7 research outputs found
BLOCKCHAIN FUNDAMENTAL: PRINCIPAL AND APPLICATION
No full text<p>BLOCKCHAIN FUNDAMENTAL: PRINCIPAL AND APPLICATION</p>
An innovation proving of knowledge bases for automated reasoning for information analysis
No full textThis paper examines the capability of using information Bases (KBs) in automatic reasoning for superior facts analysis. KBs, as a form of information representation, are uniquely suited for automated reasoning as they shop various kinds of domain-specific statistics in a single pool of declarative facts. The paper explores how to reliably and efficiently use such declarative data shops connecting one of a kind forms of artificial evidence and techniques. Right here, a new technique is proposed that unites the efficiency of RDF and the expressiveness of text-primarily based documents for representing the research context. An extension of the RDF format to represent reasoning steps and knowledge entities extra as it should be is proposed and tested on a repository of actual-global clinical and genetics datasets. The outcomes prove that the approach can improve computation of diverse components of studies workflow consisting of gene-disorder mapping tasks, whilst retaining affordable computational complexity. The proposed KB extends the capacity to generate efficient and powerful records analysis for research contexts and may result in new paradigms of automated reasonin
Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications
No full textAccurate and non-invasive sleep staging is essential for evaluating sleep quality and diagnosing neurological and sleep disorders. Addressing the variations in electroencephalogram (EEG) and electrooculogram (EOG) signals across different sleep stages, this study introduces a transcranial focused ultrasound (tFUS) based multimodal feature fusion deep learning model (MFDL) for automated sleep staging. The proposed framework integrates two one-dimensional convolutional neural networks (1D-CNNs) to extract sleep-relevant features from EEG and EOG signals, followed by an adaptive feature fusion module that dynamically assigns weights based on feature significance. By enhancing discriminative features and suppressing irrelevant ones, the model generates a robust multimodal representation of sleep information. Furthermore, a bidirectional long short-term memory (Bi-LSTM) network captures temporal dependencies in sleep stage transitions, improving classification accuracy. The effectiveness of MFDL is validated on the publicly available Sleep-EDF dataset, achieving 94.1% accuracy, 88.2% Kappa coefficient, and 81.9% MF1 score. Notably, the recall rates for the challenging N1 and REM sleep stages are significantly enhanced to 64.6% and 93.5%, respectively. These results highlight the potential of MFDL in enhancing tFUS-based neuromodulation by providing precise, data-driven sleep state monitoring, paving the way for advanced non-invasive brain stimulation technologies in next-gen clinical applications
IOT-based cyber security identification model through machine learning technique
No full textManual vulnerability evaluation tools produce erroneous data and lead to difficult analytical thinking. Such security concerns are exacerbated by the variety, imperfection, and redundancies of modern security repositories. These problems were common traits of producers and public vulnerability disclosures, which make it more difficult to identify security flaws through direct analysis through the Internet of Things (IoT). Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting databases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators. This proposed methodology's full potential has yet to be fulfilled, both in terms of its execution and its significance for security evaluation in application software. The study shows that the recommended mental security methodology works better when addressing the above inadequacies and the constraints of variation among cybersecurity alert mechanisms. Intriguing trade-offs are presented by the experimental analysis of our program, in particular the ensemble method that detects tendencies of computational security defects on data sources
Elevating intelligent voice assistant chatbots with natural language processing, and OpenAI technologies
No full textBusinesses can offer support to customers outside of usual business hours and across time zones by employing chatbots, which can provide round-the-clock support. Chatbots can react to user inquiries quickly, reducing wait times and improving customer satisfaction. It becomes challenging for the chatbot to differentiate between two queries that users pose that carry the same meaning, making it harder for it to understand and react appropriately. The aim of this research is to develop a chatbot capable of understanding the semantic meaning of questions as well as recognizing various speech patterns, accents, and dialects to provide accurate responses. In this research, we have implemented a voice-enabled chatbot system where users can verbally pose questions, and the chatbot provides responses through voice assistance. The architecture incorporates several key components: a question-answer database, OpenAI embedding for semantic representation, and OpenAI text-to-speech (TTS) and speech-to-text (STT) for audio-to-text and text-to-audio conversion, respectively. Specifically, OpenAI embedding is utilized to encode questions and responses into vector representations, enabling efficient similarity calculations. Additionally, extreme gradient boosting (XGBoost) is trained on OpenAI embeddings to identify similarities between user queries and questions within the dataset. This framework allows for seamless interaction between users and the chatbot, leveraging state-of-the-art technologies in natural language processing (NLP) and speech recognition. The outcome demonstrates that the XGBoost model delivers excellent outcomes when it is trained on OpenAI embedding and tuned with the particle swarm optimizer (PSO). The OpenAI-generated embedding has good potential for capturing sentence similarity and provides excellent information for models trained on it
An overview of natural language processing techniques for information analysis
No full textNatural Language Processing (NLP) is an increasingly essential and rapidly advancing discipline of laptop technological know-how and artificial intelligence, centered on the development of algorithms and methods for analyzing big our bodies of natural language textual content. Records analysis entails extracting applicable records from natural language files, knowing how to interpret the textual content, and the way to practice the evaluation to diverse obligations. NLP strategies for data evaluation can consist of entity recognition, part-of-speech tagging, sentiment analysis, textual content type, summarization, query processing, and topic detection. Entity reputation is the method of tagging and assigning semantic labels to words or phrases in a text, frequently with the purpose of categorizing them by kind or class. The give up result is a set of entities that could then be in addition analyzed with other NLP strategies. Component-ofspeech tagging is the process of labeling words in a text primarily based on their part of speech. This can help become aware of vital phrases in a frame of text, helping in semantic or syntactic analyse
