Search results for: multispectral algorithms

Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

Procedia PDF Downloads 152

Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

Procedia PDF Downloads 476

Authors: Mrinmoy Dhara, Vivek K. Sengar, Shovan L. Chattoraj, Soumiya Bhattacharjee

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Remote sensing techniques have emerged as an asset for various geological studies. Satellite images obtained by different sensors contain plenty of information related to the terrain. Digital image processing further helps in customized ways for the prospecting of minerals. In this study, an attempt has been made to map the hydrothermally altered zones using multispectral and hyperspectral datasets of South East Rajasthan. Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion (Level1R) dataset have been processed to generate different Band Ratio Composites (BRCs). For this study, ASTER derived BRCs were generated to delineate the alteration zones, gossans, abundant clays and host rocks. ASTER and Hyperion images were further processed to extract mineral end members and classified mineral maps have been produced using Spectral Angle Mapper (SAM) method. Results were validated with the geological map of the area which shows positive agreement with the image processing outputs. Thus, this study concludes that the band ratios and image processing in combination play significant role in demarcation of alteration zones which may provide pathfinders for mineral prospecting studies.

Keywords: ASTER, hyperion, band ratios, alteration zones, SAM

Procedia PDF Downloads 279

Authors: Malik Muhammad Akhtar, Tanzeel Khan

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Various natural and anthropogenic factors are affecting recharge processes in urban areas due to intense urban expansion; land-use/landcover change (LULC) and climate considerably influence the ecosystem functions. In Quetta, a terrible transformation of LULC has occurred due to an increase in human population and rapid urbanization over the past years; according to the Pakistan Bureau of Statistics, the increase of population from 252,577 in 1972 to 2,275,699 in 2017 shows an abrupt rise which in turn has affected the aquifer recharge capability, vegetation, and precipitation at Quetta. This study focuses on the influence of population growth and LULC on groundwater table level by employing multi-temporal, multispectral satellite data during the selected years, i.e. 2014, 2017, and 2020. The results of land classification showed that barren land had shown a considerable decrease, whereas the urban area has increased over time from 152.4sq/km in 2014 to 195.5sq/km in 2017 to 283.3sq/km in 2020, whereas surface-water area coverage has increased since 2014 because of construction of few dams around the valley. Rapid urbanization stresses limited hydrology resources, and this needs to be addressed to conserve/sustain the resources through educating the local community, awareness regarding water use and climate change, and supporting artificial recharge of the aquifers.

Keywords: climate changes, urbanization, GIS, land use, Quetta, watershed

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Authors: Muhammad Luqman, Yusuf Kurniawan

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S-Boxes is one of the linear parts of the cryptographic algorithm. The existence of S-Box in the cryptographic algorithm is needed to maintain non-linearity of the algorithm. Nowadays, modern cryptographic algorithms use an S-Box as a part of algorithm process. Despite the fact that several cryptographic algorithms today reuse theoretically secure and carefully constructed S-Boxes, there is an evaluation characteristic that can measure security properties of S-Boxes and hence the corresponding primitives. Analysis of an S-Box usually is done using manual mathematics calculation. Several S-Boxes are presented as a Truth Table without any mathematical background algorithm. Then, it’s rather difficult to determine the strength of Truth Table S-Box without a mathematical algorithm. A comprehensive analysis should be applied to the Truth Table S-Box to determine the characteristic. Several important characteristics should be owned by the S-Boxes, they are Nonlinearity, Balancedness, Algebraic degree, LAT, DAT, differential delta uniformity, correlation immunity and global avalanche criterion. Then, a comprehensive tool will be present to automatically calculate the characteristics of S-Boxes and determine the strength of S-Box. Comprehensive analysis is done on a deterministic process to produce a sequence of S-Boxes characteristic and give advice for a better S-Box construction.

