scholarly journals Towards secure deep learning architecture for smart farming-based applications

2020 ◽  
Author(s):  
R. Udendhran ◽  
M. Balamurugan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning System ◽  
Security Threats ◽  
Cloud Infrastructure ◽  
Threat Analysis ◽  
Smart Farming ◽  
Agricultural Applications ◽  
Global Food ◽  
Global Food Supply

Abstract The immense growth of the cloud infrastructure leads to the deployment of several machine learning as a service (MLaaS) in which the training and the development of machine learning models are ultimately performed in the cloud providers’ environment. However, this could also cause potential security threats and privacy risk as the deep learning algorithms need to access generated data collection, which lacks security in nature. This paper predominately focuses on developing a secure deep learning system design with the threat analysis involved within the smart farming technologies as they are acquiring more attention towards the global food supply needs with their intensifying demands. Smart farming is known to be a combination of data-driven technology and agricultural applications that helps in yielding quality food products with the enhancing crop yield. Nowadays, many use cases had been developed by executing smart farming paradigm and promote high impacts on the agricultural lands.

scholarly journals Machine learning for human learners: opportunities, issues, tensions and threats

2020 ◽  
Author(s):  
Mary E. Webb ◽  
Andrew Fluck ◽  
Johannes Magenheim ◽  
Joyce Malyn-Smith ◽  
Juliet Waters ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ethical Issues ◽  
Practical Experience ◽  
Learning Systems ◽  
Learning System ◽  
Adaptive Behaviour ◽  
Recent Developments ◽  
Key Aspects ◽  
School Curricula

AbstractMachine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.

Extraction of drug–drug interaction using neural embedding

2018 ◽  
Vol 16 (06) ◽  
pp. 1840027 ◽  
Author(s):  
Wen Juan Hou ◽  
Bamfa Ceesay
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Interaction ◽  
Relevant Information ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Learning Approach ◽  
Learning Techniques ◽  
Drug Drug Interaction

Information on changes in a drug’s effect when taken in combination with a second drug, known as drug–drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.

scholarly journals Artificial intelligence in architecture: Generating conceptual design via deep learning

2018 ◽  
Vol 16 (4) ◽  
pp. 306-327 ◽  
Author(s):  
Imdat As ◽  
Siddharth Pal ◽  
Prithwish Basu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Field Research ◽  
Building Blocks ◽  
Learning System ◽  
Generative Adversarial Networks

Artificial intelligence, and in particular machine learning, is a fast-emerging field. Research on artificial intelligence focuses mainly on image-, text- and voice-based applications, leading to breakthrough developments in self-driving cars, voice recognition algorithms and recommendation systems. In this article, we present the research of an alternative graph-based machine learning system that deals with three-dimensional space, which is more structured and combinatorial than images, text or voice. Specifically, we present a function-driven deep learning approach to generate conceptual design. We trained and used deep neural networks to evaluate existing designs encoded as graphs, extract significant building blocks as subgraphs and merge them into new compositions. Finally, we explored the application of generative adversarial networks to generate entirely new and unique designs.

Security Threats and Defensive Approaches in Machine Learning System Under Big Data Environment

2021 ◽  
Vol 117 (4) ◽  
pp. 3505-3525
Author(s):  
Chen Hongsong ◽  
Zhang Yongpeng ◽  
Cao Yongrui ◽  
Bharat Bhargava
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning System ◽  
Security Threats ◽  
Data Environment

Deep learning algorithms for cyber security applications: A survey

2021 ◽  
pp. 1-25
Author(s):  
Guangjun Li ◽  
Preetpal Sharma ◽  
Lei Pan ◽  
Sutharshan Rajasegarar ◽  
Chandan Karmakar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Big Data ◽  
Deep Learning ◽  
Cyber Security ◽  
High Dimensional ◽  
The Internet ◽  
Security Threats ◽  
Security Issues ◽  
Security Applications

With the development of information technology, thousands of devices are connected to the Internet, various types of data are accessed and transmitted through the network, which pose huge security threats while bringing convenience to people. In order to deal with security issues, many effective solutions have been given based on traditional machine learning. However, due to the characteristics of big data in cyber security, there exists a bottleneck for methods of traditional machine learning in improving security. Owning to the advantages of processing big data and high-dimensional data, new solutions for cyber security are provided based on deep learning. In this paper, the applications of deep learning are classified, analyzed and summarized in the field of cyber security, and the applications are compared between deep learning and traditional machine learning in the security field. The challenges and problems faced by deep learning in cyber security are analyzed and presented. The findings illustrate that deep learning has a better effect on some aspects of cyber security and should be considered as the first option.

scholarly journals A Deep Learning Approach for Single Shot C-Arm Pose Estimation

10.29007/6mkk ◽  
2020 ◽  
Author(s):  
Hooman Esfandiari ◽  
Sebastian Andreß ◽  
Maternus Herold ◽  
Wolfgang Böcker ◽  
Simon Weidert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Operation Time ◽  
Learning System ◽  
Patient Specific ◽  
Single Shot ◽  
X Rays ◽  
Similar Range ◽  
Deep Learning Network ◽  
Ionizing Radiation Exposure

