scholarly journals Improve Neural Distinguishers of SIMON and SPECK

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
ZeZhou Hou ◽  
JiongJiong Ren ◽  
ShaoZhen Chen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Probability ◽  
Data Augmentation ◽  
New Model ◽  
Smt Solver ◽  
Significant Potential ◽  
Pseudorandom Permutation ◽  
Research Gap

Deep learning has played an important role in many fields, which shows significant potential for cryptanalysis. Although these existing works opened a new direction of machine learning aided cryptanalysis, there is still a research gap that researchers are eager to fill. How to further improve neural distinguishers? In this paper, we propose a new algorithm and model to improve neural distinguishers in terms of accuracy and the number of rounds. First, we design an algorithm based on SAT to improve neural distinguishers. With the help of SAT/SMT solver, we obtain new effective neural distinguishers of SIMON using the input differences of high-probability differential characteristics. Second, we propose a new neural distinguisher model using multiple output differences. Inspired by the existing works and data augmentation in deep learning, we use the output differences to exploit more derived features and train neural distinguishers, by splicing output differences into a matrix as a sample. Based on the new model, we construct neural distinguishers of SIMON and SPECK with round and accuracy promotion. Utilizing our neural distinguishers, we can distinguish reduced-round SIMON or SPECK from pseudorandom permutation better.

An Application of Machine Learning in Plant Leaves Disease Detection and Classification

2020 ◽  
Vol 17 (6) ◽  
pp. 2645-2652
Author(s):  
Sachin Dahiya ◽  
Tarun Gulati
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Automatic System ◽  
Crop Production ◽  
Plant Disease ◽  
Data Augmentation ◽  
Machine Intelligence ◽  
Disease Detection ◽  
Initial Stage

Plant disease severely affects the crop production. Food security is always a challenge because the population of the world is increasing at a rapid rate. Diseases in plants can be controlled at the initial stage with the help of automatic system that can be able to detect the wide variety of diseases before its spreading to the whole cultivation area. With the development of various machine learning and deep learning algorithms it is now possible to design such an automatic system. Deep neural network like convolution neural network are able to detect the plant disease with high accuracy. In this paper we have discussed about the deep learning techniques, CNN and its parameters, data augmentation, transfer learning and various factor that affects the performance of DL model. Recent studies that apply the machine intelligence in plant leaf disease detection are also discussed.

scholarly journals Training data augmentation for deep learning radio frequency systems

2021 ◽  
pp. 154851292199124
Author(s):  
William H Clark ◽  
Steven Hauser ◽  
William C Headley ◽  
Alan J Michaels
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Radio Frequency ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Training Data ◽  
Peak Performance ◽  
Modulation Classification ◽  
Data Types ◽  
Significant Information

Applications of machine learning are subject to three major components that contribute to the final performance metrics. Within the category of neural networks, and deep learning specifically, the first two are the architecture for the model being trained and the training approach used. This work focuses on the third component, the data used during training. The primary questions that arise are “what is in the data” and “what within the data matters?” looking into the radio frequency machine learning (RFML) field of automatic modulation classification (AMC) as an example of a tool used for situational awareness, the use of synthetic, captured, and augmented data are examined and compared to provide insights about the quantity and quality of the available data necessary to achieve desired performance levels. Three questions are discussed within this work: (1) how useful a synthetically trained system is expected to be when deployed without considering the environment within the synthesis, (2) how can augmentation be leveraged within the RFML domain, and, lastly, (3) what impact knowledge of degradations to the signal caused by the transmission channel contributes to the performance of a system. In general, the examined data types each make useful contributions to a final application, but captured data germane to the intended use case will always provide more significant information and enable the greatest performance. Despite the benefit of captured data, the difficulties and costs that arise from live collection often make the quantity of data needed to achieve peak performance impractical. This paper helps quantify the balance between real and synthetic data, offering concrete examples where training data is parametrically varied in size and source.

scholarly journals Detection of citrus leaf diseases using a deep learning technique

2021 ◽  
Vol 11 (2) ◽  
pp. 1719
Author(s):  
Ahmed R. Luaibi ◽  
Tariq M. Salman ◽  
Abbas Hussein Miry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Data Augmentation ◽  
Original Data ◽  
Training Images ◽  
Learning Technique ◽  
Data Points ◽  
Citrus Leaves

The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.

scholarly journals Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble

2021 ◽  
Vol 11 (4) ◽  
pp. 1950
Author(s):  
Haixia Qi ◽  
Yu Liang ◽  
Quanchen Ding ◽  
Jun Zou
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Data Augmentation ◽  
Learning Model ◽  
Leaf Spot Disease ◽  
Automatic Identification ◽  
Rust Disease ◽  
Single Leaf ◽  
Deep Learning Model

Peanut is an important food crop, and diseases of its leaves can directly reduce its yield and quality. In order to solve the problem of automatic identification of peanut-leaf diseases, this paper uses a traditional machine-learning method to ensemble the output of a deep learning model to identify diseases of peanut leaves. The identification of peanut-leaf diseases included healthy leaves, rust disease on a single leaf, leaf-spot disease on a single leaf, scorch disease on a single leaf, and both rust disease and scorch disease on a single leaf. Three types of data-augmentation methods were used: image flipping, rotation, and scaling. In this experiment, the deep-learning model had a higher accuracy than the traditional machine-learning methods. Moreover, the deep-learning model achieved better performance when using data augmentation and a stacking ensemble. After ensemble by logistic regression, the accuracy of residual network with 50 layers (ResNet50) was as high as 97.59%, and the F1 score of dense convolutional network with 121 layers (DenseNet121) was as high as 90.50. The deep-learning model used in this experiment had the greatest improvement in F1 score after the logistic regression ensemble. Deep-learning networks with deeper network layers like ResNet50 and DenseNet121 performed better in this experiment. This study can provide a reference for the identification of peanut-leaf diseases.

