scholarly journals Detection and Classification of Different Weapon Types Using Deep Learning

2021 ◽  
Vol 11 (16) ◽  
pp. 7535
Author(s):  
Volkan Kaya ◽  
Servet Tuncer ◽  
Ahmet Baran
Keyword(s):  
Deep Learning ◽  
Control Systems ◽  
Training Process ◽  
Security Forces ◽  
New Approach ◽  
New Model ◽  
Automatic Control Systems ◽  
Proposed Model ◽  
Computer Environment

Today, with the increasing number of criminal activities, automatic control systems are becoming the primary need for security forces. In this study, a new model is proposed to detect seven different weapon types using the deep learning method. This model offers a new approach to weapon classification based on the VGGNet architecture. The model is taught how to recognize assault rifles, bazookas, grenades, hunting rifles, knives, pistols, and revolvers. The proposed model is developed using the Keras library on the TensorFlow base. A new model is used to determine the method required to train, create layers, implement the training process, save training in the computer environment, determine the success rate of the training, and test the trained model. In order to train the model network proposed in this study, a new dataset consisting of seven different weapon types is constructed. Using this dataset, the proposed model is compared with the VGG-16, ResNet-50, and ResNet-101 models to determine which provides the best classification results. As a result of the comparison, the proposed model’s success accuracy of 98.40% is shown to be higher than the VGG-16 model with 89.75% success accuracy, the ResNet-50 model with 93.70% success accuracy, and the ResNet-101 model with 83.33% success accuracy.

scholarly journals Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

scholarly journals A Method for Assessing the Stability of Digital Automatic Control Systems (ACS) with Discrete Elements. Hypothesis and Simulation Results

2020 ◽  
Author(s):  
Vladimir Kodkin ◽  
Alexander Baldenkov ◽  
Alexander Anikin
Keyword(s):  
Automatic Control ◽  
Control Systems ◽  
Variable Structure ◽  
Sampling Interval ◽  
Discrete Elements ◽  
Digital Controllers ◽  
Variable Structure Systems ◽  
New Approach ◽  
Automatic Control Systems ◽  
The Stability

The article presents a new approach to the analysis of the stability of automatic systems with discrete links.In almost all modern automatic control systems, there are links that break signals in time. These are power controlled switches - transistors or thyristors operating in a pulsed mode and digital links in regulators.Time discretization significantly affects the stability of processes in the automatic control system. The theoretical analysis of such systems is rather complicated and requires a significant change in engineering approaches to analysis. In connection with the improvement of digital controllers and a significant increase in their performance, in recent years this problem has practically not been remembered. However, its mathematical "content" has not changed since the 80s of the 20th century, when discreteness began to play a major role among the problems hindering progress in automatic control systems, in terms of the transition to digital systems.In this paper, a new approach is proposed, which consists in interpreting the sampling operation by a link with the proposed frequency characteristic, which determines the suppression of input high-frequency signals. This link greatly simplifies engineering calculations and demonstrates the new capabilities of sampling systems. These possibilities include the rational distribution of digitalization resources - the number of bits and the sampling interval between the regulator channels, depending on the frequency range of the efficiency of these channels. Theoretical statements have been verified and confirmed by simulation. It is shown how this approach makes it possible to formulate new principles of construction of seemingly well-known controllers - PID controllers and variable structure systems (VSS).

scholarly journals Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Manjit Kaur ◽  
Vijay Kumar ◽  
Vaishali Yadav ◽  
Dilbag Singh ◽  
Naresh Kumar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Huge Number ◽  
X Ray ◽  
Screening Model ◽  
Proposed Model ◽  
Strength Pareto Evolutionary Algorithm ◽  
Chest X Ray ◽  
Deep Learning Model

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.

NEW APPROACH OF SIGNED BINARY NUMBERS MULTIPLICATION AND ITS IMPLEMENTATION ON FPGA

2021 ◽  
Vol 26 (1) ◽  
pp. 56-68
Author(s):  
Sarifuddin Madenda ◽  
Suryadi Harmanto
Keyword(s):  
Field Programmable Gate Array ◽  
Low Complexity ◽  
Negative Numbers ◽  
New Approach ◽  
New Model ◽  
Proposed Model ◽  
Field Programmable ◽  
Gate Array ◽  
Complexity Algorithm

This paper proposes a new model of signed binary multiplication. This model is formulated mathematically and can handle four types of binary multipliers: signed positive numbers multiplied by signed positive numbers (SPN-by-SPN); signed positive numbers multiplied by signed negative numbers (SPN-by-SNN); signed negative numbers multiplied by signed positive numbers (SNN-by-SPN); and signed negative numbers multiplied by signed negative numbers (SNN-by-SNN). The proposed model has a low complexity algorithm, is easy to implement in software coding and integrated in a hardware FPGA (Field-Programmable Gate Array), and is more powerful compared to the modified Baugh-Wooley's model.

