A Large-Scale k -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation

Owing to the absence of hypotheses of the underlying distributions of the data and the strong generation ability, the k -nearest neighbor (kNN) classification algorithm is widely used to face recognition, text classification, emotional analysis, and other fields. However, kNN needs to compute the similarity between the unlabeled instance and all the training instances during the prediction process; it is difficult to deal with large-scale data. To overcome this difficulty, an increasing number of acceleration algorithms based on data partition are proposed. However, they lack theoretical analysis about the effect of data partition on classification performance. This paper has made a theoretical analysis of the effect using empirical risk minimization and proposed a large-scale k -nearest neighbor classification algorithm based on neighbor relationship preservation. The process of searching the nearest neighbors is converted to a constrained optimization problem. Then, it gives the estimation of the difference on the objective function value under the optimal solution with data partition and without data partition. According to the obtained estimation, minimizing the similarity of the instances in the different divided subsets can largely reduce the effect of data partition. The minibatch k -means clustering algorithm is chosen to perform data partition for its effectiveness and efficiency. Finally, the nearest neighbors of the test instance are continuously searched from the set generated by successively merging the candidate subsets until they do not change anymore, where the candidate subsets are selected based on the similarity between the test instance and cluster centers. Experiment results on public datasets show that the proposed algorithm can largely keep the same nearest neighbors and no significant difference in classification accuracy as the original kNN classification algorithm and better results than two state-of-the-art algorithms.

DSAE – Deep Stack Auto Encoder and RCBO – Rider Chaotic Biogeography Optimization Algorithm for Big Data Classification

In today’s era Big data classification is a very crucial and equally widely arise issue is many applications. Not only engineering applications but also in social, agricultural, banking, educational and many more applications are there in science and engineering where accurate big data classification is required. We proposed a very novel and efficient methodology for big data classification using Deep stack encoder and Rider chaotic biogeography algorithms. Our proposed algorithms are the combinations of two algorithms. First one is Rider Optimization algorithm and second one is chaotic biogeography-based optimization algorithm. So, we named it as RCBO which is integration is ROA and CBBO. Our proposed system also uses the Deep stack auto encoder for the purpose of training the system which actually produced the accurate classification. The Apache spark platform is used initial distribution of the data from master node to slave nodes. Our proposed system is tested and executed on the UCI Machine learning data set which gives the excellent results while comparing with other algorithms such as KNN classification, Extreme Learning Machine Random Forest algorithms.

Implementation of Classification Algorithms in Neo4j using IPL data

The main objective of this paper is to implement the classifications algorithms in Neo4j graph database using cypher query language. For implementing the classification algorithm, we have used Indian Premier League (IPL) dataset to predict the winner of the matches using some different features. The IPL is the most popular T20 cricket league in the world. The prediction models are based on the city where the matches were played, winner of the toss and decision of the toss. In this paper we have implemented Naïve Bayes and K-Nearest Neighbors (KNN) classification algorithms using cypher query language. Different classifiers are used to predict the outcome of different games like football, volleyball, cricket etc, using python and R. In this paper we shall use cypher query language. We shall also compare and analysis the results which are given by Naïve Bayes and K-Nearest Neighbors algorithms to predict the winner of the matches.

Affinity Scores: An Individual-centric Fingerprinting Framework for Neuropsychiatric Disorders

Background: Population-centric frameworks of biomarker identification for psychiatric disorders focus primarily on comparing averages between groups and assume that diagnostic groups are (1) mutually-exclusive, and (2) homogeneous. There is a paucity of individual-centric approaches capable of identifying individual-specific fingerprints across multiple domains. To address this, we propose a novel framework, combining a range of biopsychosocial markers, including brain structure, cognition, and clinical markers, into higher-level fingerprints, capable of capturing intra-illness heterogeneity and inter-illness overlap. Methods: A multivariate framework was implemented to identify individualised patterns of brain structure, cognition and clinical markers based on affinity to other participants in the database. First, individual-level affinity scores defined a neighbourhood for each participant across each measure based on variable-specific hop sizes. Next, diagnostic verification and classification algorithms were implemented based on multivariate affinity score profiles. To perform affinity-based classification, data were divided into training and test samples, and 5-fold nested cross-validation was performed on the training data. Affinity-based classification was compared to weighted K-nearest neighbours (KNN) classification. K-means clustering was used to create clusters based on multivariate affinity score profiles. The framework was applied to the Australian Schizophrenia Research Bank (ASRB) dataset. Results: Individualised affinity scores provided a fingerprint of brain structure, cognition, and clinical markers, which described the affinity of an individual to the representative groups in the dataset Diagnostic verification capability was moderate to high depending on the choice of multivariate affinity metric. Affinity score-based classification achieved a high degree of accuracy in the training, nested cross-validation and prediction steps, and outperformed KNN classification in the training and test datasets. Conclusion: Affinity scores demonstrate utility in two keys ways: (1) Early and accurate diagnosis of neuropsychiatric disorders, whereby an individual can be grouped within a diagnostic category/ies that best matches their fingerprint, and (2) identification of biopsychosocial factors that most strongly characterise individuals/disorders, and which may be most amenable to intervention.

