This paper present a hybrid method of Newton method, Differential Evolution Algorithm (DE) and
Cooperative Coevolution Algorithm (CCA). The proposed method is used to solve the optimisation problem in
optimise the production of biochemical systems. The problems are maximising the biochemical systems production
and simultaneously minimising the total amount of chemical reaction concentration involves. Besides
that, the size of biochemical systems also contributed to the problem in optimising the biochemical systems
production. In the proposed method, the Newton method is used in dealing biochemical system, DE for optimisation
process while CCA is used to increase the performance of DE. In order to evaluate the performance
of the proposed method, the proposed method is tested on two benchmark biochemical systems. Then, the
result that obtained by the proposed method is compare with other works and the finding shows that the
proposed method performs well compare to the other works.
QSAR Studies of the Inhibitory Activity of a Series of Substituted Indole and...inventionjournals
HF method, with the basis set 6-31G (d) was employed to calculate quantum some chemical descriptors of 37 substituted Indole. The best descriptors were selected to establish the quantitative structure activity relationship (QSAR) of the inhibitory activity against isoprenylcysteine carboxyl methyltransferase (Icmt), by principal components analysis (PCA), to a multiple regression analysis (MLR), to a nonlinear regression (RNLM) and to an artificial neural network (ANN). We accordingly propose a quantitative model and we interpret the activity of the compounds relying on the multivariate statistical analysis. This study shows that the MLR and have served to predict activity, but when compared with the results given by the ANN model. We concluded that the predictions achieved by this latter is more effective and much better than other models. The statistical results indicate that the model is statistically significant and shows very good stability towards data variation in the validation method. The contribution of each descriptor to the structure-activity relationship is evaluated.
Good Parameters for PSO in Optimizing Laying Hen Diet IJECEIAES
Manual formulation of poultry diet by taking into account the fulfillment of all nutrients requirement with least cost is a difficult task. Particle Swarm Optimization (PSO) shows promising technique to solve this problem. However, there is a lack of studying a good parameter for PSO to solve feed formulation problem since PSO is sensitive to control parameter which depends on the problem. Therefore, this study investigates good swarm size, total iterations, acceleration coefficients, and inertia weight to produce a better formula. PSO with proposed good parameters is compared with other parameters. The obtained result shows that PSO with good parameters choice produces the highest fitness. Furthermore, good parameters of PSO can be used as a reference for a software developer and for further research to optimize poultry diet using PSO.
PROGRAM PHASE IN LIGAND-BASED PHARMACOPHORE MODEL GENERATION AND 3D DATABASE ...Simone Brogi
We have applied a novel approach to generate a ligand-based pharmacophore model. The pharmacophore was built from a set of 42 compounds showing activity against MCF-7 cell line derived from human mammary adenocarcinoma, using the program PHASE, implemented in the Schrödinger suite software package. PHASE is a highly flexible system for common pharmacophore identification and assessment and 3D-database creation and searching. The best pharmacophore hypothesis showed five features: two hydrogen-bond acceptors, one hydrogen-bond donor, and two aromatic rings. The structure–activity relationship (SAR) so acquired was applied within PHASE for molecular alignment in a comparative molecular field analysis (CoMFA) 3D-QSAR study. The 3D-QSAR model yielded a test set r2 equal to 0.97 and demonstrated to be highly predictive with respect to an external test set of 18 compounds (r2 =0.93). In summary, in this study we improved a previously developed Catalyst MCF-7 inhibitory pharmacophore, and established a predictive 3D-QSAR model. We have further used this model to detect novel MCF-7 cell line inhibitors through 3D database searching
Formulation and Evaluation of Intranasal Microemulsion containing RutinSagar Savale
Rutin-flavonoid-polyphenolic has gained attention in prevention of brain cancer. The low
permeability of Rutin (RU) across the blood-brain-barrier (BBB) leads to its insufficient delivery which in
turns result in low therapeutic index. Therefore, developing a novel approaches enhancing the CNS delivery
of RU are required for the treatment of Cancer. The aim of this research work was to develop in
Microemulsion (ME) loaded with RU, for CNS targeting
QSAR Studies of the Inhibitory Activity of a Series of Substituted Indole and...inventionjournals
HF method, with the basis set 6-31G (d) was employed to calculate quantum some chemical descriptors of 37 substituted Indole. The best descriptors were selected to establish the quantitative structure activity relationship (QSAR) of the inhibitory activity against isoprenylcysteine carboxyl methyltransferase (Icmt), by principal components analysis (PCA), to a multiple regression analysis (MLR), to a nonlinear regression (RNLM) and to an artificial neural network (ANN). We accordingly propose a quantitative model and we interpret the activity of the compounds relying on the multivariate statistical analysis. This study shows that the MLR and have served to predict activity, but when compared with the results given by the ANN model. We concluded that the predictions achieved by this latter is more effective and much better than other models. The statistical results indicate that the model is statistically significant and shows very good stability towards data variation in the validation method. The contribution of each descriptor to the structure-activity relationship is evaluated.
Good Parameters for PSO in Optimizing Laying Hen Diet IJECEIAES
Manual formulation of poultry diet by taking into account the fulfillment of all nutrients requirement with least cost is a difficult task. Particle Swarm Optimization (PSO) shows promising technique to solve this problem. However, there is a lack of studying a good parameter for PSO to solve feed formulation problem since PSO is sensitive to control parameter which depends on the problem. Therefore, this study investigates good swarm size, total iterations, acceleration coefficients, and inertia weight to produce a better formula. PSO with proposed good parameters is compared with other parameters. The obtained result shows that PSO with good parameters choice produces the highest fitness. Furthermore, good parameters of PSO can be used as a reference for a software developer and for further research to optimize poultry diet using PSO.
