scholarly journals Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
V. Elizabeth Jesi ◽  
Shabnam Mohamed Aslam ◽  
G. Ramkumar ◽  
A. Sabarivani ◽  
A. K. Gnanasekar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Early Stage ◽  
Region Of Interest ◽  
Structural Effect ◽  
Region Selection ◽  
Optical Region ◽  
Exact Position ◽  
Sleeping Habits

Glaucoma is a major threatening cause, in which it affects the optical nerve to lead to a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits, and so on. These kinds of causes lead to Glaucoma easily, and the effect of such disease leads to heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The major problem with this disease is that it is incurable; however, the affection stages can be reduced and the same level of effect as that for the long period can be maintained but this is possible only in the earlier stages of identification. This Glaucoma causes structural effect to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads to harmful damage to one’s eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this work, a new methodology is introduced to identify the Glaucoma in earlier stages, called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results, and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of effect by such disease using OCT images. The exact position pointed out is handled by using Region of Interest- (ROI-) based optical region selection, in which it is easy to point the optical cup (OC) and optical disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and the practical proofs are shown in the Result and Discussions section in a clear manner.

scholarly journals Energetic Glaucoma Segmentation and Classification Strategies using Depth Optimized Machine Learning Strategies

2021 ◽  
Author(s):  
ELIZABETH JESI V ◽  
SHABNAM MOHAMED ASLAM ◽  
RAMKUMAR G ◽  
SUJATHA M ◽  
ANUSHYA A ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Early Stage ◽  
Region Of Interest ◽  
World Health ◽  
Optical Region ◽  
Exact Position ◽  
Health Organization ◽  
Sleeping Habits

Abstract Glaucoma is a major threatening cause, in which it affects the optical nerve to lead a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits and so on. These kinds of causes leads to Glaucoma easily as well as the affection to such disease leads a heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The eye fluid called aqueous humor is getting blocked inside due to Glaucoma, in normal cases sometimes the fluid comes out from the eye via mesh perspective channel, but this Glaucoma blocks that channel and causes the fluid to getting locked inside and provides the permanent blockage inside. So, that the eyes are getting severe affections such as infection, random blindness in initial stages and so on. The World Health Organization analyzes and reports nearly 80 million people around the globe are affected due to some form of Glaucoma. The major problem with this disease is it is incurable, however, the affection stages can be reduced and maintain the same level of affection as it is for the long period but it is possible only earlier stages of identification. This Glaucoma causes structural affection to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads a harmful damage to one's eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility is shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this paper, a new methodology is introduced to identify the Glaucoma on earlier stages called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of affection by such disease using OCT images. The exact position point out is handled by using Region of Interest (ROI) based optical region selection, in which it is easy to point the Optical Cup (OC) and Optical Disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and shows the practical proofs on resulting section in clear manner.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals Effective Procedure to Predict Rainfall Conditions using Hybrid Machine Learning Strategies

2021 ◽  
Vol 12 (6) ◽  
pp. 209-216
Author(s):  
S.Sakthivel Et.al
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Rainfall Prediction ◽  
Long Period ◽  
Hybrid Machine ◽  
Limited Period

In the present information technology stream supports many natural disaster prediction schemes to save several people from disaster scenarios. In such case, rainfall prediction and analysis is the most important concern to take care as well as the prediction of high rainfall saves many individual's life and their assets. This kind of rainfall prediction schemes provides a facilitation to take respective precautions to avoid huge damages further. The rainfall predictions are categorized into two different variants such as Limited Period Rainfall Prediction and the long period Continuous Rainfall Prediction. Several past analysis and literatures provide accurate predictions for limited period rainfall but the major problem is to identify or predict the continuous long period rainfall. This kind of drawbacks leads many researchers to work on this domain and predict the rainfall status exactly for both limited period as well as long period continues rainfall. In this paper, a new hybrid machine learning strategy is implemented to predict the rainfall status exactly, in which the proposed methodology is named as Intense Neural Network Mining (INNM). This proposed approach of INNM analyze the rainfall prediction scenario based on two different machine learning logics such as Back Propagation Neural Network and the Rapid Miner. The general machine learning algorithms train the machine with respect to the dataset features and predict the result based on testing input. In this approach two different variants of machine learning principles are utilized to classify the resulting nature with better accuracy levels and cross-validations are providing best probabilistic results in outcome. And these two logics are integrated together to produce a new hybrid machine learning strategy to predict the rainfall status exactly and save human life against disasters. In this paper, a novel dataset is utilized from Regional Meteorological Centre Chennai to predict the rainfall summary in clear manner and the summarization of specific dataset is described on further sections. The proposed approach of INNM assures the resulting accuracy levels around 96.5% in prediction with lowest error ratio of 0.04% and the resulting portion of this paper provides a proper proof of this outcome in graphical manner.

