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The application of the brain-computer interface (BCI) is massively helpful and advantageous for disabled people. Moreover, BCI is an arrangement of software and hardware interface that provides a direct interaction between the human brain and computer devices. Therefore, in this article, A steady state visual evoked potential (SSVEP)-based BCI system is presented to identify SSVEP components from multi-channel electroencephalogram (EEG) data by minimizing background noise using an adaptive spatial filtering method. Here, the proposed adaptive spatial filtering-based SSVEP component extraction (ASFSCE) model improves reproducibility among multiple trails and identifies targets efficiently by optimizing the Eigenvalue problem. Along with that, the proposed ASFSCE model minimizes computational complexity from O(G²) to to get high target identification accuracy with faster execution. Performance results are measured using the SSVEP dataset. In this dataset, 11 subjects are used to perform experiments and 256-channel EEG data is taken. The efficiency of the proposed ASFSCE model is measured in terms of mean target detection accuracy and mean information transfer rate (ITR) in bits per minute. The average detection accuracy and ITR are evaluated by considering 23 trials for each subject. The obtained detection accuracy is 93.47% and ITR is 308.23 bpm.

<p>The uncontrolled outburst in population has led to crowd gatherings in various public places causing panic and disaster in certain unpleasant and extreme conditions. A study on the analysis of crowd accumulation has been carried out for various reasons that include management of crowd, design of a well-planned public space, the possibility for surveillance at every area and transportation systems. A lot of disasters also occurs due to uncontrollable crowd behaviour and poor crowd management. It could result in loss of property, fatalities or casualties. To avoid this, the conduct of a crowd of people has been studied and analyzed to control their movement and traffic. Hence, in this research work, integrated multi-level feature fusion (IMFF) framework is designed to predict the behaviour; further classification based on the local region is carried out to enhance the prediction. In the case of multi-level feature fusion; first level feature fusion utilizes the motion and appearance; second-level feature fusion utilizes the spatial connection and third-level utilizes the temporal connections. Further, the classification approach is integrated based on the local region is used to enhance the crowd behaviour prediction in terms of accuracy and faster. Moreover, performance evaluation is carried out considering the two distinctive datasets.</p>

The increasing number of people is a major cause of disasters that occur due to overcrowding. The gatherings of crowds in public places are a source of panic, which results in disaster. An analytical study was performed on crowd management. This is highly essential for the design of a well-planned public space, the possibility of surveillance in every area, and transportation systems. The disasters that occur due to uncontrollable crowd behaviour involve loss of property, fatalities, or casualties. To avoid this, the crowd’s behaviour was analysed. A MFF (multi-level feature fusion) framework was designed in this paper to predict behaviour. The first level of multi-level feature fusion employs motion and appearance, the second level employs spatial connections, and the third level employs temporal features. The combination of these characteristics aids in the exploitation of crowd behaviour. Furthermore, MFF was evaluated considering the web dataset, considering accuracy, precision, and recall as parameters. Comparative analysis was carried out with various existing methodologies with an accuracy of above 99 %.

Abstract: In present days we have discussed about the emerging concept of smart agriculture that makes agriculture more efficient, effective and farmers save money and time with the help of high precision algorithms and Geographic Information System (GIS).The component that drives it is GIS with Machine Learning the logical field that enables machines to learn without being carefully customized. It has developed together with huge information advances and elite registering to make new chances to disentangle, measures, and comprehends information concentrated procedures in farming operational conditions. For instance, ranchers use accuracy GPS on the field spare manure. Ranchers use precision agribusiness since they can lessen the proportion of manure fertilizer. Moreover, satellites and robots assemble vegetation, topography and atmosphere information from the sky. This information can go into developing maps for better fundamental activity.