Deep-n-Cheap: An Automated Efficient and Extensible Search Framework for Cost-Effective Deep Learning

The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.

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A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos

Scientific Reports ◽

10.1038/s41598-021-96091-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Wen Li ◽

Yuan Liang ◽

Xuan Zhang ◽

Chao Liu ◽

Lei He ◽

...

Keyword(s):

Deep Learning ◽

Low Income ◽

Dental Health ◽

Area Under The Curve ◽

Cost Effective ◽

General Purpose ◽

Dental Calculus ◽

Task Learning ◽

Large Populations ◽

Dental Diseases

AbstractRoutine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. This study proposes to screen the existence of gingivitis and its irritants, i.e., dental calculus and soft deposits, from oral photos with a novel Multi-Task Learning convolutional neural network (CNN) model. The study can be meaningful for promoting the public dental health, since it sheds light on a cost-effective and ubiquitous solution for the early detection of dental issues. With 625 patients included in this study, the classification Area Under the Curve (AUC) for detecting gingivitis, dental calculus and soft deposits were 87.11%, 80.11%, and 78.57%, respectively; Meanwhile, according to our experiments, the model can also localize the three types of findings on oral photos with moderate accuracy, which enables the model to explain the screen results. By comparing to general-purpose CNNs, we showed our model significantly outperformed on both classification and localization tasks, which indicates the effectiveness of Multi-Task Learning on dental disease detection. In all, the study shows the potential of deep learning for enabling the screening of dental diseases among large populations.

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A Review on a Deep Learning Perspective in Brain Cancer Classification

Cancers ◽

10.3390/cancers11010111 ◽

2019 ◽

Vol 11 (1) ◽

pp. 111 ◽

Cited By ~ 38

Author(s):

Gopal S. Tandel ◽

Mainak Biswas ◽

Omprakash G. Kakde ◽

Ashish Tiwari ◽

Harman S. Suri ◽

...

Keyword(s):

Deep Learning ◽

Brain Cancer ◽

Cost Effective ◽

World Health ◽

Imaging Modalities ◽

Computer Assisted ◽

Learning Paradigm ◽

Brain Scans ◽

Magnetic Resonance Imaging Mri ◽

Health Organization

A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.

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Automated, Cost-Effective Optical System for Accelerated Antimicrobial Susceptibility Testing (AST) Using Deep Learning

ACS Photonics ◽

10.1021/acsphotonics.0c00841 ◽

2020 ◽

Vol 7 (9) ◽

pp. 2527-2538

Author(s):

Calvin Brown ◽

Derek Tseng ◽

Paige M. K. Larkin ◽

Susan Realegeno ◽

Leanne Mortimer ◽

...

Keyword(s):

Deep Learning ◽

Optical System ◽

Antimicrobial Susceptibility ◽

Susceptibility Testing ◽

Cost Effective ◽

Antimicrobial Susceptibility Testing

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DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

Electronics ◽

10.3390/electronics9091533 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1533 ◽

Cited By ~ 1

Author(s):

Tuan Anh Tang ◽

Lotfi Mhamdi ◽

Des McLernon ◽

Syed Ali Raza Zaidi ◽

Mounir Ghogho ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Intrusion Detection ◽

Detection System ◽

Software Defined Networking ◽

Cost Effective ◽

The Internet ◽

Network Intrusion ◽

Effective Network ◽

Control Application

Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach.

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Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors

Sensors ◽

10.3390/s21206853 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6853

Author(s):

Hayat Khaloufi ◽

Karim Abouelmehdi ◽

Abderrahim Beni-Hssane ◽

Furqan Rustam ◽

Anca Delia Jurcut ◽

...

Keyword(s):

