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Author(s):  
Saddam Bekhet ◽  
Abdullah M. Alghamdi ◽  
Islam F. Taj-Eddin

<p>Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces and<br />CAS-PEAL-R1.</p>


Author(s):  
Sujatha Arun Kokatnoor ◽  
Balachandran Krishnan

<p>The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease.</p>


2022 ◽  
Vol 23 ◽  
pp. 100732
Author(s):  
N. Taoufik ◽  
W. Boumya ◽  
R. Elmoubarki ◽  
A. Elhalil ◽  
M. Achak ◽  
...  

2022 ◽  
Vol 13 (1) ◽  
pp. 1-12
Author(s):  
Mu-Yen Chen ◽  
Min-Hsuan Fan ◽  
Li-Xiang Huang

In recent years, vehicular networks have become increasingly large, heterogeneous, and dynamic, making it difficult to meet strict requirements of ultralow latency, high reliability, high security, and massive connections for next generation (6G) networks. Recently, deep learning (DL ) has emerged as a powerful artificial intelligence (AI ) technique to optimize the efficiency and adaptability of vehicle and wireless communication. However, rapidly increasing absolute numbers of vehicles on the roads are leading to increased automobile accidents, many of which are attributable to drivers interacting with their mobile phones. To address potentially dangerous driver behavior, this study applies deep learning approaches to image recognition to develop an AI-based detection system that can detect potentially dangerous driving behavior. Multiple convolutional neural network (CNN )-based techniques including VGG16, VGG19, Densenet, and Openpose were compared in terms of their ability to detect and identify problematic driving.


2022 ◽  
Vol 120 ◽  
pp. 105022
Author(s):  
Christophe Lin-Kwong-Chon ◽  
Cédric Damour ◽  
Michel Benne ◽  
Jean-Jacques Amangoua Kadjo ◽  
Brigitte Grondin-Pérez

2023 ◽  
Vol 55 (1) ◽  
pp. 1-44
Author(s):  
Massimiliano Luca ◽  
Gianni Barlacchi ◽  
Bruno Lepri ◽  
Luca Pappalardo

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.


2022 ◽  
Vol 16 (4) ◽  
pp. 1-19
Author(s):  
Hanrui Wu ◽  
Michael K. Ng

Hypergraphs have shown great power in representing high-order relations among entities, and lots of hypergraph-based deep learning methods have been proposed to learn informative data representations for the node classification problem. However, most of these deep learning approaches do not take full consideration of either the hyperedge information or the original relationships among nodes and hyperedges. In this article, we present a simple yet effective semi-supervised node classification method named Hypergraph Convolution on Nodes-Hyperedges network, which performs filtering on both nodes and hyperedges as well as recovers the original hypergraph with the least information loss. Instead of only reducing the cross-entropy loss over the labeled samples as most previous approaches do, we additionally consider the hypergraph reconstruction loss as prior information to improve prediction accuracy. As a result, by taking both the cross-entropy loss on the labeled samples and the hypergraph reconstruction loss into consideration, we are able to achieve discriminative latent data representations for training a classifier. We perform extensive experiments on the semi-supervised node classification problem and compare the proposed method with state-of-the-art algorithms. The promising results demonstrate the effectiveness of the proposed method.


2022 ◽  
Vol 24 ◽  
pp. 101322
Author(s):  
Emre Kuşkapan ◽  
Mohammad Ali Sahraei ◽  
Merve Kayaci Çodur ◽  
Muhammed Yasin Çodur

Fuel ◽  
2022 ◽  
Vol 310 ◽  
pp. 122248
Author(s):  
Hao Wei ◽  
Kun Luo ◽  
Jiangkuan Xing ◽  
Jianren Fan

Author(s):  
Akella S. Narasimha Raju ◽  
Kayalvizhi Jayavel ◽  
Tulasi Rajalakshmi

<span>The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme.</span>


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