LIVIVO - Search results -

Search results

Result 1 - 10 of total 17

Search options

Article ; Online: A novel framework based on the multi-label classification for dynamic selection of classifiers.

Elmi, Javad / Eftekhari, Mahdi / Mehrpooya, Adel / Ravari, Mohammad Rezaei

International journal of machine learning and cybernetics

2023 Volume 14, Issue 6, Page(s) 2137–2154

Abstract: Multi-classifier systems (MCSs) are some kind of predictive models that classify instances by combining the output of an ensemble of classifiers given in a pool. With the aim of enhancing the performance of MCSs, dynamic selection (DS) techniques have ... ...

Abstract	Multi-classifier systems (MCSs) are some kind of predictive models that classify instances by combining the output of an ensemble of classifiers given in a pool. With the aim of enhancing the performance of MCSs, dynamic selection (DS) techniques have been introduced and applied to MCSs. Dealing with each test sample classification, DS methods seek to perform the task of classifier selection so that only the most competent classifiers are selected. The principal subject regarding DS techniques is how the competence of classifiers corresponding to every new test sample classification task can be estimated. In traditional dynamic selection methods, for classifying an unknown test sample
Language	English
Publishing date	2023-01-02
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2572473-3
ISSN	1868-808X ; 1868-8071
ISSN (online)	1868-808X
ISSN	1868-8071
DOI	10.1007/s13042-022-01751-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Details ▾
- Full text online
- Order with fees

Book ; Online: SELF-VS

Mokhtarabadi, Hojjat / Bahraman, Kave / HosseinZadeh, Mehrdad / Eftekhari, Mahdi

Self-supervised Encoding Learning For Video Summarization

2023

Abstract: Despite its wide range of applications, video summarization is still held back by the scarcity of extensive datasets, largely due to the labor-intensive and costly nature of frame-level annotations. As a result, existing video summarization methods are ... ...

Abstract	Despite its wide range of applications, video summarization is still held back by the scarcity of extensive datasets, largely due to the labor-intensive and costly nature of frame-level annotations. As a result, existing video summarization methods are prone to overfitting. To mitigate this challenge, we propose a novel self-supervised video representation learning method using knowledge distillation to pre-train a transformer encoder. Our method matches its semantic video representation, which is constructed with respect to frame importance scores, to a representation derived from a CNN trained on video classification. Empirical evaluations on correlation-based metrics, such as Kendall's $\tau$ and Spearman's $\rho$ demonstrate the superiority of our approach compared to existing state-of-the-art methods in assigning relative scores to the input frames. Comment: 9 pages, 5 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition
Publishing date	2023-03-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Book ; Online: An Adaptive Image Encryption Scheme Guided by Fuzzy Models

Shariatzadeh, Mahdi / Rostami, Mohammad Javad / Eftekhari, Mahdi

2022

Abstract: A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was ... ...

Abstract	A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was achieved. Security criteria were computed after running a proposed encryption process. Then an adaptive fuzzy system decided whether to repeat the encryption process, terminate it, or run the next stage based on the achieved results and user demand. The SHA-512 hash function was employed to increase key sensitivity. Security analysis was conducted to evaluate the security of the proposed scheme, which showed it had high security and all the criteria necessary for a good and efficient encryption algorithm were met. Simulation results and the comparison of similar works showed the proposed encryptor had a pseudo-noise output and was strongly dependent upon the changing key and plain image. Comment: Iranian Journal of Fuzzy Systems (2023)
Keywords	Computer Science - Cryptography and Security ; Computer Science - Multimedia
Subject code	006
Publishing date	2022-08-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Book ; Online: A New Scheme for Image Compression and Encryption Using ECIES, Henon Map, and AEGAN

Shariatzadeh, Mahdi / Eftekhari, Mahdi / Rostami, Mohammad Javad

2022

Abstract: Providing security in the transmission of images and other multimedia data has become one of the most important scientific and practical issues. In this paper, a method for compressing and encryption images is proposed, which can safely transmit images ... ...

Abstract	Providing security in the transmission of images and other multimedia data has become one of the most important scientific and practical issues. In this paper, a method for compressing and encryption images is proposed, which can safely transmit images in low-bandwidth data transmission channels. At first, using the autoencoding generative adversarial network (AEGAN) model, the images are mapped to a vector in the latent space with low dimensions. In the next step, the obtained vector is encrypted using public key encryption methods. In the proposed method, Henon chaotic map is used for permutation, which makes information transfer more secure. To evaluate the results of the proposed scheme, three criteria SSIM, PSNR, and execution time have been used.
Keywords	Computer Science - Multimedia ; Electrical Engineering and Systems Science - Image and Video Processing
Publishing date	2022-08-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: CCFS: A cooperating coevolution technique for large scale feature selection on microarray datasets.

