State-of-the-art in artificial neural network applications: A survey

With the advent of anomaly-based intrusion detection systems, many approaches and techniques have been developed to track novel attacks on the systems. High detection rate of 98% at a low alarm rate of 1% can be achieved by using these techniques. Though anomaly-based approaches are efficient, signature-based detection is preferred for mainstream implementation of intrusion detection systems. As a variety of anomaly detection techniques were suggested, it is difficult to compare the strengths, weaknesses of these methods. The reason why industries don‟t favor the anomaly-based intrusion detection methods can be well understood by validating the efficiencies of the all the methods. To investigate this issue, the current state of the experiment practice in the field of anomalybased intrusion detection is reviewed and survey recent studies in this. This paper contains summarization study and identification of the drawbacks of formerly surveyed works.

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A Review of Anomaly based Intrusion Detection Systems

There is an abundancy of systems characterized by parabolic PDEs in science and engineering, especially in chemistry and physics. These systems have a scalar variable, we generally call time, defining the evolution of the system under consideration. The governing equation(s) involves the unknown(s) and their first order partial derivative(s) with respect to this variable. Time variant Schrodinger equations where the unknown is the wavefunction which is responsible for the probability density for the system and Liouville equations for the statistical mechanics where the unknown is somehow responsible for a density in the systems’ phase space (here we use the plurality since both case may differ from Hamiltonian to Hamiltonian). Certain PDE(s), depending on so-called spatial coordinates, govern the behavior of the system in these and similar cases even though the partial differential equation nature is not necessarily needed. Hence we give the following equation for more

MATHEMATICAL MODELS and METHODS in APPLIED SCIENCES

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

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Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Bluetooth technology is a key component of wireless communications It provides a low-energy and low-cost solution for short-range radio transmissions Bluetooth, more specifically Bluetooth Low Energy (BLE) has become the predominant technology for connecting IoT (Internet of Things) It can be found in cell phones, headsets, speakers, printers, keyboards, automobiles, children’s toys, and medical devices, as well as many other devices The technology can also be found in automated smart homes, to provide monitors and controls for lights, thermostats, door locks, appliances, security systems, and cameras Bluetooth offers convenience and ease of use, but it lacks a centralized security infrastructure As a result, it has serious security vulnerabilities, and the need for awareness of the security risks are increasing as the technology becomes more widespread This paper presents an overview of Bluetooth technology in IoT including its security, vulnerabilities, threats, and risk mitigation solutions, as well as real-life examples of exploits Our study highlights the importance of understanding attack risks and mitigation techniques involved with using Bluetooth technology on our devices Real-life examples of recent Bluetooth exploits are presented Several recommended security measures are discussed to secure Bluetooth communication

Security Vulnerabilities in Bluetooth Technology as Used in IoT

Artificial neural networks (ANN) consider classification as one of the most dynamic research and application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by back propagation algorithm. The different combinations of functions and its effect while using ANN as a classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification with suitable combination of training, learning and transfer functions. When the maximum likelihood method was compared with backpropagation neural network method, the BPNN was more accurate than maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible. Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to be more effective than other classification algorithms.

/pdf/review-on-classification-based-on-artificial-neural-networks-1vpgqzzwvn.pdf

Review on classification based on artificial neural networks

Nano silica powders were synthesized from rice husk ash, the most silica-rich raw material, using alkaline extraction followed by acid precipitation. The phase transformation during sintering, the influence of sintering temperature and pH on the particle size and anti-reflective properties of nano silica were investigated. The results showed that the amorphous SiO2 sintered at 600°C were transformed to a cristobalite structures completely during the sintering process at 800°C and 1100°C. With the increasing sintering temperature and pH, the particle size distributions (d50) were increased respectively in the range of 62–84, 192–240, and 283–329 nm at sintering temperatures of 600°C, 800°C, and 1100°C. When the sintering temperatures were increased at 1100°C, 98.15% and 96.84% of transmittances were obtained respectively at the highest and lowest points of the anti-reflection band and could be used for anti-reflective applications.

Influence of sintering temperature and pH on the phase transformation, particle size and anti-reflective properties of RHA nano silica powders

This article presents an experimental investigation of domestic refrigerator using R134a as working fluid with different concentration of Al2O3, TiO2 nano particles and nano composite particles dis...

Performance of Al2O3/TiO2 nano composite particles in domestic refrigerator

Optimal Design and Performance Analysis of Vertically Stacked Nanosheet Tunnel Field Effect Transistor

This study discusses the effect of molding temperature, pressure and time on the final compression-molded hybrid composites with polypropylene resin for its flexural strength. The results of tests ...

K. Saravanan

Papers

Review on classification based on artificial neural networks

Influence of sintering temperature and pH on the phase transformation, particle size and anti-reflective properties of RHA nano silica powders

Performance of Al2O3/TiO2 nano composite particles in domestic refrigerator

Optimal Design and Performance Analysis of Vertically Stacked Nanosheet Tunnel Field Effect Transistor

Effect of Processing Conditions on Flexural Strength Properties of Chicken Feather Fibre (CFF) and Its Hybrid Composites with Polypropylene Resin