In the emergency context of COVID-19 and its variants, rapid and accurate diagnosis based on radiographic images is of paramount importance. This avoids confusion with other types of pneumonia and ensures appropriate treatment. This paper presents a hybrid model combining five pre-trained CNNs (VGG16, VGG19, MobileNet, Inception-v3 and DenseNet201) with the SVM classifier. The study was conducted on a recent public database of radiographic images of COVID-19. Experiments on 3 different patterns (COVID-19 vs Normal), (COVID-19 vs Normal vs Lung Opacity) and (COVID-19 vs Normal vs Lung Opacity vs Viral Pneumonia) showed encouraging accuracies, with higher recognition rates compared to studies using CNN s alone. In addition, the approach was validated on a separate dataset linked to Pakistani population, achieving acceptable results.

Detection of Coronavirus Disease (COVID-19) Based on Deep Features and Support Vector Machine*

Dissecting the Role of Women in Cybersecurity and Information Technology: A Medical Perspective

This research presents a supervised machine learning approach for heart attack prediction using several models. Prompt care and preventative measures may be possible with early recognition of heart attacks, which pose a significant danger to health. This study proposes a new method that integrates the strengths of many machine learning models to improve the precision of the generated predictions. Blood pressure, cholesterol levels, age, gender, medical history, and other risk indicators are all part of the study's dataset. The dataset is preprocessed for learning the missing values and standardizing the features. Three separate ML algorithms-logistic regression, random forest, and support vector classifier (SVC)-are then trained by using the preprocessed data. The prediction ability is enhanced by stacking using the differences between each model. Then, performance metrics, including F1-score, recall, accuracy, and precision are used to evaluate the stacked model. The results of the experiments demonstrate that the multi-model approach outperforms the individual models, leading to more reliable and precise predictions. The proposed technique shows the potential to predict heart attacks and assist doctors in identifying people at high risk of experiencing a heart stroke.

Multi-Model Supervised Machine Learning Techniques for Heart Stroke Prediction

Segmentation-based offline handwritten character recognition algorithms suffered from the segmenting difficulty of interleaving and touching in handwritten manuscripts. To tackle the problem, a segmentation-free recognition algorithm based on deep learning network is proposed in this paper. The network consists of four neural layers, including input layer for image preprocessing, CNNs layer for feature extraction, BDLSTM layer for sequence prediction, and connectionist temporal classification layer for text sequence alignment and classification. Besides, a novel data processing method is performed for data length equalization. Based on this, groups of experiments, based on six typical databases, involved in evaluation indicators of character correct rate, training time cost, storage space cost and testing time cost are carried out.  The experimental results show that the proposed algorithm has better performances in accuracy and efficiency than other classical algorithms.

pdf/segmentation-free-recognition-algorithm-based-on-deep-15rs46r96y.pdf

Segmentation-free Recognition Algorithm Based on Deep Learning for Handwritten Text Image

The statistics mention that women harassment compared to the male counterparts is significantly higher all over the world. Even though the government has brought in very stringent laws for violence against women, women's safety has been a debatable and major social issue. We are in the era of Digital transformation wherein all our processes and activities are enabled through technology platforms; it is also important to implement the same and enable the women's community to make use of these platforms to make them more resilient and stronger. The technology implementation in women's safety can be categorized into physical, mental, and emotional aspects. AI-based solutions, AI-based collaborative security framework, and the process of AI-enabled security solutions to ensure the holistic security aspects of women are presented here. The increased technology implementation and the great penetration power of technology, right from rural to urban areas and from illiterate to tech-savvy communities has improved the well-being of women tremendously in the recent past. 

AI-Based Security Framework for Emotional and Personal Safety of Women

In the modern world, women now have tremendous success in every field. They can play, learn, and earn as much as men. But what about safety? Do they have the same secure environment that men and boys do? The answer is ”NO”. Women and girls have been subjected to numerous incidents, including acid throwing, rape, kidnapping, and harassment. It is common to read a lot of news like this in newspapers every day. These incidents make women feel unsafe in this society. Our freedom came a long time ago, but women still lack complete security in this society. All women can’t fight or shout all the time when some danger is happening to them. What can the physically challenged person and Children do? To make women feel safe, we designed ”Wrist Band” using IoT for Women Safety. As the sensors sense information from the body, it will always update the information such as pulse, temperature and vibration to the well wishers through the blynk app

IOT Based Wrist Band for Women Safety

Gender classification is used in numerous applications such as biometrics, criminology, surveillance, HCI, and business profiling. Although biometric factors like gait, face, hand shape, and iris have been used to classify people into genders, the majority of research has focused on facial traits due to their more recognisable qualities. This research employs fingerprints to classify gender, with the intention of being relevant for future studies. Several methods for gender classification utilising fingerprints have been presented in the literature, including ANN, KNN, Naive Bayes, the Gaussian mixture model, and deep learning-based classifiers. Although these classifiers have shown good classification accuracy, gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy, computation, and running time. In this paper, a CNN-SVM hybrid framework for gender classification from fingerprints is proposed, where preprocessing, feature extraction, and classification are the three main components. The main goal of this study is to use CNN to extract fingerprint information. These features are then sent to an SVM classifier to determine gender. The hybrid model's performance measures are examined and compared to those of the conventional CNN model. Using a CNN-SVM hybrid model, the accuracy of gender classification based on fingerprints was 99.25%. 

Gender Classification from Fingerprint Using Hybrid CNN-SVM

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.

V.Ebenezer

Author Tools

Chat about Author

Papers

IOT Based Wrist Band for Women Safety

Gender Classification from Fingerprint Using Hybrid CNN-SVM