PROGRAM PHASE IN LIGAND-BASED PHARMACOPHORE MODEL GENERATION AND 3D DATABASE ...Simone Brogi
We have applied a novel approach to generate a ligand-based pharmacophore model. The pharmacophore was built from a set of 42 compounds showing activity against MCF-7 cell line derived from human mammary adenocarcinoma, using the program PHASE, implemented in the Schrödinger suite software package. PHASE is a highly flexible system for common pharmacophore identification and assessment and 3D-database creation and searching. The best pharmacophore hypothesis showed five features: two hydrogen-bond acceptors, one hydrogen-bond donor, and two aromatic rings. The structure–activity relationship (SAR) so acquired was applied within PHASE for molecular alignment in a comparative molecular field analysis (CoMFA) 3D-QSAR study. The 3D-QSAR model yielded a test set r2 equal to 0.97 and demonstrated to be highly predictive with respect to an external test set of 18 compounds (r2 =0.93). In summary, in this study we improved a previously developed Catalyst MCF-7 inhibitory pharmacophore, and established a predictive 3D-QSAR model. We have further used this model to detect novel MCF-7 cell line inhibitors through 3D database searching
Formulation and Evaluation of Intranasal Microemulsion containing RutinSagar Savale
Rutin-flavonoid-polyphenolic has gained attention in prevention of brain cancer. The low
permeability of Rutin (RU) across the blood-brain-barrier (BBB) leads to its insufficient delivery which in
turns result in low therapeutic index. Therefore, developing a novel approaches enhancing the CNS delivery
of RU are required for the treatment of Cancer. The aim of this research work was to develop in
Microemulsion (ME) loaded with RU, for CNS targeting
quantitative structure activity relationship studies of anti proliferative ac...IJEAB
Many studies have focused on indole derivatives mainly their antiproliferative effect. The therapeutic effect of this group of molecule is very important. Quantitative structure–activity relationships (QSAR) have been applied for development relationships between physicochemical properties and their biological activities. A series of 30 molecules derived from indole is based on the quantitative structure-activity relationship (QSAR). This study was carried out using the principal component analysis (PCA) method, the multiple linear regression method (MLR), non-linear regression (RNLM), the artificial neural network (ANN) and it was validated using cross validation analysis (CV). We accordingly propose a quantitative model and we try to interpret the activity of the compounds relying on the multivariate statistical analyses. A theoretical study of series was studied using density functional theory (DFT) calculations at B3LYP/6-31G(d) level of theory for employing to calculate electronic descriptors when, the topological descriptors were computed with ACD/ChemSketch and ChemDraw 8.0 programs. The best QSAR model was found in agreement with the experimental by ANN (R = 0,99).
Each and every biological function in living organism occurs due to protein-protein interactions. The
diseases are no exception to this. Identifying one or more proteins for a particular disease and then
designing a suitable chemical compound (which is known as drug or ligand) to destroy those proteins is a
challenging topic of research in computational biology. In earlier methods, drugs were designed using only
a few chemical components and were represented as a fixed-length tree. But in reality, a drug contains
many chemical groups collectively known as pharmacophore. Moreover, the chemical length of the drug
cannot be determined before designing that drug.
In the present work, a Particle Swarm Optimization (PSO) based methodology has been proposed to find
out a suitable drug for a particular disease so that the drug-target protein interaction energy becomes
minimum. In the proposed algorithm, the drug is represented as a variable length tree and essential
functional groups are arranged in different positions of that drug. Finally, the structure of the drug is
obtained and its docking energy is minimized simultaneously. Also, the orientation of chemical groups in
the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well
inside the active site of target protein. Here, several inter-molecular forces have been considered for
accuracy of the docking energy. Results are demonstrated for three different target proteins both
numerically and pictorially. Results show that PSO performs better than the earlier methods.
Weighted Ensemble Classifier for Plant Leaf IdentificationTELKOMNIKA JOURNAL
Plant leaf identification using image can be constructed by ensemble classifier. Ensemble
classifier executes classification of various features independently. This experiment utilized texture feature
and geometry feature of plant leaf to find out which features are more powerful. Each classifier trained by
specific feature produced different accuracy rate. To integrate ensemble classifier the results of the
classification were weighted, so as the score obtained from better features contributed greater to the final
results. Weighted classification results were combined to get the final result. The proposed method was
evaluated using dataset comprises of 156 variety of plants with 4559 images. Weighting and combining
classifier used in this study were Weighted Majority Vote (WMV) and Naïve Bayes Combination. Both of
those method result showed better accuracy than using single classifier. The average accuracy of single
classifier was 61.2% for geometry classifier and 70.3% for texture classifier, while WMV method was
77.8% and Naïve Bayes Comb ination was 94.6%. The calculation of classifier’s weight b y using WMV
method produces a weight value of 0.54 for texture feature classifier and 0.46 for geometry feature
classifier.
What is QSAR?, introduction to 3D QSAR, CoMFA, CoMSIA, Case Study on CoMFA contour maps analysis and CoMSIA interactive forces between ligand and receptor, various Statistical techniques involved in QSAR
Predicting Value of Binding Constants of Organic Ligands to Beta-Cyclodextrin...Maciej Przybyłek
The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (MARSplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV).
Molecular modelling for in silico drug discoveryLee Larcombe
A slide set based on the small molecule section of "Introduction to in silico drug discovery" with more detail on molecular modelling and simulation aspects. Including a bit more on protein structure prediction
Discovery PBPK: How to estimate the expected accuracy of ISIVB and IVIVB for ...Simulations Plus, Inc.
This slideshow was presented at the 2018 - 6th Asia Pacific Regional ISSX meeting in Hangzhou, China. Chief Scientist Michael Bolger, explains how Simulations Plus’ PBPK modeling and simulation software can be used successfully in the lead optimization phase of drug discovery.
