressure vessel 4

Machine learning modeling on acoustic emission leak detectio...

引用

4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

作者： Li, Fen Li, Yuefeng Yan, Rong Wu, Yunlong Zhang, Ziqiang Xu, Xuehao Beijing Aerospace Xinfeng Mechanical Equipment Co. Ltd Beijing China

ISBN: (纸本)9798350329490

The metal-sealed pressure vessels are often used to provide a sealed pressure environment for storage and transportation of gas or liquid. Once the leakage occurs, it will cause serious financial loss and even casualties. Therefore, it is very necessary to monitor the operation state of the metal-sealed pressure vessel in time. This paper describes a real-time detecting method for metal-sealed pressure vessels, which is based on a machine learning (ML) framework and acoustic emission (AE) signal. The result of leakage detection based on AE is susceptible to different factors, such as environmental noise, the material of detected pressure vessels, and the position of the AE sensor, these factors may lead to a poor detection result. To address these drawbacks, an effective ML model, the decision tree (DT) based on AE signals is presented. At the core of this methodology, a DT model was trained on over 20 thousand AE signal records for classifying the leak sections. The experimental result illustrated that the accuracy of the proposed model to detect leakage of the metal-sealed pressure vessel is 98.3%, F1score is 0.98, which has significant advantages compared with the traditional method. Instead of tedious feature extraction, ML methods have the advantage of being simple and real-time. © 2023 IEEE.

关键词： Leak detection

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Detection Bad Code Smells By Using Deep machine learning Approaches 1

Detection Bad Code Smells By Using Deep Machine Learning App...

引用

1st international conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Yahya, Hiba M. Taha, Dujan B. College of Computer Sciences & Mathematics University of Mosul Software Department Mosul Iraq College of Computer Sciences & Mathematics University of Mosul Cyber Security Department Mosul Iraq

ISBN: (纸本)9798350306484

Code smells are an indication of deviation from design principles or implementation in the source code. Early detection of these code smells increases software quality. In contemporary times, diverse artificial neural network models are employed in software engineering to detect code smells without the need for source code access, but only large training sets. The aim of a study is to present a Recurrent Neural Network, Long Short Term Memory and Gated Recurrent Units models for detecting three types of code smells (Feature Envy, Long Method, and God Class) based on Java projects. Three code smell datasets were considered. Various performance metrics were employed for comparing the results. The experimental findings suggest that the proposed models demonstrate superior performance in identifying code smells, and augmenting the training data can improve the accuracy of predictions. © 2023 IEEE.

关键词： Odors

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Generating Realistic and Diverse Tests for LiDAR-Based Perception Systems 23

Generating Realistic and Diverse Tests for LiDAR-Based Perce...

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Christian, Garrett Woodlief, Trey Elbaum, Sebastian Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9781665457019

Autonomous systems rely on a perception component to interpret their surroundings, and when misinterpretations occur, they can and have led to serious and fatal system-level failures. Yet, existing methods for testing perception software remain limited in both their capacity to efficiently generate test data that translates to real-world performance and in their diversity to capture the long tail of rare but safety-critical scenarios. These limitations are particularly evident for perception systems based on LiDAR sensors, which have emerged as a crucial component in modern autonomous systems due to their ability to provide a 3D scan of the world and operate in all lighting conditions. To address these limitations, we introduce a novel approach for testing LiDAR-based perception systems by leveraging existing real-world data as a basis to generate realistic and diverse test cases through mutations that preserve realism invariants while generating inputs rarely found in existing data sets, and automatically crafting oracles that identify potentially safety-critical issues in perception performance. We implemented our approach to assess its ability to identify perception failures, generating over 50,000 test inputs for five state-of-the-art LiDAR-based perception systems. We found that it efficiently generated test cases that yield errors in perception that could result in real consequences if these systems were deployed and does so at a low rate of false positives.