Keywords: cryptographic properties, Truth Table S-Boxes, S-Boxes characteristic, deterministic process

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Authors: Watcharapan Sukkerd, Teeradej Wuttipornpun

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This paper aims to present non-population search algorithms called tabu search (TS), simulated annealing (SA) and variable neighborhood search (VNS) to minimize the total cost of capacitated MRP problem in multi-stage assembly flow shop with two alternative machines. There are three main steps for the algorithm. Firstly, an initial sequence of orders is constructed by a simple due date-based dispatching rule. Secondly, the sequence of orders is repeatedly improved to reduce the total cost by applying TS, SA and VNS separately. Finally, the total cost is further reduced by optimizing the start time of each operation using the linear programming (LP) model. Parameters of the algorithm are tuned by using real data from automotive companies. The result shows that VNS significantly outperforms TS, SA and the existing algorithm.

Keywords: capacitated MRP, tabu search, simulated annealing, variable neighborhood search, linear programming, assembly flow shop, application in industry

Procedia PDF Downloads 234

Authors: M. Maatar, M. Mekadem, M. Medale, B. Hadjed, B. Imine

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In this paper, numerical simulations have been carried out to study the performances of a flapping wing used as an energy collector. Metamodeling and genetic algorithms are used to detect the optimal configuration, improving power coefficient and/or efficiency. Radial basis functions and genetic algorithms have been applied to solve this problem. Three optimization factors are controlled, namely dimensionless heave amplitude h₀, pitch amplitude θ₀ and flapping frequency f. ANSYS FLUENT software has been used to solve the principal equations at a Reynolds number of 1100, while the heave and pitch motion of a NACA0015 airfoil has been realized using a developed function (UDF). The results reveal an average power coefficient and efficiency of 0.78 and 0.338 with an inexpensive low-fidelity model and a total relative error of 4.1% versus the simulation. The performances of the simulated optimum RBF-NSGA-II have been improved by 1.2% compared with the validated model.

Keywords: numerical simulation, flapping wing, energy extraction, power coefficient, efficiency, RBF, NSGA-II

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Authors: Murat Yazici

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Anomalies are irregularities found in data that do not adhere to a well-defined standard of normal behavior. The identification of outliers or anomalies in data has been a subject of study within the statistics field since the 1800s. Over time, a variety of anomaly detection techniques have been developed in several research communities. The cluster analysis can be used to detect anomalies. It is the process of associating data with clusters that are as similar as possible while dissimilar clusters are associated with each other. Many of the traditional cluster algorithms have limitations in dealing with data sets containing categorical properties. To detect anomalies in categorical data, fuzzy clustering approach can be used with its advantages. The fuzzy k-Mode (FKM) clustering algorithm, which is one of the fuzzy clustering approaches, by extension to the k-means algorithm, is reported for clustering datasets with categorical values. It is a form of clustering: each point can be associated with more than one cluster. In this paper, anomaly detection is performed on two simulated data by using the FKM cluster algorithm. As a significance of the study, the FKM cluster algorithm allows to determine anomalies with their abnormality degree in contrast to numerous anomaly detection algorithms. According to the results, the FKM cluster algorithm illustrated good performance in the anomaly detection of data, including both one anomaly and more than one anomaly.

Keywords: fuzzy k-mode clustering, anomaly detection, noise, categorical data

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: G. Barański, P. Kacejko, M. Wendeker

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The impact of the ignition advance control algorithms of the ASz-62IR-16X aircraft piston engine on a combustion process has been presented in this paper. This aircraft engine is a nine-cylinder 1000 hp engine with a special electronic control ignition system. This engine has two spark plugs per cylinder with an ignition advance angle dependent on load and the rotational speed of the crankshaft. Accordingly, in most cases, these angles are not optimal for power generated. The scope of this paper is focused on developing algorithms to control the ignition advance angle in an electronic ignition control system of an engine. For this type of engine, i.e. radial engine, an ignition advance angle should be controlled independently for each cylinder because of the design of such an engine and its crankshaft system. The ignition advance angle is controlled in an open-loop way, which means that the control signal (i.e. ignition advance angle) is determined according to the previously developed maps, i.e. recorded tables of the correlation between the ignition advance angle and engine speed and load. Load can be measured by engine crankshaft speed or intake manifold pressure. Due to a limited memory of a controller, the impact of other independent variables (such as cylinder head temperature or knock) on the ignition advance angle is given as a series of one-dimensional arrays known as corrective characteristics. The value of the ignition advance angle specified combines the value calculated from the primary characteristics and several correction factors calculated from correction characteristics. Individual cylinder control can proceed in line with certain indicators determined from pressure registered in a combustion chamber. Control is assumed to be based on the following indicators: maximum pressure, maximum pressure angle, indicated mean effective pressure. Additionally, a knocking combustion indicator was defined. Individual control can be applied to a single set of spark plugs only, which results from two fundamental ideas behind designing a control system. Independent operation of two ignition control systems – if two control systems operate simultaneously. It is assumed that the entire individual control should be performed for a front spark plug only and a rear spark plug shall be controlled with a fixed (or specific) offset relative to the front one or from a reference map. The developed algorithms will be verified by simulation and engine test sand experiments. This work has been financed by the Polish National Centre for Research and Development, INNOLOT, under Grant Agreement No. INNOLOT/I/1/NCBR/2013.

