scholarly journals Gravitation Theory Based Model for Multi-Label Classification

2017 ◽  
Vol 12 (5) ◽  
pp. 689
Author(s):  
Liwen Peng ◽  
Yongguo Liu
Keyword(s):  
High Performance ◽  
Text Categorization ◽  
Classification Algorithms ◽  
Universal Gravitation ◽  
The Past ◽  
Proposed Model ◽  
Series Of Experiments ◽  
Music Information ◽  
Better Than

The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure.

scholarly journals The Compressive Strength of High-Performance Concrete and Ultrahigh-Performance

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
E. H. Kadri ◽  
S. Aggoun ◽  
S. Kenai ◽  
A. Kaci
Keyword(s):  
Experimental Data ◽  
Compressive Strength ◽  
Silica Fume ◽  
High Performance ◽  
High Performance Concrete ◽  
Cementitious Materials ◽  
Strength Gain ◽  
Proposed Model ◽  
Partial Cement Replacement ◽  
Better Than

The compressive strength of silica fume concretes was investigated at low water-cementitious materials ratios with a naphthalene sulphonate superplasticizer. The results show that partial cement replacement up to 20% produce, higher compressive strengths than control concretes, nevertheless the strength gain is less than 15%. In this paper we propose a model to evaluate the compressive strength of silica fume concrete at any time. The model is related to the water-cementitious materials and silica-cement ratios. Taking into account the author's and other researchers’ experimental data, the accuracy of the proposed model is better than 5%.

scholarly journals Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2444
Author(s):  
Mazhar Javed Awan ◽  
Osama Ahmed Masood ◽  
Mazin Abed Mohammed ◽  
Awais Yasin ◽  
Azlan Mohd Zain ◽  
...  
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
Malware Detection ◽  
Detection Methods ◽  
Learning Framework ◽  
Malware Classification ◽  
Proposed Model ◽  
Communication Devices ◽  
Digital Assets

In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.

scholarly journals From raw ion mobility measurements to disease classification: a comparison of analysis processes

2015 ◽  
Author(s):  
Salome Horsch ◽  
Dominik Kopczynski ◽  
Jörg Ingo Baumbach ◽  
Jörg Rahnenführer ◽  
Sven Rahmann
Keyword(s):  
Ion Mobility ◽  
Peak Detection ◽  
Disease Classification ◽  
Classification Algorithms ◽  
Exhaled Breath ◽  
Major Goal ◽  
Analysis Process ◽  
Automated Algorithms ◽  
Better Than

Ion mobility spectrometry (IMS) is a technology for the detection of volatile compounds in the air of exhaled breath that is increasingly used in medical applications. One major goal is to classify patients into disease groups, for example diseased versus healthy, from simple breath samples. Raw IMS measurements are data matrices in which peak regions representing the compounds have to be identified and quantified. A typical analysis process consists of pre-processing and peak detection in single experiments, peak clustering to obtain consensus peaks across several experiments, and classification of samples based on the resulting multivariate peak intensities. Recently several automated algorithms for peak detection and peak clustering have been introduced, in order to overcome the current need for human-based analysis that is slow, subjective and sometimes not reproducible. We present an unbiased comparison of a multitude of combinations of peak processing and multivariate classification algorithms on a disease dataset. The specific combination of the algorithms for the different analysis steps determines the classification accuracy, with the encouraging result that certain fully-automated combinations perform even better than current manual approaches.

Between-within effects in survival models with cross-classified clustering: Application to the evaluation of the effectiveness of medical devices

2016 ◽  
Vol 27 (1) ◽  
pp. 312-319 ◽  
Author(s):  
Guy Cafri ◽  
Juanjuan Fan
Keyword(s):  
Medical Devices ◽  
Survival Data ◽  
Real Data ◽  
Medical Intervention ◽  
Survival Models ◽  
Medical Interventions ◽  
Proposed Model ◽  
Cross Classification ◽  
Better Than

In many medical applications involving observational survival data there will be a cross-classification of doctors and hospitals, as well as an interest in controlling for potentially confounding doctor and hospital effects when evaluating the effectiveness of a medical intervention. In this paper, we propose the use of a between-within model with cross-classified random effects and show through simulation that it performs better than alternative models. A real data example illustrates the application of the proposed model in a study of the survival of hip implants. The proposed model has broad utility in determining the effectiveness of medical interventions.

scholarly journals CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8231
Author(s):  
Xinyi Hu ◽  
Chunxiang Gu ◽  
Yihang Chen ◽  
Fushan Wei
Keyword(s):  
Traffic Classification ◽  
Traffic Data ◽  
Training Method ◽  
Proposed Model ◽  
Public Dataset ◽  
Basic Characteristics ◽  
Encrypted Traffic ◽  
Closed Environment ◽  
Better Than

With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.

scholarly journals Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1573
Author(s):  
Amin Alqudah ◽  
Ali Mohammad Alqudah ◽  
Hiam Alquran ◽  
Hussein R. Al-Zoubi ◽  
Mohammed Al-Qodah ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Recognition Rate ◽  
Second Stage ◽  
The Past ◽  
The World ◽  
Very High ◽  
Handwritten Arabic

Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.

