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Heart Stroke Risk Prediction Using Machine Learning and Deep Learning Algorithm 6th

Heart Stroke Risk Prediction Using Machine Learning and Deep...

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6th international conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Prajwala Priyanka, M. Kumar, Sheo Lakshmi Bhargav, A. Department of Computer Science Engineering CMR Engineering College Hyderabad India Department of Computer Science Engineering Nalla Narasimha Reddy Education Society’s Group of Institutions Hyderabad India

ISBN: (纸本)9789819971367

Heart attack is a catch-all term for a variety of conditions affecting the heart. Analysis of large amounts of data and comparisons between them are essential for the prediction, prevention, and management of cardiovascular illnesses including heart attacks. this paper aims to develop an Intelligent System using various ML techniques such as Naive Bayes, KNN, Random Forest and Decision Tree. Users of this web-based software will answer a prepared series of questions. Data analytics is crucial for organizing, analyzing, and controlling large data sets. It has shown beneficial in the diagnosis, prevention, and treatment of cardiovascular disease. With this in mind, we want to apply SVM- and GA-based data analytics to the challenge of identifying cardiac problems. A comparison of these algorithms’ prediction models indicates which one is the most developed. It may aid physicians in making more educated diagnoses of heart illness than would have been possible with older, more traditional methods of decision assistance. By providing efficient treatments, it contributes to cost-effective healthcare. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Heart

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A Comparative Analysis of Deep Learning Algorithms for Skin Cancer Detection 6

A Comparative Analysis of Deep Learning Algorithms for Skin ...

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

作者： Singh, Priya Kumar, Mayand Bhatia, Aman Delhi Technological University Department of Software Engineering Delhi India

ISBN: (纸本)9781665410359

In the current world, machine learning and computing techniques are being used in every industry. One of these involves the healthcare and medical industry. Already due to intensive research and analysis huge leaps have been taken in strengthening the existing system. One area in the research is Skin Cancer Detection. Skin Cancer is diagnosed when damaged DNA cells start to multiply uncontrollably. this makes the detection in the preliminary stages highly essential. this is an area where machine learning or deep learning algorithms can help in the development of models which accurately predict the onset of diseases in early stages. Some research has been done in this area but high accuracy results haven't been produced. therefore, the focus of this research work is to compare between four deep learning techniques RNN (Recurrent Neural Networks), CNN (Convolutional Neural Networks), ResNet50, and Xception to conclude which algorithm works the finest in successfully predicting the disease with high accuracy. the dataset used for the purpose of this research is the HAM10000 which is available on Kaggle. the empirical results demonstrate that the accuracy by using CNN, RNN, ResNet50, and Xception was 72%, 69%, 79%, and 93% respectively with Xception performing the best. © 2022 IEEE.

关键词： Recurrent neural networks

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Estimating Medical Insurance Cost using Linear Regression with HyperParameterization, Decision Tree and Random Forest models 13

Estimating Medical Insurance Cost using Linear Regression wi...

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13th international conference on Cloud Computing, Data Science and engineering, Confluence 2023

作者： Patidar, Sanjay Dudi, Saksham Rohit Delhi Technological University Department of Software Engineering New Delhi India

ISBN: (纸本)9781665462631

the total health expenditure refers to the total public and private funds spent on health services and amenities, medical and surgical bills and all other healthcare facilities provided. the financing for health is of great significance and plays a crucial role in health systems. To enhance the productivity of human capital, the efficiency and delivery of healthcare services must be uplifted. Reports have shown that from year 2000 to 2018 there had been a gradual escalation of global health expenses and is standing on around 10% of the total GDP of the world. Out of pocket expenses are also high in least developed nations that have lower per capita income. Even though the World Health Organization (WHO) sanctions loans to these countries, these nations are bound to use the money on industrialization only and not their healthcare, education and public welfare sectors. With inflation, the expenses of first-rate healthcare are also rising which makes it fundamental to have health and life insurance plans. Health insurance schemes insured around 514 million people in India in the year 2021, most of which were covered under government schemes only. Since the advent of COVID-19 people have realized the need for having a insurance plan. Most of the companies that are based on the health insurance sector use predictive modelling to improve their services and business process. Machine Learning (ML) algorithms are used to train a model and provide insurance costs estimations. Past data is searched for any pattern or trend in the behaviour history of consumers and then future estimations are evaluated. the proposed project is comprised of different regression models like Linear regression with hyperparameterization , regressors like Decision Tree and Random forest to estimate the approximate insurance expenditure. © 2023 IEEE.

