In recent years there is a huge increase in emphasis of interest in studying biologically inspired systems.
Artificial immune systems (AIS) are a class of computationally intelligent systems inspired by the principles and
processes of the vertebrate immune system. Computer engineers, mathematicians, philosophers and other researchers
are particularly interested in the capabilities of this system, whose complexity is comparable to that of the human brain.
AIS algorithms are machine-learning algorithms that typically exploit the immune system's characteristics
of learning and memory to solve complex problem. It attempts to take advantages and benefits of natural immune
systems for use in tackling complex problem domains. It is a class of adaptive or learning computer algorithm inspired
by function of the biological immune system, designed for and applied to difficult problems such as intrusion detection,
data clustering, and classification and search problems. In this paper, a study of artificial immune systems algorithms
like Artificial Immune system Recognition system algorithm (AIRS) , parallel AIRS and clon ALG are presented with
the possible number of applications in various domain.
Adaptive Classification of Imbalanced Data using ANN with Particle of Swarm O...ijtsrd
Customary characterization calculations can be constrained in their execution on exceedingly uneven informational collections. A famous stream of work for countering the substance of class inelegance has been the use of an assorted of inspecting methodologies. In this correspondence, we center on planning alterations neural system to properly handle the issue of class irregularity. We consolidate distinctive rebalance heuristics in ANN demonstrating, including cost delicate learning, and over and under testing. These ANN based systems are contrasted and different best in class approaches on an assortment of informational collections by utilizing different measurements, including G mean, region under the collector working trademark curve, F measure, and region under the exactness review curve. Numerous regular strategies, which can be classified into testing, cost delicate, or gathering, incorporate heuristic and task subordinate procedures. So as to accomplish a superior arrangement execution by detailing without heuristics and errand reliance, presently propose RBF based Network RBF NN . Its target work is the symphonious mean of different assessment criteria got from a perplexity grid, such criteria as affectability, positive prescient esteem, and others for negatives. This target capacity and its enhancement are reliably detailed on the system of CM KLOGR, in light of least characterization mistake and summed up probabilistic plunge MCE GPD learning. Because of the benefits of the consonant mean, CM KLOGR, and MCE GPD, RBF NN improves the multifaceted exhibitions in a very much adjusted way. It shows the definition of RBF NN and its adequacy through trials that nearly assessed RBF NN utilizing benchmark imbalanced datasets. Nitesh Kumar | Dr. Shailja Sharma "Adaptive Classification of Imbalanced Data using ANN with Particle of Swarm Optimization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25255.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/25255/adaptive-classification-of-imbalanced-data-using-ann-with-particle-of-swarm-optimization/nitesh-kumar
MultiAgent artificial immune system for network intrusion detectionAboul Ella Hassanien
This thesis implements a multi-agent anomaly network intrusion detection system inspired by biological immunity to detect and classify network attacks. It proposes five approaches, including using a genetic algorithm to generate anomaly detectors, discretizing continuous features to create homogeneity between different feature types, and applying feature selection techniques. The approaches are evaluated on datasets like NSL-KDD to generate detectors for identifying anomalous network connections using measures like Euclidean, Minkowski, and Hamming distance. While initial results are promising, further work is needed to optimize feature selection and evaluate the approaches on additional datasets and attack types.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
Applications of immune system computingUltraUploader
The document discusses various applications of artificial immune systems (AIS) computing, including computer security, pattern recognition, and optimization problems. It then provides more detail on using AIS approaches for computer virus detection. A proposed Computer Virus Immune System (CVIS) would incorporate components modeled after the biological immune system, such as generating/suppressing viruses, classifying viruses, and purging/repairing infected systems. Main challenges include replicating the parallelism of the natural immune system. Several example algorithms are also described.
Inspiration to Application: A Tutorial on Artificial Immune SystemsJulie Greensmith
A tutorial of the history and application of artificial immune systems, given as a research tutorial for the Intelligent Modelling and Analysis Research Group, School of Computer Science, University of Nottingham UK.
2000: Artificial Immune Systems - Theory and ApplicationsLeandro de Castro
This document discusses artificial immune systems, which are computational systems inspired by the biological immune system. It provides an overview of the biological immune system, including its defense mechanisms, anatomy, properties, innate and adaptive responses. It also describes how the immune system processes information, such as through clonal selection, affinity maturation, repertoire diversity, reinforcement learning, self/non-self discrimination, and immune network theory. Finally, it discusses applications of artificial immune systems in areas like anomaly detection, optimization, neural networks, learning, and pattern recognition.
This document discusses artificial immune systems and their applications in mobile ad hoc networks (MANETs). It describes various artificial immune system algorithms inspired by theoretical immunology, including negative selection, artificial immune networks, clonal selection, danger theory, and dendritic cell algorithms. These algorithms can be used for intrusion detection in MANETs to provide self-healing, self-defensive, and self-organizing capabilities to address security challenges in infrastructure-less mobile networks. Several studies have investigated applying artificial immune system approaches like negative selection and clonal selection to detect node misbehavior and classify nodes as self or non-self in MANETs.
