scholarly journals A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 578 ◽  
Author(s):  
Dengshan Li ◽  
Rujing Wang ◽  
Chengjun Xie ◽  
Liu Liu ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Detection System ◽  
Plant Diseases ◽  
Grain Production ◽  
Image Detector ◽  
Still Image ◽  
Training Models ◽  
Learning Classifier ◽  
Crop Diseases ◽  
Video Detection

Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the still-image detector for detection, and finally synthesized the frames into video. In the still-image detector, we used faster-RCNN as the framework. We used image-training models to detect relatively blurry videos. Additionally, a set of video-based evaluation metrics based on a machine learning classifier was proposed, which reflected the quality of video detection effectively in the experiments. Experiments showed that our system with the custom backbone was more suitable for detection of the untrained rice videos than VGG16, ResNet-50, ResNet-101 backbone system and YOLOv3 with our experimental environment.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

Digital Image Processing for Detecting and Classifying Plant Diseases

2017 ◽  
Vol 2 (11) ◽  
pp. 1-7
Author(s):  
Izay A. ◽  
Onyejegbu L. N.
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Programming Language ◽  
Design Methodology ◽  
Fuzzy Logic Controller ◽  
Object Oriented ◽  
Plant Diseases ◽  
Analysis And Design ◽  
Crop Diseases ◽  
Growing Population

Agriculture is the backbone of human sustenance in this world. With growing population, there is need for increased productivity in agriculture to be able to meet the demands. Diseases can occur on any part of a plant, but in this paper only the symptoms in the fruits of a plant is considered using segmentation algorithm and edge/ sizing detectors. We also looked at image processing using fuzzy logic controller. The system was designed using object oriented analysis and design methodology. It was implemented using MySQL for the database, and PHP programming language. This system will be of great benefit to farmers and will encourage them in investing their resources since crop diseases can be detected and eliminated early.

scholarly journals AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1889
Author(s):  
Francisco Luna-Perejón ◽  
Luis Muñoz-Saavedra ◽  
Javier Civit-Masot ◽  
Anton Civit ◽  
Manuel Domínguez-Morales
Keyword(s):  
Elderly Person ◽  
Fall Detection ◽  
The Elderly ◽  
Daily Life Activities ◽  
Learning Classifier ◽  
The People ◽  
Activity Types ◽  
Remote Monitoring Systems ◽  
Secondary Study

Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility.

Development of laboratory x-ray fluorescence holography equipment

2003 ◽  
Vol 18 (6) ◽  
pp. 1471-1473 ◽  
Author(s):  
Yukio Takahashi ◽  
Kouichi Hayashi ◽  
Kimio Wakoh ◽  
Naomi Nishiki ◽  
Eiichiro Matsubara
Keyword(s):  
Single Crystal ◽  
Count Rate ◽  
Detection System ◽  
High Count ◽  
Synchrotron Source ◽  
High Count Rate ◽  
X Ray ◽  
Graphite Monochromator ◽  
Time Required

Laboratory x-ray fluorescence holography equipment was developed. A single-bent graphite monochromator with a large curvature and a high-count-rate x-ray detection system were applied in this equipment. To evaluate the performance of this equipment, a hologram pattern of a gold single crystal was measured. It took two days, which was about one-third the time required for the previous measurements using the conventional x-ray source and several times that using the synchrotron source. The quality of the hologram pattern is as good as that obtained using the synchrotrons. Clear atomic images on (002) are reconstructed.

scholarly journals Applied of Alternative Business Training Models through Environmental-Based Community Empowerment for Farmer Labor

2021 ◽  
Vol 9 (5) ◽  
Author(s):  
Tenri Sau ◽  
Gufran Darma Dirawan ◽  
Ahmad Rifqi Asrib
Keyword(s):  
Training Model ◽  
Community Empowerment ◽  
Farm Workers ◽  
Knowledge Test ◽  
Business Training ◽  
Training Models ◽  
South Sulawesi ◽  
Study Results ◽  
Quality Testing

Agriculture is one of the fields that still dominates Indonesia as an agricultural country. As a substantial actor in agriculture, many farmers have not met their daily needs. This study examines the quality of alternative business training models for farmworkers using a community empowerment system. This study is part of research and development focused on product quality testing. The training model that the researcher has designed is then tested on validity, practicality, and effectiveness. The participants involved in this study were farm labourers in 2 sub-districts in Wajo Regency, South Sulawesi, Indonesia, namely Pammana and Tanasitolo sub-districts as many as 15 farm workers, two observers, and three agricultural experts. Furthermore, the instruments used in this study were: (1) validation sheet, (2) model implementation observation sheet, (3) farmworker response questionnaire, and (4) knowledge test. The data that has been collected is then analyzed quantitatively using SPSS 23.00 software. The study results show that the training model that this researcher has designed is valid, practical, and effective in increasing the knowledge of farmworkers and can be used for a broader range of users.