DEVELOPMENT OF METACOGNITIVE AND SOCIO-AFFECTIVE STRATEGIES IN EFL CLASSES AT SECONDARY SCHOOLS

2019 ◽  
Vol 2 ◽  
pp. 481
Author(s):  
Ingrida Eglė Žindžiuvienė
Keyword(s):  
Foreign Language ◽  
Language Learning ◽  
Learning Strategies ◽  
Teaching And Learning ◽  
Learning Strategy ◽  
Early Stage ◽  
Cognitive Learning ◽  
Foreign Language Learning ◽  
Affective Learning ◽  
Strategy Development

The article focuses on the development of learning strategies during the process of foreign language learning. With the obvious implementation of cognitive learning strategies, the development of other types of strategies, metacognitive and socio-affective, remains an urgent issue. Very often these latter strategies are either disregarded or dismissed during the language learning process. However, recent studies have pointed out the necessity for the development of all types of learning strategies and their implementation at an early stage of learning. Therefore, the aim of this research is to determine the scope of the development of metacognitive and socio-affective learning strategies during the process of foreign language learning. Quantitative and qualitative methods have been used to determine the frequency and mode of the above-mentioned types of learning strategies during the process of teaching and learning English as a foreign language (EFL). Two hypotheses have been raised: (1) Metacognitive strategies are often disregarded in the process of language learning in the classes for adolescents; (2) The potential of implementation of socio-affective strategies in EFL classes is underestimated in the process of language learning in the classes for adolescents. The object of the research consists of 12 selected EFL textbooks and activities included in them. The research results prove the fact that much more attention to metacognitive learning strategy development is needed during EFL classes, as these strategies strongly benefit the overall process of language acquisition. To compare, socio-affective learning strategies are more often implemented during EFL classes; however, their development is rather unsystematic. 

STRATEGI PEMBELAJARAN DAN MOTIF BERPRESTASI TERHADAP HASIL BELAJAR MATA PELAJARAN MENGGAMBAR TEKNIK

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Timbul Purba ◽  
Harun Sitompul
Keyword(s):  
Student Learning ◽  
Learning Outcomes ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Student Learning Outcomes ◽  
High Achievement ◽  
Low Achievement ◽  
Significance Level ◽  
Achievement Motive ◽  
Analysis Technique

Abstrak: Penelitian ini bertujuan: (1) hasil belajar menggambar teknik siswa yang diajar dengan strategi pembelajaran elaborasi lebih tinggi dibandingkan dengan siswa yang diajar dengan strategi pembelajaran ekspositori, (2) hasil belajar menggambar teknik siswa yang memiliki motif berprestasi tinggi lebih tinggi dibandingkan dengan siswa yang memiliki motif berprestasi rendah dan (3) interaksi antara strategi pembelajaran dengan motif berprestasi dalam mempengaruhi hasil belajar menggambar teknik siswa. Metode penelitian menggunakan metode quasi eksperimen dengan desain penelitian faktorial 2x2, sedangkan teknik analisis data menggunakan ANAVA dua jalur pada taraf signifikansi a = 0.05. Hasil penelitian diperoleh: (1) hasil belajar menggambar teknik siswa yang diajar dengan strategi pembelajaran elaborasi lebih tinggi dibandingkan dengan hasil belajar siswa yang diajar dengan strategi pembelajaran ekspositori, (2) hasil belajar menggambar teknik siswa yang memiliki motif berprestasi tinggi lebih tinggi dibandingkan dengan hasil belajar siswa yang memiliki motif berprestasi rendah dan (3) terdapat interaksi antara strategi pembelajaran dengan motif berprestasi dalam mempengaruhi hasil belajar menggambar teknik siswa.   Kata Kunci: strategi pembelajaran elaborasi dan ekspositori, motif berprestasi, hasil belajar menggambar teknik   Abstract: This research was aimed to: (1) the learning outcomes of students who are taught drawing techniques with learning strategy elaboration higher than students taught by expository learning strategy, (2) drawing techniques learning outcomes of students who have high achievement motive higher than students who have low achievement motive, and (3) the interaction between learning strategy and achievement motives in affecting student learning outcomes drawing techniques. The research method used was quasi experiment with 2 x 2 factorial design. The analysis technique used is the two-track analysis of variance ANOVA (2 x 2) with a significance level α = 0.05. The findings of the study indicate: (1) the learning outcomes of students who are taught drawing techniques with learning strategy elaboration higher learning outcomes than students taught by expository learning strategy; (2) drawing techniques learning outcomes of students who have high achievement motive higher than the learning outcomes of students who have low achievement motive; and (3) there is interaction between learning strategy and achievement motives in affecting student learning outcomes drawing techniques. Keywords: elaboration learning strategies and expository, achievement motive, the result of learning drawing techniques

