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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Pooja Goyal Md Khorrom Khan Christian Steil Sarah M. Martel Renee Bryce Christian Steil Computer Science & Engineering University of North Texas Denton Texas Computer Science Cal Poly Humboldt Arcata California

Smartphone applications run in complex environments and are sensitive to context events, i.e., changes to screen orientation, location, battery, etc. Context events and sequences make reliability, accuracy, and testing of mobile applications costly and more application development susceptible to errors. Modern smartphones have continued to increase the number of context event possibilities compared to earlier versions. Multiple context events may occur within quick intervals and complicate an application's behavior. While apps are continuously released and updated, future devices will likely have more features with more context events that will lead to even greater challenges. In this work, we implemented a model for prediction of common context event sequences by using two sequence rule mining algorithms: POERMH: Partially-Ordered Episode Rule Mining with Head Support, and TKE-Rules: Top-K Episode mining, along with a sequence prediction algorithm to increase smartphone application testing efficiency. Indeed, testing frequently encountered scenarios often relates to user perceived reliability of an app. The experiment was significantly successful at predicting next sequence in sequence of events for individual events as compared to overall performance using both POERMH and TKE-Rules in terms of Recall, Precision and F-1 score.

关键词： Conferences Predictive models prediction algorithms Mobile applications Behavioral sciences Batteries Reliability

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An Integrated Approach of MCDM Methods and Machine Learning algorithms for Employees' Churn prediction

An Integrated Approach of MCDM Methods and Machine Learning ...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Shefayatuj Johara Chowdhury Mainul Islam Mahi Sadiqul Alam Saimon Aynur Nahar Urme Rashidul Hasan Nabil Department of CSE International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Department of EEE International Islamic University Chittagong Chittagong Bangladesh

Employee churn is a notable nuisance for organizations to maintain a cost-effective position and brand strategy. This research has proposed an integrated approach to overcome this issue systematically. All employees in this study were divided into three categories using a combination of machine learning algorithms and Multi-Criteria Decision Making (MCDM) techniques. The techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytical Hierarchy Process (AHP) have been consolidated. The dataset's feature importance was retrieved, and AHP was used to determine the criteria that caused the most turnover. TOPSIS was given the derived criterion weights from AHP to rank every personnel according to their propensity to depart the organization. To estimate staff turnover, seven machine learning algorithms were applied. After comparing the results, the Random Forest algorithm produces the best accuracy for assessing employee churn.

关键词： Industries Machine learning algorithms Signal processing algorithms Companies Signal processing prediction algorithms Real-time systems

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Different algorithms for Lung Cancer Detection and prediction

Different Algorithms for Lung Cancer Detection and Predictio...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： N. Sudhir Reddy V Khanaa Bharath Institute of Higher Education and Research (BIHER) Chennai Tamil Nadu Department of Information Technology Bharath Institute of Higher Education and Research (BIHER) Chennai Tamil Nadu

In many cases we see the lung cancer play main role for human death. The premature detection of cancer is hampered by the overlapping of malignant cells. To create more effective methods for its prevention, identifying genetic and environmental causes is essential and time is a crucial aspect in identifying anomalies in target photos. The decision tree algorithm is utilized in this study to predict lung cancer, and the key patterns are examined along with their related weights and scores. The Histogram Equalization is make use of for tasks like feature extraction and image preprocessing and a neural network use different classifier that used to determine if the patient is normal or abnormal. Early detection of lung cancer will limit many treatment options, improve survival rates and lower the likelihood of invasive surgery. Therefore, a simple, affordable, and time-saving technology will yield encouraging results for lung cancer screening and prediction.

关键词： Neural networks Lung cancer Surgery prediction algorithms Classification algorithms Internet Decision trees

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Effects of Dimension Reduction Methods on Boosting algorithms for Better prediction Accuracies on Classifications of Stress EEGs

Effects of Dimension Reduction Methods on Boosting Algorithm...