Deep Learning ◽

Early Detection ◽

Cost Effective ◽

Clinical Analysis ◽

Test Methods ◽

Nucleic Acid Amplification ◽

Micro Electro Mechanical Systems ◽

Preliminary Diagnosis ◽

Computer Tomography Scan ◽

Smartphone Sensors

The COVID-19 pandemic has affected almost every country causing devastating economic and social disruption and stretching healthcare systems to the limit. Furthermore, while being the current gold standard, existing test methods including NAAT (Nucleic Acid Amplification Tests), clinical analysis of chest CT (Computer Tomography) scan images, and blood test results, require in-person visits to a hospital which is not an adequate way to control such a highly contagious pandemic. Therefore, top priority must be given, among other things, to enlisting recent and adequate technologies to reduce the adverse impact of this pandemic. Modern smartphones possess a rich variety of embedded MEMS (Micro-Electro-Mechanical-Systems) sensors capable of recording movements, temperature, audio, and video of their carriers. This study leverages the smartphone sensors for the preliminary diagnosis of COVID-19. Deep learning, an important breakthrough in the domain of artificial intelligence in the past decade, has huge potential for extracting apt and appropriate features in healthcare. Motivated from these facts, this paper presents a new framework that leverages advanced machine learning and data analytics techniques for the early detection of coronavirus disease using smartphone embedded sensors. The proposal provides a simple to use and quickly deployable screening tool that can be easily configured with a smartphone. Experimental results indicate that the model can detect positive cases with an overall accuracy of 79% using only the data from the smartphone sensors. This means that the patient can either be isolated or treated immediately to prevent further spread, thereby saving more lives. The proposed approach does not involve any medical tests and is a cost-effective solution that provides robust results.

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Performance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays

10.36227/techrxiv.16725727 ◽

2021 ◽

Author(s):

Tirupathi Karthik ◽

Vijayalakshmi Kasiraman ◽

Bhavani Paski ◽

Kashyap Gurram ◽

Amit Talwar ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Delayed Diagnosis ◽

Cost Effective ◽

Radiology Workflow ◽

X Rays ◽

Term Care ◽

X Ray ◽

Intelligence System ◽

Deep Learning Algorithm

Background and aims: Chest X-rays are widely used, non-invasive, cost effective imaging tests. However, the complexity of interpretation and global shortage of radiologists have led to reporting backlogs, delayed diagnosis and a compromised quality of care. A fully automated, reliable artificial intelligence system that can quickly triage abnormal images for urgent radiologist review would be invaluable in the clinical setting. The aim was to develop and validate a deep learning Convoluted Neural Network algorithm to automate the detection of 13 common abnormalities found on Chest X-rays. Method: In this retrospective study, a VGG 16 deep learning model was trained on images from the Chest-ray 14, a large publicly available Chest X-ray dataset, containing over 112,120 images with annotations. Images were split into training, validation and testing sets and trained to identify 13 specific abnormalities. The primary performance measures were accuracy and precision. Results: The model demonstrated an overall accuracy of 88% in the identification of abnormal X-rays and 87% in the detection of 13 common chest conditions with no model bias. Conclusion: This study demonstrates that a well-trained deep learning algorithm can accurately identify multiple abnormalities on X-ray images. As such models get further refined, they can be used to ease radiology workflow bottlenecks and improve reporting efficiency. Napier Healthcare’s team that developed this model consists of medical IT professionals who specialize in AI and its practical application in acute & long-term care settings. This is currently being piloted in a few hospitals and diagnostic labs on a commercial basis.

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Local Translation Services for Neglected Languages

10.5121/csit.2021.110110 ◽

2021 ◽

Author(s):

David Noever ◽

Josh Kalin ◽

Matthew Ciolino ◽

Dom Hambrick ◽

Gerry Dozier

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Personal Data ◽

Cost Effective ◽

Leonardo Da Vinci ◽

Original Contribution ◽

Translation Services ◽

Long Short Term Memory ◽

Translation Service ◽

New Applications

Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages. For projects with protected or personal data, translators for less popular or low-resource languages require specific compliance checks before posting to a public translation API. In these cases, locally run translators can render reasonable, cost-effective solutions if done with an army of offline, smallscale pair translators. Like handling a specialist’s dialect, this research illustrates translating two historically interesting, but obfuscated languages: 1) hacker-speak (“l33t”) and 2) reverse (or “mirror”) writing as practiced by Leonardo da Vinci. The work generalizes a deep learning architecture to translatable variants of hacker-speak with lite, medium, and hard vocabularies. The original contribution highlights a fluent translator of hacker-speak in under 50 megabytes and demonstrates a companion text generator for augmenting future datasets with greater than a million bilingual sentence pairs. A primary motivation stems from the need to understand and archive the evolution of the international computer community, one that continuously enhances their talent for speaking openly but in hidden contexts. This training of bilingual sentences supports deep learning models using a long short-term memory, recurrent neural network (LSTM-RNN). It extends previous work demonstrating an English-to-foreign translation service built from as little as 10,000 bilingual sentence pairs. This work further solves the equivalent translation problem in twenty-six additional (non-obfuscated) languages and rank orders those models and their proficiency quantitatively with Italian as the most successful and Mandarin Chinese as the most challenging. For neglected languages, the method prototypes novel services for smaller niche translations such as Kabyle (Algerian dialect) which covers between 5-7 million speakers but one which for most enterprise translators, has not yet reached development. One anticipates the extension of this approach to other important dialects, such as translating technical (medical or legal) jargon and processing health records or handling many of the dialects collected from specialized domains (mixed languages like “Spanglish”, acronym-laden Twitter feeds, or urban slang).

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