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Heart Disease Diagnosis Using the brute force Algorithm and Machine Learning Techniques

Computers, Materials & Continua 2022年第8期72卷 3195-3211页

作者： Junaid Rashid Samina Kanwal Jungeun Kim Muhammad Wasif Nisar Usman Naseem Amir Hussain Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea Department of Computer Science COMSATS University IslamabadWah Campus47040Pakistan School of Computer Science University of SydneySydneyNSW 2006Australia Centre of AI and Data Science Edinburgh Napier UniversityEdinburghEH114DYUK

Heart disease is one of the leading causes of death in the world today.Prediction of heart disease is a prominent topic in the clinical data processing.To increase patient survival rates,early diagnosis of heart disease is an important field of research in the medical field.There are many studies on the prediction of heart disease,but limited work is done on the selection of features.The selection of features is one of the best techniques for the diagnosis of heart diseases.In this research paper,we find optimal features using the brute-force algorithm,and machine learning techniques are used to improve the accuracy of heart disease prediction.For performance evaluation,accuracy,sensitivity,and specificity are used with split and cross-validation techniques.The results of the proposed technique are evaluated in three different heart disease datasets with a different number of records,and the proposed technique is found to have superior performance.The selection of optimized features generated by the brute force algorithm is used as input to machine learning algorithms such as Support Vector Machine(SVM),Random Forest(RF),K Nearest Neighbor(KNN),and Naive Bayes(NB).The proposed technique achieved 97%accuracy with Naive Bayes through split validation and 95%accuracy with Random Forest through cross-validation.Naive Bayes and Random Forest are found to outperform other classification approaches when accurately evaluated.The results of the proposed technique are compared with the results of the existing study,and the results of the proposed technique are found to be better than other state-of-the-artmethods.Therefore,our proposed approach plays an important role in the selection of important features and the automatic detection of heart disease.

关键词： Heart disease brute force machine learning feature selection

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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,etc.The proposed model uses pupil characteristics for liveness detection during the authentication process.The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities.The proposed framework consists of two-phase methodologies.In the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames.The above measurement is converted into feature space using Kohn and Clynes model-defined parameters.The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the camera.In the second phase,we perform the facial recognition process.Scale-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional vector.These features are scale,rotation,and orientation invariant and are used for recognizing facial images.The brute force matching algorithm is used for matching features of two different images.The threshold value we considered is 0.08 for good matches.To analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Error Rate,while the Replay Attack datasets showed

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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The Hubei Provincial Building Technology Project in 2021(No.43)

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Journal of World Architecture 2022年第4期6卷 61-75页

作者： Fei Wang Southwest Jiaotong University Hope College Chengdu 610400Sichuan ProvinceChina

This study aims to optimize energy consumption by modifying the train’s maximal speed and coasting velocity.The methods used in the simulation are brute force and genetic algorithm(GA).The introduction briefly introduces the aim and objectives of the study,as well as the scope and the methodology.The following section gives an overview of the current rail transit development and the existing issues.Despite the rapid development of rail transit and its successful operation,energy consumption is a major issue.The methodology of brute force and genetic algorithm is then introduced.The exact algorithm of the two methods in MATLAB is explained so as to make preparations for the latter simulation optimization.The results from the brute force and genetic algorithm methods are obtained and compared for data analysis.The driving strategy for using STS(Single Train Simulator)is then optimized for an advanced modification.By inserting more values in the code,an optimal speed profile is obtained,and the energy saving target is achieved.Overall,the energy consumption of the studied line could be decreased by optimizing the maximal speed of different sections between the stations and the coasting velocity.However,influencing factors such as service and infrastructure,application of acceleration,and braking power should also be considered as improvements in future studies.

关键词： Train speed profile Energy saving MATLAB brute force Genetic algorithm

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基于相关Nakagami衰落的无线信道仿真与硬件实现

基于相关Nakagami衰落的无线信道仿真与硬件实现

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作者：陈超南京航空航天大学

学位级别：硕士

无线通信系统的性能很大程度上受到无线信道特性的制约。随着无线通信设备的日趋复杂，通信环境日趋多样，无线信道仿真与实现的重要性越来越得到人们的重视，准确模拟通信设备在各种无线传播环境下的性能表现，对于无线通信传输技术的... 详细信息

无线通信系统的性能很大程度上受到无线信道特性的制约。随着无线通信设备的日趋复杂，通信环境日趋多样，无线信道仿真与实现的重要性越来越得到人们的重视，准确模拟通信设备在各种无线传播环境下的性能表现，对于无线通信传输技术的选择和数字接收机的设计具有重要的参考意义。因此，本文研究的基于Nakagami衰落信道的仿真与实现，不仅具有理论意义，而且具有广阔的实用前景。首先，本文概要地介绍了无线信道的衰落和统计特性，并在此基础上引入了信道建模和仿真的基础理论，接着以Nakagami衰落特性和时域相关性为主线，分别对独立和相关Nakagami衰落信道的仿真模型进行了详细的分析与比较，并在此基础上提出了模型的改进。其次，本文对信道仿真器的硬件实现进行了研究。根据性能指标对总体硬件方案进行了设计与论证，重点研究了信道仿真器的双径模块、噪声模块、多普勒频移模块、大尺度衰落模块、小尺度衰落模块共五大模块的实现技术，设计并研制出一台基于相关Nakagami衰落的信道仿真器。最后，本文对信道仿真器的各项功能和技术指标进行了测试验证，测试结果表明，信道仿真器符合信道模型的衰落分布和相关特性、能够准确地模拟出无线信道的传输特性，功能和性能上均满足课题预期的指标要求，具有良好的稳定性和可靠性。

关键词： Nakagami 信道仿真器 FPGA 小尺度衰落舍弃法 brute force 信道相关性

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Improved Template Matching Based Stereo Vision Sparse 3D Reconstruction Algorithm

Improved Template Matching Based Stereo Vision Sparse 3D Rec...

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第32届中国控制与决策会议

作者： Zhiyuan Liu Bin Song Yanning Guo Hang Xu Dept.of Control Science and Engineering Harbin Institute of Technology

Stereo vision 3 D reconstruction using keypoints matching methods can bring up problems,such as uneven distribution of points,or even mismatches.To solve the problems,we proposed a novel stereo vision 3 D reconstruction algorithm based on template matching.First,considering that the similarity between left and right image of binocular camera can be high,template matching can be brought in to minify the searching range of every keypoint,thus enhance the accuracy of matches and make the reconstruction result more complete.The specific process of the proposed method includes the following steps:templates extraction,keypoint density index calculation,template matching,keypoint matching within template matched areas,point cloud generation,and point cloud stitching.Furthermore,a simulation environment was built up in Gazebo,and 3 D reconstruction was carried out using the proposed method.Comparison experiment was carried out using brute force keypoint matching method and FLANN.Simulation results showed that the proposed method performed better and improved in both utilization of keypoints and reconstruction result.

关键词： Stereo vision Keypoints brute force Template matching Sparse 3D reconstruction

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