A retrovirus inspired algorithm for virus detection & optimizationUltraUploader
This document summarizes a research paper that proposes a new artificial immune system algorithm called REALGO for computer virus detection. REALGO is inspired by reverse transcription that occurs in retroviruses. It uses a genetic algorithm to train antibodies against known virus signatures, and allows antibodies to revert back to previously successful solutions to escape local optima during optimization. The algorithm was tested on various optimization problems and was shown to find better solutions than other evolutionary strategies in most cases. The document provides details on how the REALGO algorithm is modeled after biological immune systems and reverse transcription, and presents the algorithm using symbolic notation.
Adaptive Classification of Imbalanced Data using ANN with Particle of Swarm O...ijtsrd
Customary characterization calculations can be constrained in their execution on exceedingly uneven informational collections. A famous stream of work for countering the substance of class inelegance has been the use of an assorted of inspecting methodologies. In this correspondence, we center on planning alterations neural system to properly handle the issue of class irregularity. We consolidate distinctive rebalance heuristics in ANN demonstrating, including cost delicate learning, and over and under testing. These ANN based systems are contrasted and different best in class approaches on an assortment of informational collections by utilizing different measurements, including G mean, region under the collector working trademark curve, F measure, and region under the exactness review curve. Numerous regular strategies, which can be classified into testing, cost delicate, or gathering, incorporate heuristic and task subordinate procedures. So as to accomplish a superior arrangement execution by detailing without heuristics and errand reliance, presently propose RBF based Network RBF NN . Its target work is the symphonious mean of different assessment criteria got from a perplexity grid, such criteria as affectability, positive prescient esteem, and others for negatives. This target capacity and its enhancement are reliably detailed on the system of CM KLOGR, in light of least characterization mistake and summed up probabilistic plunge MCE GPD learning. Because of the benefits of the consonant mean, CM KLOGR, and MCE GPD, RBF NN improves the multifaceted exhibitions in a very much adjusted way. It shows the definition of RBF NN and its adequacy through trials that nearly assessed RBF NN utilizing benchmark imbalanced datasets. Nitesh Kumar | Dr. Shailja Sharma "Adaptive Classification of Imbalanced Data using ANN with Particle of Swarm Optimization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25255.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/25255/adaptive-classification-of-imbalanced-data-using-ann-with-particle-of-swarm-optimization/nitesh-kumar
MultiAgent artificial immune system for network intrusion detectionAboul Ella Hassanien
This thesis implements a multi-agent anomaly network intrusion detection system inspired by biological immunity to detect and classify network attacks. It proposes five approaches, including using a genetic algorithm to generate anomaly detectors, discretizing continuous features to create homogeneity between different feature types, and applying feature selection techniques. The approaches are evaluated on datasets like NSL-KDD to generate detectors for identifying anomalous network connections using measures like Euclidean, Minkowski, and Hamming distance. While initial results are promising, further work is needed to optimize feature selection and evaluate the approaches on additional datasets and attack types.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
Applications of immune system computingUltraUploader
The document discusses various applications of artificial immune systems (AIS) computing, including computer security, pattern recognition, and optimization problems. It then provides more detail on using AIS approaches for computer virus detection. A proposed Computer Virus Immune System (CVIS) would incorporate components modeled after the biological immune system, such as generating/suppressing viruses, classifying viruses, and purging/repairing infected systems. Main challenges include replicating the parallelism of the natural immune system. Several example algorithms are also described.
Inspiration to Application: A Tutorial on Artificial Immune SystemsJulie Greensmith
A tutorial of the history and application of artificial immune systems, given as a research tutorial for the Intelligent Modelling and Analysis Research Group, School of Computer Science, University of Nottingham UK.
2000: Artificial Immune Systems - Theory and ApplicationsLeandro de Castro
This document discusses artificial immune systems, which are computational systems inspired by the biological immune system. It provides an overview of the biological immune system, including its defense mechanisms, anatomy, properties, innate and adaptive responses. It also describes how the immune system processes information, such as through clonal selection, affinity maturation, repertoire diversity, reinforcement learning, self/non-self discrimination, and immune network theory. Finally, it discusses applications of artificial immune systems in areas like anomaly detection, optimization, neural networks, learning, and pattern recognition.
This document discusses artificial immune systems and their applications in mobile ad hoc networks (MANETs). It describes various artificial immune system algorithms inspired by theoretical immunology, including negative selection, artificial immune networks, clonal selection, danger theory, and dendritic cell algorithms. These algorithms can be used for intrusion detection in MANETs to provide self-healing, self-defensive, and self-organizing capabilities to address security challenges in infrastructure-less mobile networks. Several studies have investigated applying artificial immune system approaches like negative selection and clonal selection to detect node misbehavior and classify nodes as self or non-self in MANETs.
A retrovirus inspired algorithm for virus detection & optimizationUltraUploader
This document summarizes a research paper that proposes a new artificial immune system algorithm called REALGO for computer virus detection. REALGO is inspired by reverse transcription that occurs in retroviruses. It uses a genetic algorithm to train antibodies against known virus signatures, and allows antibodies to revert back to previously successful solutions to escape local optima during optimization. The algorithm was tested on various optimization problems and was shown to find better solutions than other evolutionary strategies in most cases. The document provides details on how the REALGO algorithm is modeled after biological immune systems and reverse transcription, and presents the algorithm using symbolic notation.
