Machine Learning

Over 10% of the world's population now suffers from chronic kidney disease (CKD), and millions die yearly. To extend the lives of those suffering and lower the cost of therapy, CKD should be detected early. Building such a multimedia-driven model is necessary to detect the illness effectively and accurately before it worsens the situation. It is challenging for doctors to identify the various conditions connected to CKD early to prevent the condition. For CKD early detection and prediction, this study introduces a novel hybrid deep learning network model (HDLNet).

This dataset is made for traditional, machine learning, and deep neural-network-based virtual sensor development and evaluation.

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Iman Sharafaldin et al. generated the real time network traffic and these are made available at the Canadian Institute of Cyber security Institute website.  The team of researchers published the network traffic data and has made the dataset publicly available in both PCAP and CSV formats. The network traffic data is generated during two days. Training Day was on January 12th, 2018 and Testing Day was on March 11th, 2018.

The risks to children of online predators in real time gaming environments have been an area of growing concern. Research towards the development of near real time capabilities has been the focus of most queries published in this area of study. In this paper, we present Protectbot, a comprehensive safety framework used to interact with users in online gaming chat rooms. Protectbot employs a variant of the GPT-2 model known as DialoGPT, a generative pre-trained transformer designed specifically for conversation.

This Named Entities dataset is implemented by employing the widely used Large Language Model (LLM), BERT, on the CORD-19 biomedical literature corpus. By fine-tuning the pre-trained BERT on the CORD-NER dataset, the model gains the ability to comprehend the context and semantics of biomedical named entities. The refined model is then utilized on the CORD-19 to extract more contextually relevant and updated named entities. However, fine-tuning large datasets with LLMs poses a challenge. To counter this, two distinct sampling methodologies are utilized.

 Performance evaluation of AI Bot responses based on various sectors of society is a crucial aspect in determining the effectiveness and reliability of AI-based Chatbots. In order to evaluate their performance, several explicit parameters are taken into account, ensuring a comprehensive assessment. These parameters encompass a range of factors, such as accuracy, relevance, coherence, clarity, and contextual understanding.

The HQA1K dataset was developed for assessing the quality of Computer Generated Holography (CGH) image renderings based on direct human input.
HQA1K is comprised of 1,000 pairs of natural images matched to simulated CGH renderings of various quality levels. The result is a diverse set of data for evaluating image quality algorithms and models.

This bearing datasets has high data quality and obvious fault characteristics, so it is a commonly used bearing fault diagnosis standard dataset. In this datasets, three unbalanced datasets under different loads are constructed to testify the recognition effect of the proposed method. The test bench is composed of 2HP (1.5KW) induction motor, fan end bearing, driver end bearing, torque translator and load motor. By using EDM technology, single point faults with different depths were machined on the inner race, outer race and rolling element of the test bearing.