Detection and Classification of Skin Lesions using Probability Map-based Region Growing with BA-KNN Classifier (Preprint)

BACKGROUND Melanoma is one of the most hazardous existing diseases, and is a kind of threatening pigmented skin lesion. Appropriate automated diagnosis of skin lesions and the categorization of melanoma may be exceptionally enhancing premature identification of melanomas. OBJECTIVE However, Models of categorization based on deterministic skin lesion may influence multi-dimensional nonlinear problem provokes inaccurate and ineffective categorization. This research presents a novel hybrid BA-KNN classification approach for pigmented skin lesions in dermoscopy images. METHODS In the first step, the skin lesion is preprocessed via automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also, a new probability map based region growing and optimal thresholding algorithm is integrated in this system to enhance the rate of accuracy. RESULTS Moreover, to attain better efficacy, an estimate of ABCD as well as geometric features are considered during the feature extraction to describe the malignancy of the lesion. CONCLUSIONS The evaluation of the experiment reveals the efficiency of the proposed approach on dermoscopy images with better accuracy

Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.

Gait Characteristics and Fatigue Profiles When Standing on Surfaces with Different Hardness: Gait Analysis and Machine Learning Algorithms

Background: Longtime standing may cause fatigue and discomfort in the lower extremities, leading to an increased risk of falls and related musculoskeletal diseases. Therefore, preventive interventions and fatigue detection are crucial. This study aims to explore whether anti-fatigue mats can improve gait parameters following long periods of standing and try to use machine learning algorithms to identify the fatigue states of standing workers objectively. Methods: Eighteen healthy young subjects were recruited to stand on anti-fatigue mats and hard ground to work 4 h, including 10 min rest. The portable gait analyzer collected walking speed, stride length, gait frequency, single support time/double support time, swing work, and leg fall intensity. A Paired sample t-test was used to compare the difference of gait parameters without standing intervention and standing on two different hardness planes for 4 h. An independent sample t-test was used to analyze the difference between males and females. The K-nearest neighbor (KNN) classification algorithm was performed, the subject’s gait characteristics were divided into non-fatigued and fatigue groups. The gait parameters selection and the error rate of fatigue detection were analyzed. Results: When gender differences were not considered, the intensity of leg falling after standing on the hard ground for 4 h was significantly lower than prior to the intervention (p < 0.05). When considering the gender, the stride length and leg falling strength of female subjects standing on the ground for 4 h were significantly lower than those before the intervention (p < 0.05), and the leg falling strength after standing on the mat for 4 h was significantly lower than that recorded before the standing intervention (p < 0.05). The leg falling strength of male subjects standing on the ground for 4 h was significantly lower than before the intervention (p < 0.05). After standing on the ground for 4 h, female subjects’ walking speed and stride length were significantly lower than those of male subjects (p < 0.05). In addition, the accuracy of testing gait parameters to predict fatigue was medium (75%). After standing on the mat was divided into fatigue, the correct rate was 38.9%, and when it was divided into the non-intervention state, the correct rate was 44.4%. Conclusion: The results show that the discomfort and fatigue caused by standing for 4 h could lead to the gait parameters variation, especially in females. The use of anti-fatigue mats may improve the negative influence caused by standing for a long period. The results of the KNN classification algorithm showed that gait parameters could be identified after fatigue, and the use of an anti-fatigue mat could improve the negative effect of standing for a long time. The accuracy of the prediction results in this study was moderate. For future studies, researchers need to optimize the algorithm and include more factors to improve the prediction accuracy.

A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction

Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature.

A Counseling System of Multiple Intelligence Theory Combined With kNN Classification Algorithm

Choosing the right career is always a big issue, an important concern for everyone. To have a job, which is suitable for you, firstly you must look at yourself, called the self, and you should be aware of what the self is then you can promote the strength of your own self and avoid your weakness. To help discover more about yourself, during researching and studying, we come up with the idea that we would propose a career counseling system based on Howard Gardner's theory. The system uses the theory of multiple intelligences (Abenti & Daradoumis, 2020) which is combined with the K-nearest neighbors (KNN) (Tang, Ying; Tang, Ying; Hare, Ryan; Wang, Fei-Yue;, 2020) algorithm to assist people and to give out a suitable suggestion about career path for them. We use the results of the eight intelligences retrieved from the KNN classification algorithm to give users the consulting for their career paths. This system is built with a dataset based on 56 multiple-choice questions. These include 48 multiple choice questions based on Howard Gardner's theory of multiple intelligences (Bravo, Leonardo Emiro Contreras; Molano, Jose Ignacio Rodriguez; Trujillo, Edwin Rivas, 2020), (businessballs, 2017) and 8 multiple choice questions which are the labels of the classifier. We divided the dataset into 8 subsets corresponding with 8 Intelligences defined by Howard Gardner with the collected dataset. In each subset, we build the KNN classifier model using KNN classification algorithm. This processing of 8 subsets come out with the results accuracy for the 8 Intelligences: linguistic intelligence (80.95%), logical-mathematical intelligence (82.14%), musical intelligence (96.43%), bodily-kinesthetic intelligence (82.14%), spatial-visual intelligence (82.14%), interpersonal intelligence (89.29%), intrapersonal intelligence (88.1%), existential intelligence (78.57%). With the outcome of 8 models, we have tested with 5 students and compared them to their actual intelligences. The comparison results tell us about the valuable potential in career path of the proposed counselling system, the advantages of this combination between Multiple Intelligence and KNN classifier.