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The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity,... more
The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well in handling large datasets such as the Bot-IoT dataset, which represents a huge amount of data about botnet attacks on IoT networks. Therefore, improving the kNN performance in classifying IoT botnet attacks is the main concern in this study by applying several feature selection techniques. The whole research process was conducted in the Rapidminer environment using three prebuilt feature selection techniques, namely, Information Gain, Forward Selection, and Backward Elimination. After comparing accuracy, precision, recall, F1 score and processing time, the combination of the kNN algorithm and the Forward Selection technique (kNN-FS) achieves the best results among others, with the highest le...
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DNA-binding proteins (DBPs) have crucial biotic activities including DNA replication, recombination, and transcription. DBPs are highly concerned with chronic diseases and are used in the manufacturing of antibiotics and steroids. A... more
DNA-binding proteins (DBPs) have crucial biotic activities including DNA replication, recombination, and transcription. DBPs are highly concerned with chronic diseases and are used in the manufacturing of antibiotics and steroids. A series of predictors were established to identify DBPs. However, researchers are still working to further enhance the identification of DBPs. This research designed a novel predictor to identify DBPs more accurately. The features from the sequences are transformed by F-PSSM (Filtered position-specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), and R-PSSM (Reduced position-specific scoring matrix). To eliminate the noisy attributes, we extended DWT (discrete wavelet transform) to F-PSSM, PSSM-DPC, and R-PSSM and introduced three novel descriptors, namely, F-PSSM-DWT, PSSM-DPC-DWT, and R-PSSM-DWT. Onward, the training of the four models were performed using LiXGB (Light eXtreme gradient boosting), XGB (eXtreme gradi...
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Acetylation is the most important post-translation modification (PTM) in eukaryotes; it has manifold effects on the level of protein that transform an acetyl group from an acetyl coenzyme to a specific site on a polypeptide chain.... more
Acetylation is the most important post-translation modification (PTM) in eukaryotes; it has manifold effects on the level of protein that transform an acetyl group from an acetyl coenzyme to a specific site on a polypeptide chain. Acetylation sites play many important roles, including regulating membrane protein functions and strongly affecting the membrane interaction of proteins and membrane remodeling. Because of these properties, its correct identification is essential to understand its mechanism in biological systems. As such, some traditional methods, such as mass spectrometry and site-directed mutagenesis, are used, but they are tedious and time-consuming. To overcome such limitations, many computer models are being developed to correctly identify their sequences from non-acetyl sequences, but they have poor efficiency in terms of accuracy, sensitivity, and specificity. This work proposes an efficient and accurate computational model for predicting Acetylation using machine l...
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Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is... more
Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can be prioritized by a manager. The Kano model is a well-known method to perform this task for an individual customer. However, it requires filling in a form, which is a difficult and time-consuming exercise. Many existing methods require less effort from the customer side to perform data collection and can be used for a group of customers; however, they are not applicable to individuals. In the present study, we develop a data-analytic method that also uses the dataset; however, it can identify the attribute category for an individual customer. The proposed method is based on the probabilistic approach to analyze changes in the customer satisfaction corresponding to variations in attribute v...
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Cloud Computing becomes an attractive proposition because of long-term potential advantage by reducing the cost of services and thereby gaining more business outcomes. It is capable of transferring facilities by using the internet in a... more
Cloud Computing becomes an attractive proposition because of long-term potential advantage by reducing the cost of services and thereby gaining more business outcomes. It is capable of transferring facilities by using the internet in a secured manner economically. It has got interested from both industry and academia. The shared utilization characteristics enhance the proficiencies of the hardware resources of cloud computing. By virtue of this feature, a cloud is being used by commercial and specific users to make available their data in form of either application or service. Security concern originates because of the sharing environment in which external administration for the migration of user's assets takes place. A comprehensive review in order to find major gaps and issues related to security comes under cloud computing has been tried to present. The issues belong to security and its possible countermeasure has been found out and present. The increased number of cloud serv...
