Federated Learning is a distributed method of Machine Learning that puts user privacy first by storing data locally and never centralizing it on a server. Numerous applications have successfully used this technique, especially those requiring sensitive data like healthcare and banking. Each training round in classical federated learning involves a complete update of all model… →
This study aimed to evaluate the effectiveness of virtual reality (VR) in the mental state and quality of sleep improvement and physical activity (PA) increase of patients diagnosed with breast cancer (BC). A total of 33 subjects divided into experimental (EG, n = 17) and control (CG, n = 16) groups were assessed with the… →
CONCLUSIONS: Introducing NF-TT early in the medical school curriculum, before students are exposed to a pervasive conditional probability formula-based approach, would offer the greatest benefit. →
CONCLUSIONS: The analgesic efficacy of supraclavicular brachial plexus blockade combined with dexamethasone, magnesium sulfate, and dexmedetomidine is significantly superior to the combination of magnesium sulfate and dexmedetomidine, and significantly superior to the use of magnesium sulfate alone. →
This study aimed to evaluate the effectiveness of different pain mitigation methods during orthodontic debonding and to evaluate pain sensitivity across various regions of the dentition. A total of 144 participants (50 males and 94 females) with metal brackets were randomly assigned to one of four groups: High-Frequency Vibration (V), Cotton Roll (CR), Elastomeric Wafer… →
Retrieval-augmented generation (RAG) systems blend retrieval and generation processes to address the complexities of answering open-ended, multi-dimensional questions. By accessing relevant documents and knowledge, RAG-based models generate answers with additional context, offering richer insights than generative-only models. This approach is useful in fields where responses must reflect a broad knowledge base, such as legal research… →
A major challenge in AI research is how to develop models that can balance fast, intuitive reasoning with slower, more detailed reasoning in an efficient way. Human cognition operates by using two systems: System 1, which is fast and intuitive, and System 2, which is slow but more analytical. In AI models, this dichotomy between… →
Natural Language Processing (NLP) is a rapidly growing field that deals with the interaction between computers and human language. As NLP continues to advance, there is a growing need for skilled professionals to develop innovative solutions for various applications, such as chatbots, sentiment analysis, and machine translation. To help you on your journey to mastering… →
One of the fundamental challenges in IR is that the classic systems are not designed to handle dynamic, multi-step tasks. Current IR frameworks rely on an immutable, predefined architecture that enables only single-step interactions; users must explicitly revise queries to get the desired results. Conventional models thus lag far behind as users increasingly request systems… →
To determine if two biological or artificial systems process information similarly, various similarity measures are used, such as linear regression, Centered Kernel Alignment (CKA), Normalized Bures Similarity (NBS), and angular Procrustes distance. Despite their popularity, the factors contributing to high similarity scores and what defines a good score remain to be determined. These metrics are… →