关键词： Machine learning

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Comparative Analysis of Random Forest and Multilayer Perceptron Algorithms for Predicting Gross Domestic Product 4

Comparative Analysis of Random Forest and Multilayer Percept...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Azeta, Ambrose A. Adewale, Muyideen D. Omote, Patrick O. Onaolapo, Funmi O. Bakare, Idowo A. Tjiraso, Steven Namibia University of Science and Technology Department of Software Engineering Namibia Africa Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Nigeria Crawford University Department of Computer and Mathematical Sciences Ogun StateNeria Nigeria

ISBN: (纸本)9798350353266

this study examines the efficacy of machine learning techniques, particularly Multilayer Perceptron (MLP), and Random Forest and in forecasting Nigeria's Gross Domestic Product (GDP). Traditional statistical methods frequently fail to capture intricate, nonlinear relationships, thus necessitating advanced techniques. the dataset employed in this research encompasses 22 instances from 2000 to 2021. It includes variables such as GDP, healthcare expenditure, population, and the index of economic freedom obtained from the World Bank as well as the Nigerian Perceptions Index for corruption. the data was prepared to undergo training and testing following the CRISP-DM methodology. the models were trained and evaluated using a five-member cross-validation method. Performance metrics indicated that MLP outperformed Random Forest, with MAE values of 16.8228 and 25.3074 and RMSE values of 23.2111 and 33.2027, respectively. these findings suggest that MLP offers more accurate GDP predictions for Nigeria, effectively managing complex data relationships. this study provides valuable insights for enhancing predictive accuracy and supporting more effective economic planning and decision-making. © 2024 IEEE.

关键词： Decision trees

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Resolving Security Issues via Quality-Oriented Refactoring: A User Study 6

Resolving Security Issues via Quality-Oriented Refactoring: ...

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6th ACM/IEEE international conference on Technical Debt (TechDebt)

作者： Gigante, Domenico Pecorelli, Fabiano Barletta, Vita Santa Janes, Andrea Lenarduzzi, Valentina Taibi, Davide Baldassarre, Maria Teresa Univ Bari Aldo Moro Bari Italy Tampere Univ Tampere Finland Vorarlberg Univ Appl Sci Dornbirn Austria Univ Oulu Oulu Finland Jheronimus Acad Data Engn Shertogenbosch Netherlands

ISBN: (纸本)9798350311945

software quality is crucial in software development: if not addressed in early phases of the software development life cycle, it may even lead to technical bankruptcy, i.e., a situation in which modifications cost more than redeveloping the application from scratch. In addition, code security must also be addressed to reduce software vulnerabilities and to comply with legal requirements. In this work, we aim to investigate the relationship between refactoring code quality and software security, with the purpose of understanding whether and to what extent improving software quality could have a positive impact on software security as well. Specifically, we investigate to what extent rule violations of a software quality tool such as SonarQube overlap with rule violations of a software vulnerability tool like Fortify Static Code Analyzer. We first compared the rules encoded in the quality models of both tools, to discover possible overlapping cases. Later, we compared the issues raised by both tools on a set of open source Java projects;we also investigated the cases in which a quality refactoring process impacts over software security (thus removing one or more vulnerabilities). We furthermore validated our results statistically. Our results show that resolving software quality issues might also resolve security issues but only in part: many security issues still persist in the source code;also, some quality aspects are more likely to be improved in respect to others. In addition, this empirical study uncovers rule co-occurrences between the two tools. this study confirms the need for using a security-oriented static analysis tool to enforce software security instead of relying only on a quality-oriented one. Results have highlighted important insights for practitioners.

