scholarly journals An improved LogNNet classifier for IoT applications

2021 ◽  
Vol 2094 (3) ◽  
pp. 032015
Author(s):  
H Heidari ◽  
A A Velichko
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Chaotic Map ◽  
Network Architectures ◽  
Direct Relation ◽  
Swarm Optimization ◽  
Computing Power ◽  
Iot Applications ◽  
Special Matrix ◽  
Iot Devices

Abstract In the age of neural networks and Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda. Designing suitable algorithms for IoT applications is an important task. The paper proposes a feed forward LogNNet neural network, which uses a semi-linear Henon type discrete chaotic map to classify MNIST-10 dataset. The model is composed of reservoir part and trainable classifier. The aim of the reservoir part is transforming the inputs to maximize the classification accuracy using a special matrix filing method and a time series generated by the chaotic map. The parameters of the chaotic map are optimized using particle swarm optimization with random immigrants. As a result, the proposed LogNNet/Henon classifier has higher accuracy and the same RAM usage, compared to the original version of LogNNet, and offers promising opportunities for implementation in IoT devices. In addition, a direct relation between the value of entropy and accuracy of the classification is demonstrated.

scholarly journals Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1432
Author(s):  
Andrei Velichko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Classification Accuracy ◽  
Network Architecture ◽  
Chaotic Behavior ◽  
Logistic Map ◽  
Simple Algorithm ◽  
Research Perspective ◽  
Logistic Mapping ◽  
Iot Devices

This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the higher-dimensional feature space. The most effective recognition of a handwritten digit from MNIST-10 occurs under chaotic behavior of the logistic map. The correlation of classification accuracy with the value of the Lyapunov exponent was obtained. An advantage of LogNNet implementation on IoT devices is the significant savings in memory used. At the same time, LogNNet has a simple algorithm and performance indicators comparable to those of the best resource-efficient algorithms available at the moment. The presented network architecture uses an array of weights with a total memory size from 1 to 29 kB and achieves a classification accuracy of 80.3–96.3%. Memory is saved due to the processor, which sequentially calculates the required weight coefficients during the network operation using the analytical equation of the logistic mapping. The proposed neural network can be used in implementations of artificial intelligence based on constrained devices with limited memory, which are integral blocks for creating ambient intelligence in modern IoT environments. From a research perspective, LogNNet can contribute to the understanding of the fundamental issues of the influence of chaos on the behavior of reservoir-type neural networks.

scholarly journals Block chain based Malware Detection using Machine Learning Algorithms for IoT enabled E-Health Applications

2021 ◽  
pp. 84-92
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Wireless Communication ◽  
Optimization Technique ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Swarm Optimization ◽  
Block Chain ◽  
Iot Devices ◽  
Health Applications

IoT devices are playing a greater role in business specially in wireless communication. IoT devices are achieving higher maturity as seen in smartdust. The aim of this research is to study the functionality of MOTES in smartdust to integrate with IoT architecture and infrastructure for optimization of wireless communication specially linked with 2.4Ghz and 5Ghz band. MOTES are being modeled in MALTAB using Artificial Neural Network integrated with optimization for speed, power and frequency linked with IoT architecture. The result proves that smartdust architecture if utilized in IoT architecture, the over all performances result of IoT devices is increased specially in bandwidth and power consumption. All the modeling result were compared for general sensor data bandwidth in ESP8266 for 2.4 Ghz, and mathematical model are presented for 5Ghz using smartdust MOTES. It is been proposed that using AI optimization technique like Ant Colonization Optimization or Particle Swarm Optimization we can mathematically model smartdust Architecture.

Designing neural network architectures for pattern recognition

2000 ◽  
Vol 15 (2) ◽  
pp. 151-170 ◽  
Author(s):  
MIROSLAV KUBAT
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Classification Accuracy ◽  
Network Architectures ◽  
Systematic Information ◽  
High Classification Accuracy ◽  
Neural Network Architectures ◽  
The Right ◽  
Relevant Material

An appropriately designed architecture of a neural network is essential to many realistic pattern-recognition tasks. A choice of just the right number of neurons, and their interconnections, can cut learning costs by orders of magnitude, and still warrant high classification accuracy. Surprisingly, textbooks often neglect this issue. A specialist seeking systematic information will soon realize that relevant material is scattered over diverse sources, each with a different perspective, terminology and goals. This brief survey attempts to rectify the situation by explaining the involved aspects, and by describing some of the fundamental techniques.