Identifying drug targets and candidate sequences is an important process and an unmet challenge in drug development. Creative Biolabs has developed an original AI-augmented drug discovery platform to accelerate drug discovery.
https://ai.creative-biolabs.com/ai-augmented-drug-discovery.htm
Creative Biolabs offers a series of AI-based antibody screening services based on the prediction of antibody-antigen binding and a unique way to find rare antibody clusters and get more candidate antibody sequences by augmenting our data-driven AI screening services.
https://ai.creative-biolabs.com/ai-based-antibody-screening-services.htm
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
This summary provides the high-level information from the document in 3 sentences:
The document proposes a Particle Swarm Optimization (PSO) based ensemble classification model to improve classification of high-dimensional biomedical datasets. It develops an optimized PSO technique to select optimal features and initialize weights for base classifiers in the ensemble model. Experimental results on microarray datasets show the proposed model achieves higher accuracy, true positive rate, and lower error rate compared to traditional feature selection based classification models.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
Soft computing is an approach to construct intelligent systems that employs techniques like fuzzy logic, neural networks, evolutionary algorithms, and swarm intelligence. It aims to model human-like expertise by being adaptive, learning, and operating with uncertainty. Soft computing techniques are used together in a complementary rather than competitive way in hybrid systems. Genetic algorithms are a popular type of evolutionary algorithm used for optimization that is inspired by biological evolution, using techniques like selection, crossover, and mutation to evolve solutions over generations.
Volume 14 issue 03 march 2014_ijcsms_march14_10_14_rahulDeepak Agarwal
1) The document presents a hybrid approach for feature subset selection that combines artificial bee colony and particle swarm optimization algorithms.
2) It applies this approach to three datasets from a public repository to select optimal feature subsets and compares the classification accuracy to other algorithms.
3) The results show the proposed hybrid approach achieves better classification accuracy on all three datasets compared to using artificial bee colony or random selection alone.
Microarray Data Classification Using Support Vector MachineCSCJournals
DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Over the past decades, Creative Biolabs has become a leader in antibody drug discovery and manufacturing, providing high quality services to clients in academia and industry around the world. Now, we are able to provide solutions to accelerate drug discovery and development by deploying artificial intelligence technologies. Here, we will briefly introduce the basics of AI-augmented drug discovery, algorithm classification, common AI models, and related services.
Artificial immune systems can be defined as abstract or metaphorical computational systems
developed using ideas, theories, and components, extracted from the immune system. Most AIS aim
at solving complex computational or engineering problems, such as pattern recognition, elimination,
and optimisation. This is a crucial distinction between AIS and theoretical immune system models.
While the former is devoted primarily to computing, the latter is focused on the modelling of the IS
in order to understand its behaviour, so that contributions can be made to the biological sciences. It is
not exclusive, however, the use of one approach into the other and, indeed, theoretical models of the
IS have contributed to the development of AIS. This paper discusses the concept of artificial immune
system. AIS has various algorithms such as: Immune Theory, Clonal Selection, negative selection.
All these are explained in this paper.
Performance Evaluation of Different Data Mining Classification Algorithm and ...IOSR Journals
This document evaluates the performance of different data mining classification algorithms and predictive analysis. It applies algorithms like decision trees, naive Bayes, k-nearest neighbor, neural networks, and support vector machines to datasets like Iris, liver disorder, and E. coli. The results show that neural networks and k-nearest neighbor achieved the best performance on these datasets, with accuracy rates up to 97.33% for Iris classification. Feature selection techniques like removing zero-weighted attributes were also found to improve some algorithm performance. Predictive analysis experiments found that neural networks and k-nearest neighbor were most accurate at predicting new class labels.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Pattern recognition system based on support vector machinesAlexander Decker
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration
and exploitation, a hybrid model with a popular meta-heuristic algorithm named
spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset
from dataset by removing the noise and outliers prior to feeding the data to the
wrapper SMO. To enhance the quality of the solution, simulated annealing is
deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SMO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.
The document describes using a genetic algorithm with an improved fitness function and inverse index representation for web data mining. It proposes modifications to an existing genetic algorithm model (GAHWM) that uses a ranking based fitness function to better determine relevant documents that match a user query. The results show the proposed genetic algorithm has improved precision, returning only relevant documents, and reduced execution time compared to the original GAHWM model.
2008: Applied AIS - A Roadmap of AIS Research in Brazil and Sample ApplicationsLeandro de Castro
The document summarizes several artificial immune system (AIS) research projects from groups in Brazil. It describes applications of AIS for (1) text clustering using an immune-inspired biclustering algorithm, (2) spam detection using an innate and adaptive AIS, and (3) optimal power flow optimization using a cluster gradient-based AIS.