关键词： Static Analysis Tools Empirical software engineering software Quality software Security

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Interactive Machine Learning Framework for Predicting Asthma Health Conditions Using XGBoost 4

Interactive Machine Learning Framework for Predicting Asthma...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Azeta, Ambrose A. Edward, Chimba S. Ekpo, Raphael H. Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Crawford University Department of Computer and Mathematical Sciences Ogun StateFaith Igbesa Nigeria

ISBN: (纸本)9798350353266

Asthma is a far reaching non-communicable sickness that affects individuals of any age and is a significant danger to human wellbeing worldwide, especially in low and middle-income countries. Its prevalence is substantial, and it remains one of the major contributors to global death rate. A substantial number of asthma development prediction models, and machine learning based approaches, exist. the problem associated with conventional methods of asthma prediction is low predictive accuracy of the model. the objective of this study is to develop an interactive Machine Learning (iML) framework for predicting Asthma Health conditions in selected African countries. the model was tested using dataset obtained from National Survey of Children's Health (NSCH). the machine learning technique used was XGBoost, and model development including preprocessing, training, and tested were carried out using Python programming language on Google Collaboratory (Collab) environment. the results of benchmarking of the performance accuracy of XGBoost shows 90.6% which is higher than the existing results in literature for Decision Tree (90.49%), KNN (90.28%), Naïve Bayes (82.7%). the study found out that the model developed by the researchers performed comparatively well in predicting asthma when compared with some existing state-of-the-art machine learning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust predictive Energy-saving Cruise Control of A Four-wheel Electric Vehicle with An Air Contioning System via A Generalized Proportional Integral Observer 6

Robust Predictive Energy-saving Cruise Control of A Four-whe...

引用

6th CAA international conference on Vehicular Control and Intelligence, CVCI 2022

作者： Wang, Jiangtong Tong, Jiageng Qian, Ziyuan Yang, Junqin Liu, Jinhao Dai, Chen Sun, Hao Li, Shihua School of Automation Southeast University Nanjing China College of Software Engineering Southeast University Suzhou China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

In this paper, a model predictive control method based on a generalized proportional integral observer (GPIO) is proposed for energy saving tracking control of a four-wheel pure electric vehicle (EV) with an air conditioning (A/C) system. In the process of the EV modeling, an accuracy-enhanced prediction model is obtained by fully exploiting the disturbance estimation based upon the GPIO technique. For the energy saving problem of the EV system, the power and efficiency loss of driving motors are introduced into the cost function for simultaneous optimization. Meanwhile, the speed limitation and some tracking tasks are regarded as constraint conditions in the optimization problem. then, a robust predictive energy-saving control law is obtained by solving a receding horizon constrained optimization problem. For the A/C system, a nonlinear model predictive control (NMPC) problem is formulated and solved online to minimize the energy consumption. Finally, numerical simulations show that the proposed control strategy is more energy-efficient without sacrificing normal control performance. © 2022 IEEE.

关键词： Model predictive control

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thermomechanical Simulation of Uniaxial One-Way Shape Memory Effect of Ni-Ti Based SMAs by FEM 6th

Thermomechanical Simulation of Uniaxial One-Way Shape Memory...

引用

6th international conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures, SMAR 2022

作者： Ye, Mao Chen, Tao Li, Xuan-Wei Tongji University Shanghai200092 China Department of Structural Engineering Tongji University Shanghai200092 China

ISBN: (纸本)9789819933617

Shape memory alloys (SMAs) are smart metallic materials possessing special properties which consist of shape memory effect (SME) and superelasticity (SE). the application of SMAs in the field of civil engineering is comparatively new but increasing, especially in recent years. While SMAs are used for different purposes, one of their typical uses regarding SME is to prestress structural components as a strengthening approach. For decades, constitutive models of Ni-Ti based SMAs were proposed and developed. this work presents numerical simulations of SME by FEM. With a user-defined material subroutine (UMAT), some typical thermomechanical models were conducted in finite element software ABAQUS. Specifically, the uniaxial one-way shape memory effect of Ni-Ti based SMA wires was simulated, including the process of pretensioning and thermal activating (with displacement constraint). the stress-strain and stress-temperature relations of the process were obtained and recovery stresses were calculated. In future works, the simulation of SME may help investigate the prestress effect when it comes to the strengthening of structural components. © Tongji University Press 2024.

