5,751 research outputs found
An Efficient Authentication Approach with Optimization Algorithms and Elliptical Curve Cryptography for Cloud Environment
The fast-emerging of cloud computing technology today has sufficiently benefited its wide range of users from individuals to large organizations. It carries an attractive characteristic by renting myriad virtual storages, computing resources and platform for users to manipulate their data or utilize the processing resources conveniently over Internet without the need to know the exact underlying infrastructure which is resided remotely at cloud servers. Security is very important for any kind of networks. As a main communication mode, the security mechanism for multicast is not only the measure to ensure secured communications, but also the precondition for other security services. Attacks are one of the biggest concerns for security professionals. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies. This paper presents a dual optimizer based key generation method for the improving the authentication with Elliptical Curve Cryptography (ECC) encryption algorithm. The optimal private and secret key for the encryption and decryption are obtained with the optimization techniques like Animal Migration Optimization (AMO), and Brain Storm Optimization (BSO) for strengthening the security in the Cloud Computing environment
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological
behaviors of fish schooling in nature, viz., the preying, swarming, following
and random behaviors. Owing to a number of salient properties, which include
flexibility, fast convergence, and insensitivity to the initial parameter
settings, the family of AFSA has emerged as an effective Swarm Intelligence
(SI) methodology that has been widely applied to solve real-world optimization
problems. Since its introduction in 2002, many improved and hybrid AFSA models
have been developed to tackle continuous, binary, and combinatorial
optimization problems. This paper aims to present a concise review of the
family of AFSA, encompassing the original ASFA and its improvements,
continuous, binary, discrete, and hybrid models, as well as the associated
applications. A comprehensive survey on the AFSA from its introduction to 2012
can be found in [1]. As such, we focus on a total of {\color{blue}123} articles
published in high-quality journals since 2013. We also discuss possible AFSA
enhancements and highlight future research directions for the family of
AFSA-based models.Comment: 37 pages, 3 figure
EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
During the past decade, with the significant progress of computational power
as well as ever-rising data availability, deep learning techniques became
increasingly popular due to their excellent performance on computer vision
problems. The size of the Protein Data Bank has increased more than 15 fold
since 1999, which enabled the expansion of models that aim at predicting
enzymatic function via their amino acid composition. Amino acid sequence
however is less conserved in nature than protein structure and therefore
considered a less reliable predictor of protein function. This paper presents
EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the
Enzyme Commission number of enzymes based only on their voxel-based spatial
structure. The spatial distribution of biochemical properties was also examined
as complementary information. The 2-layer architecture was investigated on a
large dataset of 63,558 enzymes from the Protein Data Bank and achieved an
accuracy of 78.4% by exploiting only the binary representation of the protein
shape. Code and datasets are available at https://github.com/shervinea/enzynet.Comment: 11 pages, 6 figure
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
Bibliometric of Feature Selection Using Optimization Techniques in Healthcare using Scopus and Web of Science Databases
Feature selection technique is an important step in the prediction and classification process, primarily in data mining related aspects or related to medical field. Feature selection is immersive with the errand of choosing a subset of applicable features that could be utilized in developing a prototype. Medical datasets are huge in size; hence some effective optimization techniques are required to produce accurate results. Optimization algorithms are a critical function in medical data mining particularly in identifying diseases since it offers excellent effectiveness in minimum computational expense and time. The classification algorithms also produce superior outcomes when an objective function is built using the feature selection algorithm. The solitary motive of the research paper analysis is to comprehend the reach and utility of optimization algorithms such as the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO) in the field of Health care.
The aim is to bring efficiency and maximum optimization in the health care sector using the vast information that is already available related to these fields. With the help of data sets that are available in the health care analysis, our focus is to extract the most important features using optimization techniques and work on different algorithms so as to get the most optimized result.
Precision largely depends on usefulness of features that are taken into consideration along with finding useful patterns in those features to characterize the main problem. The Performance of the optimized algorithm finds the overall optimum with less function evaluation. The principle target of this examination is to optimize feature selection technique to bring an optimized and efficient model to cater to various health issues.
In this research paper, to do bibliometric analysis Scopus and Web of Science databases are used. This bibliometric analysis considers important keywords, datasets, significance of the considered research papers. It also gives details about types, sources of publications, yearly publication trends, significant countries from Scopus and Web of Science. Also, it captures details about co-appearing keywords, authors, source titles through networked diagrams. In a way, this research paper can be useful to researchers who want to contribute in the area of feature selection and optimization in healthcare. From this research paper it is observed that there is a lot scope for research for the considered research area. This kind of research will also be helpful for analyzing pandemic scenarios like COVID-19
A Tent L\'evy Flying Sparrow Search Algorithm for Feature Selection: A COVID-19 Case Study
The "Curse of Dimensionality" induced by the rapid development of information
science, might have a negative impact when dealing with big datasets. In this
paper, we propose a variant of the sparrow search algorithm (SSA), called Tent
L\'evy flying sparrow search algorithm (TFSSA), and use it to select the best
subset of features in the packing pattern for classification purposes. SSA is a
recently proposed algorithm that has not been systematically applied to feature
selection problems. After verification by the CEC2020 benchmark function, TFSSA
is used to select the best feature combination to maximize classification
accuracy and minimize the number of selected features. The proposed TFSSA is
compared with nine algorithms in the literature. Nine evaluation metrics are
used to properly evaluate and compare the performance of these algorithms on
twenty-one datasets from the UCI repository. Furthermore, the approach is
applied to the coronavirus disease (COVID-19) dataset, yielding the best
average classification accuracy and the average number of feature selections,
respectively, of 93.47% and 2.1. Experimental results confirm the advantages of
the proposed algorithm in improving classification accuracy and reducing the
number of selected features compared to other wrapper-based algorithms
Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers
This work was partially supported by the National Natural Science Foundation of China (61403206, 61876089,61876185), the Natural Science Foundation of Jiangsu Province (BK20141005), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (14KJB520025), the Engineering Research Center of Digital Forensics, Ministry of Education, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.Peer reviewedPostprin
Improved Multi-Verse Optimizer Feature Selection Technique With Application To Phishing, Spam, and Denial Of Service Attacks
Intelligent classification systems proved their merits in different fields including cybersecurity. However, most cybercrime issues are characterized of being dynamic and not static classification problems where the set of discriminative features keep changing with time. This indeed requires revising the cybercrime classification system and pick a group of features that preserve or enhance its performance. Not only this but also the system compactness is regarded as an important factor to judge on the capability of any classification system where cybercrime classification systems are not an exception. The current research proposes an improved feature selection algorithm that is inspired from the well-known multi-verse optimizer (MVO) algorithm. Such an algorithm is then applied to 3 different cybercrime classification problems namely phishing websites, spam, and denial of service attacks. MVO is a population-based approach which stimulates a well-known theory in physics namely multi-verse theory. MVO uses the black and white holes principles for exploration, and wormholes principle for exploitation. A roulette selection schema is used for scientifically modeling the principles of white hole and black hole in exploration phase, which bias to the good solutions, in this case the solutions will be moved toward the best solution and probably to lose the diversity, other solutions may contain important information but didn’t get chance to be improved. Thus, this research will improve the exploration of the MVO by introducing the adaptive neighborhood search operations in updating the MVO solutions. The classification phase has been done using a classifier to evaluate the results and to validate the selected features. Empirical outcomes confirmed that the improved MVO (IMVO) algorithm is capable to enhance the search capability of MVO, and outperform other algorithm involved in comparison
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