Keywords: algorithm, combustion process, radial engine, spark plug

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Sangwon Han, Chengquan Jin

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Time-cost trade-off (TCTO) is one of the most significant part of construction project management. Despite the significance, current TCTO analysis, based on the Critical Path Method, does not consider resource constraint, and accordingly sometimes generates an impractical and/or infeasible schedule planning in terms of resource availability. Therefore, resource constraint needs to be considered when doing TCTO analysis. In this research, genetic algorithms (GA) based optimization model is created in order to find the optimal schedule. This model is utilized to compare four distinct scenarios (i.e., 1) initial CPM, 2) TCTO without considering resource constraint, 3) resource allocation after TCTO, and 4) TCTO with considering resource constraint) in terms of duration, cost, and resource utilization. The comparison results identify that ‘TCTO with considering resource constraint’ generates the optimal schedule with the respect of duration, cost, and resource. This verifies the need for consideration of resource constraint when doing TCTO analysis. It is expected that the proposed model will produce more feasible and optimal schedule.

Keywords: time-cost trade-off, genetic algorithms, critical path, resource availability

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Authors: Ahmet Parlak, Celalettin Bilgen

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In order to create an effective early warning system, Identification of the risks, preparation and carrying out risk modeling of risk scenarios, taking into account the shortcomings of the old disaster scenarios should be used to improve the system. In the light of this, the importance of risk modeling in creating an effective early warning system is understood. In the scope of TAFRISK project risk modeling trend analysis report on risk modeling developed and a demonstration was conducted for Risk Modeling for flood and mass movements. For risk modeling R&D, studies have been conducted to determine the information, and source of the information, to be gathered, to develop algorithms and to adapt the current algorithms to Turkey’s conditions for determining the risk score in the high disaster risk areas. For each type of the disaster; Disaster Deficit Index (DDI), Local Disaster Index (LDI), Prevalent Vulnerability Index (PVI), Risk Management Index (RMI) have been developed as disaster indices taking danger, sensitivity, fragility, and vulnerability, the physical and economic damage into account in the appropriate scale of the respective type.

Keywords: disaster, hazard, risk modeling, sensor

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Authors: Luyao Zhang, Tianyu Wu, Jiayi Li, Carlos-Gustavo Salas-Flores, Saad Lahrichi

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Recent advances in AI have made algorithmic trading a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating algorithmic trading of stock and crypto tokens. Moreover, we provide comparative case studies for four conventional algorithms, including moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage. Our study offers a systematic way to program and compare different trading strategies. Moreover, we implement our algorithms by object-oriented programming in Python3, which serves as open-source software for future academic research and applications.

Keywords: algorithmic trading, AI for finance, fintech, machine learning, moving average crossover, volume weighted average price, sentiment analysis, statistical arbitrage, pair trading, object-oriented programming, python3

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Authors: Khuram Shahzad, Omar Usman Khan

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Quantum bit string comparators (QBSC) operate on two sequences of n-qubits, enabling the determination of their relationships, such as equality, greater than, or less than. This is analogous to the way conditional statements are used in programming languages. Consequently, QBSCs play a crucial role in various algorithms that can be executed or adapted for quantum computers. The development of efficient and generalized comparators for any n-qubit length has long posed a challenge, as they have a high-cost footprint and lead to quantum delays. Comparators that are efficient are associated with inputs of fixed length. As a result, comparators without a generalized circuit cannot be employed at a higher level, though they are well-suited for problems with limited size requirements. In this paper, we introduce a generalized design for the comparison of two n-qubit logic states using just two ancillary bits. The design is examined on the basis of qubit requirements, ancillary bit usage, quantum cost, quantum delay, gate operations, and circuit complexity and is tested comprehensively on various input lengths. The work allows for sufficient flexibility in the design of quantum algorithms, which can accelerate quantum algorithm development.