Project report: Investigating the effect of cellular objectives on genome-sca...Jarle Pahr
Report from a half-semester master-level project carried out at the department of biotechnology, Norwegian University of Science and Technology. Describes a MATLAB-based framework for comparing experimental metabolic flux data with model predictions and evaluating objective functions.
quantitative structure activity relationship studies of anti proliferative ac...IJEAB
Many studies have focused on indole derivatives mainly their antiproliferative effect. The therapeutic effect of this group of molecule is very important. Quantitative structure–activity relationships (QSAR) have been applied for development relationships between physicochemical properties and their biological activities. A series of 30 molecules derived from indole is based on the quantitative structure-activity relationship (QSAR). This study was carried out using the principal component analysis (PCA) method, the multiple linear regression method (MLR), non-linear regression (RNLM), the artificial neural network (ANN) and it was validated using cross validation analysis (CV). We accordingly propose a quantitative model and we try to interpret the activity of the compounds relying on the multivariate statistical analyses. A theoretical study of series was studied using density functional theory (DFT) calculations at B3LYP/6-31G(d) level of theory for employing to calculate electronic descriptors when, the topological descriptors were computed with ACD/ChemSketch and ChemDraw 8.0 programs. The best QSAR model was found in agreement with the experimental by ANN (R = 0,99).
Each and every biological function in living organism occurs due to protein-protein interactions. The
diseases are no exception to this. Identifying one or more proteins for a particular disease and then
designing a suitable chemical compound (which is known as drug or ligand) to destroy those proteins is a
challenging topic of research in computational biology. In earlier methods, drugs were designed using only
a few chemical components and were represented as a fixed-length tree. But in reality, a drug contains
many chemical groups collectively known as pharmacophore. Moreover, the chemical length of the drug
cannot be determined before designing that drug.
In the present work, a Particle Swarm Optimization (PSO) based methodology has been proposed to find
out a suitable drug for a particular disease so that the drug-target protein interaction energy becomes
minimum. In the proposed algorithm, the drug is represented as a variable length tree and essential
functional groups are arranged in different positions of that drug. Finally, the structure of the drug is
obtained and its docking energy is minimized simultaneously. Also, the orientation of chemical groups in
the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well
inside the active site of target protein. Here, several inter-molecular forces have been considered for
accuracy of the docking energy. Results are demonstrated for three different target proteins both
numerically and pictorially. Results show that PSO performs better than the earlier methods.
Weighted Ensemble Classifier for Plant Leaf IdentificationTELKOMNIKA JOURNAL
Plant leaf identification using image can be constructed by ensemble classifier. Ensemble
classifier executes classification of various features independently. This experiment utilized texture feature
and geometry feature of plant leaf to find out which features are more powerful. Each classifier trained by
specific feature produced different accuracy rate. To integrate ensemble classifier the results of the
classification were weighted, so as the score obtained from better features contributed greater to the final
results. Weighted classification results were combined to get the final result. The proposed method was
evaluated using dataset comprises of 156 variety of plants with 4559 images. Weighting and combining
classifier used in this study were Weighted Majority Vote (WMV) and Naïve Bayes Combination. Both of
those method result showed better accuracy than using single classifier. The average accuracy of single
classifier was 61.2% for geometry classifier and 70.3% for texture classifier, while WMV method was
77.8% and Naïve Bayes Comb ination was 94.6%. The calculation of classifier’s weight b y using WMV
method produces a weight value of 0.54 for texture feature classifier and 0.46 for geometry feature
classifier.
What is QSAR?, introduction to 3D QSAR, CoMFA, CoMSIA, Case Study on CoMFA contour maps analysis and CoMSIA interactive forces between ligand and receptor, various Statistical techniques involved in QSAR
Predicting Value of Binding Constants of Organic Ligands to Beta-Cyclodextrin...Maciej Przybyłek
The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (MARSplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV).
Molecular modelling for in silico drug discoveryLee Larcombe
A slide set based on the small molecule section of "Introduction to in silico drug discovery" with more detail on molecular modelling and simulation aspects. Including a bit more on protein structure prediction
Discovery PBPK: How to estimate the expected accuracy of ISIVB and IVIVB for ...Simulations Plus, Inc.
This slideshow was presented at the 2018 - 6th Asia Pacific Regional ISSX meeting in Hangzhou, China. Chief Scientist Michael Bolger, explains how Simulations Plus’ PBPK modeling and simulation software can be used successfully in the lead optimization phase of drug discovery.
Project report: Investigating the effect of cellular objectives on genome-sca...Jarle Pahr
Report from a half-semester master-level project carried out at the department of biotechnology, Norwegian University of Science and Technology. Describes a MATLAB-based framework for comparing experimental metabolic flux data with model predictions and evaluating objective functions.
Semi-automatic model to colony forming units countingIJECEIAES
Colony forming units counting is a conventional process carry out in bacteriological laboratories, and it is used to follow the behavior of bacteria in different conditions. Currently exist different systems, automatic or semiautomatic, to counting colony forming units exits, but, in general, many laboratories continue using manual counting, which consumes considerable time and effort from researchers and laboratory employees. This paper presents a mathematical model carry out to segment the colony forming units and, in this way, counting them from a digital image of the sample. The method uses the color space information of some points in the image and shows good behavior for images with many or few colony forming units in the sample, according to manual counting. The results show efficiencies close to 98% with MacConkey agar.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAijejournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneousl y using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multiproblem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
In recent years, consumers and legislation have been pushing companies to optimize their activities in such a way as to reduce negative environmental and social impacts more and more. In the other side, companies
must keep their total supply chain costs as low as possible to remain competitive.This work aims to develop a model to traveling salesman problem including environmental impacts and to identify, as far as possible, the contribution of genetic operator’s tuning and setting in the success and
efficiency of genetic algorithms for solving this problem with consideration of CO2 emission due to transport. This efficiency is calculated in terms of CPU time consumption and convergence of the solution. The best transportation policy is determined by finding a balance between financial and environmental
criteria.Empirically, we have demonstrated that the performance of the genetic algorithm undergo relevant
improvements during some combinations of parameters and operators which we present in our results part.