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DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung,... more
DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung, breast, and liver cancer) and chronic diseases (i.e. AIDS/HIV, asthma), while other kinds are involved in antibiotics, steroids, and anti-inflammatory drugs designing. These crucial processes are closely related to DBPs types. DBPs are categorized into single-stranded DNA-binding proteins (ssDBPs) and double-stranded DNA-binding proteins (dsDBPs). Few computational predictors have been reported for discriminating ssDBPs and dsDBPs. However, due to the limitations of the existing methods, an intelligent computational system is still highly desirable. In this work, features from protein sequences are discovered by extending the notion of dipeptide composition (DPC), evolutionary difference formula (EDF), and K-separated bigram (KSB) into the position-speci...
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Let h 1 z and h 2 z be two nonvanishing holomorphic functions in the open unit disc with h 1 0 = h 2 0 = 1 . For some holomorphic function q z , we consider the class consisting of normalized holomorphic functions f whose ratios f z / z q... more
Let h 1 z and h 2 z be two nonvanishing holomorphic functions in the open unit disc with h 1 0 = h 2 0 = 1 . For some holomorphic function q z , we consider the class consisting of normalized holomorphic functions f whose ratios f z / z q z and q z are subordinate to h 1 z and h 2 z , respectively. The majorization results are obtained for this class when h 1 z is chosen either h 1 z = cos z or h 1 z = 1 + sin z or h 1 z = 1 + z and h 2 z = 1 + sin z .
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Background:The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins.... more
Background:The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological processes. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites.Methodology:In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features a...
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Real-time video communication has become one of the most significant applications extensively used by homogeneous/heterogeneous wireless network technologies, such as Wi-Fi, the Internet of things, the wireless sensor network (WSN), 5G,... more
Real-time video communication has become one of the most significant applications extensively used by homogeneous/heterogeneous wireless network technologies, such as Wi-Fi, the Internet of things, the wireless sensor network (WSN), 5G, etc. This leads to enhanced deployment of multimedia streaming applications over wireless network technologies. In order to accomplish the optimal performance of real-time multimedia streaming applications over the homogeneous/heterogeneous wireless network, it is therefore necessary to develop a simulation tool-set that effectively measures the quality of service (QoS) for different multimedia streaming applications over transport layer protocols. This paper proposes an autonomous simulation tool (AST) that is entirely independent from the source code of transport layer protocols. Furthermore, the AST is integrated into NS-2 to evaluate the QoS of real-time video streaming over numerous transport layer protocols and it uses new QoS measurement tools...
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Background:In various biological processes and cell functions, Post Translational Modifications (PTMs) bear critical significance. Hydroxylation of proline residue is one kind of PTM, which occurs following protein synthesis. The... more
Background:In various biological processes and cell functions, Post Translational Modifications (PTMs) bear critical significance. Hydroxylation of proline residue is one kind of PTM, which occurs following protein synthesis. The experimental determination of hydroxyproline sites in an uncharacterized protein sequence requires extensive, time-consuming and expensive tests.Methods:With the torrential slide of protein sequences produced in the post-genomic age, certain remarkable computational strategies are desired to overwhelm the issue. Keeping in view the composition and sequence order effect within polypeptide chains, an innovative in-silico predictor via a mathematical model is proposed.Results:Later, it was stringently verified using self-consistency, cross-validation and jackknife tests on benchmark datasets. It was established after a rigorous jackknife test that the new predictor values are superior to the values predicted by previous methodologies.Conclusion:This new mathem...
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The diversity of applications developed with different programming languages, application/data architectures, database systems and representation of data/information leads to heterogeneity issues. One of the problem challenges in the... more
The diversity of applications developed with different programming languages, application/data architectures, database systems and representation of data/information leads to heterogeneity issues. One of the problem challenges in the problem of heterogeneity is about heterogeneity data in term of semantic aspect. The semantic aspect is about data that has the same name with different meaning or data that has a different name with the same meaning. The semantic data mapping process is the best solution in the current days to solve semantic data problem. There are many semantic data mapping technologies that have been used in recent years. This research aims to compare and analyze existing semantic data mapping technology using five criteria’s. After comparative and analytical process, this research provides recommendations of appropriate semantic data mapping technology based on several criteria’s. Furthermore, at the end of this research we apply the recommended semantic data mappin...
