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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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Microsoft Malware Detection using Machine Learning 6

Microsoft Malware Detection using Machine Learning

引用

6th international conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Garg, Aayushi Tahilramani, Nikunj Chauhan, Parth Patel, Ritu Silver Oak University Electronics & Communication Dept Ahmedabad India Aiie Information Communication Technology Dept. Ahmedabad India Sage University Computer Engineering Dept Madhya Pradesh India

ISBN: (纸本)9781665410359

A malware is any software intentionally designed to cause damage to a computer, server, client or network. Malware is very challenging issue and major concern for privacy of data. this detection can give information about Malicious and Benign data. Machine learning contain Evaluation measures like multi-class log loss, Confusion matrix, precision and recall these four measures are used. Exploratory data analysis, ANOVA, Feature engineering and evaluation are methods for classification machine learning models. In this paper Decision tree model is used for the classification. Max depth for 16 value gives more accuracy score 0.63 as compare to Max depth for 32 value which gives accuracy score 0.5. © 2022 IEEE.

关键词： Malware

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Dleep Learning based Regularization as defense against Clean Label Targeted Data Poisoning 6

Dleep Learning based Regularization as defense against Clean...

引用

6th international conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Parihar, Anil Singh Singh Kalra, Jushaan Java, Abhinav Delhi Technological University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665410359

Adversarial examples are doctored inputs to predictive models intentionally designed with a malicious purpose - to manipulate the inference time outputs of the machine learning system. One such recent threat to Deep Neural Networks is the targeted clean label data poisoning attack, wherein the adversary modifies training samples in the dataset without modifying the original labels. A model trained on these seemingly benign examples is likely to misclassify specific queries to targeted labels. While ample research is ongoing on the ways to generate and attack deep models, there is little work in mitigating such attacks. this research work employs a simpler strategy of making the model robust to poison samples during training and study the effect of regularization techniques on such attacks. Specifically, this research work proposes a strong pruning and autoencoder-based strategies to defend against data poisoning attacks and show its effectiveness on various deep models. the proposed methods reduce attacker efficacy by 23% and can serve as strong baselines for future work on defense against such adversarial attacks. © 2022 IEEE.

关键词： Deep neural networks

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A Wearable Breath Sound Monitoring Device Using the DWT-based MFCC Optimization Algorithm 6

A Wearable Breath Sound Monitoring Device Using the DWT-base...

引用

6th international conference on Electronics Technology, ICET 2023

作者： Li, Yanxiu Chang, Ridong Li, Nianqiang University of Birmingham School of Engineering Birmingham United Kingdom University of Jinan Science and Engineering Jinan China

ISBN: (纸本)9798350337693

Respiratory sounds are the most important feature in determining whether a person with asthma has an attack. the aim of this work is to find the most suitable recognition algorithm for identifying respiratory sounds in daily life environments and to port it to a stand-alone circuit system with a microcontroller as the controller. In this paper, the DWT-MFCC, i.e., the Mel frequency cepstral coefficients (MFCC) algorithm optimized using the discrete wavelet transform(DWT), is proposed as the feature extraction algorithm and the support vector machine (SVM) is used as the classifier. And all the feature combinations that can be obtained from the optimized algorithm, including the optimized MFCC and the optimized MFCC + statistical features, are compared with the feature combinations used in related studies, such as linear predictive cepstral coefficients (LPCC), statistical features, LPCC + statistical features, or the original MFCC. the classifier was cross-validated using MATLAB software. the combination of optimized MFCC + statistical features could achieve an accuracy of up to 96%, with an average accuracy of 92.56%. this is the highest accuracy and most consistent performance of all possible combinations. After porting the recognition algorithm to a stand-alone circuit with ESP32-WROOM-32 as the controller, the desired result was achieved. © 2023 IEEE.

