Deep Learning: A Revolutionizing Approach To Brain Tumor Classification Using MRI

Brain cancer, caused by tumors formed through the irregular and unchecked proliferation of brain cells, poses significant risks, including permanent brain damage and even death if left unmanaged. The number of individuals impacted by brain tumors (BT) is rising worldwide. Positional accuracy and tumor size play a key role in traditional treatments. Thus, creating an automated and meticulous approach to deliver critical information to healthcare professionals is of utmost importance. The integration of various imaging modalities with machine learning (ML) along with its various subsets like deep learning (DL) has enhanced physicians’ ability to identify tumor types with increased precision and reduced time. This paper aims to provide insights into recently developed systems that utilize these techniques to analyze medical imaging for BT diagnosis. Lastly, this paper discusses the major challenges faced by DL algorithms in BT classification and potential advancements in this field. Lastly the paper discusses the use of YOLOv8 and compares its results with the customized CNN (CCNN) BT classification technique. The accuracies obtained were 98.94% and 96.88% respectively

Contextual Locally Generated Suggestions

This disclosure describes techniques for automatic generation and presentation of communication parameters via a virtual assistant dialog to a user that is engaged in another activity, e.g., driving a vehicle. The parameters are generated locally based on user-permitted contextual factors. Users can select from the presented options by using any combination of voice input, touch/gesture input, etc. The generated parameters can include, e.g., call/message recipients, message content, etc. The recipients are suggested based on prior on-device activity. Upon user selection of a message recipient, message content options are provided based on ranking candidate messages based on the context. With user permission, appropriate contextual information, e.g.. a destination, current location, etc. is included in the body of the candidate message

Contextually Performing Query Processing On-device or On a Remote Server

When a user issues a query, e.g., a spoken query to a user device such as a smartphone, smart speaker, in-car device, etc., the query may be processed locally on-device and additionally, remotely on a server (if permitted by the user). The determination of whether a query is processed locally or on a remote device is typically based on whether the device has a network connection. Local processing of queries can consume device resources. When the device is simultaneously in use for other critical tasks, such resource demand can have a negative impact on such tasks. This disclosure describes the use of a trained machine learning model that takes into account user-permitted contextual factors to determine whether query processing is to be performed on-device or on a remote server

Simulated Alternatives for Data Augmentation in Machine Learning

In domains such as automated speech recognition and search, pre-collected data is often used to understand user input and map it to an intention. In such systems, the context of the user query is important in arriving at the correct response. However, in many use cases, the amount of context to resolve the concept is limited. This disclosure describes data augmentation techniques that increase the amount of data available to understand user input. The techniques described herein can be used to automatically generate data that is adjacent to known correct observations and distinct from most incorrect observations. The techniques described in this disclosure enable growing a seed data set into a much larger corpus

Perceived stress among gravid and its effect on their oral health in Sri Ganganagar, Rajasthan, India

Background: The gestation period presents unique stresses that challenges overall psychological adaptation of a women. The present study is designed especially to focus on evaluating the effect of perceived stress on pregnant women and its effect on their oral health.Methods: A cross-sectional study was conducted among 18-30 years old, pregnant women in the Sri Ganganagar city. Prior to the clinical examination a questionnaire was used in order to collect the information which comprised of three parts and were completed through an interview. The first part comprised of demographics questionnaire, second part included oral hygiene questionnaire and third part was perceived Stress Scale. Descriptive analysis described demographics and socioeconomic characteristics. Multivariate analysis was used to describe the association between stress and various characteristics. Chi-square and Kruskal-wallis test was used to study the association of independent variables with level of stress.Results: The high stress was reported among those females who were aged > 25 years (46.66%), living in rural areas (73.33%), were employed (57.77%), were in third trimester of pregnancy (96.66%) and had no previous pregnancy experience (67.77%). The mothers in high stress group had high levels of dental diseases when compared to the other two groups (low and moderate stress).Conclusions: Based on the results, this study emphasizes on the need for a continued effort to improve the mental and oral health status of gravid women so as to reduce the incidences of psychological and physical troubles in this population predicted

Item-Language Model for Conversational Recommendation

Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abilities have been extended with multi-modality to include image, audio, and video capabilities. Recommender systems, on the other hand, have been critical for information seeking and item discovery needs. Recently, there have been attempts to apply LLMs for recommendations. One difficulty of current attempts is that the underlying LLM is usually not trained on the recommender system data, which largely contains user interaction signals and is often not publicly available. Another difficulty is user interaction signals often have a different pattern from natural language text, and it is currently unclear if the LLM training setup can learn more non-trivial knowledge from interaction signals compared with traditional recommender system methods. Finally, it is difficult to train multiple LLMs for different use-cases, and to retain the original language and reasoning abilities when learning from recommender system data. To address these three limitations, we propose an Item-Language Model (ILM), which is composed of an item encoder to produce text-aligned item representations that encode user interaction signals, and a frozen LLM that can understand those item representations with preserved pretrained knowledge. We conduct extensive experiments which demonstrate both the importance of the language-alignment and of user interaction knowledge in the item encoder.Comment: 15 pages, 3 figure