PENGARUH STRATEGI PEMBELAJARAN DAN TIPE KEPRIBADIAN SISWA TERHADAP HASIL BELAJAR EKONOMI

2017 ◽  
Vol 10 (2) ◽  
pp. 151
Author(s):  
Harningsih Fitri Situmorang
Keyword(s):  
Learning Outcomes ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Problem Based Learning ◽  
Personality Type ◽  
Student Learning Outcomes ◽  
Personality Types ◽  
Descriptive Statistics ◽  
Scheffe Test ◽  
Inferential Statistic

Abstrak: Penelitian ini bertujuan :(1) Untuk mengetahui hasil belajar ekonomi siswa yang diajar dengan strategi pembelajaran berbasis masalah lebih tinggi dari siswa yang diajar dengan strategi pembelajaran ekspositori. (2) Untuk mengetahui hasil belajar  ekonomi siswa yang memiliki tipe kepribadian ekstrovert dan siswa yang memiliki kepribadian introvert. (3) Untuk mengetahui interaksi antara strategi pembelajaran dengan tipe kepribadian  terhadap hasil belajar Ekonomi. Metode penelitian yang digunakan adalah kuasi eksperimen dengan desain faktorial 2 x 2. Uji statistik yang digunakan adalah statistik deskriptif untuk menyajikan data dan dilanjutkan dengan statistik inferensial dengan menggunakan ANAVA dua jalur dengan taraf signifikan α = 0,05 yang dilanjutkan dengan uji Scheffe. Hasil penelitian menunjukkan: (1) hasil belajar ekonomi siswa yang diajarkan dengan strategi pembelajaran berbasis masalah lebih tinggi dari pada hasil belajar ekonomi siswa yang diajarkan dengan strategi pembelajaran ekspositori; (2) hasil belajar ekonomi siswa yang memiliki kepribadian ekstrovert lebih tinggi dari pada hasil belajar ekonomi siswa yang memiliki tipe kepribadian introvert; (3) terdapat interaksi antara strategi pembelajaran dengan tipe kepribadian  dalam mempengaruhi hasil belajar siswa. Hipotesis ini menunjukkan bahwa strategi pembelajaran berbasis masalah lebih tepat daripada model pembelajaran ekspositori dalam meningkatkan hasil belajar ekonomi siswa, dan siswa yang memiliki tipe kepribadian ekstrovert akan memperoleh hasil yang lebih baik dari pada siswa yang memiliki tipe kepribadian introvert. Kata Kunci: strategi pembelajaran, tipe kepribadian, hasil belajar ekonomi. Abstract: This study aims: (1) To find out the results of students' economic learning taught by problem-based learning strategy is higher than students who are taught by expository learning strategy. (2) To know the economic learning result of students who have extrovert personality type and students who have introverted personality. (3) To know the interaction between learning strategy with personality type to Economic learning result. The research method used is quasi experiment with 2 x 2 factorial design. Statistical test used is descriptive statistics to present the data and continued with inferential statistic by using two way ANOVA with significant level α = 0,05 followed by Scheffe test. The results showed: (1) the students 'economic learning outcomes taught with problem-based learning strategy is higher than the students' economic learning outcomes taught with expository learning strategies; (2) the students 'economic learning outcomes that have extroverted personality is higher than the students' economic learning outcomes that have introverted personality types; (3) there is interaction between learning strategy with personality type in influencing student learning outcomes. This hypothesis suggests that problem-based learning strategies are more appropriate than expository learning models in improving students' economic learning outcomes, and students with extroverted personality types will achieve better outcomes than students with introverted personality types. Keywords: learning strategy, personality type, economic learning result

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.

