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The present work includes the temporal modeling of the oviposition activity of the Aedes aegypti mosquito, a vector of viral diseases such as Dengue, Chicungunya and Zika, based on time series of data extracted from earth observation satellite images. Unlike previous works, Machine Learning techniques that are capable of capturing nonlinear relationships between variables, such as artificial neural...
State of the art network intrusion detection systems are heavily influenced by signature based techniques for detecting threats which are extracted from raw packet captures and firewall logs. With the recent emergence of cloud computing and big data analytics, supervised machine learning is also being used to detect deviations of the network traffic patterns from already-known normal patterns. Subsequently,...
Snort is an open source network intrusion detection and prevention system (IDS/IPS) utilizing a rule-driven language, its shortcoming is unable to detect new attacks. This paper explores how to integrate Artificial Intelligence into Snort IDS/IPS, which enables IDS/IPS adapt to networks and detect anomalies. As for preprocessors of Snort IDS, a learning algorithm such as artificial neural network...
Incorporating knowledge from domain expert to a classifier is one of the techniques which require to be considered in solving imbalanced dataset problems. In this study, the proposed technique is a development to extend the process for imbalanced dataset where the individual classification system has already been designed for balanced data set. This paper introduces a methodology and preliminary results...
This research work presents a systematic investigational study of an interesting challenging phenomenon observed in natural world. Mainly, presented study concerned with conceptual interdisciplinary analysis and evaluation of quantified learning creativity phenomenon. Associated with diverse aspects of measurable behavioral learning performance. That's observed by two diverse natural biological systems'...
Local scour around bridge abutment is a time-dependent complex phenomenon encountered world-wide. It is difficult to establish a general empirical model that can be applied to all abutment conditions. In this paper, Radial basis function (RBF) Network has been used to predict the maximum scour depth around bridge abutment. An appropriate model is identified using experimental data from literature...
Out of several antenna design techniques the Artificial Neural Network (ANN) based method is suitable for prediction of characteristic parameters of loop antenna by considering transmit - receive conditions of practical communication set-ups. The predicted set of parameters can be used to fix dimensions of a loop antenna which involves theoretical calculations. This work proposes an approach to determine...
Artificial Neural Networks (ANNs) have been used as a promising tools for many applications. In recent years, a computer-aided design approach based on ANNs has been introduced to microwave modeling, simulation and optimization. In this work, the characteristics parameters of the conductor-backed asymmetric coplanar waveguide (CB - ACPW) with one lateral ground plane have been determined with the...
The purpose of this paper is to analysis EEG spectrogram image using Artificial Neural Network (ANN) for brainwave balancing application. Time-frequency approach or spectrogram image processing technique is used to analyze EEG signals. The Gray Level Co-occurrence Matrix (GLCM) texture feature was extracted from spectrogram image and passed through Principal components analysis (PCA) to reduce the...
Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address...
Breast cancer is the most frequent cancer and the most frequent cause of cancer induced death in women in the world. Diagnosis and prognosis of this cancer can be done through the radiological, surgical, and pathologic assessments of breast tissue samples. In developing countries, testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration...
Traditionally e-learning systems are emphasized on the online content generation and most of them fail in considering the requirements and learning styles of end user, while representing it. Therefore, appears the need for adaptation to the user's learning behavior. Adaptive e-learning refers to an educational system that understands the learning content and the user interface according to pedagogical...
In this paper, we analyze the potential of combining wireless sensor networks with artificial neural networks (ANNs) to build a "smart forest-fire early detection sensory system" (SFFEDSS). We outline our new SFFEDS system in which temperature, light and smoke data from low-cost sensor nodes spread out on the forest bed is aggregated into information. This information is spatially and temporally...
This paper presents an optimizing methodology for implementing a multi-layer perceptron (MLP) neural network in a Field Programmable Gate Array (FPGA) device. In order to obtain an efficient implementation, a compromise of time and area is needed. Starting from simulation in the learning phase with fixed point operators, we have developed a methodology which allows the automatic generation of a VHDL...
The relationship of road conditions and time change on the basis of people-machine-environmental coupling is researched. A prediction method of time recursive to confirm the shortest time of route is proposed. This method is as an accumulated experience basing on the idea of supervised learning in artificial neural network, colligating with the difference of road conditions during different time section,...
A novel hybrid method based on feature extraction and neural network for short-term load forecasting was presented. It is well known that temperature information is very important for load forecasting, but the local structure of temperature sensitive information is not adopted in the literature. The proposed model adopts an integrated architecture to handle the local temperature sensitive information...
We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set...
In this paper, a novel approach for online motor fault diagnosis is proposed based on artificial neural network (ANN) trained by immune clustering and genetic algorithm (IGA). The IGA is employed to adaptively optimize the structure of the radial basis function neural network (RBFNN). The clonal selection principle is responsible for how the centres will represent the training data set. The immune...
Artificial intelligence technologies have been applied in a number of systems to achieve learning and intelligent behavior. In this paper an artificial neural network is used to implement multimode authentication through information fusion. An information fusion model uses metrics computed from the identity attributes using Shannon's information theory. Initialisation of the artificial neural network...
In this paper, the basic principle of support vector machine is introduced firstly, Then a new method to diagnosis fault for high voltage circuit breakers is presented based on the introduction of wavelet packet and characteristic entropy. The new method decomposes vibration signals with wavelet packet, and extracts entropy parameters from the restructured signals at the third level. Finally, the...
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