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The paper utilizes the Karnaugh map in a pedagogical calculation of the Shapley-Shubik index of voting power, which is perhaps the most prominent such index for managerial, political, and engineering voting systems. The Karnaugh map has... more
The paper utilizes the Karnaugh map in a pedagogical calculation of the Shapley-Shubik index of voting power, which is perhaps the most prominent such index for managerial, political, and engineering voting systems. The Karnaugh map has features that allow it to readily implement this calculation, namely: (a) it can conveniently represent pseudo-switching functions of the form g= B_2^n →R, (b) it can immediately detect the number of orderings in going from one of its configurations (cells) to another, and (c) it visually identifies a pair of configurations that is critical w. r. t. a certain voter, which are a primitive winning coalition of which this voter is a member and a corresponding primitive losing coalition that results when this voter defects. The paper offers a detailed description of these three features and of the associated calculations using the celebrated system of the European Economic Community as a running example.
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The continuous increase in Android malware applications (apps) represents a significant danger to the privacy and security of users’ information. Therefore, effective and efficient Android malware app-classification techniques are needed.... more
The continuous increase in Android malware applications (apps) represents a significant danger to the privacy and security of users’ information. Therefore, effective and efficient Android malware app-classification techniques are needed. This paper presents a method for Android malware classification using optimized ensemble learning based on genetic algorithms. The suggested method is divided into two steps. First, a base learner is used to handle various machine learning algorithms, including support vector machine (SVM), logistic regression (LR), gradient boosting (GB), decision tree (DT), and AdaBoost (ADA) classifiers. Second, a meta learner RF-GA, utilizing genetic algorithm (GA) to optimize the parameters of a random forest (RF) algorithm, is employed to classify the prediction probabilities from the base learner. The genetic algorithm is used to optimize the parameter settings in the RF algorithm in order to obtain the highest Android malware classification accuracy. The ef...
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This note proposes a minor modification of a recently-developed method that achieves two-level multiple- output logic minimization via the constrained minimization of a single function. The modified method is simpler and more efficient... more
This note proposes a minor modification of a recently-developed method that achieves two-level multiple- output logic minimization via the constrained minimization of a single function. The modified method is simpler and more efficient than the original one, but unlike the original method, it does not guarantee exact minimality except for small-size circuits.
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ABSTRACT
ABSTRACT
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ABSTRACT Stochastic Symmetric Nets (SSNs) are a High-Level Stochastic Petri Net formalism which provides a parametric system description and an efficient analysis technique that exploit system symmetries to automatically aggregate its... more
ABSTRACT Stochastic Symmetric Nets (SSNs) are a High-Level Stochastic Petri Net formalism which provides a parametric system description and an efficient analysis technique that exploit system symmetries to automatically aggregate its states. Even if significant reductions can be achieved in highly symmetric models, the reduced state space can still be too large to derive and/or solve the underlying stochastic process, so that Monte Carlo simulation and fluid approximation remain the only viable ways that need to be explored. In this paper, we contribute to this line of research by proposing a new approach based on fluid approximation to automatically derive from an SSN model a set of ordinary differential equations (ODEs) which mimic the system behavior, and by showing how the SSN formalism allows us to define an efficient translation method which reduces the size of the corresponding ODE system with an automatic exploitation of system symmetries. Additionally, some case studies are presented to show the effectiveness of the method and the relevance of its application in practical cases.
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ABSTRACT We consider in this paper a queueing system where, at a service completion epoch, the server decides to serve the next customer or take a vacation, to perform some auxiliary work. If a vacation is taken, the server again has to... more
ABSTRACT We consider in this paper a queueing system where, at a service completion epoch, the server decides to serve the next customer or take a vacation, to perform some auxiliary work. If a vacation is taken, the server again has to decide, at the end of the vacation, whether to take another one. This process continues for a maximum of K vacations. We call this a binomial schedule. We further assume that the server is unreliable and may break down anytime while providing service. Analysis of this system yields to the explicit expression of the optimal number of vacations K<sup align="right"> * . Numerical illustrations are provided.