During a typical fluoroscopic guided surgery, it is common to acquire multiple x-ray images to correctly position the C-arm. This can be a long process resulting in an in- crease in operation time and ionizing radiation exposure. Our purpose in this study is to implement a machine learning system for predicting the position of the C-arm based on the intraoperative radiographs. The prediction is achieved by training a Deep Learning Network based on Digitally Reconstructed Radiographs. We first showed a high prediction accuracy (4.5 mm and 1.1o) when patient-specific training was implemented. Additionally, we demonstrated a similar range of accuracy by applying transfer-learning on the last lay- ers of the network while reducing the processing time by 83%. In conclusion, in this study, we propose a C-arm position prediction system based on machine learning that can po- tentially reduce the number of intraoperatively acquired X-rays in a common orthopaedic surgical procedure.

scholarly journals MLPM: Machine Learning Package Manager

2020 ◽  
Author(s):  
Xiaozhe Yao
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Community ◽  
Source Code ◽  
Learning Systems ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Third Party ◽  
Small Companies ◽  
Run Test

In recent years, the machine learning community has witnessed rapid growth in the development of deep learning and its application. Unlike other software that can be installed through the package manager, developing machine learning systems usually need to search for the source code or start from scratch, debug and then deploy to production. It usually costs much for small companies and research institutions to run, test, evaluate, deploy and monitor machine learning system. In this paper, we proposed a machine learning package manager aiming to assist users1) find potentially useful models,2) resolve dependencies,3) deploy as HTTP service. By using the MLPM, users are enabled to easily adopt existing and well-established machine learning algorithms and libraries to their project within few steps. MLPM also allows third-party extensions to be installed, which makes the system customizable according to users’ workflow.rpose

scholarly journals Apparel-based deep learning system design for apparel style recommendation

2019 ◽  
Vol 31 (3) ◽  
pp. 376-389 ◽  
Author(s):  
Congying Guan ◽  
Shengfeng Qin ◽  
Yang Long
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recommendation System ◽  
Learning Technologies ◽  
The Body ◽  
Learning System ◽  
The Other ◽  
Training Data ◽  
Support Vector ◽  
Content Type

Purpose The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert. Design/methodology/approach This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion. Findings The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable. Originality/value The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

scholarly journals Exploration of machine algorithms based on deep learning model and feature extraction

2021 ◽  
Vol 18 (6) ◽  
pp. 7602-7618
Author(s):  
Yufeng Qian ◽  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Lifelong Learning ◽  
Learning Algorithm ◽  
Learning Model ◽  
Learning System ◽  
Classification Error ◽  
Model Learning ◽  
Deep Learning Model

<abstract> <p>The study expects to solve the problems of insufficient labeling, high input dimension, and inconsistent task input distribution in traditional lifelong machine learning. A new deep learning model is proposed by combining feature representation with a deep learning algorithm. First, based on the theoretical basis of the deep learning model and feature extraction. The study analyzes several representative machine learning algorithms, and compares the performance of the optimized deep learning model with other algorithms in a practical application. By explaining the machine learning system, the study introduces two typical algorithms in machine learning, namely ELLA (Efficient lifelong learning algorithm) and HLLA (Hierarchical lifelong learning algorithm). Second, the flow of the genetic algorithm is described, and combined with mutual information feature extraction in a machine algorithm, to form a composite algorithm HLLA (Hierarchical lifelong learning algorithm). Finally, the deep learning model is optimized and a deep learning model based on the HLLA algorithm is constructed. When K = 1200, the classification error rate reaches 0.63%, which reflects the excellent performance of the unsupervised database algorithm based on this model. Adding the feature model to the updating iteration process of lifelong learning deepens the knowledge base ability of lifelong machine learning, which is of great value to reduce the number of labels required for subsequent model learning and improve the efficiency of lifelong learning.</p> </abstract>

scholarly journals Conceptual view approach of Machine Learning Based Recommendation System

2021 ◽  
Vol 23 (07) ◽  
pp. 1165-1173
Author(s):  
G NageswaraRao ◽  
◽  
P Om Sreeja ◽  
T Anusha ◽  
K Kethan Surya Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Human Brain ◽  
Recommendation System ◽  
Motion Pictures ◽  
Learning System ◽  
Learning Approaches ◽  
Learning Models ◽  
Neural Learning

The paper explains the use of machine learning approaches and especially throws light on the issue of user-based recommender frameworks. The new sort of framework which has been received by this exploration is a blend of profound learning baed and client recommender type arrangement of AI. Therefore, the model of a hybrid system of deep learning system has been incorporated into this research which used the convolutional neural learning models. This system of learning has been explained as the method which is used to study various users’ preferences in order to see their clicks. The information utilizes considering the inclinations or proposals of the clients is utilized in such a manner to direct these machines. In the client proposals frameworks, the innovation of computerized reasoning is utilized with the goal that the machines could learn things like a human brain. In the section of the literature review, the researcher has emphasized the various models which are used in machine learning. The systems which play a role in the users’ recommender systems involve examining the preferences of these users who use these systems. The system which has been utilized for this exploration is examining different characters who watch various motion pictures which have a place with two classifications of activity and parody. Thus, the information which has been gathered examined and anticipated the inclinations of these clients by considering the aa around gave information. Hence, there are various datasets that are used in this paper to predict the users’ preferences.

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