scholarly journals Deep Learning Based on Residual Networks for Automatic Sorting of Bananas

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Abdulkader Helwan ◽  
Mohammad Khaleel Sallam Ma’aitah ◽  
Rahib H. Abiyev ◽  
Selin Uzelaltinbulat ◽  
Bengi Sonyel
Keyword(s):  
Deep Learning ◽  
Intelligent System ◽  
Deep Structure ◽  
Real Life ◽  
Generalization Capability ◽  
Residual Learning ◽  
Simulation Results ◽  
Human Operators ◽  
Comparative Results ◽  
Selection Of

This study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residual learning-based network “ResNet-50” is designed to sort out fruits, particularly bananas into healthy or defective classes. The design of the system is implemented by using transfer learning that uses the stored knowledge of the deep structure. Datasets of bananas have been collected for the implementation of the deep structure. The simulation results of the designed system have shown a great generalization capability when tested on test (unseen) banana images and obtained high accuracy of 99%. The simulation results of the designed residual learning-based system are compared with the results of other systems used for grading the bananas. Comparative results indicate the efficiency of the designed system. The developed system can be used in food processing industry, in real-life applications where the accuracy, cost, and speed of the intelligent system will enhance the production rate and allow meeting the demand of consumers. The system can replace or assist human operators who can exert their energy on the selection of fruits.

scholarly journals A BAYESIAN APPROACH BASED ON ACQUISITION FUNCTION FOR OPTIMAL SELECTION OF DEEP LEARNING HYPERPARAMETERS: A CASE STUDY WITH ENERGY MANAGEMENT DATA

2020 ◽  
Vol 2 (1) ◽  
pp. 22-27
Author(s):  
MUHAMMAD ALI ◽  
Krishneel Prakash ◽  
Hemanshu Pota
Keyword(s):  
Deep Learning ◽  
Energy Management ◽  
Bayesian Approach ◽  
Learning Algorithm ◽  
Real Life ◽  
Model Parameters ◽  
Optimal Parameters ◽  
Comparison Results ◽  
Selection Of

With the recent rollout of smart meters, huge amount of data can be generated on hourly and daily basis. Researchers and industry persons can leverage from this big data to make intelligent decisions via deep learning (DL) algorithms. However, the performance of DL algorithms are heavily dependent on the proper selection of parameters. If the hyperparameters are poorly selected, they usually lead to suboptimal results. Traditional approaches include a manual setting of parameters by trial and error methods which is time consuming and difficult process.  In this paper, a Bayesian approach based on acquisition is presented to automatic selection of optimal parameters based on provided data. The acquisition function was established to search for the best parameter from the input space and evaluate the next points based on past observations. The tuning process identifies the best model parameters by iterating the objective function and minimizing the loss for optimizable variables such as learning rate and Hidden layersize. To validate the presented approach, we conducted a case study on real-life energy management datasets while constructing a deep learning model on MATLAB platform. A performance comparison was drawn with random parameters and optimal parameters selected by presented approach. The comparison results illustrate that the presented approach is effective as it brings a notable improvement in the performance of learning algorithm.

IMPLEMENTASI PENENTUAN ARSITEKTUR MASJID MODERN DI ACEH MENGGUNAKAN PEMODELAN SISTEM CERDAS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Soraya Masthura Hasan ◽  
T Iqbal Faridiansyah
Keyword(s):  
Fuzzy Inference System ◽  
Architectural Design ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Selection Process ◽  
System Modeling ◽  
Inference System ◽  
Normal Limits ◽  
Roof Structure ◽  
Selection Of

Mosque architectural design is based on Islamic culture as an approach to objects and products from the Islamic community by looking at their suitability and values and basic principles of Islam that explore more creative and innovative ideas. The purpose of this system is to help the team and the community in seeing the best mosque in the top order so that the system can be used as a reference for the team and the community. The variables used in the selection of modern mosques include facilities and infrastructure, building structure, roof structure, mosque area, level of security and facilities. The system model used is a fuzzy promethee model that is used for the modern mosque selection process. Fuzzy inference assessment is used to determine the value of each variable so that the value remains at normal limits. Fuzzy values will then be included in promethee assessment aspects. The highest promethee ranking results will be made a priority for the best mosque ranking. This fuzzy inference system and promethee system can help the management team and the community in determining the selection of modern mosques in aceh in accordance with modern mosque architecture. Intelligent System Modeling System In Determining Modern Mosque Architecture in the City of Aceh, this building will be web based so that all elements of society can see the best mosque in Aceh by being assessed by all elements of modern mosque architecture.Keywords: Fuzzy inference system, Promethe, Option of  Masjid

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

scholarly journals Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

2021 ◽  
Author(s):  
Sayan Surya Shaw ◽  
Shameem Ahmed ◽  
Samir Malakar ◽  
Laura Garcia-Hernandez ◽  
Ajith Abraham ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Real Life ◽  
Class Imbalance ◽  
Ring Theory ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Swarm Optimization ◽  
Imbalance Problem ◽  
Representative Samples ◽  
Selection Of

AbstractMany real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

An Analysis on the Forming Load of AA 2024 Aluminium Alloy in Combined and Sequence Operation Process

2006 ◽  
Vol 519-521 ◽  
pp. 949-954 ◽  
Author(s):  
Beong Bok Hwang ◽  
J.H. Shim ◽  
Jung Min Seo ◽  
H.S. Koo ◽  
J.H. Ok ◽  
...  
Keyword(s):  
Finite Element ◽  
Load Capacity ◽  
Low Cost ◽  
Forming Load ◽  
Mechanical Press ◽  
Combined Operation ◽  
Aa 2024 ◽  
Load Characteristics ◽  
Simulation Results ◽  
Selection Of

This paper is concerned with the analysis of the forming load characteristics of a forward-backward can extrusion in both combined and sequence operation. A commercially available finite element program, which is coded in the rigid-plastic finite element method, has been employed to investigate the forming load characteristics. AA 2024 aluminum alloy is selected as a model material. The analysis in the present study is extended to the selection of press frame capacity for producing efficiently final product at low cost. The possible extrusion processes to shape a forward-backward can component with different outer diameters are categorized to estimate quantitatively the force requirement for forming forward-backward can part, forming energy, and maximum pressure exerted on the die-material interfaces, respectively. The categorized processes are composed of combined and/or some basic extrusion processes such as sequence operation. Based on the simulation results about forming load characteristics, the frame capacity of a mechanical press of crank-drive type suitable for a selected process could be determined along with securing the load capacity and with considering productivity. In addition, it is suggested that different load capacities be selected for different dimensions of a part such as wall thickness in forward direction and etc. It is concluded quantitatively from the simulation results that the combined operation is superior to sequence operation in terms of relatively low forming load and thus it leads to low cost for forming equipments. However, it is also known from the simulation results that the precise control of dimensional accuracy is not so easy in combined operation. The results in this paper could be a good reference for analysis of forming process for complex parts and selection of proper frame capacity of a mechanical press to achieve low production cost and thus high productivity.

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