scholarly journals CoopECC: A Collaborative Cryptographic Mechanism for the Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wassim Jerbi ◽  
Abderrahmen Guermazi ◽  
Omar Cheikhrouhou ◽  
Hafedh Trabelsi
Keyword(s):  
Cluster Head ◽  
Computation Time ◽  
Cryptographic Protocol ◽  
Computing Power ◽  
Security Issues ◽  
Iot Applications ◽  
Iot Devices ◽  
Power And Energy ◽  
The Internet Of Things ◽  
Constrained Devices

The emergence of IoT applications has risen the security issues of the big data sent by the IoT devices. The design of lightweight cryptographic algorithms becomes a necessity. Moreover, elliptic curve cryptography (ECC) is a promising cryptographic technology that has been used in IoT. However, connected objects are resource-constrained devices, with limited computing power and energy power. Driven by these motivations, we propose and develop a secure cryptographic protocol called CoopECC which leverages the organization of IoT nodes into cluster to distribute the load of cluster head (CH) among its cluster members. This technique proves that it optimizes the resource consumption of the IoT nodes including computation and energy consumption. Performance evaluation, done with TOSSIM simulator, shows that the proposed protocol CoopECC outperforms the original ECC algorithm, in terms of computation time, consumed energy, and the network’s lifespan.

Evolutionary Optimization of Residual Neural Network Architectures for Modulation Classification

2021 ◽  
Author(s):  
Erma Perenda ◽  
Sreeraj Rajendran ◽  
Gerome Bovet ◽  
Sofie Pollin ◽  
Mariya Zheleva
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Network Architecture ◽  
Communication Systems ◽  
Domain Knowledge ◽  
Low Complexity ◽  
Initial Population ◽  
Network Architectures ◽  
Modulation Classification ◽  
Fold Reduction

Automatic Modulation Classification (AMC) receives significant interest in the context of current and future wireless communication systems. Deep learning emerged as a powerful AMC tool, as it allows for the joint learning of discriminative features, and signal classification. However, the optimization of Deep Neural Network (DNN) architectures for AMC is a manual and time-consuming process that requires profound domain knowledge and much effort. Moreover, most proposed solutions focus mainly on classification accuracy, while optimization of network complexity is neglected. In this paper, we propose a novel bi-objective memetic algorithm, BO-NSMA, to search optimal DNN architectures for AMC to maximize classification accuracy and minimize network complexity. We show that BO-NSMA, with a small initial population of six individuals and only ten generations, finds a DNN architecture that outperforms all human-crafted State-of-the-Art (SoA) models. BO-NSMA discovered the first low-complexity Convolutional Neural Network (CNN)-based model, which achieves slightly better performance than costly Recurrent Neural Network (RNN)-based approaches, allowing a 2.8-fold reduction in network complexity with 0.7% performance improvement. Compared to its counterparts from Network Architecture Search (NAS), BO-NSMA finds the best architecture, which achieves up to 18.24% accuracy gain and up to a 78.71-fold reduction in network complexity.

A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification

2020 ◽  
pp. 1458-1479
Author(s):  
Nabil M. Hewahi ◽  
Enas Abu Hamra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Classification Accuracy ◽  
Hybrid Approach ◽  
Particle Swarm ◽  
Mathematical Methods ◽  
Swarm Optimization ◽  
Artificial Neural Network Ann ◽  
Domain Independent

Artificial Neural Network (ANN) has played a significant role in many areas because of its ability to solve many complex problems that mathematical methods failed to solve. However, it has some shortcomings that lead it to stop working in some cases or decrease the result accuracy. In this research the authors propose a new approach combining particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to increase the classification accuracy of ANN. The proposed approach utilizes the advantages of both PSO and GA to overcome the local minima problem of ANN, which prevents ANN from improving the classification accuracy. The algorithms start with using backpropagation algorithm, then it keeps repeating applying GA followed by PSO until the optimum classification is reached. The proposed approach is domain independent and has been evaluated by applying it using nine datasets with various domains and characteristics. A comparative study has been performed between the authors' proposed approach and other previous approaches, the results show the superiority of our approach.

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