关键词： Support vector machines

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Path Complexity Analysis for Interprocedural Code 24

Path Complexity Analysis for Interprocedural Code

引用

46th international conference on software engineering: Companion, ICSE-Companion 2024

作者： Kaniyur, Mira Cavalcante-Studart, Ana Yang, Yihan Park, Sangeon Chen, David Lam, Duy Bang, Lucas Harvey Mudd College ClaremontCA United States

ISBN: (纸本)9798400705021

INTRODUCTION: Symbolic execution's path explosion is a critical issue in software testing, quantified by Asymptotic Path Complexity (APC) [3]. APC, more precise than cyclomatic [6] or NPAth [7] complexities, measures the effort to cover paths in code analysis [1]. It's vital for testing, setting limits on path growth for tools like Klee [4], focusing previously on intraprocedural code [2, 8]. Our advancement, APC-IP, extends APC to interprocedural analysis, enhancing scalability and encompassing earlier models. Contributions. We claim the following research contributions. (1) APC-IP Formalization: Extension of the theory and algorithms established for APC to account for interprocedural functions;(2) Optimization Over All Previous APC Approaches. Replacing theoretical steps in previous algorithms to improve performance for both interprocedural and intraprocedural code;(3) APC-IP Implementation. Implementing APC-IP atop Metrinome, an existing APC analysis tool;(4) APC-IP Experimental Validation. APC-IP computes accurate APC for both intraprocedural and interprocedural, and is the fastest option to process complex source code. © 2024 IEEE Computer Society. All rights reserved.

关键词： software testing

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A Dynamic Load Balancing Algorithm for Stable Pushing of Docker Images in Nginx 6

A Dynamic Load Balancing Algorithm for Stable Pushing of Doc...

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6th international conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2024

作者： Xi, Hajie Hu, Song Zhang, He Zhang, Shuang Weng, Jingnong Huang, Jian School of Computer Science and Engineering Beihang University Beijing China Research Institute of Highway Ministry of Transport State Key Lab of Transportation System Beijing China Transportation Comprehensive Enforcement Corps Beijing China School of Software Beihang University Beijing China

ISBN: (纸本)9798331541798

With the rapid development of artificial intelligence, a wide variety of deep learning models have emerged across various fields. Nevertheless, the quality of these models varies significantly, making the effective evaluation of model performance a pressing issue. In some open data competitions, remote evaluation based on Docker images has increasingly become a novel and reliable approach, providing a consistent environment to ensure the fairness of model evaluations. However, it imposes high demands on hardware resources, typically requiring dedicated resources for each participant to serve as Docker Registries for the pushing and management of Docker images. In this paper, we extend the load balancing module of the Nginx server by integrating Lua scripts and propose a Cache-based Dynamic Smooth Weighted Round-robin (CDSWRR) algorithm to optimize the pushing process of Docker images. By constructing a unified load balancing entry, the proposed approach simplifies the deployment of Docker Registry clusters. Experiments show that compared to weighted round-robin and IP hash algorithms of Nginx, the CDSWRR algorithm is able to ensure stable pushing of Docker images and reduce both response error rate and response time. © 2024 IEEE.

关键词： Deep learning

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Forecasting User Payment Behavior Using Machine Learning 1

引用

6th international conference on Applied Machine Learning and Data Analytics, AMLDA 2023

作者： Vora, Deepali Choudhary, Rupali Kumar, Aman Kadam, Payal Lavale Pune India College of Engineering Pune India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

the payment prediction model is a software application that uses machine learning algorithms to predict future payments based on historical data. these models are crucial for organizations like credit card companies, banks, and other financial institutions that depend on recurring payments from their clients businesses may determine the possibility that consumers will miss their payments by utilizing payment prediction models, and they can then take action to either avoid or lessen the effects of any prospective non-payment. this system works on real-time data, data goes to different stages including data collection and storage, data pre-processing and cleaning, feature engineering, and model development. the system uses machine learning algorithms such as linear regression, decision trees, or XG boost to predict future payments based on historical payment data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： User interfaces

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predictive Analytics in Agriculture: Machine Learning for Farm Suitability Evaluation 6

Predictive Analytics in Agriculture: Machine Learning for Fa...

引用

6th Parul international conference on engineering and Technology, PICET 2024

作者： Mishra, Trapti Nair, Pramod S. Department of Computer Science Medic-Caps University India