QPLC: A Novel Multimodal Biometric Score Fusion MethodCSCJournals
In biometrics authentication systems, it has been shown that fusion of more than one modality (e.g., face and finger) and fusion of more than one classifier (two different algorithms) can improve the system performance. Often a score level fusion is adopted as this approach doesn’t require the vendors to reveal much about their algorithms and features. Many score level transformations have been proposed in the literature to normalize the scores which enable fusion of more than one classifier. In this paper, we propose a novel score level transformation technique that helps in fusion of multiple classifiers. The method is based on two components: quantile transform of the genuine and impostor score distributions and a power transform which further changes the score distribution to help linear classification. After the scores are normalized using the novel quantile power transform, several linear classifiers are proposed to fuse the scores of multiple classifiers. Using the NIST BSSR-1 dataset, we have shown that the results obtained by the proposed method far exceed the results published so far in the literature.
In our homes or offices, security has been a vital issue. Control of home security system remotely always offers huge advantages like the arming or disarming of the alarms, video monitoring, and energy management control apart from safeguarding the home free up intruders. Considering the oldest simple methods of security that is the mechanical lock system that has a key as the authentication element, then an upgrade to a universal type, and now unique codes for the lock. The recent advancement in the communication system has brought the tremendous application of communication gadgets into our various areas of life. This work is a real-time smart doorbell notification system for home Security as opposes of the traditional security methods, it is composed of the doorbell interfaced with GSM Module, a GSM module would be triggered to send an SMS to the house owner by pressing the doorbell, the owner will respond to the guest by pressing a button to open the door, otherwise, a message would be displayed to the guest for appropriate action. Then, the keypad is provided for an authorized person for the provision of password for door unlocking, if multiple wrong password attempts were made to unlock, a message of burglary attempt would be sent to the house owner for prompt action. The main benefit of this system is the uniqueness of the incorporation of the password and messaging systems which denies access to any unauthorized personality and owner's awareness method.
Augmented reality, the new age technology, has widespread applications in every field imaginable. This technology has proven to be an inflection point in numerous verticals, improving lives and improving performance. In this paper, we explore the various possible applications of Augmented Reality (AR) in the field of Medicine. The objective of using AR in medicine or generally in any field is the fact that, AR helps in motivating the user, making sessions interactive and assist in faster learning. In this paper, we discuss about the applicability of AR in the field of medical diagnosis. Augmented reality technology reinforces remote collaboration, allowing doctors to diagnose patients from a different locality. Additionally, we believe that a much more pronounced effect can be achieved by bringing together the cutting edge technology of AR and the lifesaving field of Medical sciences. AR is a mechanism that could be applied in the learning process too. Similarly, virtual reality could be used in the field where more of practical experience is needed such as driving, sports, neonatal care training.
Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus, it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus, the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.
Graphs have become the dominant life-form of many tasks as they advance a
structure to represent many tasks and the corresponding relations. A powerful
role of networks/graphs is to bridge local feats that exist in vertices as they
blossom into patterns that help explain how nodal relations and their edges
impacts a complex effect that ripple via a graph. User cluster are formed as a
result of interactions between entities. Many users can hardly categorize their
contact into groups today such as “family”, “friends”, “colleagues” etc. Thus,
the need to analyze such user social graph via implicit clusters, enables the
dynamism in contact management. Study seeks to implement this dynamism
via a comparative study of deep neural network and friend suggest algorithm.
We analyze a user’s implicit social graph and seek to automatically create
custom contact groups using metrics that classify such contacts based on a
user’s affinity to contacts. Experimental results demonstrate the importance
of both the implicit group relationships and the interaction-based affinity in
suggesting friends.
This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.
In the wake of the sudden replacement of wood and kerosene by gas cookers for several purposes in Nigeria, gas leakage has caused several damages in our homes, Laboratories among others. installation of a gas leakage detection device was globally inspired to eliminate accidents related to gas leakage. We present an alternative approach to developing a device that can automatically detect and control gas leakages and also monitor temperature. The system detects the leakage of the LPG (Liquefied Petroleum Gas) using a gas sensor, then triggred the control system response which employs ventilator system, Mobile phone alert and alarm when the LPG concentration in the air exceeds a certain level. The performance of two gas sensors (MQ5 and MQ6) were tested for a guided decision. Also, when the temperature of the environment poses a danger, LED (indicator), buzzer and LCD (16x2) display was used to indicate temperature and gas leakage status in degree Celsius and PPM respectively. Attension was given to the response time of the control system, which was ascertained that this system significantly increases the chances and efficiency of eliminating gas leakage related accident.
Feature selection problem is one of the main important problems in the text and data mining domain. This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared by four criteria: Precision, Recall, F-measure and Time to build model. The results showed that the improved ICHI feature selection got almost all the best results in comparison with other methods.
In this paper Gentoo Penguin Algorithm (GPA) is proposed to solve optimal reactive power problem. Gentoo Penguins preliminary population possesses heat radiation and magnetizes each other by absorption coefficient. Gentoo Penguins will move towards further penguins which possesses low cost (elevated heat concentration) of absorption. Cost is defined by the heat concentration, distance. Gentoo Penguins penguin attraction value is calculated by the amount of heat prevailed between two Gentoo penguins. Gentoo Penguins heat radiation is measured as linear. Less heat is received in longer distance, in little distance, huge heat is received. Gentoo Penguin Algorithm has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.
08 20272 academic insight on applicationIAESIJEECS
This research has thrown up many questions in need of further investigation.There was an expressive quantitative-qualitative research, which a common investigation form was used in.The dialogue item was also applied to discover if the contributors asserted the media-based attitude supplements their learning of academic English writing classes or not.Data recounted academic” insights toward using Skype as a sustaining implement for lessons releasing based on chosen variables: their occupation, year of education, and knowledge with Skype discovered that there were no important statistical differences in the use of Skype units because of medical academics major knowledge. There are statistically important differences in using Skype units. The findings also, disclosed that there are statistically significant differences in using Skype units due to the practice with Skype variable, in favors of academics with no Skype use practice. Skype instrument as an instructive media is a positive medium to be employed to supply academic medical writing data and assist education. Academics who do not have enough time to contribute in classes believe comfortable using the Skype-based attitude in scientific writing. They who took part in the course claimed that their approval of this media is due to learning academic innovative medical writing.
Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision-Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.
Notary is an official authorized to make an authentic deed regarding all deeds, agreements and stipulations required by a general rule. Activities carried out at the notary office such as recording client data and file data still use traditional systems that tend to be manual. The problem that occurs is the inefficiency in data processing and providing information to clients. Clients have difficulty getting information related to the progress of documents that are being taken care of at the notary's office. The client must take the time to arrive to the notary's office repeatedly to check the progress of the work of the document file. The purpose of this study is to facilitate clients in obtaining information about the progress of the work in progress, and make it easier for employees to process incoming documents by implementing an administrative system. This system was developed with the waterfall system development method and uses the Multi-Channel Access Technology integrated in the website to simplify the process of delivering information and requesting information from clients and to clients with Telegram and SMS Gateway. Clients will come to the office only when there is a notification from the system via Telegram or SMS notifying that the client must come directly to the notary's office, thus leading to an efficient time and avoiding excessive transportation costs. The overall functional system can function properly based on the results of alpha testing. The results of beta testing conducted by distributing the system feasibility test questionnaire to end users, get a percentage of 96% of users agree the system is feasible to be implemented.
In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal reactive power problem. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents’ numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.
In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.
In this paper, Mine Blast Algorithm (MBA) has been intermingled with Harmony Search (HS) algorithm for solving optimal reactive power dispatch problem. MBA is based on explosion of landmines and HS is based on Creativeness progression of musicians-both are hybridized to solve the problem. In MBA Initial distance of shrapnel pieces are reduced gradually to allow the mine bombs search the probable global minimum location in order to amplify the global explore capability. Harmony search (HS) imitates the music creativity process where the musicians supervise their instruments’ pitch by searching for a best state of harmony. Hybridization of Mine Blast Algorithm with Harmony Search algorithm (MH) improves the search effectively in the solution space. Mine blast algorithm improves the exploration and harmony search algorithm augments the exploitation. At first the proposed algorithm starts with exploration & gradually it moves to the phase of exploitation. Proposed Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) has been tested on standard IEEE 14, 300 bus test systems. Real power loss has been reduced considerably by the proposed algorithm. Then Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) tested in IEEE 30, bus system (with considering voltage stability index)- real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.
Artificial Neural Networks have proved their efficiency in a large number of research domains. In this paper, we have applied Artificial Neural Networks on Arabic text to prove correct language modeling, text generation, and missing text prediction. In one hand, we have adapted Recurrent Neural Networks architectures to model Arabic language in order to generate correct Arabic sequences. In the other hand, Convolutional Neural Networks have been parameterized, basing on some specific features of Arabic, to predict missing text in Arabic documents. We have demonstrated the power of our adapted models in generating and predicting correct Arabic text comparing to the standard model. The model had been trained and tested on known free Arabic datasets. Results have been promising with sufficient accuracy.
In the present-day communications speech signals get contaminated due to
various sorts of noises that degrade the speech quality and adversely impacts
speech recognition performance. To overcome these issues, a novel approach
for speech enhancement using Modified Wiener filtering is developed and
power spectrum computation is applied for degraded signal to obtain the
noise characteristics from a noisy spectrum. In next phase, MMSE technique
is applied where Gaussian distribution of each signal i.e. original and noisy
signal is analyzed. The Gaussian distribution provides spectrum estimation
and spectral coefficient parameters which can be used for probabilistic model
formulation. Moreover, a-priori-SNR computation is also incorporated for
coefficient updation and noise presence estimation which operates similar to
the conventional VAD. However, conventional VAD scheme is based on the
hard threshold which is not capable to derive satisfactory performance and a
soft-decision based threshold is developed for improving the performance of
speech enhancement. An extensive simulation study is carried out using
MATLAB simulation tool on NOIZEUS speech database and a comparative
study is presented where proposed approach is proved better in comparison
with existing technique.
Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.
Attenuation correction designed for PET/MR hybrid imaging frameworks along with portion making arrangements used for MR-based radiation treatment remain testing because of lacking high-energy photon weakening data. We present a new method so as to uses the learned nonlinear neighborhood descriptors also highlight coordinating toward foresee pseudo-CT pictures starting T1w along with T2w MRI information. The nonlinear neighborhood descriptors are acquired through anticipating the direct descriptors interested in the nonlinear high-dimensional space utilizing an unequivocal constituent guide also low-position guess through regulated complex regularization. The nearby neighbors of every near descriptor inside the data MR pictures are looked during an obliged spatial extent of the MR pictures among the training dataset. By that point, the pseudo-CT patches are evaluated through k-closest neighbor relapse. The planned procedure designed for pseudo-CT forecast is quantitatively broke downward on top of a dataset comprising of coordinated mind MRI along with CT pictures on or after 13 subjects.
The cognitive radio prototype performance is to alleviate the scarcity of spectral resources for wireless communication through intelligent sensing and quick resource allocation techniques. Secondary users (SU’s) actively obtain the spectrum access opportunity by supporting primary users (PU’s) in cognitive radio networks (CRNs). In present generation, spectrum access is endowed through cooperative communication-based link-level frame-based cooperative (LLC) principle. In this SUs independently act as conveyors for PUs to achieve spectrum access opportunities. Unfortunately, this LLC approach cannot fully exploit spectrum access opportunities to enhance the throughput of CRNs and fails to motivate PUs to join the spectrum sharing processes. Therefore, to overcome this con, network level cooperative (NLC) principle was used, where SUs are integrated mutually to collaborate with PUs session by session, instead of frame based cooperation for spectrum access opportunities. NLC approach has justified the challenges facing in LLC approach. In this paper we make a survey of some models that have been proposed to tackle the problem of LLC. We show the relevant aspects of each model, in order to characterize the parameters that we should take in account to achieve a spectrum access opportunity.
