A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

Face Recognition Through Multi-Resolution Images

In this paper, we introduce a new method for face recognition in multi-resolution images. The proposed method is composed of two phases: an off-line phase and an inference phase. In the off-line phase, we built the Kernel Partial Least Squares (KPLS) regression model to map the LR facial features to HR ones. The KPLS predictor was then used in the inference phase to map HR features from LR features. We applied in both phases the Block-Based Discrete Cosine Transform (BBDCT) descriptor to enhance the facial feature description. Finally, the identity matching was carried out with the K-Nearest Neighbor (KNN) classifier. Experimental study was conducted on the AR and ORL databases and the obtained results proved the efficiency of the proposed method to deal with LR and VLR face recognition problem.

The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition

Security systems must be continuously developed in order to cope with new challenges. One example of such challenges is the proliferation of sexual harassment against women in public places, such as public toilets and public transportation. Although separately designated toilets or waiting and seating areas in public transports are provided, enforcing these restrictions need constant manual surveillance. In this paper we propose an automatic gender classification system based on an individual’s facial characteristics. We evaluate the performance of QLRBP and MLLPQ as feature extractors combined with SVM or kNN as classifiers. Our experiments show that MLLPQ gives superior performance compared to QLRBP for either classifier. Furthermore, MLLPQ is less computationally demanding compared to QLRBP. The best result we achieved in our experiments was the combination of MLLPQ and kNN classifier, yielding an accuracy rate of 92.11%.

Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight

The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to sense whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 80.95, 72.50 and 86.05%, for data collected at S-band, C-band and W-band, respectively, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper.

Brain Tumor Classification using Machine Learning

This paper presents a technical analysis of tumor data with Machine Learning and Classification Approach. Feature parameters which are dependent for classification of tumor are used for analyzing and classifying the class of tumor. In the classification of tumor, KNN-Classifier is implemented with cross validating accuracy score and tuning hyper parameters. Experimental simulation for best average score for K makes it to the cross validation. Approaching the prediction with the best accuracy score, hyper parameters of KNN Classifier states the best score. Using Principal Component Analysis on the data, miss-classification of tumor class in data is visualized. Aims: To declare and analyse tumor data from the source of MRI, CT scan, etc. for medication of tumor. To utilize smart predictions for the upcoming tumor patients using Machine Learning. Study Design: Tumor classification using K Nearest Neighbor algorithm and analysis of the miss-classification. Methodology: We included 11 different studies and research papers which were relevant with tumor classification. Research papers include classification of tumors with different supervised learning approaches. Our proposed analysis and classification give visualization of two classes of tumor. Results: The Project results in classification of tumor data using Machine Learning and analyzing the miss-classification of tumor. In implementation of KNN Algorithm, the accuracy score after cross validation and tuning K values is 0.97. The confusion matrix shows 4 false positives and 1 false negative value in testing. Conclusion: Less miss-classification of tumor results best accuracy score and more efficient working on testing data. Visualizing the classification with 3-dimensional scatter plots made the analysis accurate.

KNN Algorithm Used for Heart Attack Detection

Machine Learning, a branch of artificial intelligence, has become more accurate than human medical professionals in predicting the incidence of heart attack or death in patients at risk of coronary artery disease. In this paper, we attempt to employ Artificial Intelligence (AI) to predict heart attack. For this purpose, we employed the popular classification technique named the K-Nearest Neighbor (KNN) algorithm to predict the probability of having the Heart Attack (HA). The dataset used is the cardiovascular dataset available publicly on Kaggle, knowing that someone suffering from cardiovascular disease is likely to succumb to a heart attack. In this work, the research was conducted using two approaches. We use the KNN classifier for the first time, aided by using a correlation matrix to select the best features manually and faster computation, and then optimize the parameters with the K-fold cross-validation technique. This improvement led us to have an accuracy of 72.37% on the test set.

Emotion recognition from syllabic units using k-nearest-neighbor classification and energy distribution

In this article, we present an automatic technique for recognizing emotional states from speech signals. The main focus of this paper is to present an efficient and reduced set of acoustic features that allows us to recognize the four basic human emotions (anger, sadness, joy, and neutral). The proposed features vector is composed by twenty-eight measurements corresponding to standard acoustic features such as formants, fundamental frequency (obtained by Praat software) as well as introducing new features based on the calculation of the energies in some specific frequency bands and their distributions (thanks to MATLAB codes). The extracted measurements are obtained from syllabic units’ consonant/vowel (CV) derived from Moroccan Arabic dialect emotional database (MADED) corpus. Thereafter, the data which has been collected is then trained by a k-nearest-neighbor (KNN) classifier to perform the automated recognition phase. The results reach 64.65% in the multi-class classification and 94.95% for classification between positive and negative emotions.