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Electronics and Electrical Engineering Technology (EEET), International Conference on

作者： Doreen Y. Y. Sim C. K. Chong School of Computer Science Faculty of Science and Engineering University of Nottingham (Malaysia campus) Selangor Malaysia

ISBN: (数字)9798350395594

ISBN: (纸本)9798350395600

This research aims to investigate the effects of various dimension reduction methods, namely Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA) on the prediction accuracies of the stressful state in the EEG signaling when performing different mental tasks. The dataset used for this research is the SAM-40 dataset. It consists of 40 subjects performing three different mental tasks, i.e. Arithmetic Problem Solving Task, Stroop Color Word Test and Mirror Image Recognition Task. Each task was carried out with 3 trials. The results after applying the different dimension reduction methods of PCA, ICA and LDA to different boosting algorithms were analyzed and compared meticulously. These boosting algorithms are mainly the ensemble techniques of AdaBoostM1 and RUSBoost algorithms. Among all the experimented results shown, the LDA induced boosted classification methods showed the best prediction accuracy result, i.e. around $30 \%$ of prediction accuracy improvement.

关键词： Dimensionality reduction Accuracy Human factors prediction algorithms Feature extraction Boosting Electroencephalography

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Endurance prediction Based on Hidden Markov Model and Programming Optimization for 28nm 1Mbit Resistive Random Access Memory Chip

IEEE ELECTRON DEVICE LETTERS

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IEEE ELECTRON DEVICE LETTERS 2023年第6期44卷 919-922页

作者： Zheng, Xu Wu, Lizhou Dong, Danian Yu, Jie Lai, Jinru Sun, Wunxuan Xue, Xiaoyong Chen, Bing Pang, Wan Xu, Xiaoxin Chinese Acad Sci Inst Microelect Key Lab Microelect Device & Integrated Technol Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China Fudan Univ Sch Microelect Shanghai 200433 Peoples R China Fudan Univ Frontier Inst Chip & Syst Shanghai 200438 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

We proposed a state transition probability model based on Hidden Markov Model (HMM), which can predict the lifetime for different endurance failure modes. The prediction span of this model is 500 cycles, and the accuracy rate can reach 83.6 %. Based on the prediction results, we proposed an optimized programming algorithm to rescue the failing cells during endurance. The failure rate of memory chip is reduced by 38 % after 5000 cycles. The forecasting model is effective and of practical value since it is based on the data from 28nm 1Mbit Resistive Random Access Memory (RRAM) chips, including the peripheral circuit noise, machine noise and device randomness. This prediction model is significant for promoting RRAM application and improving memory chip utilization.

关键词： Hidden Markov models Predictive models Resistance Programming prediction algorithms Training Maintenance engineering Endurance prediction model RRAM reliability

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Improving Accuracy of Heart Disease prediction through Machine Learning algorithms

Improving Accuracy of Heart Disease Prediction through Machi...

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Mohammed Ali Shaik Radhandi Sreeja Safa Zainab Panthangi Shiva Sowmya Thipparthy Akshay Sudireddy Sindhu School of Computer Science &Artificial Intelligence SR University Warangal Telangana State India

One of the main causes of death in the world is heart disease. Early heart disease detection and treatment can lower mortality rates and enhance quality of life which is the major challenge. “Machine learning” algorithms can accurately predict the likelihood of getting the heart disease by using data like: “age, gender, lifestyle factors, medical history, and laboratory testing”. Building a ML model for “heart disease prediction” which is merely relies on the various relevant factors is the primary goal of this paper. For this research project, we used 4 different datasets which comprises of distinct factors that are relevant to heart disease. The model building is made through ML algorithms: “Random forest, K-nearest neighbour, logistic regression, and decision tree”. The study demonstrates that, when compared to other ML techniques, logistic regression and KNN provide better prediction accuracy in a shorter amount of time.

关键词： Heart Training Machine learning algorithms Soft sensors Buildings prediction algorithms Artificial intelligence

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prediction of Diabetes using Machine Learning algorithms

Prediction of Diabetes using Machine Learning Algorithms

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Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON)

作者： Khushi Sidana Computer Science Narsee Monjee Institute of Management Studies Mumbai India

Diabetes, a chronic and lethal disease is affecting individuals not only in India but all over the world at an exponential rate. This disease, if not predicted and left untreated can be very dangerous. Hence, with the rise in the prevalence of this disease, multiple researchers have been trying to find the most optimal methods for predicting diabetes using Machine Learning algorithms (ML) and Data Mining. In this paper, the objective is to predict diabetes using five supervised machine learning algorithms and compare their performance and accuracy. For the same, I compared the performance of Decision Tree, Logistic Regression, K Nearest Neighbours, Random Forest Tree and Support Vector Machine algorithms to get the best possible accuracy. Furthermore, owing to the fact that a larger and better dataset can improve the model’s accuracy, I tested this theory by comparing the results of two separate datasets and achieved an accuracy of 97% using the Random Forest algorithm. This paper also discusses the impact overfitting and underfitting can have on the overall outcome. I hope this paper along with other such research works contributes to helping doctors and other personnel in the medical field to predict diabetes using machine learning algorithms.

