Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.

Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

Diabetes mellitus,generally known as diabetes,is one of the most common diseases worldwide.It is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin injections.Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/dL.Diabetes is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood cells.Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error.The primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing diabetes.Historically,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of diabetes.However,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals.Therefore,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data.The traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be programmable.Each switch in SDN keeps track of forwarding information in a flow table.The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets.Due to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding capabilities.So,the SDN must handle traffic from across the whole network.The flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy consumption.Whenever the flow table is abused and overflowing,the usual regulations cannot be executed quickly.In this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low rate.This study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)controller.The efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and throughput.The findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G network.By putting the proposed flow method and checking whether a packet may move from point A to point B without breaking certain regulations,the evaluation tool examines every flow against a set of criteria.The FFNN with DT and K-means algorithms obtain accuracies of 96.29%and 97.51%,respectively,in the identification of collision flows,according to the experimental outcome when associated with existing methods from the literature.

DDoS Detection in SDN using Machine Learning Techniques

Software-defined network(SDN)becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure.The SDN controller is considered as the operating system of the SDN based network infrastructure,and it is responsible for executing the different network applications and maintaining the network services and functionalities.Despite all its tremendous capabilities,the SDN face many security issues due to the complexity of the SDN architecture.Distributed denial of services(DDoS)is a common attack on SDN due to its centralized architecture,especially at the control layer of the SDN that has a network-wide impact.Machine learning is now widely used for fast detection of these attacks.In this paper,some important feature selection methods for machine learning on DDoS detection are evaluated.The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller.A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks.The experimental results show that the Random forest(RF)classifier trains the more accurate model with 99.97%accuracy using features subset by the Recursive feature elimination(RFE)method.

Real-time prediction of projectile penetration to laminates by training machine learning models with finite element solver as the trainer

Studies on ballistic penetration to laminates is complicated,but important for design effective protection of structures.Experimental means of study is expensive and can often be dangerous.Numerical simulation has been an excellent supplement,but the computation is time-consuming.Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model.A large number of finite element models were developed;the residual velocities of projectiles from finite element simulations were used as the target data and processed to produce sufficient number of training samples.Study focused on steel 4340tpolyurea laminates with various configurations.Four different 3D shapes of the projectiles were modeled and used in the training.The trained neural network and decision tree model was tested using independently generated test samples using finite element models.The predicted projectile velocity values using the trained machine learning models are then compared with the finite element simulation to verify the effectiveness of the models.Additionally,both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples.Performance of both the models was evaluated and compared.Models trained with Finite element simulation data samples were found capable to give more accurate predication,compared to the models trained with experimental data,because finite element modeling can generate much larger training set,and thus finite element solvers can serve as an excellent teacher.This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.

Social Engineering Attack Classifications on Social Media Using Deep Learning

In defense-in-depth,humans have always been the weakest link in cybersecurity.However,unlike common threats,social engineering poses vulnerabilities not directly quantifiable in penetration testing.Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware.Social Engineering(SE)in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic.In this paper,a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory(RNN-LSTM)to identify well-disguised SE threats in social media posts.We use a custom dataset crawled from hundreds of corporate and personal Facebook posts.First,the social engineering attack detection pipeline(SEAD)is designed to filter out social posts with malicious intents using domain heuristics.Next,each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data.Then,we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering.The experimental result showed that the Social Engineering Attack(SEA)model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts.The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.

基于BiLSTM-XGBoost混合模型的储层岩性识别

储层岩性分类是地质研究基础,基于数据驱动的机器学习模型虽然能较好地识别储层岩性,但由于测井数据是特殊的序列数据,模型很难有效提取数据的空间相关性,造成模型对储层识别仍存在不足.针对此问题,本文结合双向长短期循环神经网络(bidirectional long short-term memory,BiLSTM)和极端梯度提升决策树(extreme gradient boosting decision tree,XGBoost),提出双向记忆极端梯度提升(BiLSTM-XGBoost,BiXGB)模型预测储层岩性.该模型在传统XGBoost基础上融入了BiLSTM,大大增强了模型对测井数据的特征提取能力.BiXGB模型使用BiLSTM对测井数据进行特征提取,将提取到的特征传递给XGBoost分类模型进行训练和预测.将BiXGB模型应用于储层岩性数据集时,模型预测的总体精度达到了91%.为了进一步验证模型的准确性和稳定性,将模型应用于UCI公开的Occupancy序列数据集,结果显示模型的预测总体精度也高达93%.相较于其他机器学习模型,BiXGB模型能准确地对序列数据进行分类,提高了储层岩性的识别精度,满足了油气勘探的实际需要,为储层岩性识别提供了新的方法.

基于机器学习的智能化电力设备巡检技术应用

阐述数据的采集和预处理方法,通过支持向量机和决策树等机器学习算法进行模型训练,实现对设备状态的智能诊断和异常检测,并以数据可视化方式进行展示。针对巡检过程中出现的异常情况,提出基于神经网络的异常分类和定位方案。

机器学习在网络入侵检测中的应用

随着网络的快速发展,网络安全成为计算机网络中一个重要的研究方向。网络攻击日益频繁,传统的安全防护产品存在漏洞,入侵检测作为信息安全的重要防护手段弥补了防火墙的不足,提供了有效的网络入侵检测措施,保护网络安全。然而传统的入侵检测系统存在许多问题,基于机器学习的入侵检测方法实现了对网络攻击的智能检测,提高了入侵检测的效率,降低了漏报率和误报率。本文首先简要介绍机器学习的部分算法,然后对机器学习算法在网络入侵检测中的应用进行深入的分析,比较各个算法在入侵检测应用中的优势和缺点,最后总结了机器学习的应用前景,为获得性能良好的网络入侵检测和防御系统奠定基础。

