scholarly journals AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 982 ◽  
Author(s):  
Khaled Almgren ◽  
Murali Krishnan ◽  
Fatima Aljanobi ◽  
Jeongkyu Lee
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Marketing Strategies ◽  
Multimedia Data ◽  
Visual Features ◽  
Image Detection ◽  
Real World Application ◽  
Real World Applications ◽  
Scanned Images

The processing and analyzing of multimedia data has become a popular research topic due to the evolution of deep learning. Deep learning has played an important role in addressing many challenging problems, such as computer vision, image recognition, and image detection, which can be useful in many real-world applications. In this study, we analyzed visual features of images to detect advertising images from scanned images of various magazines. The aim is to identify key features of advertising images and to apply them to real-world application. The proposed work will eventually help improve marketing strategies, which requires the classification of advertising images from magazines. We employed convolutional neural networks to classify scanned images as either advertisements or non-advertisements (i.e., articles). The results show that the proposed approach outperforms other classifiers and the related work in terms of accuracy.

scholarly journals ProtTox: Toxin identification from Protein Sequences

2020 ◽  
Author(s):  
Sathappan Muthiah ◽  
Debanjan Datta ◽  
Mohammad Raihanul Islam ◽  
Patrick Butler ◽  
Andrew Warren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Synthetic Biology ◽  
Real World ◽  
Protein Sequences ◽  
Structural Diversity ◽  
Manual Annotation ◽  
Real World Applications ◽  
Automated Machine Learning

AbstractToxin classification of protein sequences is a challenging task with real world applications in healthcare and synthetic biology. Due to an ever expanding database of proteins and the inordinate cost of manual annotation, automated machine learning based approaches are crucial. Approaches need to overcome challenges of homology, multi-functionality, and structural diversity among proteins in this task. We propose a novel deep learning based method ProtTox, that aims to address some of the shortcomings of previous approaches in classifying proteins as toxins or not. Our method achieves a performance of 0.812 F1-score which is about 5% higher than the closest performing baseline.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals SECURITY ARCHITECTURE FOR REAL- WORLD APPLICATIONS OF WIRELESS SENSOR NETWORK

2013 ◽  
pp. 181-184
Author(s):  
SHUBHI PRIYA ◽  
SONALI SINGHA
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Real World ◽  
Wireless Sensor ◽  
Security Architecture ◽  
Security Model ◽  
Road Tunnel ◽  
Security Framework ◽  
Real World Application ◽  
Real World Applications

With the recent capability of being applicable in enormous fields, the Wireless Sensor Networks (WSN) have drawn attention of researchers and industries in diverse areas. Being deployed in areas that are hostile, WSN pose lots of difficulties and challenges to the research fraternity. In this paper, we discuss a generalized security framework for real world application of WSN. In particular we have considered the scenario of civil engineering field, a road tunnel. The proposed work mainly focus on security model for self adaptive lighting Model for road tunnel in WSN, and hence called Smart tunnel which provide the safety of the tunnels.

scholarly journals Deep Learning Method for Quantitative Analysis of Covid -19 using CT Chest Images

2021 ◽  
Author(s):  
Indrajeet Kumar ◽  
Jyoti Rawat
Keyword(s):  
Deep Learning ◽  
Diagnostic Tests ◽  
Ct Images ◽  
Rapid Diagnosis ◽  
Chest Ct ◽  
Visual Features ◽  
Learning Module ◽  
Volumetric Ct ◽  
The Cost

Abstract The manual diagnostic tests performed in laboratories for pandemic disease such as COVID19 is time-consuming, requires skills and expertise of the performer to yield accurate results. Moreover, it is very cost ineffective as the cost of test kits is high and also requires well-equipped labs to conduct them. Thus, other means of diagnosing the patients with presence of SARS-COV2 (the virus responsible for COVID19) must be explored. A radiography method like chest CT images is one such means that can be utilized for diagnosis of COVID19. The radio-graphical changes observed in CT images of COVID19 patient helps in developing a deep learning-based method for extraction of graphical features which are then used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID19 from given volumetric CT images of patient’s chest by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network is deployed for classifying the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of which 349 images belong to COVID19 positive cases while remaining 397 belong negative cases of COVID19. The extensive experiment has been completed with the accuracy of 98.4 %, sensitivity of 98.5 %, the specificity of 98.3 %, the precision of 97.1 %, F1score of 97.8 %. The obtained result shows the outstanding performance for classification of infectious and non-infectious for COVID19 cases.

Face Counting and Mask Detection using Deep Learning

2020 ◽  
Vol 11 (12) ◽  
pp. 709-714
Author(s):  
Janani Prabu ◽  
Sai Saranesh ◽  
Dr.S. Ajitha
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Strong Solution ◽  
Real World ◽  
Human Communication ◽  
Human Ability ◽  
Real World Applications ◽  
The Face

Face is one among the foremost important human's biometrics which is used frequently in every day human communication and due to some of its unique characteristics plays a major role in conveying identity and emotion. So far numerous methods have been proposed for face recognition, but it's still remained very challenging in real world applications and up to date; there is no technique which equals human ability to recognize faces despite many variations in appearance that the face can have in a scene and provides a strong solution to all situations.

Deep learning architecture using rough sets and rough neural networks

Kybernetes ◽  
2017 ◽  
Vol 46 (4) ◽  
pp. 693-705 ◽  
Author(s):  
Yasser F. Hassan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Soft Computing ◽  
Rough Set ◽  
Real World ◽  
Rough Sets ◽  
Decision Table ◽  
Content Type ◽  
Real World Applications ◽  
Decision Tables

Purpose This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications. Design/methodology/approach The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables. Findings The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system. Research limitations/implications The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables. Practical implications The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent. Social implications The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems Originality/value This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

scholarly journals Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6661
Author(s):  
Lars Schmarje ◽  
Johannes Brünger ◽  
Monty Santarossa ◽  
Simon-Martin Schröder ◽  
Rainer Kiko ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Supervised Classification ◽  
Limited Information ◽  
Classification Problems ◽  
Previous State ◽  
Supervised Methods ◽  
Real World Datasets ◽  
Fuzzy Labels

Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes. For underwater classification, and uncurated real-world datasets in general, clean class boundaries can often not be given due to a limited information content in the images and transitional stages of the depicted objects. This leads to different experts having different opinions and thus producing fuzzy labels which could also be considered ambiguous or divergent. We propose a novel framework for handling semi-supervised classifications of such fuzzy labels. It is based on the idea of overclustering to detect substructures in these fuzzy labels. We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels. We show that our framework is superior to previous state-of-the-art semi-supervised methods when applied to real-world plankton data with fuzzy labels. Moreover, we acquire 5 to 10% more consistent predictions of substructures.

Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

2020 ◽  
pp. 274-294
Author(s):  
Yilin Yan ◽  
Min Chen ◽  
Saad Sadiq ◽  
Mei-Ling Shyu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Imbalanced Data ◽  
Network Models ◽  
Multimedia Data ◽  
Neural Network Models ◽  
Minority Class ◽  
Imbalanced Data Classification

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.

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