Brain Computer Interface ( BCI) is a computerized system that acquires brain signals, extracts and classifies features during different mental activities, and converts them into correct control signals, and transfers them to external devices. BCI helps people with motor disabilities. Real-time application of a BCI system needs an efficient classification of motor tasks. Motor Imagery task identification based on EEG signals is still challenging for researchers. Extraction of robust, mutual information and discriminative features which can be converted into device commands is the biggest challenge in Motor Imagery BCI system. This study aims to analyse the effectiveness of motor and motor imagery classification for left hand and right-hand movements. The motor and motor imagery of left and right-hand movements is defined using statistical features of a higher order that are fed to classifier SVM and Random Forest Classifier. Using SVM classifier, for motor action the classification accuracy of 62.5% was reached and for motor imagery classification accuracy of 45.83% was reached. Using random forest classifier, for motor action the classification accuracy of 80.2% was reached and for motor imagery classification accuracy of 64.58% was reached.

Even since the 40s the scope of natural language processing has been primal dismay in computer science and Artificial Intelligence. It aspires to include the next strive forward in Artificial Knowledge which can perform both computers and Individual work with better malleability and apprehension. It incorporates various methods like machine translation, speech recognition, online learning, auto tutor etc. Researchers recalled it as a potential bridge that can amalgamate human spoken language and computer which uses programming language and binary codes. Since it is an impossible task to prepare a computer to recognize human natural language, further techniques and enhancements will foster the demanding yet rewarding and innovative computational trends. This paper confers a restrained domain metaphysical model for agriculture cultivation question answering system. The question answering system has been noted as a significant tactic for knowledge engineering research. Ontologies facilitate the computers in deciphering the restrain domain concept of semantics. Thus forming a significant technique for the question-answering system. This paper inculcates introduction ontology and the definition of a domain ontology for agriculture cultivation. The paper also focused on presenting the restricted domain ontology models and concept level vector space model of information retrieval.

Concrete is the most widely used building material. In worldwide the high rise buildings are constructed by using high strength concrete mode only. Growing demand for construction materials necessitated the usage of alternative materials in the production of conventional concrete. Now a days fine aggregate source is reduced due to high consumption, so some material is needed to replacing of sand. The objective of this work is to study the strength and durability properties of concrete. Here M 70 grade concrete used and concrete containing copper slag as partial replacement of fine aggregate and mineral admixture as partial replacement of cement in the concrete mix design. Copper slag content has been 40% as a replacement of fine aggregate and silica fume 5%,10%,15% & 20% and GGBS 5%,10%,15% & 20% as a replacement of cement respectively .This research paper study on strength test& Durability test. The test results show 40% replacement of fine aggregate as copper slag gives them more strength. And Silica fume & GGBS as partial replacement of cement (up to 15%). From the results, it was observed that the use of copper slag and mineral admixture in concrete has shown a considerable increase in strength and reduction of the cost when compared with normal concrete.

90 percent of the motors used in industries are induction motors due to its simple design and high robustness. Total Harmonic Distortion of output voltage waveform keeps decreasing as the number of voltage levels increases. The main consideration while selecting a multilevel inverter is its quality of the output voltage waveform which is assessed in terms of the amount of harmonics content present in it. So, Total Harmonic Distortion (THD) value in the output voltage waveform is one of the important criteria in the multilevel inverter (MLI). The 15 level hybrid multilevel inverter circuit was simulated in Matlab/Simulink environment and the results were experimentally analysed for a resistive load. C code was developed to generate PWM pulses. Arduino microcontroller is used to feed the PWM pulses to the switches in the inverter circuit. From the overall analysis, it is proved that the Equal Area Criteria (EAC) method is superior to the other PWM methods.

Predicting the price of used cars is one of the significant and interesting areas of analysis. As an increased demand in the second-hand car market, the business for both buyers and sellers has increased. For reliable and accurate prediction it requires expert knowledge about the field because of the price of the cars dependent on many important factors. This paper proposed a supervised machine learning model using KNN (K Nearest Neighbor) regression algorithm to analyze the price of used cars. We trained our model with data of used cars which is collected from the Kaggle website. Through this experiment, the data was examined with different trained and test ratios. As a result, the accuracy of the proposed model is around 85% and is fitted as the optimized model.