LAUTECH Journal of Engineering and Technology (LAUJET)
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THERMODYNAMIC EVALUATION OF A LOW-PRESSURE FISH DRYER
No full textFish products are major sources protein supplements for human nourishment composing of low - fat diet cherished by consumers for health wise consequence. Quite substantial amount of this products during harvest cum handling are lost to spoilage due to lack of short time drying technology systems and untimely removal of moisture content amidst activities of bacteria. The inappropriate and inadequate processing methods must be eliminated by providing advance short time drying outfits to assist the local processors to stall the activities of bacteria orchestrating fish spoilage. To meet this short time drying needs and increase fish storability and shelf life for overall good productivity, an indigenous affordable vacuum dryer was conceived and developed locally. The unit was fabricated using locally sourced materials and was tested on water cans and cat fish samples. A constant vacuum head of 3.6 KPa was attained at pumping rate of 320 l/hr, at observed temperature range of 38 and 42oC, energy and power level of 498.6 KJ and 13.6W with an effective moisture diffusivity 8.41×10-7m2/s and 2.05×10-7m2/s were recorded for the fish samples; gutted and un-gutted respectively dried within 10 hours. Total moisture content removal efficiency of 85% was attained This method of drying was very effective in drying the fish samples but still requires further optimization studies to scale up the unit for commercial purpose
DEVELOPMENT OF AN OPTIMIZED INTELLIGENT MACHINE LEARNING APPROACH IN FOREX TRADING USING MOVING AVERAGE INDICATORS
Get PDFThis research presents the development of an optimized intelligent machine learning approach in Forex trading using two variants of Moving Average indicators. The main aim of the Expert Advisor (EA) development is to introduce a new intelligent model for automated execution of trades in the Forex market, reducing potential losses due to human errors and sentimental factors in trading Forex. In developing this trading model, Momentum strategy was used since it takes advantage of market swings, along with Machine Learning - Genetic algorithm, being a type of supervised learning used in training the past historical data based on selected trading parameters in a Meta Trader 4 (MT4) platform. The new Expert Advisor –Exponential Moving Average (ESMA) was built using the MQL4 language which is based on C++ for programming specific trading strategies and easily facilitates automated trading. The result is an optimized intelligent trading system that implements the intersection of the two moving averages at various periods, to execute trades autonomously with a profit pass rate of 75% visible from the Optimization chart of the MetaTrader 4 (MT4) platfor
CHARACTERISATION OF THE NIGERIAN-GROWN EUCALYPTUS CAMALDULENSIS TIMBER SPECIE ACCORDING TO EN 338 (2009) AND NCP 2 (1973)
Get PDF In this work, the Nigerian-grown Eucalyptus camaldulensis timber specie was characterized based on the NCP 2 (1973) and EN 338 (2009) code. The specie was obtained from timber markets in Sabon Gari, Zaria and Fanteka, Kaduna, North-western Nigeria. The elastic modulus, bending strength (using four-point flexural test) and density of the timber as stated in EN 384 (2004) were determined at their various moisture contents with which other respective derived properties were obtained. The experiments were carried out using a 500 kN capacity Universal Testing Machine at the Department of Civil Engineering laboratory, Ahmadu Bello University, Zaria. Results obtained indicate that the mean density of Eucalyptus camaldulensis timber is 975.9 kg/m3 at an adjusted moisture content of 18%. The flexural strength of Eucalyptus camaldulensis timber species was determined to be 69.02 N/mm2 and the mean Modulus of Elasticity of 5409.4 N/mm2. With these results, Eucalyptus camaldulensis was allocated to strength class D60 based on EN 338 (2009) and strength class N1 based on NCP 2 (1973) classification systems which makes it suitable for bridge construction, railway sleepers, pier construction as well as heavy duty flooring
A HYBRIDIZED ENCRYPTION SCHEME BASED ON ELLIPTIC CURVE CRYPTOGRAPHY FOR SECURING DATA IN SMART HEALTHCARE
Get PDFRecent developments in smart healthcare have brought us a great deal of convenience. Connecting common objects to the Internet is made possible by the Internet of Things (IoT). These connected gadgets have sensors and actuators for data collection and transfer. However, if users' private health information is compromised or exposed, it will seriously harm their privacy and may endanger their lives. In order to encrypt data and establish perfectly alright access control for such sensitive information, attribute-based encryption (ABE) has typically been used. Traditional ABE, however, has a high processing overhead. As a result, an effective security system algorithm based on ABE and Fully Homomorphic Encryption (FHE) is developed to protect health-related data. ABE is a workable option for one-to-many communication and perfectly alright access management