Digital platforms are being utilized a growing amount frequently every day. Youngsters have a strong devotion to digital media, notably to social media platforms. These platforms exhibit a combination of messages and content, some of which could be abusive and affect young people's learning environments. These days, youngsters predominantly comprehend slang phrases and utterances from these internet channels. This has an impact on both their moral evolution and cognitive blossoming. We have assembled a vernacular sentence identifi-cation approach in this study, especially for the transliterated Bengali language. Sentences possessing filthy phrases or terms are pinpointed operating the Bi-directional LSTM method. For the first time, We have introduced a remarkable Repulsive Levy random walk established Particle Swarm Optimization (PSO) algorithm to obtain the optimum scales of the hyper-parameters within Bi-directional LSTM. To assist the swarms update their location and attain the goal, Repulsive Levy is utilized for the first time in this study. As a result of the fitness score incentive distribution, which the swarms have never experienced before, they can now improve their velocity integrating the Repulsive Levy method. The proposed Repulsive Levy- PSO algorithm is compared to a few cutting-edge techniques, and it is evident from the comparison that the proposed method exhibits the optimal fitness score for the rastrigin objective function of 1 × +10-59 and thus outperforms the other methods.

The rapid growth of natural language processing (NLP) applications, such as text summarization, speech recognition, information extraction, and machine translation, has led to the development of structured query language (SQL) for extracting information from structured data. However, due to limited resources, converting Natural Language (NL) queries to SQL in Bengali is challenging. This article proposes an unsupervised machine learning model to find semantically Bengali closed words that can generate SQL from NL queries in Bengali. The main objective of the proposed system is to provide support in the creation of patient-oriented explanations and educational resources by simplifying intricate medical terminology. The major findings of the proposed system are as follows: The use of machine translation in the field of medicine facilitates the dissemination of healthcare information to a diverse international audience and improves the performance of entity recognition tasks, including the identification of medical conditions, drugs, or procedures within clinical notes or electronic health data. This system allows a naive user to extract health-related information from a healthcare-structured database without any knowledge of SQL. The system accepts a query and generates a response according to the query in Bengali language. Query tokenization and stop word removal are carried out in the preprocessing stage, and unsupervised machine learning techniques are implemented to process the input query sentence. Tokenized words are converted into vectors using the skip-gram model, with noise-contrastive estimation (NCE) applied to discriminate between actual and irrelevant words. Stochastic gradient descent (SGD) optimizes the model by randomly choosing a small amount of data from the dataset and using cosine similarity to measure closer words. The semantically closer words are found using an unsupervised learning method to generate the SQL.

Profound subject knowledge is necessary for education to spark creative or formative projects and boost individual's dynamism. Acquiring a magnificent education is crucial because it permits us to live completely, make wise decisions, and overcome obstacles in life. A learner's academic journey is continued in early education, which also permits them to become more adept at discriminating between inequity and fairness and equips them for a broader spectrum of activities in higher education. Alongside superficial constraints like threats or financial crises, learners must compose a plethora of delightful and demanding activities in addition to their studies. Therefore, learners can experience moments of cognitive disorientation and progressively acquire cognitive ailments such as stress or depression, to name a couple. Thus, this research suggests a novel Bi-directional Encoder Representations from Transformer (BERT) method for ascertaining learners' cognitive declines. This research utilizes the BERT-Base architecture, and the hyper-tuning of the hyperparameters is carried out by the innovative Tweaked Shuffled Frog Leaping Algorithm technique. Although there is a high need for language models these days, there are currently no specific language models that can reliably specify a learner's cognitive deficits according to psychological inqueries. Thus, this work offers a novel strategy known as CogniBERT in response to this. Our approach confounds some of the weaknesses of the BERT methodology by choosing the right scaling factors during training. In terms of training, validation, and testing accuracies, the experimental result surpasses the other approaches, obtaining 99.56%, 98.97%, and 99.29%, respectively, when the suggested method is contrasted with a few state-of-the-art methods.