Keywords: quantum comparator, quantum algorithm, space-efficient comparator, comparator

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Authors: Linghui Meng, James Atlas, Deborah Munro

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There is an increasing demand for prosthetics capable of mimicking natural limb movements and hand gestures, but precise movement control of prosthetics using only electrode signals continues to be challenging. This study considers the implementation of machine learning as a means of improving accuracy and presents an initial investigation into hand gesture recognition using models based on electromyographic (EMG) signals. EMG signals, which capture muscle activity, are used as inputs to machine learning algorithms to improve prosthetic control accuracy, functionality and adaptivity. Using logistic regression, a machine learning classifier, this study evaluates the accuracy of classifying two hand gestures from the publicly available Ninapro dataset using two-time series feature extraction algorithms: Time Series Feature Extraction (TSFE) and Convolutional Neural Networks (CNNs). Trials were conducted using varying numbers of EMG channels from one to eight to determine the impact of channel quantity on classification accuracy. The results suggest that although both algorithms can successfully distinguish between hand gesture EMG signals, CNNs outperform TSFE in extracting useful information for both accuracy and computational efficiency. In addition, although more channels of EMG signals provide more useful information, they also require more complex and computationally intensive feature extractors and consequently do not perform as well as lower numbers of channels. The findings also underscore the potential of machine learning techniques in developing more effective and adaptive prosthetic control systems.

Keywords: EMG, machine learning, prosthetic control, electromyographic prosthetics, hand gesture classification, CNN, computational neural networks, TSFE, time series feature extraction, channel count, logistic regression, ninapro, classifiers

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Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

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This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC

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Authors: H. Fuad Usman, M. Rafay Khan Sial, Shahzaib Hamid

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The trend of renewable energy resources has been amplified due to global warming and other environmental related complications in the 21st century. Recent research has very much emphasized on the generation of electrical power through renewable resources like solar, wind, hydro, geothermal, etc. The use of the photovoltaic cell has become very public as it is very useful for the domestic and commercial purpose overall the world. Although a single cell gives the low voltage output but connecting a number of cells in a series formed a complete module of the photovoltaic cells, it is becoming a financial investment as the use of it fetching popular. This also reduced the prices of the photovoltaic cell which gives the customers a confident of using this source for their electrical use. Photovoltaic cell gives the MPPT at single specific point of operation at a given temperature and level of solar intensity received at a given surface whereas the focal point changes over a large range depending upon the manufacturing factor, temperature conditions, intensity for insolation, instantaneous conditions for shading and aging factor for the photovoltaic cells. Two improved algorithms have been proposed in this article for the MPPT. The widely used algorithms are the ‘Incremental Conductance’ and ‘Perturb and Observe’ algorithms. To extract the maximum power from the source to the load, the duty cycle of the convertor will be effectively controlled. After assessing the previous techniques, this paper presents the improved and reformed idea of harvesting maximum power point from the photovoltaic cells. A thoroughly go through of the previous ideas has been observed before constructing the improvement in the traditional technique of MPP. Each technique has its own importance and boundaries at various weather conditions. An improved technique of implementing the use of both ‘Perturb and Observe’ and ‘Incremental Conductance’ is introduced.

Keywords: duty cycle, MPPT (Maximum Power Point Tracking), perturb and observe (P&O), photovoltaic module

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Authors: Toni Maristela C. Estabillo, Michaela V. Matienzo, Mikaela L. Sabangan, Rosette M. Tienzo, Justine L. Bahinting

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This study is about blind watermarking on images with different categories and properties using two algorithms namely, Discrete Wavelet Transform and Patchwork Algorithm. A program is created to perform watermark embedding, extraction and evaluation. The evaluation is based on three watermarking criteria namely: image quality degradation, perceptual transparency and security. Image quality is measured by comparing the original properties with the processed one. Perceptual transparency is measured by a visual inspection on a survey. Security is measured by implementing geometrical and non-geometrical attacks through a pass or fail testing. Values used to measure the following criteria are mostly based on Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The results are based on statistical methods used to interpret and collect data such as averaging, z Test and survey. The study concluded that the combined DWT and Patchwork algorithms were less efficient and less capable of watermarking than DWT algorithm only.