of encrypting data in a cloud environment. Without needing to decode the encrypted data, cloud servers can use the FHE algorithm to take valid actions on it. Because of its potential to provide excellent security with a tiny key size, elliptic curve cryptography (ECC) algorithm is also used. As a result, when compared to related existing methods in the literature, the suggested hybridized algorithm (ABE-FHE-ECC) has reduced computation and storage overheads. A comprehensive safety evidence clearly shows that the suggested method is protected by the Decisional Bilinear Diffie-Hellman postulate. The experimental results demonstrate that this system is more effective for devices with limited resources than the conventional ABE when the system’s performance is assessed by utilizing standard model
EXPERIMENTAL INVESTIGATION OF THE POTENTIAL OF LIQUIFIED PETROLEUM GAS IN VAPOUR COMPRESSION REFRIGERATION SYSTEM
Get PDFThe essence of refrigeration systems cannot be overemphasized especially in this part of the globe. Perishable items are to be preserved for some periods before usage while human comfort should also be also be paramount since we are in the northern hemisphere of the globe. The device hat doe this uses refrigerants as working fluids which are traditional harmful to human beings through depletion of the ozone layer. Majorly Ozone layer protects the earth from warming which could lead to flooding. Common economical refrigerants like CFCs (Chlorofluorocarbons) have been discovered to be harmful to the earth. This article therefore, experimented the quantity replacement of CFCs with Liquefied Petroleum Gas in various mixes. The LPG (Liquefied Petroleum Gas) used consists a mixture of propane and butane in the ratio 6:4 by mass. The blend of the two refrigerants were shaped essentially by blending at least two single-part refrigerants, the GWP (Global Warming Potentials) of a refrigerant mix is the mass-weighted normal of GWPs of individual parts in the mix. That is, to compute the GWP of a mix, one essentially adds the GWP of the singular parts with respect to their (GWP (LPG) x M (LPG)) + (GWP(R-134a) x M(R-134a)) = GWP (blend). From the evaluated GWP of the 6 different % mass composition, the % mass of (100%/0%) was the only refrigerant to adhere to the preferred GWP<150. The mass composition of blend (100%/0%) LPG/R-134a was first performed. In-order to achieve this, 8kg of each of the refrigerant was used. The blend was formed in an empty cylinder which was measured as 2482g with the aid of a digital beam balance, by gradually injecting LPG into the empty cylinder till the mass percentage of the 2000g entered, making the mass read as 4,482g (i.e., 2482g of the empty cylinder + 2000g of LPG). Based on the above observations, it could be inferred that the COP (Coefficient of Performance) of mixed refrigerants blends was higher than that of R-134a indicating that each of the blend exhibit higher performance. The experiment discovered that LPG could be used in the place of R134a without affecting the operation efficiency of a vapor compression refrigeration system. The study concludes that LPG offers the best alternative when the COP and flammability are combined as performance metric
PREDICTING COVID-19 FROM CHEST X-RAY IMAGES USING OPTIMIZED CONVOLUTION NEURAL NETWORK
Get PDFMachine learning is emerging as a unique powerful method to improve the diagnosis and prognosis of several multifactorial diseases, including COVID-19. The COVID-19 pandemic is a major threat, and it has severe impact on the health and life of many people worldwide. The recent advances in computer vision made possible by various computational method has paved the way for computer assisted diagnosis in fighting COVID-19. Early detection of the COVID-19 through accurate diagnosis, may decrease the patient’s mortality rate. Chest X-ray images are crucial and mostly used for the diagnosis of this disease. Thus, this study used optimized Convolution Neural Network (OCNN) to support the diagnosis of COVID-19 using chest x-ray. Particle Swarm Optimization (PSO) was applied to optimize the network of CNN for improved performance. The dataset used in this study was acquired from Kaggle repository. The dataset contains the Chest X-Ray images of COVID-19 patients and normal patients. The model is created, and the results have been evaluated by using the various evaluation metrics, i.e., sensitivity, false positive rate, precision, accuracy, and prediction time. The approach adopted in this study enhances CNN by making it free from iterative adjustment of weights which increases the computational speed to a higher extent. The experimental results reveal that the proposed technique achieved an improved performance which indicates the very high accuracy of the proposed model
PARAMETER ESTIMATION OF ARIMA USING GOAL PROGRAMMING
