scholarly journals Deep Learning-Based Pest Surveillance System for Sericulture

2021 ◽  
Author(s):  
Thippesha D ◽  
Pramodh B R
Keyword(s):  
Invasive Species ◽  
Deep Learning ◽  
Surveillance System ◽  
Labour Force ◽  
Learning System ◽  
Annual Production ◽  
Vespa Orientalis ◽  
Learning Techniques ◽  
Manual Labour ◽  
Apanteles Glomeratus

Every year sericulture farmers lose a sizeable amount of revenue because of pest attacks on silkworms. In 2011 the annual production of silk is fall by about 50% because of pest attacks [1]. To prevent these losses constant monitoring of the environment is required. But this constant surveillance can’t be achievable by manual labour force but it can be achievable by using deep learning techniques. This article presents a deep learning system that is trained and tested for detecting invasive species which can cause harm to silkworms such as Oecophylla smargdina, Vespa orientalis, Sycanus collaris, Hierodulla bipapilla, Canthecona furcellata, Blepharipa zebina and Apanteles glomeratus.

scholarly journals Online Public Shaming Approach using Deep Learning Techniques

2021 ◽  
Vol 23 (3) ◽  
Author(s):  
Mehdi Surani ◽  
◽  
Ramchandra Mangrulkar ◽  
Keyword(s):  
Deep Learning ◽  
Hate Speech ◽  
Severe Depression ◽  
Learning System ◽  
Online Bullying ◽  
Digital World ◽  
Learning Techniques ◽  
Proposed Model ◽  
Social Media Platforms ◽  
The Impact

Public shaming on social media platforms like Twitter / Instagram / Facebook etc. have recently increased from the past years. This results in affecting an individual’s social, political, mental and financial life. The impact can range from mild bullying to severe depression. With the growing leniency on these social platforms, many people have started misusing the opportunity by turning to online bullying and hate speech. When something is posted online, it stays there forever and it becomes extremely hard taking something out of the digital world. Manually locating and categorizing such comments is a lengthy procedure and just cannot be relied upon. To solve this challenge, automation was performed to identify and classify the shamers. This has been done using the classic SVM model which worked on a given quantity of data. To identify the negative content being posted and discussed online, this paper further explores the deep learning system which can successfully classify these content pieces into proper labels. The text-based Convolution Neural Network (CNN) is the proposed model in this paper for this analysis.

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 A Probabilistic-Based Deep Learning Model for Skin Lesion Segmentation

2021 ◽  
Vol 11 (7) ◽  
pp. 3025
Author(s):  
Adekanmi Adeyinka Adegun ◽  
Serestina Viriri ◽  
Muhammad Haroon Yousaf
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Probabilistic Model ◽  
Learning System ◽  
Gaussian Kernel ◽  
Complex Nature ◽  
Low Contrast ◽  
Learning Techniques ◽  
Cancer Diseases

The analysis and detection of skin cancer diseases from skin lesion have always been tedious when done manually. The complex nature of skin lesion images is one of the key reasons for this. The skin lesion images contain noise and artifacts such as hairs, oil and bubbles, blood vessels, and skin lines. They also have variegated colors, low contrast, and irregular borders. Various computational approaches have been designed in the past for aiding in the detection and diagnosis of skin cancer diseases using skin lesion images. The existing techniques have been limited due to the interference of the aforementioned features of skin lesion. Recently, machine learning techniques, in particular the deep learning techniques have been used for the detection of skin cancer. However, they are still limited to the fuzzy and irregular borders of skin lesion images coupled with the low contrast that exists between the diseased lesion and healthy tissues. In this paper, we utilized a probabilistic model for the enhancement of a fully convolutional network-based deep learning system to analyze and segment skin lesion images. The probabilistic model employs an efficient mean-field approximate probabilistic inference approach with a fully connected conditional random field that utilizes a Gaussian kernel. The probabilistic model further performs a refinement of skin lesion borders. The whole framework is tested and evaluated on publicly available skin lesion image datasets of ISBI 2017 and PH2. The system achieved a better performance, having an accuracy of 98%.

scholarly journals Machine Health Surveillance System by Using Deep Learning Sparse Autoencoder

2021 ◽  
Author(s):  
Faizan Ullah ◽  
Abdu Salam ◽  
Muhammad Abrar ◽  
Masood Ahmad ◽  
Fasee Ullah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Surveillance System ◽  
Low Cost ◽  
Research Area ◽  
Ensemble Classifier ◽  
Health Surveillance ◽  
Industrial Manufacturing ◽  
Learning Techniques ◽  
Widespread Availability ◽  
Machine Health

Abstract Deep learning is a rapidly growing research area having state of art achievement in various applications including but not limited to speech recognition, object recognition, machine translation, and image segmentation. In the current modern industrial manufacturing system, Machine Health Surveillance System (MHSS) is achieving increasing popularity because of the widespread availability of low cost sensors internet connectivity. Deep learning architecture gives useful tools to analyze and process these vast amounts of machinery data. In this paper, we review the latest deep learning techniques and their variant used for MHSS. We used Gearbox Fault Diagnosis dataset in this paper that contains the sets of vibration attributes recorded by SpectraQuest’s Gearbox Fault Diagnostics Simulator. In addition, the authors used the variant of auto encoders for feature extraction to achieve higher accuracy in machine health surveillance. The results showed that the bagging ensemble classifier based on voting techniques achieved 99% accuracy.

scholarly journals AndroAnalyzer: android malicious software detection based on deep learning

2021 ◽  
Vol 7 ◽  
pp. e533
Author(s):  
Recep Sinan Arslan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Smart Devices ◽  
Android Malware ◽  
Original Dataset ◽  
Learning Techniques ◽  
Technological Developments

Background Technological developments have a significant effect on the development of smart devices. The use of smart devices has become widespread due to their extensive capabilities. The Android operating system is preferred in smart devices due to its open-source structure. This is the reason for its being the target of malware. The advancements in Android malware hiding and detection avoidance methods have overridden traditional malware detection methods. Methods In this study, a model employing AndroAnalyzer that uses static analysis and deep learning system is proposed. Tests were carried out with an original dataset consisting of 7,622 applications. Additional tests were conducted with machine learning techniques to compare it with the deep learning method using the obtained feature vector. Results Accuracy of 98.16% was achieved by presenting a better performance compared to traditional machine learning techniques. Values of recall, precision, and F-measure were 98.78, 99.24 and 98.90, respectively. The results showed that deep learning models using trace-based feature vectors outperform current cutting-edge technology approaches.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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