Adapting New Data In Intrusion Detection SystemsCSCJournals
Most of the introduced anomaly intrusion detection system (IDS) methods focus on achieving better detection rates and lower false alarm rates. However, when it comes to real-time applications many additional issues come into the picture. One of them is the training datasets that are continuously becoming outdated. It is vital to use an up-to-date dataset while training the system. But the trained system will become insufficient if network behaviors change. As well known, frequent alteration is in the nature of computer networks. On the other hand it is costly to continually collect and label datasets while frequently training the system from scratch and discarding old knowledge is a waste. To overcome this problem, we propose the use of transfer learning which benefits from the previous gained knowledge. The carried out experiments stated that transfer learning helps to utilize previously obtained knowledge, improves the detection rate and reduces the need to recollect the whole dataset.
Classification Of Iris Plant Using Feedforward Neural Networkirjes
The classification and recognition of type on the basis of individual features and behaviors constitute
a preliminary measure and is an important target in the behavioral sciences. Current statistical methods do not
always yield satisfactory answers. A Feed Forward Artificial Neural Network is the computer model inspired by
the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the
other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural
network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant
species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural
networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal
width, petal length, and petal width. Using this data set a Neural Network (NN) is used for the classification of
iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of
the neural system in iris class identification.
DETECTION OF LIVER INFECTION USING MACHINE LEARNING TECHNIQUESIRJET Journal
This document discusses using machine learning techniques to detect liver infections. It provides an overview of various machine learning methods that have been applied to medical data related to the liver, including supervised learning algorithms like naive Bayes classifiers, k-nearest neighbors, and support vector machines. Deep learning techniques like deep neural networks are also mentioned. The goal is to automatically predict liver diseases early based on complex data from electronic health records, images, genomics and other sources to help doctors and improve patient care and outcomes.
Deep learning methods applied to physicochemical and toxicological endpointsValery Tkachenko
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...AM Publications
Toddler family cadre is a community members work voluntarily in fostering and providing information to parents of toddlers about how to properly care for children. Toddler Family cadre desperately need training to increase their skills. There are still a few Toddler family cadres who get training so that the knowledge and skills of parents and other family members in developing toddlers' growth through physical stimulation, motoric intelligence, emotional and social economy as well as possible are still lacking. The purpose of this study is to develop an Android- assisted Toddler family cadre training model in Demak. This research is research in tian research and development. The research location was in Demak Regency. Toddler family cadres became the object of this research. Development of Toddler family cadre training models assisted by Android in Demak is feasible to be used as an effort to improve Toddler Family cadres' capabilities.
TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...AM Publications
In recent years the use of composite materials in structural components has become increasingly common in a wide range of engineering applications. Composite materials offer numerous advantages over more conventional materials because of their superior specific properties, but a serious obstacle to a more widespread use of these materials is their high sensitivity to localized impact loading. This paper presents an experimental study to assess the impact response of drop weight impact tests on fiber reinforced polymer composites with deferent load and damage identification of composite using Non-destructive testing techniques ultrasonic testing (UT) C scan. In the study includes checking the strength of the specimen, plotting of graphs between the height and the impact energy obtained and tabulating the results after conducting the various functional tests.
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Identifying drug targets and candidate sequences is an important process and an unmet challenge in drug development. Creative Biolabs has developed an original AI-augmented drug discovery platform to accelerate drug discovery.
https://ai.creative-biolabs.com/ai-augmented-drug-discovery.htm
Creative Biolabs offers a series of AI-based antibody screening services based on the prediction of antibody-antigen binding and a unique way to find rare antibody clusters and get more candidate antibody sequences by augmenting our data-driven AI screening services.
https://ai.creative-biolabs.com/ai-based-antibody-screening-services.htm
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
This summary provides the high-level information from the document in 3 sentences:
The document proposes a Particle Swarm Optimization (PSO) based ensemble classification model to improve classification of high-dimensional biomedical datasets. It develops an optimized PSO technique to select optimal features and initialize weights for base classifiers in the ensemble model. Experimental results on microarray datasets show the proposed model achieves higher accuracy, true positive rate, and lower error rate compared to traditional feature selection based classification models.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
Soft computing is an approach to construct intelligent systems that employs techniques like fuzzy logic, neural networks, evolutionary algorithms, and swarm intelligence. It aims to model human-like expertise by being adaptive, learning, and operating with uncertainty. Soft computing techniques are used together in a complementary rather than competitive way in hybrid systems. Genetic algorithms are a popular type of evolutionary algorithm used for optimization that is inspired by biological evolution, using techniques like selection, crossover, and mutation to evolve solutions over generations.
Volume 14 issue 03 march 2014_ijcsms_march14_10_14_rahulDeepak Agarwal
1) The document presents a hybrid approach for feature subset selection that combines artificial bee colony and particle swarm optimization algorithms.
2) It applies this approach to three datasets from a public repository to select optimal feature subsets and compares the classification accuracy to other algorithms.
3) The results show the proposed hybrid approach achieves better classification accuracy on all three datasets compared to using artificial bee colony or random selection alone.
