Heart Disease Diagnosis Using the Brute Force Algorithm and Machine Learning Techniques

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.

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

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 a 92.1%Equal Error Rate.

FPGA Implementation of Non-Linear Cryptography

The paper focuses on the design and Field Programmable Gate Array (FPGA) implementation of embedded system for time based dual encryption scheme with Delay Compulsion Function (DCF) and also illustrates the application of DCF in time based cryptography. Further, the strength of the time based FPGA encryption algorithm with and without using DCF is analyzed using a Nios II processor. This proposed scheme enhances the security of vital data against Brute force attack by incorporating a temporal key distribution where two different keys encrypt the data simultaneously, one being the regular key and the other being the time. The time is included using a dynamically varying number of shifts thereby allowing the system to wait for the duration and this forms the second dimension of the key. Presently, available encryption systems suffer from Brute Force attack in which all the key combinations are tried in order to find the correct key. In such a case, the time taken for breaking the key depends on the speed of the system used for cryptanalysis. The proposed system adds complexity by using dynamically varying sequence of operations, by including the time as a second dimension of the key besides minimizing the possibility of Brute Force attack and increasing the time required for cryptanalysis irrespective of the system capability. As the proposed system needs concurrent execution and real time processing, the system is implemented using Altera Stratix II FPGA and the results are presented.

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

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.