Early Stage Detection of Psoriasis Using Artificial Intelligence and Image Processing

2020 ◽  
pp. 1199-1208
Author(s):  
D. R. Kalbande ◽  
Uday Khopkar ◽  
Avinash Sharma ◽  
Neil Daftary ◽  
Yash Kokate ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Early Stage

scholarly journals Artificial Intelligence for early Detection of Breast Cancer and Classification of Mammographic Masses

2020 ◽  
Vol 8 (6) ◽  
pp. 4314-4320
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Image Processing ◽  
Early Stage ◽  
Probabilistic Neural Network ◽  
Breast Lesions ◽  
Test Image ◽  
False Positive Detection ◽  
Processing Techniques

Every single year thousands of women endure painful and invasive surgery to remove breast lesions. Most of the time the mammographic image analysis leads to false positive detection and the majority of this actions reveal the lesions to be benign. Refining present detection and diagnostic tool is a major priority of our work. MATLAB R2015a is been used to develop the algorithm, which aids in detection of breast cancer in its early stage. The algorithm comprises of image processing and applying artificial intelligence where in the system is trained with a set of images so that when the input or the test image is given, the algorithm performs the image processing techniques and then applies the Probabilistic Neural Network (PNN) technique for detection of cancer. The system performance is also been calculated in order to estimate its reliability.

scholarly journals Pest Detection in Plants Using Convolutional Neural Network

2021 ◽  
Vol 9 (11) ◽  
pp. 1583-1592
Author(s):  
Savita Sharma
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Climate Changes ◽  
Agricultural Sector ◽  
Predictive Analytics ◽  
Early Stage ◽  
Maximum Yield ◽  
Disease Classification ◽  
The World ◽  
Pest Detection

Abstract: Agriculture or farming is an imperative occupation since the historical backdrop of humanity is kept up. Artificial Intelligence is leading to a revolution in the agricultural practices. This revolution has safeguarded the crops from being affected by distinct factors like climate changes, porosity of the soil, availability of water, etc. The other factors that affect agriculture includes the increase in population, changes in the economy, issues related to food security, etc. Artificial Intelligence finds a lot of applications in the agricultural sector also which includes crop monitoring, soil management, pest detection, weed management and a lot more. Significant problems for sustainable farming include detection of illness and healthy monitoring of plants. Therefore, plant disease must automatically be detected with higher precision by means of image processing technology at an early stage. It consists of image capturing, preprocessing images, image segmentation, extraction of features and disease classification. The digital image processing method is one of those strong techniques used far earlier than human eyes could see to identify the tough symptoms. Considering different climatic situations in various regions of the world that impact local weather conditions. These climate changes affect crop yield directly. There is a great demand for such a platform in the world of today which would enable the farmer market his farm products. We have proposed in this study a system where farmers can sell their products directly to customers without the intervention of distributors and traders. The predictive analytics system is necessary for the farmer to get the maximum yield which benefit the farmer. This may be done if the environment, market conditions and knowledge of the timely planning of farms are known properly. Keywords: Pest Detection, Artificial Intelligence, Agriculture, Image processing, Convolutional Neural Networks

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

scholarly journals Micro Computed Tomography Analysis of Four-Way Conversion Catalysts using Artificial Intelligence-Enabled Image Processing

2021 ◽  
Vol 27 (S1) ◽  
pp. 1028-1029
Author(s):  
Robert Palomino ◽  
Ke-Bin Low ◽  
Chunxin Ji ◽  
Ivan Petrovic ◽  
Florian Waltz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Image Processing ◽  
Micro Computed Tomography ◽  
Tomography Analysis

Observation of Fatigue Damage to Gear Tooth Using Image Processing

2007 ◽  
Author(s):  
Tomoya Masuyama ◽  
Takuya Ikeda ◽  
Satoshi Yoshiizumi ◽  
Katsumi Inoue
Keyword(s):  
Image Processing ◽  
Acoustic Emission ◽  
Strain Distribution ◽  
Cyclic Load ◽  
Early Stage ◽  
Gear Tooth ◽  
Digital Camera ◽  
Correlation Method ◽  
Point Of View ◽  
Strain Diagram

The detection of damage in early stage of fatigue is important for a reliable evaluation of gear life and strength. From this point of view, the variation of strain distribution in a tooth due to cyclic load contains useful information because the fatigue crack will initiate as a result of the accumulation of plastic strain. Meanwhile, digital image equipments are widely used in our life and the performance is in progress. We took digital pictures of cyclic loaded tooth by the digital camera and compared with the picture of no load to find displacement. The strain distribution of tooth is calculated by the correlation method using those pictures. The initiation of a micro crack is observed by the method. It is also confirmed by the detection of acoustic emission wave with higher energy. The variation of stress-strain diagram in fatigue process is presented, and this illustrates the increase of strain in the final stage of fatigue.

Integrating Image Computation in Undergraduate Level Data-Structure Education

1998 ◽  
Vol 12 (08) ◽  
pp. 1071-1080 ◽  
Author(s):  
Sudeep Sarkar ◽  
Dmitry Goldgof
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Data Structure ◽  
Data Structures ◽  
Science Curriculum ◽  
Early Stage ◽  
Real Life ◽  
South Florida ◽  
The University ◽  
Structure Education

There is a growing need for expertise both in image analysis and in software engineering. To date, these two areas have been taught separately in an undergraduate computer and information science curriculum. However, we have found that introduction to image analysis can be easily integrated in data-structure courses without detracting from the original goal of teaching data structures. Some of the image processing tasks offer a natural way to introduce basic data structures such as arrays, queues, stacks, trees and hash tables. Not only does this integrated strategy expose the students to image related manipulations at an early stage of the curriculum but it also imparts cohesiveness to the data-structure assignments and brings them closer to real life. In this paper we present a set of programming assignments that integrates undergraduate data-structure education with image processing tasks. These assignments can be incorporated in existing data-structure courses with low time and software overheads. We have used these assignment sets thrice: once in a 10-week duration data-structure course at the University of California, Santa Barbara and the other two times in 15-week duration courses at the University of South Florida, Tampa.

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