关键词： software Testing and Validation machine learning

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Correlation based Feature Selection and Hybrid machine learning Approach for Forecasting Disease Outbreaks 4

Correlation based Feature Selection and Hybrid Machine Learn...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Bhunia, Swayon Abirami, S. Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350300093

According to WHO, Dengue is a viral infection transmitted to humans through the bite of infected mosquitoes i.e., Aedes aegypti mosquitoes. There is currently no known cure for dengue or severe dengue. Artificial Intelligence (AI) in the form of machine learning (ML) allows software programs to predict outcomes more correctly without explicit instructions. machine learning algorithms use historical data as input to forecast new output values. The aim of this study is to identify, evaluate and interpret suitable hybrid algorithms/approaches relevant to the application of machine learning in limiting the spread of deadly disease outbreaks. It focuses on finding a way of predicting the next dengue fever local epidemic by comparing the bench mark approaches available until now. For this the study proposes the use of XGBoost coupled with Moving Average Rolling Features in order to learn the long-term temporal relations in the features to get accurate predictions. The dataset used for evaluating the proposed approach contains number of cases in the two locations: San Juan and Iquitos and it includes information on temperature, precipitation, humidity, vegetation, and what time of the year the data was obtained. A correlation analysis-based feature selection along with Moving Average Rolling Features has been used for getting more precise data implemented with ML approach resulting in MS E 11.37 in San Juan and MSE 6.37 in Iquitos. © 2023 IEEE.

关键词： Forecasting

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Effectiveness of Ensemble Classifier Over State-Of-Art machine learning Classifiers for Predicting software Faults in software Modules 3rd

Effectiveness of Ensemble Classifier Over State-Of-Art Machi...

引用

3rd international conference on machine learning, Image Processing, Network Security and Data Sciences, MIND 2021

作者： Gupta, Mansi Rajnish, Kumar Bhattacharya, Vandana Department of Computer Science and Engineering BIT Mesra Ranchi India

ISBN: (纸本)9789811958670

software fault prediction (SFP) is the process of creating a model that software practitioners may use to identify defective classes/modules prior to the testing phase. The software development team leader will be able to better manage resources and decrease the testing effort if faulty modules can be predicted prior to the testing phase. Experiments and studies are being carried out in order to develop a reliable model. The software engineering process relies heavily on classification techniques that help in the prediction of software faults. According to several research, utilizing ensembles is typically more accurate than using single classifiers. Using ensemble learning approaches, this study offers a comparative analysis in software fault prediction. In this paper, state-of-art classifiers (Naive Bayes, Decision Table, Random Tree, and J48) as well as ensemble classifiers (AdaBoost, Gradient Boosting machine, and XGBoost) are tested for SFP. The experiments were performed using the three versions of Eclipse Dataset (Eclipse 2.0, Eclipse 2.1, and Eclipse 3.0). Accuracy, F1 scores, precision, and recall are four frequently used measures for performance assessment. The results demonstrate that ensemble techniques can improve classification outcomes in SFP, with the XGBoost method providing the best results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Adaptive boosting

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Social Media Marketing Using the AIML Algorithm

Social Media Marketing Using the AIML Algorithm

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2023 international conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Dharani, D. Leela Vij, Renu Ansari, Mohammed Saleh Al Srinivas, Amedapu P.V.P Siddhartha Institute of Technology Department of Information Technology Vijayawada India University School of Business Chandigarh University Department of Ait Management Mohali India University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9798350317060

This study aims to investigate how artificial intelligence (AI) has altered online marketing through a social media. The authors began their study of how people embrace new technologies by using the UTAUT framework. The research team used statistics to conclude how people embrace and utilize new technologies. In this article, the writers investigate the connections between 'social media marketing', 'machine learning', and 'social media.' Given the incidence with which 'ML methods are used in SMM campaigns, 'consumer familiarity with 'AI-based technology built upon three pillars. (Listenership/opinion/image analysis).' In a survey of over 100 individuals from all across the globe responded. This means that internet marketing strategies are given greater attention. A social media marketer may train a computer using algorithms for machine learning to identify potentially malicious trends in online postings Therefore, social media gives new views on which to base studies. This course may be taken digitally in its entirety. The data demonstrate that people considering using AI Media software are naturally curious and rely on the importance of its suggestion. As part of the Futures Web study, developers of AI-powered media software will conduct user interviews to see how its eventual end users plan to put the program's features to use. The present study remained statistical, and its respondents were selected at chance from all over the globe. Thus, its findings should be interpreted with caution. © 2023 IEEE.

关键词： machine learning

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machine learning Algorithms for Intrusion Detection Performance Evaluation and Comparative Analysis 4

Machine Learning Algorithms for Intrusion Detection Performa...