Recommender systems enhance the user experience by integrating factual and behavioral practices, enhancing learning strategies, and granting academic opportunities based on learners subjective interests. Cognitive intelligence, a learner's capability to interpret and synthesize cognitive abilities, is crucial to assemble effective recommendations. However, a student encounters diverse adversities in his or her academic life, whether it is his or her academic pacers or outward circumstances. As a result, a learner consequently experienced severe academic setbacks. Thus, this paper proposes a novel approach to identify cognitive deficits of learners in a school or college before recommending any scholastic content or pertaining in any academic activity. We have employed a modified Bi-directional Gated Recurrent Unit (Bi-GRU) method in conjunction with Shuffled Frog Leaping Algorithm (SFLA). Our approach overcomes some of the shortcomings of conventional Bi-GRU method in choosing the right scaling parameters during training. Bi-GRU method is opted for detecting the cognitive deficits of a learner and SFLA is applied over Bi-GRU in order to hyper-Tune the parameters. The proposed method is compared with a few cutting-edge methods, and the experimental result outperforms the other methods in training, validation, and testing accuracies, achieving 99.34%, 98.98%, and 99.19% respectively.

This paper proposes a disease detection system where it receives the query in form of symptoms of the disease in the Bengali language. This system is able to handle natural language queries in Bengali. The proposed system assists a layman to detect a probable disorder or disease in their body using disease symptoms. The proposed research work is challenging due to insufficient resources in vernacular languages like Bengali. This system receives a description of the patient's symptoms in the Bengali language and after processing the natural language text, it detects any potential disorders or diseases that may have occurred. This research work has been implemented separately by using the two most popular sequential prediction models. One is Bi-directional Long Short-Term Memory (Bi-LSTM) and the other is Bi-directional Gated Recurrent Unit (Bi-GRU). Both Bi-GRU and Bi-LSTM have provided significant results on a dataset of 3714 samples. The raw clinical text categorization data has been gathered from the Kaggle to build the detection model. The performances of disease detectability of both models have been measured using precision, recall and f1-score. The accuracy of the proposed system using the Bi-LSTM and the Bi-GRU models are 97.85% and 99.73%, respectively.

Machine intelligence is an essential branch of computer science that imitates human behavior. A human recognizes every real-world object and incident by their distinguishable features. If a computerized system incorporates human cognitive abilities, the system will be able to classify many real-world phenomena accurately. Therefore, an automated system that detects the disease based on symptoms has been addressed. Bidirectional Encoder Representations from Transformers (BERT) has been used to design this system. The BERT model has been developed based on the Transformers concept, forecasting a token by looking at every token in the series. The proposed system recognizes each and every individual's illness in the Bengali language. Punctuation marks are stripped from the user-supplied query which is in form of disease symptoms. Tokens are obtained from the individual's query. Tokens that have been filtered are sent to the proposed system. The proposed method employs filtered tokens to diagnose the disorder. The BERT model is trained using a dataset of diseases and their symptoms obtained from Kaggle. Precision, recall, fl-score, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) have been used to describe the proposed system's performance. The accuracy of the proposed system is 98.12% whereas RMSE, MSE, and MAE exhibit 1.8618, 1.0852, and 1.0826 respectively.

A temporal database may contain time-related data and handling such kind of database is a challenging task. It is difficult to extract data from a temporal database using natural language (NL) queries. Many NL queries to database interfaces have been developed but these interfaces are not suitable to handle time-related NL queries. The proposed system can handle time-related NL queries in the healthcare domain. A temporal database has been created where various Autism Spectrum Disorders (ASD) real-life problem statements have been stored with time. The proposed system is able to extract data from a temporal database using time-related NL queries with aggregation. Time tick has been assigned for handling temporal queries whereas aggregation methods have been used for handling computational type queries. Auxiliary verbs are used as an indication of whether the NL query is a temporal query. The proposed system has the capacity to produce structured query language (SQL) from temporal and computational types of queries which are frequently occurring in NL. This system will help the parents of an autistic child to enhance their knowledge about autism after getting many real-life autism problems according to time.