Microarray Data Classification Using Support Vector MachineCSCJournals
DNA microarrays allow biologist to measure the expression of thousands of genes simultaneously on a small chip. These microarrays generate huge amount of data and new methods are needed to analyse them. In this paper, a new classification method based on support vector machine is proposed. The proposed method is used to classify gene expression data recorded on DNA microarrays. It is found that the proposed method is faster than neural network and the classification performance is not less than neural network.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Over the past decades, Creative Biolabs has become a leader in antibody drug discovery and manufacturing, providing high quality services to clients in academia and industry around the world. Now, we are able to provide solutions to accelerate drug discovery and development by deploying artificial intelligence technologies. Here, we will briefly introduce the basics of AI-augmented drug discovery, algorithm classification, common AI models, and related services.
Artificial immune systems can be defined as abstract or metaphorical computational systems
developed using ideas, theories, and components, extracted from the immune system. Most AIS aim
at solving complex computational or engineering problems, such as pattern recognition, elimination,
and optimisation. This is a crucial distinction between AIS and theoretical immune system models.
While the former is devoted primarily to computing, the latter is focused on the modelling of the IS
in order to understand its behaviour, so that contributions can be made to the biological sciences. It is
not exclusive, however, the use of one approach into the other and, indeed, theoretical models of the
IS have contributed to the development of AIS. This paper discusses the concept of artificial immune
system. AIS has various algorithms such as: Immune Theory, Clonal Selection, negative selection.
All these are explained in this paper.
Performance Evaluation of Different Data Mining Classification Algorithm and ...IOSR Journals
This document evaluates the performance of different data mining classification algorithms and predictive analysis. It applies algorithms like decision trees, naive Bayes, k-nearest neighbor, neural networks, and support vector machines to datasets like Iris, liver disorder, and E. coli. The results show that neural networks and k-nearest neighbor achieved the best performance on these datasets, with accuracy rates up to 97.33% for Iris classification. Feature selection techniques like removing zero-weighted attributes were also found to improve some algorithm performance. Predictive analysis experiments found that neural networks and k-nearest neighbor were most accurate at predicting new class labels.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...ahmad abdelhafeez
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
Pattern recognition system based on support vector machinesAlexander Decker
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration
and exploitation, a hybrid model with a popular meta-heuristic algorithm named
spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset
from dataset by removing the noise and outliers prior to feeding the data to the
wrapper SMO. To enhance the quality of the solution, simulated annealing is
deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SMO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.
The document describes using a genetic algorithm with an improved fitness function and inverse index representation for web data mining. It proposes modifications to an existing genetic algorithm model (GAHWM) that uses a ranking based fitness function to better determine relevant documents that match a user query. The results show the proposed genetic algorithm has improved precision, returning only relevant documents, and reduced execution time compared to the original GAHWM model.
2008: Applied AIS - A Roadmap of AIS Research in Brazil and Sample ApplicationsLeandro de Castro
The document summarizes several artificial immune system (AIS) research projects from groups in Brazil. It describes applications of AIS for (1) text clustering using an immune-inspired biclustering algorithm, (2) spam detection using an innate and adaptive AIS, and (3) optimal power flow optimization using a cluster gradient-based AIS.
Adapting New Data In Intrusion Detection SystemsCSCJournals
Most of the introduced anomaly intrusion detection system (IDS) methods focus on achieving better detection rates and lower false alarm rates. However, when it comes to real-time applications many additional issues come into the picture. One of them is the training datasets that are continuously becoming outdated. It is vital to use an up-to-date dataset while training the system. But the trained system will become insufficient if network behaviors change. As well known, frequent alteration is in the nature of computer networks. On the other hand it is costly to continually collect and label datasets while frequently training the system from scratch and discarding old knowledge is a waste. To overcome this problem, we propose the use of transfer learning which benefits from the previous gained knowledge. The carried out experiments stated that transfer learning helps to utilize previously obtained knowledge, improves the detection rate and reduces the need to recollect the whole dataset.
Classification Of Iris Plant Using Feedforward Neural Networkirjes
The classification and recognition of type on the basis of individual features and behaviors constitute
a preliminary measure and is an important target in the behavioral sciences. Current statistical methods do not
always yield satisfactory answers. A Feed Forward Artificial Neural Network is the computer model inspired by
the structure of the Human Brain. It views as in the set of artificial nerve cells that are interconnected with the
other neurons. The primary aim of this paper is to demonstrate the process of developing the Artificial Neural
network based classifier which classifies the Iris database. The problem concerns the identification of Iris plant
species on the basis of plant attribute measurements. This paper is related to the use of feed forward neural
networks towards the identification of iris plants on the basis of the following measurements: sepal length, sepal
width, petal length, and petal width. Using this data set a Neural Network (NN) is used for the classification of
iris data set. The EBPA is used for training of this ANN. The results of simulations illustrate the effectiveness of
the neural system in iris class identification.
DETECTION OF LIVER INFECTION USING MACHINE LEARNING TECHNIQUESIRJET Journal
This document discusses using machine learning techniques to detect liver infections. It provides an overview of various machine learning methods that have been applied to medical data related to the liver, including supervised learning algorithms like naive Bayes classifiers, k-nearest neighbors, and support vector machines. Deep learning techniques like deep neural networks are also mentioned. The goal is to automatically predict liver diseases early based on complex data from electronic health records, images, genomics and other sources to help doctors and improve patient care and outcomes.