In this paper, the author provides insights and lessons that can be learned from colleagues at American universities about their online education experiences. The literature review and previous studies of online educations gains are explored and summarized in this research. Emerging trends in online education are discussed in detail, and strategies to implement these trends are explained. The author provides several tools and strategies that enable universities to ensure the quality of online education. At the end of this research paper, the researcher provides examples from Arab universities who have successfully implemented online education and expanded their impact on the society. This research provides a strategy and a model that can be used by universities in the Middle East as a roadmap to implement online education in their regions.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Power plants release a large amount of water vapor into the
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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1. Indonesian Journal of Electrical Engineering and Computer Science
Vol. 8, No. 1, October 2017, pp. 27 ∼ 35
DOI: 10.11591/ijeecs.v8.i1.pp27-35 27
Optimisation of Biochemical Systems Production
using Hybrid of Newton method, Differential
Evolution Algorithm and Cooperative Coevolution
Algorithm
Mohd Arfian Ismail*1
, Vitaliy Mezhuyev 1
, Kohbalan Moorthy 1
, Shahreen Kasim 2
, and
Ashraf Osman Ibrahim 3,4
1
Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Pahang, Malaysia
2
Soft Computing and Data Mining Centre, Faculty of Computer Science and Information Technology,
Universiti Tun Hussein Onn, Johor, Malaysia
3
Faculty of computer Science and Information Technology, Alzaiem Alazhari University, Khartoum North
13311, Sudan
4
Arab Open University, Khartoum, Sudan
*Corresponding author, e-mail: arfian@ump.edu.my
Abstract
This paper present a hybrid method of Newton method, Differential Evolution Algorithm (DE) and
Cooperative Coevolution Algorithm (CCA). The proposed method is used to solve the optimisation problem in
optimise the production of biochemical systems. The problems are maximising the biochemical systems pro-
duction and simultaneously minimising the total amount of chemical reaction concentration involves. Besides
that, the size of biochemical systems also contributed to the problem in optimising the biochemical systems
production. In the proposed method, the Newton method is used in dealing biochemical system, DE for opti-
misation process while CCA is used to increase the performance of DE. In order to evaluate the performance
of the proposed method, the proposed method is tested on two benchmark biochemical systems. Then, the
result that obtained by the proposed method is compare with other works and the finding shows that the
proposed method performs well compare to the other works.
Keywords: Newton method, Differential Evolution Algorithm, Cooperative Coevolutioan Algorithm, Biochem-
ical systems, Computational Intelligence
Copyright c 2017 Institute of Advanced Engineering and Science. All rights reserved.
1. Introduction
Biomass is a good alternative to produce the biofuel. This is because the biomass is a
plant-based resource that can be used to replace the limited biofuel. Nowadays, the demand of
biomass is increase where it leads to competition of land and plant [1, 2, 3]. Recently, many re-
searchers have focus on manipulating the microorganism activity in order to produce the biomass
rather than really on increasing the land and plant. This is because manipulating the microorgan-
ism is far cheaper and reduce time rather than increase the land or plant. But, the biomass that
extracted from manipulating the microorganism activity has a limitation where the production is low
[4, 5]. Due to that, many researcher have focus on optimisation the production of biomass. One
way to improve the biomass production is the optimisation of biochemical systems production by
fine-tuning the reactions value in biochemical systems.
The optimisation of the production in biochemical systems can be performed because the
biochemical system can be represented by a nonlinear equations system. In the nonlinear equa-
tions system, each variable is used to represent each reactions of biochemical systems. The
process of fine-tuning the reactions value can be performed by change the variables value. Fine-
tuning process of the variables in nonlinear equations system becomes a hard task if involves a
large biochemical systems. This is because a large biochemical systems contains with many reac-
tions and involves many interaction between reaction. In order to overcome this situation, this paper
Received May 6, 2017; Revised September 2, 2017; Accepted September 15, 2017
2. 28 ISSN: 2502-4752
present an automated method to fine-tuning the variables in nonlinear equations system. The pro-
posed method hybrid the Newton method, Differential Evolution Algorithm (DE) and Cooperative
Coevolution Algorithm (CCA).
In optimisation of biochemical systems production, the biochemical systems can be mod-
elled by mathematical model, which is generalised mass action (GMA) model. During the opti-
misation, there are several constraints involve which are steady state condition and reaction con-
centration constraint. The steady state condition make all the equations in GMA model equal to
0 where it make the optimisation process become the process of solving a nonlinear equations
system. There are various methods that can be used in solving a nonlinear equations system such
as Newton method, Secant method and Bisection method. In this study, Newton method is used
because Newton method is fast in solving the system [6], simple to used [7, 8] and very widely
used in solving nonlinear equations system [9, 10, 11].
For fine-tuning the reactions value in biochemical systems, an optimisation method is
needed. The reason of fine-tuning is to discover the suitable value that produce the high pro-
duction of biochemical systems. The fine-tuning process become complicated when involves a
complex biochemical system where contains with many reactions and involves many interaction
between them. Because of that, an optimisation method is need. There are various method can
be applied such as genetic algorithm (GA), DE, and Particle Swarm Optimisation (PSO) algorithm.
This study used DE because DE offer several advantages such as DE involves few parameters
[12, 13] and DE is more robust on several problems when compare to other [14].
In the optimisation of biochemical systems production, two factors that need to be con-
sidered which are the production and the total of chemical reaction concentrations involves. In
addition, a large of biochemical systems that content with many reactions and interaction between
them also contribute to the difficulty in optimisation process. Because of these factors, this make
the representation of the solution become complex. This make the optimisation proses become
hard and complicated. In order to overcome these issues, this study use CCA in order to simplify
the representation of the solution by dividing the complete into multiple sub-solutions.
In this paper, the hybrid of Newton method, GA and CCA is proposed and discuss in detail.
The aim of the proposed method is to solve the problems in optimisation of biochemical systems
production which are to improve the biochemical systems production and at the same time reduce
the total of chemical reaction concentrations involves. In the proposed method, the function of
Newton method is to solve the nonlinear equations system, DE is used in optimisation process
where DE is used to fine-tuning process while CCA is utilised to improve the performance of DE.
