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Software Fault Prediction Using Optimal Classifier Selection: An Ensemble Approach 2

Software Fault Prediction Using Optimal Classifier Selection...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Agrawalla, Bikash Reddy, B. Ramachandra Dept. of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India

Fault prediction is the process of using data analysis and machine learning models to anticipate potential defects or faults in the software system. Using only the base machine learning models for software fault prediction leads to limited performance, difficulty in handling non-linear relationships and imbalanced data, inadequate feature representation, and limited complexity handling. Hence, in order to overcome these challenges, this paper proposes a new technique for the selection of classifiers that forms a heterogeneous ensemble. The main goal is to remove or trim out the classifiers that show poor performance compared to the other base classifiers, which can result into a more effective ensemble and can produce better results. The algorithm proposed in this paper finds a set of classifiers that can perform better than using all the classifiers. The challenge that was faced was how to identify the poor-performing classifiers. This challenge is dealt with by performing an experiment using different threshold values to choose the trimmed set of classifiers. For evaluation of the proposed model, 8 different benchmark software fault datasets were used, which are taken from PROMISE and the Apache repository, and AUC is used as the performance measure. The results obtained after the experimental analysis demonstrate the effectiveness of our algorithm compared to the traditional approaches, which used all the base classifiers. There is a significant increase in the AUC values for 6 datasets out of 8, while using the average of probabilities and majority voting, it was seen that there is improvement in 7 out of 8 datasets used. The best-performing dataset by using the average of probabilities is ARC, where the AUC values increase from 0.6505 to 0.694, and while using majority voting, the best-performing dataset is XALAN, where the AUC values increase from 0.5455 to 0.679. From this, it can be seen that the proposed ensemble approach achieved higher AUC values for the

关键词： Decision trees

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Robotics Process Automation Technologies for Data Collection and Analysis 15

Robotics Process Automation Technologies for Data Collection...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Usman, K. Hayath, T.M. Shafiulla, Mohammed Dadapeer Ballari Institute of Technology and Management Dept. of Computer Science and Engineering Karnataka Ballari India

ISBN: (纸本)9798350370249

Data collection and analysis are critical aspects of various business processes. However, these tasks can be time-consuming, prone to errors, and delays employee productivity when done manually, especially when we have to deal with huge data. Robotic Process Automation (RPA) technologies offer a promising solution in form of a BOT, by automating tedious tasks related with data handling. A survey by The Economist corporation believe that automation kick launches digital transformation, robotic process automation (RPA) has evolved from an emerging technology to a must-have digital transformation tool for enterprises to compete globally. An increasing number of companies are now using RPA to automate normal business processes in a non-intrusive and cost-effective way. On the other hand, decision-making, pattern recognition, and many other business-related tasks benefit greatly from the use of big data and analytics. This paper explores the application of RPA in data collection and analysis, examining its functionalities, benefits, and limitations. In practical, Bot produces error-free work and takes 94.44% less time for data collection compared to a manual process, which encourages all firms to adopt to RPA technologies. © 2024 IEEE.

关键词： Automation Data collection Process Robotics RPA

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LSTM Based Approach to Detect Fake News 2

LSTM Based Approach to Detect Fake News

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Khare, Sakhi Singh, Pushpa Kumar, Pankaj Gl Bajaj Institute of Technology Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

In the age of technology, every day, we ingest a variety of news data, either willingly or accidentally. People will always use various social media apps and keep looking for information. The information passed through these social media apps is only sometimes valid. Many organizations or individuals spread the news to influence the mass public. This type of information is called fake news. Fake news is a common problem that naturally comes with social media. Detection of counterfeit news with ubiquitous data from social media is almost impossible. Fake news kills real journalism and spreads false information in society. Researchers are still looking for better solutions. In this paper, we find a better solution with Natural Language Processing (NLP). We used fake news challenge datasets for training and testing purposes. We aim to classify the news item into four stance labels: agree, disagree, discuss, and unrelated. We developed a Long Short-Term Memory (LSTM) based solution to organize the news article in the given stance label. We find that our approach classifies data effectively with 99% of accuracy © 2024 IEEE.