Multi-wavelength follow-up of FRB 180309

We report on the results of multi-wavelength follow-up observations with Gemini, VLA, and ATCA, to search for a host galaxy and any persistent radio emission associated with FRB 180309. This FRB is among the most luminous FRB detections to date, with a luminosity of $> 8.7\times 10^{32}$ erg Hz$^{-1}$ at the dispersion-based redshift upper limit of 0.32. We used the high-significance detection of FRB 180309 with the Parkes Telescope and a beam model of the Parkes Multibeam Receiver to improve the localization of the FRB to a region spanning approximately $\sim2'\times2'$. We aimed to seek bright galaxies within this region to determine the strongest candidates as the originator of this highly luminous FRB. We identified optical sources within the localization region above our r-band magnitude limit of 24.27, fourteen of which have photometric redshifts whose fitted mean is consistent with the redshift upper limit ($z < 0.32$) of our FRB. Two of these galaxies are coincident with marginally detected "persistent" radio sources of flux density 24.3$\mu$Jy beam$^{-1}$ and 22.1$\mu$Jy beam$^{-1}$ respectively. Our redshift-dependent limit on the luminosity of any associated persistent radio source is comparable to the luminosity limits for other localized FRBs. We analyze several properties of the candidate hosts we identified, including chance association probability, redshift, and presence of radio emission, however it remains possible that any of these galaxies could be the host of this FRB. Follow-up spectroscopy on these objects to explore their H$\alpha$ emission and ionization contents, as well as to obtain more precisely measured redshifts, may be able to isolate a single host for this luminous FRB.Comment: 14 pages, 7 figures; Accepted for publication in AAS Journals (ApJ

Ensemble Fractional Sensitivity: A Quantitative Approach to Neuron Selection for Decoding Motor Tasks

A robust method to help identify the population of neurons used for decoding motor tasks is developed. We use sensitivity analysis to develop a new metric for quantifying the relative contribution of a neuron towards the decoded output, called “fractional sensitivity.” Previous model-based approaches for neuron ranking have been shown to largely depend on the collection of training data. We suggest the use of an ensemble of models that are trained on random subsets of trials to rank neurons. For this work, we tested a decoding algorithm on neuronal data recorded from two male rhesus monkeys while they performed a reach to grasp a bar at three orientations (45°, 90°, or 135°). An ensemble approach led to a statistically significant increase of 5% in decoding accuracy and 25% increase in identification accuracy of simulated noisy neurons, when compared to a single model. Furthermore, ranking neurons based on the ensemble fractional sensitivities resulted in decoding accuracies 10%–20% greater than when randomly selecting neurons or ranking based on firing rates alone. By systematically reducing the size of the input space, we determine the optimal number of neurons needed for decoding the motor output. This selection approach has practical benefits for other BMI applications where limited number of electrodes and training datasets are available, but high decoding accuracies are desirable

Creation of an Interactive Dashboard to Facilitate Early Detection of Cardiac Amyloidosis in African American Veterans

Background Cardiac amyloidosis (CA) is an underdiagnosed cause of heart failure (HF) that disproportionately impacts men of African descent. Without a standardized method of screening and scattered patient health information, clinicians must integrate data that spans multiple disease systems and is stored across the electronic health record.Objectives The aim of this project was to create a dashboard to facilitate identification of high-risk African American (AA) veterans who would benefit from CA screening tests. This paper described the development of the dashboard and identified barriers and opportunities in dashboard development.Methods Three Veterans Affairs (VA) health systems participated in this project. Microsoft Structured Query Language (SQL) Report Builder was utilized to create an interactive dashboard that refreshes daily through stored procedures using SQL Server Integration Services and the SQL Server Job Agent. Inclusion criteria included AA patients less than 90 years old with a history of HF. The 2023 American College of Cardiology/American Heart Association consensus statement on diagnosis and treatment of transthyretin CA was the source of evidence in creating the inclusion criteria and parameters of interest.Results The final dashboard contained 1,732 HF patients who met inclusion criteria, of which 949 (55%) were identified as high risk. We faced several challenges in this project, including time required for dashboard development, limited team experience in specifying dashboard requirements, identifying informatics counterparts at all sites, and standardizing data across three VA hospitals.Conclusion In this clinical improvement project, we created a dashboard that identifies AA veterans with HF at risk for CA and that can help to mitigate the impact of CA on this population