scholarly journals Real-Time Bot Detection from Twitter Using the Twitterbot+ Framework

2020 ◽  
Vol 26 (4) ◽  
pp. 496-507
Author(s):  
Kheir Daouadi ◽  
Rim Rebaï ◽  
Ikram Amous
Keyword(s):  
Real Time ◽  
Time Use ◽  
Detection System ◽  
Twitter Data ◽  
Benchmark Datasets ◽  
Filtering Technique ◽  
Bot Detection ◽  
Independent Features ◽  
Quality Of Research

Nowadays, bot detection from Twitter attracts the attention of several researchers around the world. Different bot detection approaches have been proposed as a result of these research efforts. Four of the main challenges faced in this context are the diversity of types of content propagated throughout Twitter, the problem inherent to the text, the lack of sufficient labeled datasets and the fact that the current bot detection approaches are not sufficient to detect bot activities accurately. We propose, Twitterbot+, a bot detection system that leveraged a minimal number of language-independent features extracted from one single tweet with temporal enrichment of a previously labeled datasets. We conducted experiments on three benchmark datasets with standard evaluation scenarios, and the achieved results demonstrate the efficiency of Twitterbot+ against the state-of-the-art. This yielded a promising accuracy results (>95%). Our proposition is suitable for accurate and real-time use in a Twitter data collection step as an initial filtering technique to improve the quality of research data.

A Mechanism for Securing Hybrid Cloud Outsourced Data

2015 ◽  
pp. 73-83 ◽  
Author(s):  
Abdullah El-Haj ◽  
Shadi Aljawarneh
Keyword(s):  
Quality Of Service ◽  
Communication Networks ◽  
Security Policy ◽  
Detection System ◽  
Network Access ◽  
Network Resources ◽  
Outsourced Data ◽  
Secure Data Transmission ◽  
Cloud Network

The existing research related to security mechanisms only focuses on securing the flow of information in the communication networks. There is a lack of work on improving the performance of networks to meet quality of service (QoS) constrains for various services. The security mechanisms work by encryption and decryption of the information, but do not consider the optimised use of the network resources. In this paper the authors propose a Secure Data Transmission Mechanism (SDTM) with Preemption Algorithm that combines between security and quality of service. Their developed SDTM enhanced with Malicious Packets Detection System (MPDS) which is a set of technologies and solutions. It enforces security policy and bandwidth compliance on all devices seeking to access Cloud network computing resources, in order to limit damage from emerging security threats and to allow network access only to compliant and trusted endpoint devices.

Plant Disease Detection Using Machine Learning Approaches

2021 ◽  
pp. 122-130
Author(s):  
Sukanta Ghosh ◽  
Shubhanshu Arya ◽  
Amar Singh
Keyword(s):  
Plant Disease ◽  
Crop Yields ◽  
Detection System ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Detection ◽  
Learning Approaches ◽  
Factors Affecting ◽  
Plant Leaf ◽  
Market Situation

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

Makespan of routing and security in Cross Centric Intrusion Detection System (CCIDS) over black hole attacks and rushing attacks in MANET

2019 ◽  
Vol 7 (4) ◽  
pp. 162-176
Author(s):  
Rajendran N. ◽  
Jawahar P.K. ◽  
Priyadarshini R.
Keyword(s):  
Quality Of Service ◽  
Intrusion Detection ◽  
Response Time ◽  
Intrusion Detection System ◽  
Security Policy ◽  
Ad Hoc ◽  
Detection System ◽  
Content Type ◽  
The Cost

Purpose The purpose of this paper is to apply security policies over the mobile ad hoc networks. A mobile ad hoc network refers to infrastructure-less, persistently self-designing systems; likewise, there is a noteworthy innovation that supplies virtual equipment and programming assets according to the requirement of mobile ad hoc network. Design/methodology/approach It faces different execution and effectiveness-based difficulties. The major challenge is the compromise of performance because of unavailable resources with respect to the MANET. In order to increase the MANET environment’s performance, various techniques are employed for routing and security purpose. An efficient security module requires a quality-of-service (QoS)-based security policy. It performs the task of routing and of the mobile nodes, and it also reduces the routing cost by finding the most trusted node. Findings The experimental results specify that QoS-based security policy effectively minimizes the cost, response time as well as the mobile makespan (routing cost and response time) of an application with respect to other existing approaches. Research limitations/implications In this paper, the authors proposed an enhancement of Cross Centric Intrusion Detection System named as PIHNSPRA Routing Algorithm (PIHNSPRA). Practical implications It maps the security with the secure IDS communication and distributes the packets among different destinations, based on priority. This calculation is proposed for the purpose of routing and security by considering greatest throughput with least routing cost and reaction time. Social implications When the concept is applied to practical applications. Quality of Service introduced in the proposed research reduces the cost of routing and improves the throughput. Originality/value The proposed calculation is tested by NS2 simulator and the outcomes showed that the execution of the calculation is superior to other conventional algorithms.

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