Major contributors to impairment are neurological conditions such epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease, and others. These neurodegenerative conditions are life-altering, and those afflicted with them must fight a new battle every day. This manuscript presents the implementation of a Thought-to-Text Conversion BCI system. The system employs a joint CNN-LSTM deep neural network to learn high-level features from MI-EEG signals. To eliminate various artifacts from EEG signals, pre-processing is employed, such as utilizing a band-pass filter. Although an Auto-encoder layer is included, it has been found to decrease efficiency. The XGBoost Classifier is utilized to classify EEG signals into five EEG imagery tasks. This Deep Learning model is then pickled using Python and deployed to the server. The server prompts the development of a GUI that maps the input EEG signals to corresponding alphabets. The Neural Network is trained on multiple open-sourced EEG data-sets, utilizing various optimization algorithms to achieve high system accuracy and desirable efficiency for real-time applications. The textual output obtained can be utilized to aid in the rehabilitation of paralysis and stroke patients. The proposed hybrid model achieved an accuracy of 96.89% on testing data.while taking approximately 6 seconds per letter to be typed on the GUI screen.
This paper explores the potential of leveraging data science to advance the United Nations Sustainable Development Goals (SDGs). Data science is a multidisciplinary field that uses statistical and computational methods to extract insights and knowledge from data. The SDGs are 17 interrelated goals designed to promote sustainable development and eradicate poverty. The paper discusses how data science techniques can be applied to each of the 17 SDGs and also mentions the role of AI in achieving the SDGs. While data science presents a tremendous opportunity to advance the SDGs, it poses challenges like data privacy or quality. The paper reviews several examples of how data science has been used to address specific SDGs, such as ending hunger, promoting health and well-being, and reducing inequalities. The paper also discusses some of the challenges and opportunities for leveraging data science in the context of the SDGs, including issues related to data quality, privacy, and equity. Overall, the paper argues that data science has the potential to be a powerful tool for advancing the SDGs, but that careful attention must be paid to the ethical and social implications of data-driven approaches.
Cyberbullying has become a prevalent issue in the current era of technology. The prevalence of such content on online platforms not only perpetuates harmful behavior but also poses a threat to online communities and individuals' mental health. To address this issue there is a growing need for automated systems to detect toxic comments on social media. In this work, our focus is on utilizing natural language processing (NLP) techniques to distinguish between praise and insult in online comments on Reddit, a popular social media platform. In particular, we first benchmarked the classification performance using Term-Frequency Inverse Document Frequency (TF-IDF) features and then employed two different deep-learning-based approaches, Keras Embedding, and Global vectors for word representation (GloVe). Our results show that whereas the TF-IDF features achieve an accuracy of 83%, GloVe achieves an accuracy of 89%, but Keras Embeddings achieved an accuracy of 93%. Although these results are relatively poorer than the baseline classification accuracy of 97.8%, the size of our proposed solution is around 28 MB compared to 340 MB of the baseline model XLNet model, thereby making our solution more viable for real-time applications. These results suggest that there is ample opportunity for further research and development in this area, with the potential to make a real difference in the fight against cyberbullying.
The amelioration of information technology and the infiltration of it almost all the sectors of world have resulted in massive efflux of data. The rampant increase in the generation of data has introduced to a new term called, Big data. Big data is letting industry set the pace of future research and development. The need rose to further refine the data and get most out of it. Scientists and researchers performed analytic on the Big data to analyze it for patterns and behaviors hidden inside the data. Big data analytics aid in understanding the data through various tools, algorithms and frameworks. It allows stakeholders to make business plans and strategies according to the revelations from data. There are various tools and technologies available to perform analytics on the data. The most popular among scientists and researchers are real-time database and Big data analytics frameworks. In the paper, we evaluate the performance of the tools and technologies of Big data analytics. Mongodb and Firebase are real-time databases implemented in this study and Impala and Hive are the BDA frameworks. The parameter to perform the comparison is the query execution time. It is found out that Impala and Hive gave lesser execution time than Mongodb and Firebase. The query execution time of Impala on 50 mb dataset and 100 mb dataset was recorded to be 4 ms and 14 ms respectively. Hence, Big data Analytics Frameworks are better performing analytics tools for Big data Analytics.
The increasing demand of inductors in various industries reflects the attention of scientific communities to identify various solutions based on the magnetic flux density. This paper presents the design and simulation analysis of various inductors being investigated in COMSOL. The performance of straight line and circularly bent shaped inductors (nested inductor) is investigated based on simulation of the same dimension, size inductor in COMSOL. The achieved results predict that by changing the shape of the inductor from straight line to circularly bent, keeping all other parameters including permeability the same the inductance of the inductor is increased from $0.38568\mu\mathrm{H}$ . to $0.7525\mu\mathrm{H}$ . The magnetic flux density in a straight-line inductor is increased from 4mT to 6mT in a circularly bent or nested inductor.