Deep learning methods applied to physicochemical and toxicological endpointsValery Tkachenko
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
Similar to Application of Artificial Immune System Algorithms in Dataset Classification (20)
DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...AM Publications
Toddler family cadre is a community members work voluntarily in fostering and providing information to parents of toddlers about how to properly care for children. Toddler Family cadre desperately need training to increase their skills. There are still a few Toddler family cadres who get training so that the knowledge and skills of parents and other family members in developing toddlers' growth through physical stimulation, motoric intelligence, emotional and social economy as well as possible are still lacking. The purpose of this study is to develop an Android- assisted Toddler family cadre training model in Demak. This research is research in tian research and development. The research location was in Demak Regency. Toddler family cadres became the object of this research. Development of Toddler family cadre training models assisted by Android in Demak is feasible to be used as an effort to improve Toddler Family cadres' capabilities.
TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...AM Publications
In recent years the use of composite materials in structural components has become increasingly common in a wide range of engineering applications. Composite materials offer numerous advantages over more conventional materials because of their superior specific properties, but a serious obstacle to a more widespread use of these materials is their high sensitivity to localized impact loading. This paper presents an experimental study to assess the impact response of drop weight impact tests on fiber reinforced polymer composites with deferent load and damage identification of composite using Non-destructive testing techniques ultrasonic testing (UT) C scan. In the study includes checking the strength of the specimen, plotting of graphs between the height and the impact energy obtained and tabulating the results after conducting the various functional tests.
THE USE OF FRACTAL GEOMETRY IN TILING MOTIF DESIGNAM Publications
In this paper I will present the use of fractal geometry to design tile motifs. A fractal is a geometric figure that combines the several characteristics among others: its parts have the same form as the whole, fragmented, and formation by iteration. The concept of fractals has been spread over all fields of sciences, technology, and art. This paper aims to provide an algorithm to creating motifs of tile algorithm for create the tile motif consists of base, iteration, coloration and duplication. In order to help the reader better understand the algorithm, I will present some script using Matlab. We describe a mathematically based algorithm that can fill a spatial region with sequence of randomly placed which may be transformed copies of one motif or several motifs. By using this algorithm, I can produce thousand variety of aesthetically pleasing tile motifs, of which we show a number of examples.
TWO-DIMENSIONAL INVERSION FINITE ELEMENT MODELING OF MAGNETOTELLURIC DATA: CA...AM Publications
Two-dimensional resistivity analysis of magnetotelluric data has been done at “Z” geothermal area which is located in southern part of Indonesia. The objective is to understand subsurface structure beneath reasearch area based on 2-D modeling of magnetotelluric data. The inversion finite element method were used for numerical simulations which requires discretization on the boundary of the modeling domain. The modeling results of magnetotelluric data shows relativity structure dissemination: 0-10 ohm.m in a thickness of 1 km (Clay Cap), 10-100 ohm.m with 1-2 km depth respectively (reservoir zone), and on a scale of 100-1000 ohm.m in a depth of 2-3 km (heat source zone). The result of relativity structure can be used to delineate an area with geothermal prospect around 12 km2.
USING THE GENETIC ALGORITHM TO OPTIMIZE LASER WELDING PARAMETERS FOR MARTENSI...AM Publications
This document presents an optimization of laser welding parameters for martensitic stainless steel using a genetic algorithm. The algorithm aims to minimize the difference between the actual and desired weld size (width and depth) by optimizing laser power, welding speed, and fiber diameter. The genetic algorithm was run 10 times with a population of 30 over 200 iterations each time. The results showed errors between optimized and experimental values of less than 5% for the parameters. The study demonstrates that genetic algorithms can effectively optimize laser welding parameters to achieve a preset weld size.
ANALYSIS AND DESIGN E-MARKETPLACE FOR MICRO, SMALL AND MEDIUM ENTERPRISESAM Publications
The Ministry of Cooperatives and Small and Medium Enterprises launched in 2018 the number of Micro, Small and Medium Enterprises (MSMEs) in Indonesia as many as 58.97 million people. It is predicted that the number of MSMEs players in 2019 will amount to 59.2 million. This shows that the Indonesian people have made changes in the field of family economics which initially as consumptive are now productive. The community prefers to carry out activities that can increase family income. Future MSMEs remain the mainstay of the national economy. In accordance with the government roadmap, in 2020 e-commerce transactions are predicted to reach Rp1,300 trillion or equivalent to USD130 billion. According to data from the Central Statistics Agency (BPS), the contribution of MSMEs to Indonesia's Gross Domestic Product (GDP) reached 61.41%, with the number of MSMEs reaching almost 60 million units. However, only around 8% or 3.79 million of the 59.2 million MSMEs players have used online platforms to market their products. Based on the above problems, researchers conducted research on the analysis and display of E-Marketplace for MSMEs in Indonesia. The type of research used is action research. The object of research is MSMEs which are under the Office of Industry and Trade of Sragen Regency. The method of data collection is by techniques: (1) interview, (2) documentation (3) observation, (4) literature study. The researcher uses the waterfall method in developing the system. The research team has successfully analyzed the E-Market place according to the results of data collection. The research team has succeeded in designing the E-Marketplace for MSMEs. E-Marketplace designed can be used by admin, MSME and user. Admin is in charge of managing E-Marketplace and has full access rights. MSMEs can register online and manage their products in E-Marketplace. Users or buyers can search data in E-Marketplace as desired. To make transactions, users can interact directly with MSMEs according to the data provided in E-Marketplace. E-Marketplace can be used for marketing together MSMEs products. This e-marketplace can be accessed at www.umkmonline.com
REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS AM Publications
Remote sensing technology's increasing accessibility helps us observe research and learn about our globe in ways we could only imagine a generation ago. Guides to profound knowledge of historical, conceptual and practical uses of remote sensing which is increasing GIS technology. This paper will go briefly through remote sensing benefits, history, technology and the GIS and remote sensing integration and their applications. Remote sensing (RS) is used in mapping the predicted and actual species and dominates the ecosystem canopy.