舰艇对空中来袭目标意图的预判方法

[目的]为使舰艇能在短时间内正确预判空中来袭目标的意图,提出应用异质集成学习器解决该模糊不确定性分类问题。[方法]首先选取极限学习机、决策树、Skohonen神经网络和学习矢量化(LVQ)神经网络4种子学习器,使用集成学习结合策略构建异质集成学习器;然后利用该集成学习器训练测试训练集100次,得到该分类实验平均准确率和计算时间。为提高准确率,进行了集成修剪,剔除"劣质"的LVQ神经网络,重新构建效率更高的异质集成学习器,其实验结果具有极高的精度,但计算耗时长。为此,提出对Skohonen神经网络子分类器做"线下训练、线上调用"的改进。[结果]仿真实验表明,从探测到空中目标到预判出各来袭目标意图总用时为4.972 s,预判精度为99.93%,很好地满足了精度和实时性要求。[结论]该研究为作战决策提供了一种新颖而有效的方法,同时也为小样本分类识别问题提供了一种较好的实现途径。

机器学习在奶牛临床疾病预测中的应用

机器学习是指计算机通过大量数据训练及分析来模拟人类的学习行为,从而获得新的知识和技能,是人工智能的核心。近年来,机器学习在奶牛疾病预测领域的应用已成为国际上的研究热点。论文介绍了利用奶牛机体生理指标和生产数据建立奶牛疾病预测模型的方法,重点阐述了如何利用决策树和神经网络筛选奶牛疾病风险因子、预测疾病和疾病分类。同时,综述了机器学习预测代谢性疾病、跛行、乳房炎、热应激和传染性疾病的进展。

基于机器学习的入侵检测方法对比研究

随着网络安全形势日趋严峻,入侵检测技术已经成为保障网络安全的一种重要手段。因此把机器学习的理论和方法引入入侵检测已成为一种共识,并且近些年来在这一研究领域取得了不错的进展。文章对比分析了不同机器学习方法在入侵检测上的应用。首先,介绍机器学习的一般化过程,对典型机器学习方法的理论进行对比分析。然后,对不同机器学习方法做仿真研究,观察性能变化。最后,在仿真的基础上对不同模型进行横向比较。文章在仿真实验的基础上得出了较为可靠的结论,对找出具有性能优势的机器学习方法具有重要意义。

基于Yahoo的信息自动分类器的原理与设计

本文介绍了一种基于Ｙａｈｏｏ层次的自动分类器，此分类器主要是把基于文本数据的机器学习技巧用于Ｙａｈｏｏ的层次结构；讨论了分类文档过程中的文档表示、功能选择和学习方法及相关的算法。

基于机器学习的上海市空气质量预测方法研究

空气质量与气象因子之间存在较强的非线性关系且多数学者仅基于单一方法对该问题进行研究和改进,导致空气质量预测精度不佳。为更好地预测上海市空气质量,选取2016-2021年上海市空气质量数据,分别使用BP神经网络、决策树和支持向量机算法构建空气质量预测模型对次日空气质量等级进行预测。研究结果表明:①支持向量机的预测精度最高,CART决策树次之,BP神经网络预测效果最差;②在4类基于不同核函数和分类方法的支持向量机模型中,基于线性核函数和一对多分类方法的支持向量机预测准确率最高,为80%;③当空气质量为良时,预测值和真实值的重合度高。将机器学习方法应用于空气质量预报合理有效,可为市民出行提供参考建议。

基于机器学习的花卉分类算法研究

介绍数据挖掘和机器学习基础知识,通过使用统计分类算法:分类和回归决策树、朴素贝叶斯分类器、神经网络、支持向量机,对UCI数据库上的花卉数据集进行分类,得到各种算法的分类性能评价指标并详细分析算法影响分类准确度的原因。

面向智能驾驶行为的机器学习

基于机器学习的智能驾驶行为分析是智能车辆发展的方向和难点之一.驾驶行为的知识获取和表达,涉及机器学习中的诸多算法.首先对机器学习在驾驶行为识别判断、建模预测、智能决策等方面的研究进行了分析,进而对驾驶行为分析中的几种主要机器学习算法进行了较为全面的总结,最后给出了各种算法的优缺点.

基于分类的机器学习方法中的决策树算法

阐述数据挖掘的分类及目的,总结分类器的构造方法,讲述分类中决策树的构建和修剪.

一种混合型多概念获取系统(英文)

文章实现混合型多概念获取系统 HMCAS( hybrid multi- concept acquisition system) .无论在离散值或连续值输入下 ,HMCAS系统都可以实现增量式教师学习 .HMCAS的核心算法 HMCAP基于事例空间的概率分布 ,结合了符号学习和神经网络学习 ,能够以混合型判定树形式产生概念描述 .

基于典型样本的Prototype决策树

文章将SVM算法和GA－NN－C4．5算法的思路结合起来，提出了用典型样本产生原型（Prototype）的方法。实验结果表明，基于典型样本的Prototype决策树搜索效果更好、判决精度更高。

机器学习理论在入侵检测技术中的应用研究

入侵检测系统是主动保障网络信息安全的重要方法。本文针对大规模、高带宽网络环境下,入侵检测技术存在的不足,提出将机器学习理论应用到入侵检测系统中。文章简要介绍几种适合用于入侵检测系统中的机器学习算法,并建立基于机器学习理论的入侵检测系统框架。利用机器学习的算法不仅能检测到一些已知的攻击,还可以通过自我学习检测到未知的攻击。