Detection of Shotgun Surgery and Message Chain Code Smells using Machine Learning Techniques

2019 ◽  
Vol 6 (2) ◽  
pp. 34-50
Author(s):  
Thirupathi Guggulothu ◽  
Salman Abdul Moiz
Keyword(s):  
High Performance ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Code Smells ◽  
Design Problems ◽  
Chain Code ◽  
Learning Techniques ◽  
Machine Leaning ◽  
Code Smell ◽  
Better Than

Code smell is an inherent property of software that results in design problems which makes the software hard to extend, understand, and maintain. In the literature, several tools are used to detect code smell that are informally defined or subjective in nature due to varying results of the code smell. To resolve this, machine leaning (ML) techniques are proposed and learn to distinguish the characteristics of smelly and non-smelly code elements (classes or methods). However, the dataset constructed by the ML techniques are based on the tools and manually validated code smell samples. In this article, instead of using tools and manual validation, the authors considered detection rules for identifying the smell then applied unsupervised learning for validation to construct two smell datasets. Then, applied classification algorithms are used on the datasets to detect the code smells. The researchers found that all algorithms have achieved high performance in terms of accuracy, F-measure and area under ROC, yet the tree-based classifiers are performing better than other classifiers.

Reduced the Risk in Agriculture Management for Government Using Support Vector Regression with Dynamic Optimization Parameters

2017 ◽  
Vol 15 (2) ◽  
pp. 243-261 ◽  
Author(s):  
Chien-Pang Lee
Keyword(s):  
Agricultural Policy ◽  
Real Life ◽  
Agricultural Management ◽  
Support Vector ◽  
The Past ◽  
Real Life Data ◽  
Proposed Model ◽  
Optimization Parameters ◽  
Agriculture Management ◽  
Better Than

A good agricultural policy can reduce the risk of agricultural management. In the past, the traditional statistical methods were always used for assisting agricultural management. However, the assumptions of traditional methods might not fit for real life data, that would affect the decision of agricultural management. For this reason, this paper uses big data analysis to propose a novel prediction model without any assumption to forecast agricultural output for reducing the risk. According to the result, the proposed model is better than the existing models in terms of prediction accuracy. Accordingly, the proposed model can be suggested for reducing the risk of agricultural management of government.

scholarly journals Pathological Analysis of Blood Cells Using Deep Learning Techniques

2020 ◽  
Vol 13 ◽  
Author(s):  
Virender Ranga ◽  
Shivam Gupta ◽  
Priyansh Agrawal ◽  
Jyoti Meena
Keyword(s):  
Deep Learning ◽  
Blood Cells ◽  
Well Being ◽  
Input Image ◽  
Medical Sciences ◽  
Human Errors ◽  
Deep Dive ◽  
Proposed Model ◽  
Better Than

Introduction: The major area of work of pathologists is concerned with detecting the diseases and helping the patients in their healthcare and well-being. The present method used by pathologists for this purpose is manually viewing the slides using a microscope and other instruments. But this method suffers from a lot of problems, like there is no standard way of diagnosing, human errors and it puts a heavy load on the laboratory men to diagnose such a large number of slides daily. Method: The slide viewing method is widely used and converted into digital form to produce high resolution images. This enables the area of deep learning and machine learning to deep dive into this field of medical sciences. In the present study, a neural based network has been proposed for classification of blood cells images into various categories. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the blood images with an accuracy of 95.24%. Result: After training the models on 20 epochs. The plots of training accuracy, testing accuracy and corresponding training loss, testing loss for proposed model is plotted using matplotlib and trends. Discussion: The performance of proposed model is better than existing standard architectures and other work done by various researchers. Thus, the proposed model enables the development of pathological system which will reduce human errors and daily load on laboratory men. This can also in turn help pathologists in carrying out their work more efficiently and effectively. Conclusion: In the present study, a neural based network has been proposed for classification of blood cells images into various categories. These categories have significance in the medical sciences. When input image is passed through the proposed architecture and all the hyper parameters and dropout ratio values are used in accordance with proposed algorithm, then model classifies the images with an accuracy of 95.24%. This accuracy is better than standard architectures.. Further it can be seen that the proposed neural network performs better than present related works carried by various researchers.

scholarly journals Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7987
Author(s):  
Naresh K. Trivedi ◽  
Vinay Gautam ◽  
Abhineet Anand ◽  
Hani Moaiteq Aljahdali ◽  
Santos Gracia Villar ◽  
...  
Keyword(s):  
Neural Network ◽  
Crop Yield ◽  
High Performance ◽  
Early Stage ◽  
Tomato Plants ◽  
Leaf Disease ◽  
Proposed Model ◽  
Tomato Diseases

Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

Sign in / Sign up

Export Citation Format

Share Document