Development of an intelligent scorpion detection technique using vibration analysis

A possible solution to address the problem of Scorpion stings is the capability of detecting its presence earlier before it stings. This paper presents efforts in Scorpion detection using substrate vibration modelling approach. An eight stage approach has been presented in this work. Using sinusoidal signal, signal representing Scorpion behaviour was firstly sampled and then amplified before transmitting to a nearby receiving module. The received signal undergoes filtering for noise removal before being modelled for coefficients determination. The computed coefficients were then clustered for analysis of behavioural determination. Results obtained in this work show that the proposed technique can be used for Scorpion detection

Design and development of a new Shariah compliant dirham based Islamic market

The return of Islamic currencies consisting of the Dinar and Dirham calls for the return of the Islamic Trade and market. The Islamic Trade represents a frame by which the Islamic currencies operate in accordance to Islamic shariah exemplify by the earlier Muslims. For trades to exist, Islamic markets have got to be established and the characteristics of an Islamic market includes (1) right of place in the market until the completion of transaction, (2) no private ownership of the market place (3) no rent or tax levied on the usage of the market place. A new Islamic trading and market that complies with the aforementioned shariah is hereby proposed. The proposed system consists of integrating a platform of registered sellers to a marketplace in the form of a vending machine. The vending machine is made up of two different modules, namely the seller module and the buyer module. The login information provided for each registered seller is used to configure the unit selling price by the seller and this information is also used for online sales monitoring and alert. The buyer module is made up of an automatic dirham coin sensing device and the product selection switch. Insertion of dirham coin triggered the sensing and detection unit for coin validation. On completion of validation process, the acknowledgement unit reads the status of the products selection switch to detect the selected product. The End point module comprising of the sms gateway, the return unit and the delivery unit handles the completion of the transaction activity. This innovative shariah compliant Islamic market gives any registered user the ability to trade on any of the available e-market space in the vending machine until the transaction completes without being the owner of the machine

A programmable dirham coin based Hajj saving electronic device

Hajj, one of the five pillars of religion of Islam requires long-term saving. As most of the Muslim majority countries are developing countries and also facing financial instabilities, savings tend to loose value due to depreciation of the national currencies, political and economic conditions of the countries. As a solution to this problem, hajj saving in gold has been proposed has the solution to this paper money devaluation. Previous studies have proven that there is a significant effect in cost of hajj when it is priced in gold oppose to paper currency

Two level Differential Evolution algorithms for ARMA parameters estimatio

The problem of determining simultaneously the model order and coefficient of an Autoregressive Moving Average (ARMA) model is examined in this paper. An Evolutionary Algorithm (EA) comprising two-level Differential Evolution (DE) optimization scheme is proposed. The first level searches for the appropriate model order while the second level computes the optimal/sub-optimal corresponding parameters. The performance of the algorithm is evaluated using both simulated ARMA models and practical rotary motion system. The results of both examples show the effectiveness of the proposed algorithm over a well known conventional technique

A new method of vascular point detection using artificial neural network

Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5x5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database

ARTIFICIAL NEURAL NETWORK EMBEDDED OPTIMAL MOBILE COMMUNICATION SYSTEM

Recent advancement in information and communication has witnessed the involvement and application of embedded system in mobile communications and Internet of Things (IoT) deployment in form of Multiple Operator Enabled SIM (MOES) System. This newly introduced system has shown to have capability to switch from one network to another seamlessly based on Received Signal Strength (RSS). However, such switching capability has shown not to be optimal in nature due to non-application of Artificial Intelligence (AI) in network selection process. Hence, this paper aims at developing Artificial Neural Network (ANN) based system for optimal network selection using RSS. The RSS parameters were collected over time using an existing hardware which reads RSS and the parameters were compiled. The datasets obtained were from four different Mobile Network Operators (MNOs) and used as prediction parameters which were used as input and target parameters for ANN trainings and testing. The parameters were simulated and the results obtained showed high accuracy of 0.99 as the most stable value which means that the developed method was efficient for optimal selection of network as the output regression plots were linear and the graph results of the trainings showed high selection of best RSS values when compared to the actual value results obtained and plotted on the graph. The performance evaluation was carried out by checking the accuracy of the system and the result obtained shows that the system is efficient with 1 as the highest regression value obtained and can be deployed for handing over from one mobile network to another

Application of modeling techniques to diabetes diagnosis

In recent times, the introduction of complex-valued neural networks (CVNN) has widened the scope and applications of real-valued neural network (RVNN) and parametric modeling techniques. In this paper, new expert systems for automatic diagnosis and classification of diabetes using CVNN and RVNN based parametric modeling approaches have been suggested. Application of complex data normalization technique converts the real valued input data to complex valued data (CVD) by the process of phase encoding over unity magnitude. CVNN learn the relationship between the input and output phase encoded data during training and the coefficients of Complex-valued autoregressive (CAR) model can be extracted from the complex-valued weights and coefficients of the trained network. Classification of the obtained CAR or RVAR model coefficients results in required distinct classes for diagnosis purpose. Similar operations can be performed for real-valued autoregressive technique except for CVD normalization. The effect of data normalization techniques, activation functions, learning rate, number of neurons in the hidden layer and the number of epoch using the suggested techniques on PIMA INDIA diabetes dataset have been evaluated in this paper. Results obtained compares favorably with earlier reported results

RETINA FUNDUS IMAGE MASK GENERATION USING PSEUDO PARAMETRIC MODELING TECHNIQUE

The use of vascular intersection as one of the symptoms for monitoring and diagnosis of diabetic retinopathy from fundus images have been widely reported in literatures. In this work, a new hybrid approach that makes use of three different methods of vascular intersection detection namely Modified cross-point number (MCN), Combine Cross Points (CNN) and Artificial Neural Network (ANN) is hereby proposed. Result obtained from the application of this technique to both simulated and experimental shows a very high accuracy and precision value in detecting both bifurcation and cross over points. Thus an improvement in bifurcation and vascular point detection and a good tool in the monitoring and diagnosis of diabetic retinopathy

Classification of retinal images based on statistical moments and principal component analysis

Early diagnosis of Diabetic Retinopathy (DR) has been suggested as a good measure of preventing blindness associated with Diabetes. Some of the reported methodologies of Retinal Images (RI) classification for early diagnosis of DR have been shown to involve several steps and approaches for effective and accurate diagnosis. Thus, this paper investigates the classification of RI using a two-stage procedure. The first stage includes the extraction of blood vessels from RI belonging to healthy and diabetes retinal images using a modified local entropy thresholding algorithm. In the second stage, different features are extracted including statistical moments and principal components. The set of extracted features is combined into one feature vector and fed into a Sequential Minimal Optimization (SMO) classifier. The obtained result is encouraging with an average accuracy of 68.33 %