ISBN: (纸本)9798350369748

Given the rapid increase in global population, advancements in agricultural technology are crucial for ensuring food security and promoting sustainable farming practices. the study suggests a new way to determine whether a farm is suitable for a given crop by combining several machine learning algorithms, namely Restricted Boltzmann Machines, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks (RBMs). Our objective is to combine these models so that we can better understand the patterns in agricultural data that are influenced by multiple variables, including weather, soil, crop yields, and other environmental factors. For the purpose of handling consecutive data and predicting future climatic trends that affect farming suitability, our long short-term memory (LSTM) model is designed. By expertly analysing patterns in satellite pictures and other spatial data, the CNN component assesses land qualities. Lastly, important factors influencing agricultural production are extracted using the RBM's feature learning abilities. Our integrated model was trained using a massive dataset collected from various agro-climatic zones. How well the model projected, given both present and future conditions, whether a plot of land would be appropriate for a specific crop was used to evaluate its effectiveness. the results demonstrate that our technique can reliably forecast which farms would be the best fit. Additionally, our model can assist lawmakers and farmers in making educated decisions regarding crop rotation, land usage, and operational management. A paradigm shift in precision agriculture could result from this. this research has the potential to improve crop yield, sustainability, and resilience to climate change by pushing the development of intelligent agricultural systems forward. © 2024 IEEE.

关键词： Crop rotation

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A Comprehensive Study of the Machine Learning with Federated Learning Approach for Predicting Heart Disease 6

A Comprehensive Study of the Machine Learning with Federated...

引用

6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharma, Pakhi Sharma, Sanjiv KIET Group of Institutions Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350304480

Heart disease is a leading cause of mortality worldwide, resulting in millions of deaths annually. As individuals age and their physical condition deteriorates, the risk of developing heart disease increases. To mitigate this risk, predictive models leveraging machine learning and artificial intelligence have emerged as valuable tools for early diagnosis and treatment. In this review paper, we introduce the Google-pioneered concept of federated learning as a means to address concerns about data safety in the context of heart disease prediction. Federated learning, also known as collaborative learning, employs a technique wherein an algorithm is trained through multiple independent sessions, each utilizing its own dataset. this paper aims to provide a comprehensive investigation of recent machine learning approaches and databases employed in predicting the occurrence of cardiovascular disease. © 2023 IEEE.

关键词： Deep Learning Federated Learning Heart Disease Machine Learning

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Optimizing predictive Maintenance in Smart Factories Using Random Forests and Internet of things Sensors 8

Optimizing Predictive Maintenance in Smart Factories Using R...

引用

8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Preethi, E. Ahmed, A. Sarfaraz Shyamala, G. Vasukidevi, G. Sunil, Tummapudi Atheeswaran, Athiraja Rajalakshmi College of Engineering Rajalakshmi Nagar Thandalam Department of Computer Science and Design Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sr University Department of Civil Engineering Telangana Warangal India R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Cse Medchal Malkajgiri India Hyderabad India

ISBN: (纸本)9798350367904

In the rapidly evolving landscape of Industry 4.0, smart factories leverage advanced technologies such as the Internet of things (IoT) and machine learning to enhance operational efficiency. predictive maintenance (PdM) is a critical aspect of this transformation, aiming to anticipate and mitigate equipment failures before they occur. However, existing PdM models often suffer from high false positive rates and inadequate scalability. this study addresses these issues by proposing an optimized predictive maintenance framework utilizing Random Forests and IoT sensors, tailored specifically for smart factory environments. Our methodology integrates a comprehensive IoT sensor network within the factory, collecting real-time data on various parameters such as temperature, vibration, and machine utilization. this data is preprocessed to handle noise and outliers, followed by feature extraction to identify the most significant predictors of equipment failure. We employ an optimized Random Forest algorithm, enhanced with a novel dynamic threshold adjustment technique and an adaptive learning mechanism to improve prediction accuracy and model robustness. the model's performance is evaluated using a large dataset collected from multiple manufacturing units over a period of six months. the proposed framework significantly outperforms traditional PdM approaches. Our optimized Random Forest model achieved an accuracy of 9 6. 8 %, a precision of 9 4. 5 %, and a recall of 9 7. 2 %, compared to 88.3 %, 85.7 %, and 89.1% respectively for the baseline models. Additionally, the dynamic threshold adjustment technique reduced the false positive rate by 40%, while the adaptive learning mechanism enhanced the model's ability to generalize. © 2024 IEEE.

关键词： predictive maintenance

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning 27

Determining the Critical Factors of Antenatal Care Follow-Up...

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27th international conference on Soft Computing and Measurements, SCM 2024

作者： Bessmertny, Igor A. Enkomaryam, Bidru C. Mamuye, Adane L. Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia University of Gondar Department of Computer Science Gondar Ethiopia

ISBN: (纸本)9798350363708

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. this study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. the 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. the model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman's age, and HIV testing to predict antenatal care follow-up in the study area. © 2024 IEEE.

关键词： Machine learning

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