Get PDFGoal programming (GP) is an improved technique of the linear programming model that is suitable for multi-criteria decision making for organizations that have multiple objectives that are usually not measurable in the same units. Autoregressive integrated moving average model (ARIMA) is useful in predicting future behavior based on past behaviors. It is also useful for forecasting when there is any relationship between values especially in a time series in nature, the values before and the values after them. In this paper, we examine the application of goal programming as a mathematical tool for estimating the parameters of time series forecasting models such as the estimation of ARIMA model’s parameters using traditional estimation and goal programming. Ordinary Least Squares (OLS) is used to estimate the ARIMA parameters using a linear constrained goal programming set. Maximum likelihood estimation and goal programming methods have been studied and compared using mean absolute error (MAE). We show that the GP prediction's mean absolute error values in the data set were significantly lower than those attained by the ARIMA model. These findings suggest that the prediction equations derived by goal programming were more accurate than those generated through maximum likelihood estimation. This can be formulated as minimizing the sum of absolute errors using goal programming as opposed to the ARIMA model's sum of squares error
AN IMPROVED CRYPTO-STEGANOGRAPHIC TECHNIQUE FOR DATA HIDING USING MODIFIED LEAST SIGNIFICANT BIT AND RIVEST SHAMIR ADLEMAN ALGORITHMS
Get PDFThis study is aimed at developing an improved LSB technique by overcoming the standard LSB technique's high imperceptibility and transparency, especially in JPEG and BMP formats which are the characteristics of a truly secure and reliable security technique for remote data transmission. The creation of a modified LSB (mLSB+DWT) algorithm combined with RSA would enhance secured data transmission and image security. The standard LSB was modified to create a balance between the algorithm's exploration and exploitation stages so as to improve quantity solution in detecting high energy coefficient (optimal wavelet coefficient) of DWT and to resolve conflicting requirements of different parameters and properties of digital images. The techniques achieved improved Average difference, Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Image Fidelity and Normalized Cross Correlation (NCC), indicating a higher quality measurement between the original and compressed images using different format
PROXIMATE COMPOSITION OF TANNIA (Xanthosoma sagittifolium) FLOUR AS INFLUENCED BY PRETREATMENT AND DRYING TEMPERATURE
Get PDFDrying is an important operation in processing fresh tannia cormel into flour with better storability. Product characteristics and drying variables could affect the final product's quality and consumers’ acceptability. This study was therefore designed to investigate the effects of blanching time (5, 10, and 15 minutes) and drying temperature (60, 70 and 80°C) on selected proximate composition of oven-dried tannia flour. Response Surface Method (RSM) of 2 factors, 3 levels Historical Data Design (HDD) second-order polynomial model was adopted for the experimental design. Flour was produced from fresh and pretreated tannia cormels and proximate analysis of the flour samples was carried out using standard methods. Data obtained were statistically analyzed at 5% level of significance. Moisture content (wet basis), carbohydrate, protein, ash, crude fibre and fat content of the flour samples were within the ranges 4.43-12.74, 77.34-84.71, 2.22-4.22, 2.47-4.69, 0.34-2.50 and 0.63-3.72%, respectively. Samples dried at 60oC and blanched for 12.74 minutes had the best quality attributes with the optimum response values of 83.19% carbohydrate, 3.56% protein, 3.80% ash, 0.98% crude fibre and 1.96% fat with 7.01% moisture content. Extended blanching period is recommended to obtain high-quality flour with improved storage stability. Proper combination of drying temperature and blanching period that will result in desired proximate composition of tannia flour can be achieved based on the findings of this study
TECHNICAL ANALYSIS AND SIMULATION OF 4G WIRELESS NETWORK HANDOFF DECISION
Get PDFThis paper presents the technical analysis and simulation of 4G wireless network handoff decision. The integration of numerous new technologies into 4G services which provide faster wireless internet access, makes 4G technology an extremely complicated technology. Vertical handoff poses a great challenge in communication channel and this contributes to unbearable life for subscribers. The method used involves the handoff process for inter-nodes handoff, together with matching network loads using three phases of operation. The performance of the four handoff algorithms was optimized, compared and evaluated using MATLAB/Simulink. The results obtained shows that at 6ms of time to trigger, the results of the proposed handoff algorithm had the highest optimized ratio value of 18225.701 and also, at 1 beta level, the proposed algorithm had the lowest optimized ratio value of 9255.701. Again, at 1.5 alpha level, the proposed algorithm had the highest optimized ratio value of 3012.701. The experimented results produced the minimum handoff delay of 1000.701 when compared with the other three algorithms. In conclusion, the results realized have improved the handoff decisions in order to achieve a reliable signal strength in wireless network