EVALUATE THE STRAIN ENERGY ERROR FOR THE LASER WELD BY THE H-REFINEMENT OF TH...AM Publications
Currently, the finite element method (FEM) is still one of the useful tools in numerical simulation for technical problems. With this method, a continuum model presented by a certain number of elements with a simple approximation field causes the presence of discretization error in solutions. This paper considers the butt weld by laser which subjected the tension for AISI 1018 steel highness 8 mm. The aim of the study is to use the h-refinement of the FEM in estimation the strain energy error for the laser weld mentioned. The results show that the stability of the h-refinement shown by the value of the relative error of the strain energy is quite small, specifically; FEM is less than 5.7% and extra is no more than 3.7%.
HMM APPLICATION IN ISOLATED WORD SPEECH RECOGNITIONAM Publications
Speech recognition is always being an all-time trendy topic for discussion and also for researches and we see a major application in our life. This paper provides the work done on the application of Hidden Markov model to implement isolated word speech recognition on MATLAB and to develop and train the system for set of self-selective words for specific user (user dependent) to get maximum efficiency in word recognition system. Which uses the forward and Baum-welch algorithm and fitting Gaussian of the Baum-welch algorithm for all the iteration perform. We use a sample of 7 alphabets which are recorded in 15 different ways giving total of 105 word to use for training with each word with 15 variations. This system can be used in real world in system security using voice security system and mainly for children and impaired people.
PEDESTRIAN DETECTION IN LOW RESOLUTION VIDEOS USING A MULTI-FRAME HOG-BASED D...AM Publications
Detecting pedestrians in low resolution videos is a challenging task, due to the small size of pedestrians in the images and the limited information. In practical outdoor surveillance scenarios the pedestrian size is usually small. Existing state-of-the-art pedestrian detection methods that use histogram of oriented gradient (HOG) features have poor performance in this problem domain. To compensate for the lack of information in a single frame, we propose a novel detection method that recognizes pedestrians in a short sequence of frames. Namely, we take the single-frame HOG-based detector and extend it to multiple frames. Our detector is applied to regions containing potential moving objects. In the case of video taken from a moving camera on an aerial platform, video stabilization is first performed to register the frames. A classifier is then applied to features extracted from spatio-temporal volumes surrounding the potential moving objects. On challenging stationary and aerial video datasets, our detection accuracy outperforms several state-of-the-art algorithms.
The aim of this paper is to help the blind people to identify and catch the public transport vehicles with the help of Light Fidelity technology. It is a Navigation aid. When the bus arrives at the bus stand, transmitter in the bus transmits the light signals and receiver in the stick, receives the light signals and a sound signal is generated through the speaker present in the stick. The sound message contains the bus number and the destination of the bus. In addition to this, if the person is absconded or lost, details of the location will be sent to his/her family members by pressing a button. This is made possible with the help of Global System for Mobile (GSM). Finally, presence of water can be detected along the blind person’s path, with the help of water sensors.
EFFECT OF SILICON - RUBBER (SR) SHEETS AS AN ALTERNATIVE FILTER ON HIGH AND L...AM Publications
A digital radiography delivers a radiation dose to patients; therefore it poses potential risk to the patients. One effort to reduce dose is carried out using a radiation filter, e.g. Silicone Rubber (SR) sheet. The purpose of this research was to determine the impact of the SR sheet on the high contrast objects (HCO) and the low contrast objects (LCO). The dose reduction was determined from attenuation x-rays before and after using the SR sheet. Assessment of HCO and LCO was observed from CDR TOR phantom at tube voltage of 48 kVp and tube current of 8 mAs. The physical parameter to assess image quality was the Signal to Noise Ratio (SNR) value in LCO. The maximum x-ray attenuation using the SR sheet is 48.82%. The visibility of the HCO remains the same, namely 16 objects; however the LCO slighly decreases from 14 objects to 13 objects after using the SR sheet. The SNR value decreases with an average value of 15.17%.Therefore, the SR sheet as a alternative filter has no effect on the HCO and has realtively little effect on the LCO. Thus, the SR sheet potentially is used for radiation protection in patients, especially on examinations that do not require low contrast resolution.
UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...AM Publications
Immunization is the key strategy to curb communicable diseases which are the number one killer of children under five. Immunization prevents mortalities of approximating three million children under five annually. This study aimed to assess utilization of immunization services among children under five of age in Kirinyaga County, Kenya.
REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...AM Publications
The article presents the study of cryptographic transformations of the Kuznyechik algorithm in relation to differential analysis and the translation of their representations into a more convenient form for cryptanalysis. A simplification of the type of transformations of the algorithm to algebraic the form, in which cryptanalysis software will be more effective. Since the description of the algorithm in the analytical form allows for 16 cycles of execution of the shift register with linear feedback, each of which will be carried out 16 operations of multiplication and 15 operations of addition, reduced to 16 multiplying and 15 the operations of addition. The result is an algebraic form of a linear transformation (from a shift register with linear feedback to the multiplication of the matrix in a finite field). In the future, the algebraic type of transformation can be used to effectively carry out differential cryptanalysis.
Optical character recognition (OCR) is process of classification of optical patterns contained in a digital image. The process of OCR Recognition involves several steps including pre-processing, segmentation, feature extraction, classification. Pre-processing is for done the basic operation on input image like noise reduction which remove the noisy signal from image. Segmentation stage for segment the given image into line by line and segment each character from segmented line. Future extraction calculates the characteristics of character. A Radial Basis Function Neural Network (RBFNN) is used to classification contains the database and does the comparison.
Surveillance refers to the task of observing a scene, often for lengthy periods in search of particular objects or particular behaviour. This task has many applications, foremost among them is security (monitoring for undesirable behaviour such as theft or vandalism), but increasing numbers of others in areas such as agriculture also exist. Historically, closed circuit TV (CCTV) surveillance has been mundane and labour Intensive, involving personnel scanning multiple screens, but the advent of reasonably priced fast hardware means that automatic surveillance is becoming a realistic task to attempt in real time. Several attempts at this are underway.
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENTAM Publications
Interest in air pollution investigation of urban environment due to existence of industrial and commercial activities along with vehicular emission and existence of buildings and streets which setup natural barrier for pollutant dispersion in the urban environment has increased. The air pollution modelling is a multidisciplinary subject when the entire cities are taken under consideration where urban planning and geometries are complex which needs a large software packages to be developed like Operational Street Pollution Model (OSPM), California Line Source model (CALINE series) etc. On overviewing various works it can be summarized that the air pollutant dispersion in urban street canyons and all linked phenomenon such as wind flow, pollutant concentrations, temperature distribution etc. generally depend on wind speed and direction, building heights and density, road width, source and intensity of air pollution, meteorological variables like temperature, humidity etc. A unique and surprising case is observed every time on numerous combinations of these factors. The main aim of this study is to simulate the atmospheric pollutant dispersion for given pollutant like carbon monoxide, sulphur dioxide and nitrogen dioxide and given atmospheric conditions like wind speed and direction. Computational Fluid Dynamics (CFD) simulation for analysing the atmospheric pollutant dispersion is done after natural airflow analysis. Volume rendering is done for variables such as phase 2 volume fraction and velocity with resolution as 250 pixels per inch and transparency as 20%. It can be observed that all the three pollutant namely nitrogen dioxide, sulphur dioxide and carbon monoxide the phase 2 volume fraction changes from 0 to 1. The wind velocity changes from 3.395×10-13 m/s to 1.692×102 m/s. The dispersion of pollutants follow the sequence Sulphur dioxide>Carbon monoxide>Nitrogen dioxide.
PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...AM Publications
In this article, we have extracted keratin from deccani wool waste and prepared the wool keratin based Chitosan nanofibers by electrospinning technique. The prepared nanofibers mat were prepared with different weight percent ratio like 1wt.%, 3wt.% and 5wt.% with respect to polymer i.e Chitosan. The physicochemical and filtration properties of wool keratin based Chitosan nanofibers were studied. Wool keratin based Chitosan nanofibers were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), differential scanning calorimetry (DSC) and scanning electron microscopy (FESEM). The filtration efficiency of keratin Chitosan nanofibers were investigated through DOP test and heavy metal removal capacity of evaluated through Atomic absorption spectroscopy. FTIR results were showed that Keratin gets compatible with Chitosan. XRD patterns revealed keratin was in crystalline nature and increase the crystalline nature of Chitosan nanofibers. FESEM images showed that uniform nanofibers generation with average fiber diameter 80nm. Nanofibers filtration efficiency against a particulate matter in air was obtained more than 99.53% and excellent property of removal of heavy metal.
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...AM Publications
Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulation analysis, it has been found that proposed improved version of regulated load balancing approach has shown better performance in terms of cost, response time and data processing time.
A MODEL BASED APPROACH FOR IMPLEMENTING WLAN SECURITY AM Publications
This paper presents various security features and configurations commonly implemented in WLANs and their aggregated security levels and then proposes a model that enables implementation and evaluation of WLAN security
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.