scholarly journals A COMPREHENSIVE STUDY ON APPLICATION OF DEEP LEARNING IN BRAIN TUMOR DETECTION

2021 ◽  
Vol 23 (07) ◽  
pp. 977-994
Author(s):  
Josmy Mathew ◽  
◽  
Dr. N. Srinivasan ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumours ◽  
Data Augmentation ◽  
State Of The Art ◽  
Network Models ◽  
Data Sets ◽  
Learning Approaches ◽  
Neural Network Models ◽  
Comprehensive Study

Deep Learning is an area of machine learning which, because of its capability to handle a large quantity of data, has demonstrated amazing achievements in each field, notably in biomedicine. Its potential and abilities were evaluated and utilised with an effective prognosis in the identification of brain tumours with MRI pictures. The diagnosis of MRI images by computer-aided brain tumours includes tumour identification, segmentation and classification. Many types of research have concentrated in recent years on conventional or basic machine learning approaches in the detection of brain tumours. Throughout this overview, we offer a comprehensive assessment of the surveys that have been reported so far and the current approaches for detecting tumours. Our review examines the major processes in deep learning approaches for detecting brain tumours including preprocessing, extraction of features and classification and their performance and limitations. We also explore state-of-the-art neural network models to identify brain tumours through extensive trials with and without data augmentation. This review also discusses existing data sets for brain tumour detection assessments.

scholarly journals EventDrop: Data Augmentation for Event-based Learning

2021 ◽  
Author(s):  
Fuqiang Gu ◽  
Weicong Sng ◽  
Xuke Hu ◽  
Fangwen Yu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Augmentation ◽  
Dynamic Range ◽  
Low Cost ◽  
Training Data ◽  
Event Data ◽  
Deep Networks ◽  
Time Latency ◽  
Event Based

The advantages of event-sensing over conventional sensors (e.g., higher dynamic range, lower time latency, and lower power consumption) have spurred research into machine learning for event data. Unsurprisingly, deep learning has emerged as a competitive methodology for learning with event sensors; in typical setups, discrete and asynchronous events are first converted into frame-like tensors on which standard deep networks can be applied. However, over-fitting remains a challenge, particularly since event datasets remain small relative to conventional datasets (e.g., ImageNet). In this paper, we introduce EventDrop, a new method for augmenting asynchronous event data to improve the generalization of deep models. By dropping events selected with various strategies, we are able to increase the diversity of training data (e.g., to simulate various levels of occlusion). From a practical perspective, EventDrop is simple to implement and computationally low-cost. Experiments on two event datasets (N-Caltech101 and N-Cars) demonstrate that EventDrop can significantly improve the generalization performance across a variety of deep networks.

scholarly journals Identifikasi Foto Wanita Berhijab dari Majalah Untuk Pembuatan Katalog Busana Muslim Otomatis Memanfaatkan Convolutional Neural Network

2019 ◽  
Vol 1 (2) ◽  
pp. 85-91
Author(s):  
M. Najamudin Ridha ◽  
Endang Setyati ◽  
Yosi Kristian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Large Scale ◽  
Data Augmentation ◽  
Rectified Linear Unit

Abstrak—Perkembangan Fashion Muslim di Indonesia terus meningkat, disisi lain terobosan baru pada Deep Learning dengan memadukan arsitektur seperti dropout regularizations dan Rectified Linear Unit (ReLU) sebagai fungsi aktivasi dan data augmentation, mampu mencapai terobosan pada large scale image classification. Penelitian ini menggunakan metode deteksi objek wajah dengan Haar Cascades Classification untuk mendapatkan sample dataset wajah dan preprocessing data testing untuk dilanjutkan pada metode machine learning untuk klasifikasi citra dengan Convolutional Neural Network. Dataset yang digunakan adalah kumpulan katalog busana online, dataset yang sudah di preprocessing dibagi menjadi dua kategori, yaitu Hijab untuk semua citra wanita berhijab, dan Non Hijab untuk citra yang bukan wanita berhijab. selanjutnya klasifikasi citra menggunakan data ujicoba majalah digital terbitan Hijabella, Joy Indonesia dan Scarf Indonesia. Semakin besar resolusi citra input untuk preprocessing pada majalah digital, maka akan semakin banyak objek citra yang terdeteksi, dengan meningkatkan jumlah dataset untuk training dan validasi, mampu menambah hasil akurasi yang didapatkan, terjadi peningkatan akurasi pada dataset 2.500 wajah perkategori ke 5.000 wajah perkategori dengan resolusi 720p meningkat dari rata-rata 81.30% menjadi 82.31%, peningkatan rata-rata 1.01% dan tertinggi 2.14%, sedangkan resolusi 1080p meningkat dari rata-rata 83.03% menjadi 83.68%, peningkatan rata-rata 0.65% dan tertinggi 1.73%, akurasi tertinggi adalah sebesar 84.72% menggunakan model dataset 5.000 secara acak perkategori.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

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