For using software applications, user interfaces that can be used includes command line, graphical user interface (GUI), menu driven, form-based, natural language, etc. The mainstream user interfaces include GUI and web-based, but occasionally the need for an alternative user interface arises. A chatbot based conversational user interface fits into this space. The chatbot is a class of bots that have existed in the chat platforms. The user can interact with them via graphical interfaces or widgets, and the trend is in this direction. They generally provide a stateful service i.e. the application saves data of each session. On a college's website, one often doesn't know where to search for some kind of information. It becomes difficult to extract information for a person who is not a student or employee there. The solution to these comes up with a college inquiry chat bot, a fast, standard and informative widget to enhance college website's user experience and provide effective information to the user. Chat bots are an intelligent system being developed using artificial intelligence (AI) and natural language processing (NLP) algorithms. It has an effective user interface and answers the queries related to examination cell, admission, academics, users' attendance and grade point average, placement cell and other miscellaneous activities.

A Brain Computer Interface (BCI) system provides a method for controlling a peripheral device. A BCI may use the magnetic, electrical, or metabolic activity of the brain. Electro-encephalography (EEG) is a non-invasive technique. It is popular for BCI research and is preferred due to its high temporal resolution, low cost of devices, convenience and movability. BCI based applications have massive potential in assistive devices, health care, and amusement industry. A regular BCI system comprises of these steps: signal acquisition, pre-processing, feature extraction and classification. An EEG contains the impulsive electrical activity of the brain taken from electrodes placed on the scalp of the subject. The EEG signal is then processed to remove noise and enhance the signal for analysing further. Features are mined from the amplitude and frequency of the recorded analog signals which can be transformed into feature vectors, and given as input to a classifier. Since EEG is non-stationary in nature, vulnerable to artifacts and has high variability, we need algorithms that efficiently extract relevant features and classify the signals accurately. This study reviews some recent applications of BCI and the feature extraction techniques used by them. Machine learning algorithms typically used in EEG-based BCI applications are also studied.

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The conventional BCI system experiences several issues such as background noise interference, lower precision rate and high cost. Hence, a novel speech recognition model which is based on the optimized Deep-CNN is proposed in this research article so as to restrain the issues related to the conventional speech recognition method. The significance of the research relies on the proposed method algorithm known as Aquila-eagle optimization, which effectively tunes the parameters of Deep-CNN. The most significant features are extracted in the feature selection process, which enhance the precision of the speech recognition model. Further unwanted noises in the EEG signals are constructively removed in the pre-processing stage to boost the accuracy of the Deep-CNN classifier.From the experimental outcomes it is demonstrated that the proposed Aquila-eagle-based DeepCNN outperformed other state-of-the-art techniques in terms of accuracy, precision, and recall with the values of 93.11%, 90.89%, and 93.11%, respectively.