This paper proposes a natural language (NL) query-based response system in the Healthcare domain. Today, autism spectrum disorder (ASD) is a big issue among children and most parents are unaware of this disorder. Awareness about this disorder is very important and very much needed for parents to identify ASD in their children at an early stage to start treatment. Our proposed system will extract information about ASD from a database using NL queries. Sometimes the NL query may ask for some aggregate data from the database which may be difficult to process this proposed system is able to handle conventional queries with aggregation and generates responses from the healthcare database. A combinational algorithm has been approached where SQL has been generated from the NL query.

Various search strategies are used to search the data from the database. Adapting the searching language and grasping its numerous syntaxes are the key hurdles that a user encounters when accessing these data. Thus, we propose a system that translates natural language queries into Structured Query Language (SQL) queries and retrieves the relevant data from a database. This proposed system allows inexperienced users to access a database without prior knowledge of query languages. The current approach applies machine learning and rule-based approaches because the machine learning approach gives better results for large-size data, whereas the rule-based approach performs well in small-size datasets. This system receives health queries in Bengali. Tokenization is applied to the user's query. The Bengali Natural Language Processing (BNLP) toolkit removes punctuation marks from the token list. After removing punctuation marks, the proposed system uses a predefined Bengali stop words list to provide a score for each token. The score facilitates the finding of nominal words. The stemming method is performed to obtain the nominal root word. The pattern is created to generate all possible nominal compounds in Bengali. A new set of proposed rules and named entity recognition module of the BNLP toolkit is utilized to predict entities and attributes using the pattern. The proposed system maintains a healthcare database. Finally, the SQL is formed using entities, and attributes and the relevant result is obtained from the database.

The digital India program encourages Indian citizens to become conversant with e-services which are primarily English language-based services. However, the vast majority of the Indian population is comfortable with vernacular languages like Bengali, Assamese, Hindi, etc. The rural villagers are not able to interact with the Relational Database Management system in their native language. Therefore, create a system that produces SQL queries from natural language queries in Bengali, containing ambiguous words. This paper proposes a Bengali Query Processor named Extended Bengali language Query Processing System (XBLQPS) to handle queries containing ambiguous words posted to a Healthcare Information database in the electronic domain. The Healthcare Information database contains doctor, hospital and department details in the Bengali language. The proposed system provides support for the Bengali-speaking Indian rural population to efficiently fetch required information from the database. The proposed system extracts the Bengali root word by removing the inflectional part and categorizing them to a specific part of speech (POS) using modified Bengali WordNet. The proposed system uses manually annotated parts of speech detection of a word based on Bengali WordNet. Patterns of noun phrases are generated to detect the correct noun phrase as well as entity and attribute(s). Entity and attributes are used to prepare the semantic table which is utilized to create the Structured Query Language (SQL). The simplified LESK method is utilized to resolve ambiguous Bengali phrases in this query processing system. The accuracy, precision, recall and F1 score of the system is measured as 70%, 74%, 73%, and 73% respectively.

Bengali is the seventh most widely spoken language in the world. Many researchers are working on developing Bengali language based information retrieval, question-answering, query-response systems. The proposed Bengali Language Query Processing System (BLQPS) is based on natural language query-response model. Bengali language has been used in the model to extract knowledge data from a default database. The system is based on scoring and pattern generation algorithm that is able to generate structure query language (SQL) from natural language query in Bengali with the help of a synonym database. The proposed system is domain based and a large number of words have been initialized in the synonym database. The SQL is formulated from semantic analysis. Further, the generated SQL has been used to extract knowledge data in Bengali language from the default database.