scholarly journals Traditional Bangladeshi Sports Video Classification Using Deep Learning Method

2021 ◽  
Vol 11 (5) ◽  
pp. 2149
Author(s):  
Moumita Sen Sarma ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Easy Access ◽  
Learning Method ◽  
Cultural Significance ◽  
Sports Video ◽  
Temporal Features ◽  
Average Accuracy ◽  
Proposed Model

Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal features from the videos. In this regard, a new Traditional Bangladeshi Sports Video (TBSV) dataset is constructed containing five classes: Boli Khela, Kabaddi, Lathi Khela, Kho Kho, and Nouka Baich. A key contribution of this paper is to develop a scratch model by incorporating the two most prominent deep learning algorithms: convolutional neural network (CNN) and long short term memory (LSTM). Moreover, the transfer learning approach with the fine-tuned VGG19 and LSTM is used for TBSV classification. Furthermore, the proposed model is assessed over four challenging datasets: KTH, UCF-11, UCF-101, and UCF Sports. This model outperforms some recent works on these datasets while showing 99% average accuracy on the TBSV dataset.

scholarly journals Review methods for breast cancer detection using artificial intelligence and deep learning methods

2021 ◽  
pp. 98-102
Author(s):  
Maryam Naderan
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
High Performance ◽  
False Negative ◽  
Main Idea ◽  
New Approach ◽  
Training Time ◽  
Proposed Model ◽  
Convolutional Autoencoder

Nowadays, there are many related works and methods that use Neural Networks to detect the breast cancer. However, usually they do not take into account the training time and the result of False Negative (FN) while training the model. The main idea of this paper is to compare already existing methods for detecting the breast cancer using Deep Learning Algorithms. Moreover, since the breast cancer is one of the most common lethal cancers and early detection helps prevent complications, we propose a new approach and the use of the convolutional autoencoder. This proposed model has shown high performance with sensitivity, precision, and accuracy of 93,50%, 91,60% and 93% respectively.

scholarly journals Deepenz: prediction of enzyme classification by deep learning

2021 ◽  
Vol 22 (2) ◽  
pp. 1108
Author(s):  
Hamza Chehili ◽  
Salah Eddine Aliouane ◽  
Abdelhafedh Bendahmane ◽  
Mohamed Abdelhafid Hamidechi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Great Capacity ◽  
Intensive Training ◽  
Enzyme Classification ◽  
New Methods ◽  
Proposed Model

<span>Previously, the classification of enzymes was carried out by traditional heuritic methods, however, due to the rapid increase in the number of enzymes being discovered, new methods aimed to classify them are required. Their goal is to increase the speed of processing and to improve the accuracy of predictions. The Purpose of this work is to develop an approach that predicts the enzymes’ classification. This approach is based on two axes of artificial intelligence (AI): natural language processing (NLP) and deep learning (DL). The results obtained in the tests  show the effectiveness of this approach. The combination of these two tools give a model with a great capacity to extract knowledge from enzyme data to predict and classify them. The proposed model learns through intensive training by exploiting enzyme sequences. This work highlights the contribution of this approach to improve the precision of enzyme classification.</span>

scholarly journals Automated Prediction of the Liquid Paraffins Viscosity

2021 ◽  
Vol 2096 (1) ◽  
pp. 012084
Author(s):  
B A Grigoriev ◽  
D V Boldyrev
Keyword(s):  
Experimental Data ◽  
Control Systems ◽  
Raw Materials ◽  
Petroleum Products ◽  
Physicochemical Analysis ◽  
New Approach ◽  
Automatic Control Systems ◽  
Systems Software ◽  
Construction Of Self ◽  
Predicted Values

Abstract The article deals with an approach to the construction of self-adjusting automatic control systems, in which parametric adaptation occurs when the properties of technological raw materials change during its processing due to changes in the parameters of state. A new algorithm for predicting the viscosity of hydrocarbon liquids is proposed, which can be used as part of the control systems software. The main dependences are obtained on the basis of reliable experimental data on the viscosity of normal C8–C20 alkanes, which are similar in properties to commodity petroleum products. The data of physicochemical analysis are used as the initial data for the computation. Based on the theory of corresponding states, a new approach to scaling the viscosity using a set of characteristic parameters is developed and technique for their determination is proposed. The method is tested in the temperature range (0.4 – 0.7)TC at pressures up to 10 MPa. It is shown that the deviation of the predicted values from the experimental data is comparable to the error of the viscosity measurement.

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