关键词： Fake News LSTM NLP Word Embedding

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Beyond the ROC Curve: Classification Trees Using Cost-Optimal Curves, with Application to Imbalanced Datasets 41

Beyond the ROC Curve: Classification Trees Using Cost-Optima...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Gabidolla, Magzhan Zharmagambetov, Arman Carreira-Perpiñán, Miguel Á. Dept. of Computer Science and Engineering University of California Merced United States United States

Important applications such as fraud or spam detection or churn prediction involve binary classification problems where the datasets are imbalanced and the cost of false positives greatly differs from the cost of false negatives. We focus on classification trees, in particular oblique trees, which subsume both the traditional axis-aligned trees and logistic regression, but are more accurate than both while providing interpretable models. Rather than using ROC curves, we advocate a loss based on minimizing the false negatives subject to a maximum false positive rate, which we prove to be equivalent to minimizing a weighted 0/1 loss. This yields a curve of classifiers that provably dominates the ROC curve, but is hard to optimize due to the 0/1 loss. We give the first algorithm that can iteratively update the tree parameters globally so that the weighted 0/1 loss decreases monotonically. Experiments on various datasets with class imbalance or class costs show this indeed dominates ROC-based classifiers and significantly improves over previous approaches to learn trees based on weighted purity criteria or over- or undersampling. Copyright 2024 by the author(s)

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Unleashing Machine Learning for Hepatitis C Prediction: A Holistic Exploration of Clinical Insights

Unleashing Machine Learning for Hepatitis C Prediction: A Ho...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Al-Amain Janin, Fatema Tuj Ahmed Robin, Faizul Ahmed, Shifat Mohi Uddin, Khandaker Mohammad Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Hamburg University of Applied Sciences Dept. of Information and Electrical Engineering Hamburg Germany

ISBN: (纸本)9798331529765

Chronic diseases, characterized by long-term effects and persistent symptoms, pose significant challenges to human health and well-being. The hepatitis C virus (HCV) is the cause of chronic hepatitis C, which affects 58 million people globally. It also causes 290,000 deaths annually due to liver cancer and cirrhosis, two conditions connected to HCV. Taking advantage of machine learning (ML) algorithms' ability to analyze large datasets and identify patterns and relationships that aid in diagnosis, this effort focuses on employing algorithms for machine learning to identify and forecast hepatitis C precisely. Various techniques were utilized, including Random Forest, Cat Boost, SVM, and others to create predictive models using blood values from 615 patients, including both hepatitis patients and healthy blood donors. The dataset underwent comprehensive preprocessing, including handling missing values, scaling, and dimensionality reduction via Principal Component Analysis (PCA). Various ML algorithms were evaluated, and the Cat Boost classifier achieved the highest accuracy of 99.17%, recall of 92.00%, precision of 100.00% and F1-score is 96.00%. This study demonstrates the potential of ML in enhancing HCV diagnosis, offering less invasive and cost-effective alternatives to traditional methods, and providing predictive insights for personalized treatment plans. The findings underscore the importance of advanced data preprocessing and feature selection in improving model efficiency. This study adds to the ongoing efforts to lessen the prevalence of hepatitis C worldwide, aligning with the World Health Organization's goal to significantly decrease new HCV cases and related deaths by 2030. © 2024 IEEE.

关键词： Adversarial machine learning

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AI Enhanced-Smart Genome Editing: Integration of CRISPR-Cas9 with Artificial Intelligence for Cancer Treatment 5

AI Enhanced-Smart Genome Editing: Integration of CRISPR-Cas9...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Kumar, D. Nitesh Chowdhary, D. Lavanya Pathuri, Trinadh Katta, Priyanka Arya, Leena Dept. of Computer Science & Engineering Koneru Lakshmaiah Education Foundation AP Guntur India

ISBN: (纸本)9798350361155

The CRISPR-Cas9 genome editing system is a new technology that allows the modification of secret vital material using disruptions insertions or amendments of DNA sequence. It is an easy, widely accepted, and correct representation of gene modification. The potential for treating different genetic diseases is enormous. This paper discusses the latest approach to CRISPR-Cas9 integration with AI, aimed at improving efficacy, as well as the safety of cancer treatment. This paper highlights the cutting-edge integration of CRISPRCas9 with Artificial Intelligence (AI) to enhance the efficacy and safety of cancer treatment. Artificial intelligence is a simulation of human intelligence for computers, indicates the latest developments in the field, and mentions clinical trials' ability to help further the treatment of cancer and overcome current and upcoming challenges. Finally, the application of (AI) in the modification of the CRISPR/Cas9 system is highlighted. Taking case scenarios and futuristic approaches, this paper reveals excellent implementation initiatives by learning from the Clinical revolution together with human technology and aimed at a future in which new technologies improve patient outcomes without sacrificing ethics or putting patients first. © 2024 IEEE.