MODIFIKASI MODEL PEMBELAJARAN QUANTUM LEARNING DENGAN STRATEGI PEMBELAJARAN TUGAS DAN PAKSA

2019 ◽  
Vol 2 ◽  
Author(s):  
Erna Pebriana ◽  
Bela Mustika Sari ◽  
Yasa Abdurrahman
Keyword(s):  
Student Learning ◽  
Learning Outcomes ◽  
Learning Strategies ◽  
Learning Process ◽  
Learning Strategy ◽  
Mathematics Learning ◽  
Learning Approaches ◽  
Quantum Learning ◽  
Task Strategies ◽  
Improve Student Learning

This writing aims to make students more active and disciplined in the learning process and can also increase creativity and learning outcomes. The low mathematics learning outcomes are not only due to difficult mathematics, but are caused by several factors which include students themselves, teachers, learning approaches, and learning environments that are interconnected with each other. To improve the ability and results of learning it is necessary to make modifications to the task learning strategy and force. Quantum learning is a tip, a guide, a strategy and an entire learning process that can sharpen understanding and memory, and make learning a pleasant and useful process. Task and Forced Learning Strategies are strategies that focus on giving assignments and a little coercion so that students complete their tasks on time so that the learning process can run effectively. Therefore, the writer modifies the model of quantum learning with task and forced learning strategies, the results of this modification show that learning with quantum learning models with forced and task strategies can improve the learning process so that students become more disciplined in doing tasks, can motivate student learning, and can improve student learning outcomes.

PENGARUH STRATEGI PEMBELAJARAN E-LEARNING DAN MINAT BELAJAR TERHADAP HASIL BELAJAR AKUNTANSI

Akademika ◽  
2019 ◽  
Vol 8 (01) ◽  
pp. 81-100
Author(s):  
Eva Kristiyani ◽  
Iffah Budiningsih
Keyword(s):  
Learning Outcomes ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Sampling Technique ◽  
Student Group ◽  
Design Factor ◽  
Significant Difference ◽  
E Learning ◽  
Learning Interest ◽  
Equivalent Class

The aim of this research is to know the influence of e-learning learning strategy and interest in learning to accounting learning result. This research was conducted at SMK Permata Bangsa Kelurahan Jakasetia, South Bekasi Subdistrict, Bekasi City involving 56 samples taken with random sampling technique to the equivalent class. Instrument used in this research is the accounting test and questionnaire interest in student learning; and the data analysis using two-way ANAVA and Tukey Test. The results of this study obtained: (1) there is a significant difference between the learning outcomes of students who are taught with e-learning learning strategies and expository strategies in which the results of student accounting learning taught by e-learning strategy is higher than the students taught by strategy expository learning. (2) There is an interaction between students who are taught using learning strategies with interest in learning on accounting learning outcomes. (3) This means that the result of group accounting learning which is taught using e-learning learning strategy is significantly higher than that taught using expository learning strategy in students who have high learning interest. (4) While the learning result of student group accounting that is taught using e-learning strategy is same as learning result which is taught using expository learning strategy to students who have low learning interest, influenced by student environment factor and learning design factor in research.

THE EFFECT OF LEARNING STRATEGY ON MATHEMATICS LEARNING OUTCOMES IN CLASS IV STUDENTS AT PRIMARY SCHOOL 09 KABA WETAN

2020 ◽  
Vol 3 (1) ◽  
pp. 67-83
Author(s):  
Ahmad Abdul Rochim ◽  
Siti Bandiah
Keyword(s):  
Learning Outcomes ◽  
Learning Strategies ◽  
Design Research ◽  
Learning Strategy ◽  
Interactive Learning ◽  
Mathematics Learning ◽  
Problem Based Learning ◽  
Sampling Technique ◽  
Analysis Technique ◽  
Class Iv

The accuracy in choosing a learning strategy is a very important part in efforts to improve the achievement of student learning outcomes. Therefore this study aims to determine the effect of learning strategies on mathematics learning outcomes. This study uses a 2x2 factorial design research. Through this design the effects of Interactive learning strategies and problem-based learning will be compared to student mathematics learning outcomes. The population in this study were all students of grade IV SDN 09 Kaba Wetan, totaling 76 students, consisting of 2 classes. To determine the sample class, a random sampling technique is used. The sample classes used were 2 classes totaling 76 students, class IV-A as an Interactive class and class IV-B as a problem-based class. The data analysis technique used is descriptive and inferential statistical techniques. And testing the analysis requirements is the normality test using the Lilifors Test, while the homogeneity requirements are using the F Test and Barlett Test. After testing the analysis requirements, the two-way variance analysis of Analilsis is performed. The results of this study indicate that there is an interaction effect between learning strategies on student mathematics learning outcomes. So that the selection of appropriate learning strategies is influenced by the ability of teachers to understand the characteristics of their students. In the learning strategy applied by the teacher can optimize student mathematics learning outcomes by choosing class strategies namely Interactive learning and problem based learning classes.

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