In the following section, the explanation of the proposed method is discussed in detail. Then,
the model and experimental data is describe in detail where two benchmark biochemical systems
are used namely the Saccharomyces cerevisiae (S.cerevisiae) pathway and the Escherichia Coli
(E.coli) pathway. After that, the experimental result and discussion is presented before this paper
was conclude in conclusion.
2. A Hybrid Method of Newton Method, Differential Evolution Algorithm and Cooperative
Coevolution Algorithm
This section is about the discussion of the proposed method. The proposed method hybrid
Newton method, DE and CCA. In the proposed method, Newton method is utilised to deal with
nonlinear equations system, DE is used in optimisation process and CCA is embodied into DE in
order to improve the performance of DE by simplifies the chromosome representation. Figure 1
shows the proposed method in flowchart form. The detail steps in the proposed method are as
follows:
Step 1: Generate the initial solution. In the first step, the first generation of m solution is gen-
erated separately in n sub-population (the number of sub-population is equal to the number of
variables that need to be tuned). The variable (in nonlinear equations system) is represented by
sub-chromosome. The sub-chromosome is in binary format. The sub-chromosome is generated
randomly and in a specific format (depends on the value of chemical reaction concentration).
Step 2: Form the complete chromosome. The complete solution is form in this step by combine all
IJEECS Vol. 8, No. 1, October 2017 : 27 ∼ 35
3. IJEECS ISSN: 2502-4752 29
Figure 1. The flowchar of the proposed method
sub-chromosomes from all sub-populations. The sub-chromosome is selected based on their fit-
ness value where the sub-chromosome that has lowest fitness value is selected and then combine
with other sub-chromosome from each sub-population. This is because the selection process is in-
tended to minimise the total amount of chemical reaction concentration involves. Figure 2 depicted
the generation of sub-chromosome until the formation of complete chromosome.
Step 3: Evaluate the complete chromosome. In this step, the complete chromosome is decoded
into variables form. At this stage, the Newton method is used in solving the nonlinear equations
system. Besides that, two termination conditions are applied which are; the maximum number of
generation is reach and all the chemical reaction concentration value is in their range. The process
move forward to Step 6 if these conditions are meet, otherwise the process enter the next step.
Step 4: Decompose the complete chromosome. In this step, the complete chromosome is decom-
posed into multiple sub-chromosomes. After that, all sub-chromosomes went back into their own
sub-population for reproduction process.
Step 5: Produce new generation. This step is intended to improve the solution by producing the next
generation of the solution. This step happens in all sub-population. The mutation and crossover
process are applied on all sub-chromosome.
Step 6: Return the best solution. This is the final step. In this step, the best solution is given.
3. Model and Experimental Data
In order to test the performance of the proposed method, two benchmark biochemical sys-
tems are used which are the optimisation of the ethanol production in S. cerevisiae pathway and the
optimization of the trp biosynthesis in E. Coli. A Java program based on JAMA version 1.0.3 and
jMetal [15] are used. The JAMA program is used in dealing with nonlinear equations system while
Optimisation of biochemical systems production using hybrid of Newton method, ... (M.A. Ismail)
4. 30 ISSN: 2502-4752
Figure 2. The process of formation the complete chromosome
jMetal for optimisation process. The JAMA can be obtained from http://math.nist.gov/javanumerics/jama/
and jMetal can be downloaded from http://jmetal.sourceforge.net. The detail description of two
benchmark biochemical systems are describe in the next sub section.
3.1. Optimisation of the ethanol production in Saccharomyces cerevisiae pathway
The proposed method is used to optimise the ethanol production in S.cerevisiae pathway.
The detail description of this pathway can be found in [16]. In this pathway, the nonlinear equations
system can be represented as follows:
Vin − VHK = 0
VHK − VP F K − VCarb = 0
VP F K − VGAP D − 0.5VGro = 0 (1)
2VGAP D − VP K = 0
2VGAP D + VP K − VHK − VCarb − VP F K − VAT P ase = 0
where at steady state conditions, these chemical reaction concentrations (denoted by V ) have the
following value:
Vin = 0.8122X−0.2344
2 Y1
VHK = 2.8632X0.7464
1 X0.0243
5 Y2
VP F K = 0.5232X0.7318
2 X−0.3941
5 Y3
VCarb = 8.904 × 10−4
X8.6107
2 Y7 (2)
VGAP D = 7.6092 × 10−2
X0.6159
3 X0.1308
5 Y4
VGro = 9.272 × 10−2
X0.05
3 X0.533
4 X−0.0822
5 Y8
VP K = 9.471 × 10−2
X0.05
3 X0.533
4 X−0.0822
5 Y5
VAT P ase = X5Y6
IJEECS Vol. 8, No. 1, October 2017 : 27 ∼ 35
5. IJEECS ISSN: 2502-4752 31
In this biochemical system, the ethanol production is given by VP K and it became the
fitness function of complete chromosome. This lead to the improving the production as follows:
max F1 (v) = VP K (3)
For the total of chemical reaction concentrations involves, it can be formulated as follow:
min F2 =
5
j=1
Xj +
6
j=6
Yj (4)
where the range of X is set between 0.2 to 1.2 and Y in the range of 0 to 50 [17, 18].
3.2. Optimisation of the tryptophan biosynthesis in Escherichia Coli pathway
In this pathway, the proposed method is used to optimise the trp production. Xiu et al. has
explained in detail of this pathway[19]. For this pathway, the nonlinear equations system can be
formulated as follows:
V11 − V12 = 0
V21 − V22 = 0 (5)
V31 − V32 − V33 − V34 = 0
All reaction concentration (denoted by V ) has the following values at steady state condition:
V11 = 0.6403X−5.87×10−4
3 X−0.8332
5
V12 = 1.0233X1X0.0035
4 X0.9965
11
V21 = X1
V22 = 1.4854X2X−0.1349
4 X0.8651
12 (6)
V31 = 0.5534X2X−0.5573
3 X0.5573
6
V32 = X3X4
V33 = 0.9942X7.0426×10−4
3 X7
V34 = 0.8925X3.5×10−6
3 X0.9760
4 X8X−0.0240
9 X−3.5×10−6
10
The trp production is given by reaction V34 thus it become the fitness function of the com-
plete chromosome. This lead to the improving the production as follows:
max F1 = V34 (7)
For the total of chemical reaction concentrations involves, it can be formulated as follow:
min F2 =
6
j=1
Xj + X8 (8)
where the range of X1 to X3 is between 0.8 to 1.2, X4 between 0 to 0.00624, X5 between 4 to 10,
X6between 500 to 5000 and between X8 0 to 1000 [17, 18].