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

作者： Irfan, B. Md. Poornima, V. Mohana Kumar, S. Aswal, Upendra Singh Krishnamoorthy, N. Maranan, Ramya Nalsar University of Law Department of Information Technology Telengana Hyderabad500101 India VISTAS Pallavaram Department of Computer Science Chennai India Ramaiah Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560054 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Vellore Institute of Technology Vellore Campus Department of Software Systems and Engineering Tamilnadu Vellore632014 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai600124 India

ISBN: (纸本)9798350300888

The security of computer networks is increasingly difficult to maintain due to the rising complexity and frequency of cyber-attacks. Important tools for finding and neutralizing these dangers are intrusion detection systems. This study sets out to do a thorough examination and comparison of the efficacy of several machine learning algorithms for use in intrusion detection. This research study evaluates the efficacy of several machine learning algorithms in correctly categorizing instances of network traffic as normal or invasive via extensive experiments performed on representative datasets. Algorithms like random forests, decision trees, SVMs, DL models and NNs are all being tested and rated. Effectiveness is measured and compared using a variety of performance indicators including accuracy, recall, precision, false positive rate, and F1-score. The results of this study emphasize the potential of deep learning models and Random Forests for use in intrusion detection and add to the body of knowledge around machine learning methods for this task. Professionals in the field of network security might use the results to their advantage when building intrusion detection systems. Future research areas are also mentioned, which will hopefully lead to even greater improvements in the field and safer, more reliable intrusion detection systems. © 2023 IEEE.

关键词： Decision trees

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Attention-based deep learning approach for CSI feedback under 5G TDL channel

Attention-based deep learning approach for CSI feedback unde...

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2023 international conference on machine learning and Automation, conf-MLA 2023

作者： Peng, Hanli School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

In 5G communication systems, accurate channel state information (CSI) is indispensable for signal detection and regulation at the base station side. However, frequent CSI feedback from users leads to excessive system overhead. To tackle this challenge, this paper puts forward a novel deep learning framework - HCNet based on attention mechanism and autoencoder, aiming to efficiently compress and reconstruct the high-dimensional time-varying CSI matrices in an end-to-end approach. The proposed framework incorporates a self-attention module in the encoder to explicitly capture global dependencies within the CSI matrix. Meanwhile, the decoder adopts gated recurrent unit networks to fully exploit inter-feature correlations and redundancy. To evaluate the performance, simulations are conducted using datasets conforming to current 5G time-varying TDL channel models. Results demonstrate superior performance over existing deep learning based feedback networks. Specifically, the proposed framework can reduce the normalized mean square error of CSI reconstruction by 4 dB under various compression ratios which confirms the effectiveness of the attention-enhanced autoencoder structure for compressive CSI sensing and feedback in practical dynamic communication systems. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Channel state information

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Water Quality Classification Using machine learning 8

Water Quality Classification Using Machine Learning

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8th IEEE international conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Arifin, Fikri Firas Tajul Idrus, Zanariah Halim, Shamimi A. Ahmarofi, Ahmad Afif Ahmad, Khairul Adilah Shah Alam College of Computing Informatics and Mathematics Selangor Shah Alam Malaysia Kedah Branch College of Computing Informatics and Mathematics Merbok Kedah Malaysia

ISBN: (纸本)9798350315516

Water quality is crucial as it directly affects the ecosystem and human health. However, current water quality classification methods are inefficient because they do not compare prediction accuracy between machine learning methods. In this regard, the objective of this study is to classify water quality based on the proposed machine learning tools. To fulfill that, a preliminary study was conducted by collecting related information in the research domain through articles, electronic books, and online databases. The data collection for the prototype's dataset was obtained from an electronic book published by the Pakistan Council of Research in Water Resources 2021. Subsequently, the data pre-processing phase was conducted by using WEKA software which includes the crucial steps to transform the data into a cleaner format and make the model more accurate. The model for each technique was developed using Python in Jupyter Notebook. The results of the accuracy score for each model were also conducted in this phase. The findings of this research show that the Decision Tree model performs excellently with an accuracy of 97.37% compared to the Support Vector machine and K-Nearest Neighbour models, with an accuracy of 95.69% and 74.72%, respectively. Consequently, implementing a multi-class classification system can help future researchers classify more accurately and reduce the misclassification of water quality. © 2023 IEEE.

关键词： Decision trees

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