The Fuzzy Min-Max Neural Network (FMNN) Classification algorithm gained prominence due to its distinct characteristics, such as adaptability for incremental learning, nonlinear classification, and single-pass training. There were many extensions to FMNN since it was incepted. Among them, a couple of works have been done to improve the scalability of FMNN. The MapReduce framework has been widely employed in recent years to scale machine learning algorithms. The existing MapReduce-based FMNN (MRCFMNN) that uses centroid for classification was found to have limitations regarding generalizability due to outliers. This work proposes medoid-based algorithms MRMFMNN_1 and MRMFMNN_2 to overcome the limitations associated with MRCFMNN. The Apache Spark cluster was used to perform the comparative experimental study between the proposed approaches MRMFMNN_1, MRMFMNN_2, and existing state-of-the-art approaches MRCFMNN, MRFMNN using benchmark large decision systems. The experimental outcomes empirically determine the supremacy of the proposed MRMFMNN_1 algorithm by accomplishing improved generalizability over existing MRCFMNN and MRFMNN.

Fuzzy Rough Set Theory (FRS) is an effective method for feature subset selection (reduct computation) for numeric decision systems. Several Fuzzy Rough Set reduct computation approaches were proposed using Fuzzy Similarity Matrix (FSM) or Fuzzy Discernibility Matrix (FDM) constructed on object space. However, constructing FDM or FSM on object space increases the complexity of the algorithm. In a stand-alone scenario, a representative instance-based reduct computation approach is useful in increasing the scalability while computing an approximate reduct. The FMNN-FRS algorithm is one such approach where the given dataset is represented by a much smaller interval-valued decision system using Fuzzy Min–Max Neural Network (FMNN) pre-processing. This work aims to further increase the scalability of FMNN-FRS by designing a novel MapReduce approach (MR_FMNN_FRRC) using centroid-based FMNN. We also proposed two more representative instances-based approaches using MapReduce through FMNN pre-processing. The relevance of the proposed approaches is assessed through a comparative experimental analysis with state-of-the-art Fuzzy Discernibility Matrix (FDM)-based MapReduce algorithm(MR_IFDMFS), Distributed Fuzzy Rough Set (DFRS)-based feature selection, MapReduce based Approach for Fuzzy Decision Reduct Computation (MR_IMQRA), and Hybrid-FMNN-FRS. The results amply validate that the proposed approaches achieve higher scalability with an ability to induce generalizable classification models while achieving dependency measures almost equal to 1.

This paper presents a fixed transconductance circuit based on the impedance matching. The proposed technique does not depend on the square law model of the transistors and tracks the resistor for all operating regions of the transistor. Unlike its predecessors, the transconductance of the transistor tracks for a wide range of external resistors. Such techniques find applications in analog and mixed-signal circuits like data converters, voltage- controlled oscillators, amplifiers, etc. The proposed circuit is implemented in the TSMC 65nm LP process, and it occupies an area of 55μm x 24μm. The transconductance varies about 0.8% over a nominal value of 125μS across process corners and temperature. Monte Carlo simulation results show a standard deviation of 4.64 μS over a mean of 125.8 μS.

Fuzzy Min-Max Neural Network (FMNN) Classifier has acquired significance owing to its unique properties of single-pass training, non-linear classification, and adaptability for incremental learning. Since its inception in 1992, FMNN has witnessed several extensions, modifications, and utilization in various applications. But very few works are done in the literature for enhancing the scalability of FMNN. In recent years, MapReduce framework is used extensively for scaling machine learning algorithms. The existing MapReduce approach for FMNN (MRFMNN) is found to be having limitations in load balancing and in achieving good generalizability. This work proposes MRCFMNN Algorithm for overcoming these limitations. MRCFMNN induces an ensemble of centroid-based FMNN Classifiers for achieving higher generalizability with load balancing. Four ensemble strategies are proposed for combining the individual classifier results. The comparative experimental results using benchmark large decision systems were conducted on Apache Spark MapReduce cluster. The results empirically establish the relevance of the proposed MRCFMNN by achieving significantly better classification accuracy in most of the datasets over MRFMNN.

The signs of asthma, a long-term inflammatory condition of the airways, include wheezing, throat tightness, coughing, and breathing difficulties. The attack of an asthma, which can be fatal, is the fast worsening of these symptoms. Another severe, irreversible airflow restriction in the lungs is caused by respiratory COPD, which encompasses emphysema and chronic bronchitis. In this project, a machine learning-based algorithm is for predicting asthma risk is presented (ML). PEFR, which are widely used external tools like peak flow meters and recognized asthma risk predictors, are frequently monitored. This study shows a relationship between the ambient particle matter(PM) and the weather outdoors. According to the best peak flow value each person was able to acquire, the results are divided into two groups: Safe and Risk circumstances. The link between indoor PM and weather data is mapped to the found values using a convolutional neural network (CNN) architecture. The suggested method's root mean square and mean absolute error accuracy metrics are contrasted with those of current deep neural network (DNN)-based methods. Additionally, the accuracy of the classification methods KNN and SVM are carried out. The new data set's asthma category may be predicted more accurately thanks to the application of SVM, KNN, and CNN classification. Python 3.7 is the coding language employed.