4. Experimental results and discussions
In producing the best result, several experiments are performed. Table 1 list the DE pa-
rameters setting used. For CCA, the number of sub-populations depend on the variables in non-
linear equations system the need to be tuned. For the S.cerevisiae pathway, the number of sub-
populations is 11 while for E.coli pathway, the number of sub-populations is 7. For the Newton
Optimisation of biochemical systems production using hybrid of Newton method, ... (M.A. Ismail)
6. 32 ISSN: 2502-4752
Table 1. The DE parameters
Parameter S.cerevisiae pathway E.coli pathway
Mutation (Scaling factor) 0.8 0.7
Crossover 0.2 0.2
method, fixed parameter used for both pathway; the number of iteration is 100 and the tolerance
value is 10−6
.
In S.cerevisiae pathway, the best result obtained by the proposed method is 52.7269 in
maximising the ethanol production while 295.2405 in minimising the total of chemical concentration
involves. The detail result, average result and comparison with other methods are listed in Table 2.
From Table 2, it can be observed that the performance of the proposed method is outperform the
result from other works in maximising the ethanol production and at the same time minimising the
total amount of chemical reaction concentration involves.
Table 2. The detail result obtained by the proposed method in S.cerevisiae pathway
Parameter This work Work by [20] Work by [18] Work by [21]
X1 1.113 1.14 1.102 1.11
X2 1.053 1.05 1.046 1.03
X3 1.127 1.15 1.141 1.13
X4 1.164 1.17 1.171 1.18
X5 0.92 1.12 1.113 51.14
Y1 49.972 49.97 50 49.99
Y2 49.810 44.77 45.953 45.83
Y3 49.90 49.89 50 49.92
Y4 47.333 47.26 47.772 47.97
Y5 48.062 48 48.366 48.30
Y8 49.792 49.75 50 49.79
F1 52.727 52.084 52.512 52.57
F2 295.241 295.28 297.664 297.384
Meanwhile, the best result produce by the proposed method in E.coli pathway is 3.9988
in maximising the trp production and 6015.5871 in minimising the total of chemical concentration
involves. The detail result, average result and comparison with other methods are listed in Table
3. Same observation with S.cerevisiae pathway, the performance of the proposed method also
perform better when it compare to other works.
Table 3. The detail result obtained by the proposed method in E.coli pathway
Parameter This work Work by [22] Work by [23] Work by [18] Work by [21]
X1 1.191 1.19 1.2 1.2 1.11
X2 1.119 1.15 1.15 1.12 1.114
X3 0.8 0.8 0.8 0.8 0.8
X4 0.0054 0.0041 0.004 0.0054 0.0054
X5 4.037 4 4 4.011 4.75
X6 5000 5000 5000 5000 5000
X8 1000 1000 1000 1000 1000
F1 3.999 3.06 3.06 3.95 3.98
F2 6015.5871 6016.38 6016.57 6016.57 6016.22
Besides that, the comparison between multi sub-population that used in this study with
single population (dont use CCA). The purpose of CCA is to enhance the performance of DE in
minimising the total amount of chemical reaction concentration involves. Several experiments are
IJEECS Vol. 8, No. 1, October 2017 : 27 ∼ 35
7. IJEECS ISSN: 2502-4752 33
conducted using parameters setting in Table 1. Figure 3 and Figure 4 depicted the bar graph of the
comparison between multi sub-population with single population in S.cerevisiae pathway and E.coli
pathway. From that figures, it can be seen clearly that all the results of multi sub-population are
lower compare to the results obtained by single population. It can be concluded that, the CCA able
to improve the performance of DE in minimising the total amount of chemical reaction concentration
involves.
Figure 3. The comparison of multi population and single population in S.cerevisiae pathway
Figure 4. The comparison of multi population and single population in E.coli pathway
In order to show the consistency in apply CCA, the proposed method is compared with the
method that not use CCA (only use Newton method and DE). About 100 independent experiments
are performed. Figure 5 and Figure 6 show the comparison in box plot form. Figure 5 show
the ethanol production in S.cerevisiae pathway while Figure 6 show the trp production in E.coli
pathway. From the figures, the result produce by the proposed method are not too wide compare
to the result that not use CCA. From this observation, it can be explained that the propose method
able to produce a consistent result if the experiment run several times.
Figure 5. The boxplot of the ethanol production
Optimisation of biochemical systems production using hybrid of Newton method, ... (M.A. Ismail)
8. 34 ISSN: 2502-4752
Figure 6. The boxplot of the trp production
5. Conclusion
This paper has proposed a hybrid method of Newton method, DE and CCA. The proposed
method is proposed to overcome the problems in optimisation of biochemical systems where the
problems are to maximise the biochemical systems production and simultaneously minimise the
total amount of chemical reaction concentration involves. The proposed method works by view the
biochemical systems as nonlinear equation system. Firstly, the Newton method is used to solve the
nonlinear equations system. Then, DE is used in optimisation process. The performance of DE is
drop when applied on alrge and complex biochemical systems and CCA is utilised to improve the
performance of DE. The proposed method is applied on benchmark biochemical systems and the
experimental result show that the performance is outperform the other works.
Acknowledgement
Special thanks and appreciation to the editor and anonymous reviewers that reviewed this
paper. The author also would thanks to the sponsor from RDU Grant Vot No. RDU1603115 form
Universiti Malaysia Pahang.
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