关键词： Support vector machines Machine learning algorithms Medical services Microwave communication prediction algorithms Microwave theory and techniques Diabetes

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Analysis of Diabetic prediction Using Machine Learning algorithms on BRFSS Dataset

Analysis of Diabetic Prediction Using Machine Learning Algor...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Lakshmi H.N. A. Srinivasa Reddy Kritika Naidu Department of ET CVR College of Engineering Hyderabad India Department of CSIT CVR College of Engineering Hyderabad India

Due to the detrimental effects it has on everyone's health, diabetes is a chronic condition that still poses a serious threat to the global population. It is a metabolic disorder that increases blood sugar levels and increasing the risk of heart disease, kidney failure, stroke, issues with the nerves and heart, among other issues. Over the years, several scholars have sought to create reliable diabetes prediction models. Due to a lack of adequate data sets and prediction techniques, this discipline still faces many unsolved research issues, which forces researchers to apply big data analytics and ML-based methodology. The paper investigates healthcare prediction analytics and addresses the issues using four different machine learning methods. This study has utilized the Early detection and Binary 012 databases. Based on these datasets, the precision, recall, and accuracy of KNNs and Random Forest methods are calculated. The study's findings may be valuable to health professionals, stakeholders, students, and researchers engaged in diabetes prediction research and development because SVM performs better than KNN and Logistic Regression.

关键词： Support vector machines Heart Machine learning algorithms Sociology Predictive models prediction algorithms Diabetes

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Performance Analysis of Machine Learning algorithms Using Bagging Ensemble Technique for Software Fault prediction

Performance Analysis of Machine Learning Algorithms Using Ba...

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International Conference on Information Systems and Computer Networks (ISCON)

作者： Roshan Samantaray Himansu Das School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

Software development comes with a lot of challenges. Developers face various issues with performance and bugs. These issues increase with the scale of the project and if fewer individuals work on the development. It has become necessary to fix various bugs during development to ensure better performance and reduce the chances of failure during the deployment of the software. As a result of this, faults in the software must be predicted during the earlier stages of development. This would help in reducing the cost of maintenance of the software post-deployment. Multiple software fault prediction (SFP) approaches have been proposed to tackle this problem. These approaches can be improved by implementing ensemble techniques. In this paper, we study the effect of the bagging technique and how it helps to improve the predictive capability across various datasets. These datasets are provided and made open source by NASA. Decision Tree Classifier (DTC), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Support Vector Classifier (SVC) were used as base classifiers for the bagging method. Random forest (RFC) is an ensemble learning algorithm that uses the bagging technique. Based on the outcome of the results, it was concluded that RFC was the best-performing algorithm.

关键词： Machine learning algorithms Software algorithms NASA Support vector machine classification Static VAr compensators Gaussian processes prediction algorithms

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Vehicle Efficiency prediction using Machine Learning algorithms

Vehicle Efficiency Prediction using Machine Learning Algorit...

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Smart Data Intelligence (ICSMDI), International Conference on

作者： Parvathi R Ashish Choudhary Pulak Jain Om Kajave School Of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai Tamil Nadu India

The performance analysis and efficiency of a vehicle play a prominent role and a very necessary step to do in today's scenario. There are various instances when the user feels reluctant to discard the vehicle. In such cases where the user is ignorant of the fact to discard the car, the concerned authorities must come forward to check whether the user is using the car beyond the limit. Therefore, there is an increasing need to save the environment and nature to live a sustainable life. The performance analysis of the car is based on the engine type, number of engine cylinders, fuel type, etc. This study predicts the mpg value by using machine learning models like Random Forest (RF), K-Nearest Neighbors (KNN), XG-Boost, Ridge Regression, Lasso Regression, etc. and based on that it is compared with the optimum value of mpg and hence one can reach to a decision to discard the vehicle.

关键词： Training Machine learning algorithms Predictive models prediction algorithms Cleaning Performance analysis Automobiles

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