Keywords: blind watermarking, discrete wavelet transform algorithm, patchwork algorithm, digital watermark

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Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

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Authors: Niharika Verma, Sandeep Santosh

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DOA technique is a localization technique used in the communication field. Various algorithms have been developed for direction of arrival estimation like MUSIC, ROOT MUSIC, etc. These algorithms depend on various parameters like antenna array elements, number of snapshots and various others. Basically the MUSIC spectrum is evaluated and peaks obtained are considered as the angle of arrivals. The angles evaluated using this process depends on the scanning interval chosen. The accuracy of the results obtained depends on the coarseness of the interval chosen. In this paper, golden section search is applied to the MUSIC algorithm and therefore, more accurate results are achieved. Initially the coarse DOA estimations is done using the MUSIC algorithm in the range -90 to 90 degree at the interval of 10 degree. After the peaks obtained then fine DOA estimation is done using golden section search. Also, the partitioning method is applied to estimate the number of signals incident on the antenna array. Dependency of the algorithm on the number of snapshots is also being explained. Hence, the accurate results are being determined using this algorithm.

Keywords: Direction of Arrival (DOA), golden section search, MUSIC, number of snapshots

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Authors: Mergim Gasia, Bojan Milovanovica, Sanjin Gumbarevic

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Better energy performance of the building envelope is one of the most important aspects of energy savings if the goals set by the European Union are to be achieved in the future. Dynamic heat transfer simulations are being used for the calculation of building energy consumption because they give more realistic energy demands compared to the stationary calculations that do not take the building’s thermal mass into account. Software used for these dynamic simulation use methods that are based on the analytical models since numerical models are insufficient for longer periods. The analytical models used in this research fall in the category of the conduction transfer functions (CTFs). Two methods for calculating the CTFs covered by this research are the Laplace method and the State-Space method. The literature review showed that the main disadvantage of these methods is that they are inadequate for heavyweight façade elements and shorter time periods used for the calculation. The algorithms for both the Laplace and State-Space methods are implemented in Mathematica, and the results are compared to the results from EnergyPlus and TRNSYS since these software use similar algorithms for the calculation of the building’s energy demand. This research aims to check the efficiency of the Laplace and the State-Space method for calculating the building’s energy demand for heavyweight building elements and shorter sampling time, and it also gives the means for the improvement of the algorithms used by these methods. As the reference point for the boundary heat flux density, the finite difference method (FDM) is used. Even though the dynamic heat transfer simulations are superior to the calculation based on the stationary boundary conditions, they have their limitations and will give unsatisfactory results if not properly used.

Keywords: Laplace method, state-space method, conduction transfer functions, finite difference method

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Enver Kucukkulahli, Pakize Erdogmus, Kemal Polat

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The 2D image segmentation is a significant process in finding a suitable region in medical images such as MRI, PET, CT etc. In this study, we have focused on 2D MRI images for image segmentation process. We have designed a GUI (graphical user interface) written in MATLABTM for 2D MRI images. In this program, there are two different interfaces including data pre-processing and image clustering or segmentation. In the data pre-processing section, there are median filter, average filter, unsharp mask filter, Wiener filter, and custom filter (a filter that is designed by user in MATLAB). As for the image clustering, there are seven different image segmentations for 2D MR images. These image segmentation algorithms are as follows: PSO (particle swarm optimization), GA (genetic algorithm), Lloyds algorithm, k-means, the combination of Lloyds and k-means, mean shift clustering, and finally BBO (Biogeography Based Optimization). To find the suitable cluster number in 2D MRI, we have designed the histogram based cluster estimation method and then applied to these numbers to image segmentation algorithms to cluster an image automatically. Also, we have selected the best hybrid method for each 2D MR images thanks to this GUI software.

