Data augmentation has proven to be effective in training neural networks. Recently, a method called RandAug was proposed, randomly selecting data augmentation techniques from a predefined search space. RandAug has demonstrated significant performance improvements for image-related tasks while imposing minimal computational overhead. However, no prior research has explored the application of RandAug specifically for audio data augmentation, which converts audio into an image-like pattern. To address this gap, we introduce AudRandAug, an adaptation of RandAug for audio data. AudRandAug selects data augmentation policies from a dedicated audio search space. To evaluate the effectiveness of AudRandAug, we conducted experiments using various models and datasets. Our findings indicate that AudRandAug outperforms other existing data augmentation methods regarding accuracy performance.

pdf/audrandaug-random-image-augmentations-for-audio-29ju19hjmr.pdf

AudRandAug: Random Image Augmentations for Audio Classification

Navigating the world without sight presents profound challenges for millions of visually impaired individuals. Echo Guidance includes a pioneering application, addresses these obstacles by facilitating independence and mobility through voice-driven interaction and an IoT smart assistive device for obstacle detection by utilizing Ultrasonic, GPS and GSM modules powered by Arduino. This study explores the design and evaluation of Echo Guidance, highlighting its utilization of advanced algorithms like RGB888 conversion, matrix transformation, and Optical Character Recognition (OCR) for real-time object recognition. The application boasts a voice-centric interface tailored to the needs of the visually impaired, seamlessly integrating day-to-day functionalities such as weather forecasting, reminders, and a calculator accessible through voice commands. Central to Echo Guidance is its innovative object detection and recognition approach, employing the MobileNetObjDetector class powered by TensorFlow Lite for swift and accurate object detection. The OverlayView class visually represents detected objects, enhancing spatial awareness. Additionally, the integration of IoT devices, such as the smart stick with ultrasonic sensors for obstacle detection and head-level obstacle detection, coupled with SOS functionality in case of any emergency using GPS and GSM modules, further enhances Echo Guidance's capabilities. This research underscores Echo Guidance's transformative potential in improving the quality of life for visually impaired individuals, representing a significant advancement in assistive technology. Prioritizing accessibility and user experience, this paper empowers individuals worldwide with newfound independence and confidence in navigating their surroundings.

Echo Guidance: Voice-Activated Application for Blind with Smart Assistive Stick Using Machine Learning and IoT

To bridge the communication barrier and improve mobility for individuals who struggle to understand emotions, this paper present a novel solution. A deep learning algorithm capable of recognizing and interpreting emotions from EEG signals. This research addresses a crucial challenge in improving the quality of life for affected individuals. By harnessing the power of EEG data analysis, the aim is to enhance their ability to engage with the world and others. In this study, using the DEAP dataset, we trained and tested the algorithm, and found that the CNN model had the highest accuracy of 92%. These results demonstrate the feasibility of using EEG signals to accurately recognize human emotions.

EEG-Based Human Emotion Recognition Using Deep Learning

This research project focuses on utilizing machine learning techniques to predict loan default among applicants in the context of financial organizations. Loan approval decisions carry substantial risks, and not all applicants can be deemed trustworthy. The primary goal of this research is to determine the feasibility of accurately presenting loan defaults by analyzing a dataset sourced from Kaggle.The project involves data analysis and the application of machine learning algorithms, including logistic regression, K Nearest Neighbor, and Decision tree. Implementation will be carried out using Python programming language within Visual Studio Code, with version control through GitHub, and eventual deployment on the AWS Cloud platform. The anticipated outcomes aim for high prediction accuracy and moderate training and validation data loss, contributing to more informed decision-making in the lending process.

Banking in the Digital Age: Predicting Eligible Customers through Machine Learning and AWS

Because of the speedy surge in social media’s expansion, the proliferation of malicious and harmful poses a substantial worry in contemporary society. The identification of hate speech on platforms like Twitter is crucial for various tasks such as controversial event extraction, AI chatterbot creation, content suggestions, and sentiment analysis. Researchers have invested considerable effort in addressing the challenging task of identifying hostile content due to the rise in hate speech and harmful information. The objective is to classify tweets as Hateful, Offensive, or neither. However, this task is highly complex due to the intricate nature of natural language constructs, encompassing different manifestations of animosity directed at various demographics, and the multitude of ways the same meaning can be expressed.Previous research has predominantly relied on manual feature extraction or employed representation-learning techniques followed by linear classifiers. Nevertheless, deep learning methods have recently demonstrated significant accuracy improvements in complex problems across speech, vision, and text applications. In this study, This paper present an idea for automatic classifications of inappropriate language and expressions of hostility using transfer learning models. In this research, leverage classified tweet datasets obtained from Kaggle and conduct experiments. Findings reveal that the multilingual-BERT model and its pre-trained version deliver superior outcomes. Specifically, pre-trained BERT model notably improves classification accuracy of hateful tweets by up to 92% when compared to other algorithms.

Multi-Modal Hate Speech Recognition Through Machine Learning

One of the most important senses in human life is vision, without it one’s life is totally filled with darkness. According to WHO globally millions of people are visually impaired estimated there are 285 million, of whom some millions are blind. Unfortunately, there are around 2.4 million people are blind in our beloved country Pakistan. Human are a crucial part of society and the blind community is a main part of society. The technologies are grown so far to make the life of humans easier more comfortable and more reliable for. However, this disability of the blind community would reduce their chance of using such innovative products. Therefore, the visually impaired community believe that they are burden to other societies and they do not capture in normal activities separates the blind people from society and because of this believe did not participate in the normally tasks of society. The visual impair people mainly face most of the problems in this real-time The aim of this work is to turn the real time world into an audio world by telling blind person about the objects in their way and can read printed text. This will enable blind persons to identify the things and read the text without any external help just by using the object detection and reading system in real time. Objective of this work: i) Object detection ii) Read printed text, using state-of-the-art (SOTA) technology.

Advanced Audio Aid for Blind People

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Danish Channa

Author Tools

Chat about Author

Papers

Advanced Audio Aid for Blind People