关键词： Finite element method

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Cybersecurity and Privacy Attacks Detection in IoT Networks with Improved Data engineering and Machine Learning Methods 9

Cybersecurity and Privacy Attacks Detection in IoT Networks ...

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9th IEEE international conference on Big Data Computing Service and Applications, BigDataService 2023

作者： Elmazi, Donald Karras, Dimitrios A. Alkholidi, Abdulsalam Capari, Klea Faculty of Engineering Dept. of Electronics and Telecommunications Tirana Albania Dept. S/W Engineering Tirana Albania Dept. General School of Science Athens Greece Tirana Albania Faculty of Engineering Sana'a University Yemen University of Rijeka Faculty of Engineering Dept. of Computer Engineering Croatia Faculty of Engineering Cit Dept. of Software Engineering Tirana Albania

ISBN: (纸本)9798350333794

Presently, the global proliferation of Internet of things (IoT) devices and applications has accelerated owing to their advantages in enhancing the business and industrial environment, as well as the daily lives of individuals. Nevertheless, IoT devices are vulnerable to malevolent network traffic, which can lead to undesirable outcomes and disrupt the operation of IoT devices. Consequently, it is imperative to devise a screening method for network traffic to identify and categorize malicious activity and reduce its harmful effects. In this regard, the present study proposes a framework and thoroughly compares single and ensemble machine learning models that are using predictive analytics to detect and classify network activity in an IoT network. Specifically, our framework and models distinguish between normal and anomalous network activity. Furthermore, network traffic is classified into eight categories, normal, denial of service (DoS) attack, Scan attack, Data Type Probing, Malicious Operation, Malicious Control, Spying attack and Wrong Setup based attack. the special focus of this paper is in identifying privacy related attacks in comparison with main cybersecurity related ones. Several supervised machine learning models have been herein implemented to characterize their performance in detecting and classifying network activities for IoT Networks based on the ensemble learning framework as well as on the single learning machines framework. Moreover, classifiers based on the statistical learning theory framework are herein involved too, including hybrid Bayesian theory models, decision trees (DT) and the ordinal Learning Modelling approach. the statistical pattern recognition based models as well as all supervised learning machines models have been evaluated on a broad benchmark dataset for IoT attacks. Besides, an improved data engineering feature processing model has been applied to the dataset under consideration to improve the accuracy of all models under co

关键词： Internet of things

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Large Language models for Test-Free Fault Localization 24

Large Language Models for Test-Free Fault Localization

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46th IEEE/ACM international conference on software engineering, ICSE 2024

作者： Yang, Aidan Z.H. Martins, Ruben Le Goues, Claire Hellendoorn, Vincent J. Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9798400702174

Fault Localization (FL) aims to automatically localize buggy lines of code, a key first step in many manual and automatic debugging tasks. Previous FL techniques assume the provision of input tests, and often require extensive program analysis, program instrumentation, or data preprocessing. Prior work on deep learning for APR struggles to learn from small datasets and produces limited results on real-world programs. Inspired by the ability of large language models (LLMs) of code to adapt to new tasks based on very few examples, we investigate the applicability of LLMs to line level fault localization. Specifically, we propose to overcome the left-to-right nature of LLMs by fine-tuning a small set of bidirectional adapter layers on top of the representations learned by LLMs to produce LLMAO, the first language model based fault localization approach that locates buggy lines of code without any test coverage information. We fine-tune LLMs with 350 million, 6 billion, and 16 billion parameters on small, manually curated corpora of buggy programs such as the Defects4J corpus. We observe that our technique achieves substantially more confidence in fault localization when built on the larger models, with bug localization performance scaling consistently with the LLM size. Our empirical evaluation shows that LLMAO improves the Top-1 results over the state-ofthe-art machine learning fault localization (MLFL) baselines by 2.3%-54.4%, and Top-5 results by 14.4%-35.6%. LLMAO is also the first FL technique trained using a language model architecture that can detect security vulnerabilities down to the code line level. © 2024 IEEE Computer Society. All rights reserved.

关键词： software testing

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