Keywords: image segmentation, clustering, GUI, 2D MRI

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Authors: Dib Djalel, Mordjaoui Mourad

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The current trend towards the exploitation of various renewable energy resources has become indispensable, so it is important to improve the efficiency and reliability of the GPV photovoltaic systems. Maximum Power Point Tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions. This paper presents a new fuzzy logic control based MPPT algorithm for solar panel. The solar panel is modeled and analyzed in Matlab/Simulink. The Solar panel can produce maximum power at a particular operating point called Maximum Power Point(MPP). To produce maximum power and to get maximum efficiency, the entire photovoltaic panel must operate at this particular point. Maximum power point of PV panel keeps on changing with changing environmental conditions such as solar irradiance and cell temperature. Thus, to extract maximum available power from a PV module, MPPT algorithms are implemented and Perturb and Observe (P&O) MPPT and fuzzy logic control FLC, MPPT are developed and compared. Simulation results show the effectiveness of the fuzzy control technique to produce a more stable power.

Keywords: MPPT, photovoltaic panel, fuzzy logic control, modeling, solar power

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Authors: Avinash Malladhi

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Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

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Authors: Uduak Umoh, Imo Eyoh, Emmauel Nyoho

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This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753.

Keywords: Machine Learning Algorithms , Interval Type-2 Fuzzy Logic, Fire Outbreak, Support Vector Machine, K-Nearest Neighbour, Principal Component Analysis

Procedia PDF Downloads 182

Authors: Taisir Eldos, Aws Kanan, Waleed Nazih, Ahmad Khatatbih

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Chemical Reaction Optimization (CRO) is an optimization metaheuristic inspired by the nature of chemical reactions as a natural process of transforming the substances from unstable to stable states. Starting with some unstable molecules with excessive energy, a sequence of interactions takes the set to a state of minimum energy. Researchers reported successful application of the algorithm in solving some engineering problems, like the quadratic assignment problem, with superior performance when compared with other optimization algorithms. We adapted this optimization algorithm to the Printed Circuit Board Drilling Problem (PCBDP) towards reducing the drilling time and hence improving the PCB manufacturing throughput. Although the PCBDP can be viewed as instance of the popular Traveling Salesman Problem (TSP), it has some characteristics that would require special attention to the transactions that explore the solution landscape. Experimental test results using the standard CROToolBox are not promising for practically sized problems, while it could find optimal solutions for artificial problems and small benchmarks as a proof of concept.

Keywords: evolutionary algorithms, chemical reaction optimization, traveling salesman, board drilling

Procedia PDF Downloads 519

Authors: Vytautas Galvanauskas, Rimvydas Simutis, Donatas Levisauskas, Vykantas Grincas, Renaldas Urniezius

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The paper deals with model-based development and implementation of efficient control strategies for recombinant protein synthesis in fed-batch E.coli cultivation processes. Based on experimental data, a kinetic dynamic model for cultivation process was developed. This model was used to determine substrate feeding strategies during the cultivation. The proposed feeding strategy consists of two phases – biomass growth phase and recombinant protein production phase. In the first process phase, substrate-limited process is recommended when the specific growth rate of biomass is about 90-95% of its maximum value. This ensures reduction of glucose concentration in the medium, improves process repeatability, reduces the development of secondary metabolites and other unwanted by-products. The substrate limitation can be enhanced to satisfy restriction on maximum oxygen transfer rate in the bioreactor and to guarantee necessary dissolved carbon dioxide concentration in culture media. In the recombinant protein production phase, the level of substrate limitation and specific growth rate are selected within the range to enable optimal target protein synthesis rate. To account for complex process dynamics, to efficiently exploit the oxygen transfer capability of the bioreactor, and to maintain the required dissolved oxygen concentration, adaptive control algorithms for dissolved oxygen control have been proposed. The developed model-based control strategies are useful in scale-up of cultivation processes and accelerate implementation of innovative biotechnological processes for industrial applications.

Keywords: adaptive algorithms, model-based control, recombinant E. coli, scale-up of bioprocesses

Procedia PDF Downloads 257

Authors: John Pomerat, Aviv Segev

Abstract:

Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.

Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning

Procedia PDF Downloads 162