关键词： Diseases

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Time-Dependent Multi-Objective Trip Planning by Ant Colony Optimization with Route API

Time-Dependent Multi-Objective Trip Planning by Ant Colony O...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Saeki, Etsushi Bao, Siya Takayama, Toshinori Togawa, Nozomu Waseda University Dept. of Computer Science and Communications Engineering Japan ZENRIN DataCom Japan

ISBN: (纸本)9798350324136

In this paper, we propose a time-dependent multi-objective trip planning using ant colony optimization. Especially, the proposed method deals with time-dependent POI factors by utilizing past-trip records with time stamps and computes time-dependent travel time by utilizing route API. Moreover, we reduced the response time from the route API calls. Compared with two conventional methods, our proposed method provided routes with high time-dependent values. Meanwhile, the number of API calls is reduced by 98.8% on average by introducing the API call reduction. © 2024 IEEE.

关键词： ant colony optimization past-trip record route API time-dependent multi-objective trip planning problem

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Bilinear Attention Based Learning on Graphs

Bilinear Attention Based Learning on Graphs

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Njanko, Adrien Rawat, Danda B. Howard University Dept. Electrical Engineering and Computer Science WashingtonDC United States

ISBN: (纸本)9798350362480

The attention mechanism is a core module in today's well-established models like Transformers and Graph Attention Networks (GAT). The attention coefficients needed typically requires heavy computational resources for stability and better predictions. This requirement limits the size of the sequence or graph a model can learn from, leading to separating connected graphs or sequences to ease training. In this paper, we introduce an attention mechanism that leverages Bilinear forms to handle sequences of arbitrary length, offering greater flexibility and scalability. This improvement not only optimizes computational resources but also offers better model performance. Furthermore, our Bilinear Attention Mechanism (BAM) can capture more complex relationships between nodes for less parameters count. © 2024 IEEE.

关键词： Contrastive Learning

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Tracking Dynamic Conditional Neural Correlation during Task Learning 46

Tracking Dynamic Conditional Neural Correlation during Task ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Zixu Chen, Shuhang Li, Mingdong Wang, Yiwen The Hong Kong University of Science and Technology Dept. Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350371499

Single neuron modulates the external stimuli, and neural population coordinates to encode information. An alternate method for examining the coordinated populational activity in neural encoding is conditional neural correlation (CNC). However, such correlations are not static during a new task learning process as neurons adapt their tunings over time for better performance. To investigate how neurons adjust their firing patterns during learning, it's essential to track the time-variant correlation. In this paper, we propose to mathematically model the dynamical CNC by implementing the integrated point process filter which incorporates neural correlation and single neural tuning into decoding. Specifically, we generate synthetic M1 neurons' firing data to simulate the dynamic change of the conditional neural correlation over days, while a rat learns a two-lever discrimination task. By comparing the tracked CNC with the designed CNC, our results show that the CNC can be better tracked over time by CIPPF than that of decoder assuming conditional independence among neurons, which indicates the possibility to better understand the brain dynamics during task learning. © 2024 IEEE.

关键词： Decoding

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Event Management System Using Generative AI 7

Event Management System Using Generative AI

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thummala, Sruthi Thammishetti, Saketh Varkol, Sharanya Thirunahari, Amarthya Lakshmi Kanthey, V.V.S. B V Raju Institute of Technology Dept. Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

This project depicts how Artificial intelligence is used in developing a "User-Engaging Event Management System". The developed system will spontaneously record all the events and online contests registered by an individual. Motivates the individual to participate the particular event by providing sufficient resources. It implements a persistent alarm or remainder system that operates continuously. The implementation of the system starts with choosing appropriate technologies (Deep learning Libraries, Natural Language Processor, DBMS, Web Development Framework and so on) focusing on generative AI followed by data collection (from existing event data, social media, crowdsourcing and so on) and preprocessing the data to ensure the quality for AI model training which integrates with app architecture. This study evaluates the effectiveness of the generative AI model in Event Management App by employing key numerical metrices. The accuracy metric gauges the model's overall correctness in generating event-related content, while precision focuses on the accuracy of positive predictions. It incorporates a robust feedback mechanism ,allowing users to provide input and receive corresponding responses, enhancing the overall user experience. The system will provide security for user information, access controls, and authentication protocols © 2024 IEEE.

关键词： Access control

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