Ebrahimpour, Mohammad K / Nezamabadi-Pour, Hossein / Eftekhari, Mahdi

Computational biology and chemistry

2018 Volume 73, Page(s) 171–178

Abstract: Recently, advances in bioinformatics lead to microarray high dimensional datasets. These kinds of datasets are still challenging for researchers in the area of machine learning since they suffer from small sample size and extremely large number of ... ...

Abstract	Recently, advances in bioinformatics lead to microarray high dimensional datasets. These kinds of datasets are still challenging for researchers in the area of machine learning since they suffer from small sample size and extremely large number of features. Therefore, feature selection is the problem of interest in the learning process in this area. In this paper, a novel feature selection method based on a global search (by using the main concepts of divide and conquer technique) which is called CCFS, is proposed. The proposed CCFS algorithm divides vertically (on features) the dataset by random manner and utilizes the fundamental concepts of cooperation coevolution by using a filter criterion in the fitness function in order to search the solution space via binary gravitational search algorithm. For determining the effectiveness of the proposed method some experiments are carried out on seven binary microarray high dimensional datasets. The obtained results are compared with nine state-of-the-art feature selection algorithms including Interact (INT), and Maximum Relevancy Minimum Redundancy (MRMR). The average outcomes of the results are analyzed by a statistical non-parametric test and it reveals that the proposed method has a meaningful difference to the others in terms of accuracy, sensitivity, specificity and number of selected features.
MeSH term(s)	Algorithms ; Computational Biology ; Databases, Factual ; Humans ; Microarray Analysis
Language	English
Publishing date	2018-04
Publishing country	England
Document type	Journal Article
ISSN	1476-928X
ISSN (online)	1476-928X
DOI	10.1016/j.compbiolchem.2018.02.006
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition.

Bardsiri, Mahshid Khatibi / Eftekhari, Mahdi

International journal of data mining and bioinformatics

2014 Volume 9, Issue 1, Page(s) 89–105

Abstract: In this paper, some methods for ensemble learning of protein fold recognition based on a decision tree (DT) are compared and contrasted against each other over three datasets taken from the literature. According to previously reported studies, the ... ...

Abstract	In this paper, some methods for ensemble learning of protein fold recognition based on a decision tree (DT) are compared and contrasted against each other over three datasets taken from the literature. According to previously reported studies, the features of the datasets are divided into some groups. Then, for each of these groups, three ensemble classifiers, namely, random forest, rotation forest and AdaBoost.M1 are employed. Also, some fusion methods are introduced for combining the ensemble classifiers obtained in the previous step. After this step, three classifiers are produced based on the combination of classifiers of types random forest, rotation forest and AdaBoost.M1. Finally, the three different classifiers achieved are combined to make an overall classifier. Experimental results show that the overall classifier obtained by the genetic algorithm (GA) weighting fusion method, is the best one in comparison to previously applied methods in terms of classification accuracy.
MeSH term(s)	Amino Acid Sequence ; Artificial Intelligence ; Computer Simulation ; Decision Support Techniques ; Models, Chemical ; Models, Molecular ; Molecular Sequence Data ; Pattern Recognition, Automated/methods ; Protein Folding ; Proteins/chemistry ; Proteins/ultrastructure ; Sequence Analysis, Protein/methods
Chemical Substances	Proteins
Language	English
Publishing date	2014-04-29
Publishing country	Switzerland
Document type	Journal Article
ISSN	1748-5673
ISSN	1748-5673
DOI	10.1504/ijdmb.2014.057776
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Book ; Online: Deep Metric Learning with Soft Orthogonal Proxies

Saberi-Movahed, Farshad / Ebrahimpour, Mohammad K. / Saberi-Movahed, Farid / Moshavash, Monireh / Rahmatian, Dorsa / Mohazzebi, Mahvash / Shariatzadeh, Mahdi / Eftekhari, Mahdi

2023

Abstract: Deep Metric Learning (DML) models rely on strong representations and similarity-based measures with specific loss functions. Proxy-based losses have shown great performance compared to pair-based losses in terms of convergence speed. However, proxies ... ...

Abstract	Deep Metric Learning (DML) models rely on strong representations and similarity-based measures with specific loss functions. Proxy-based losses have shown great performance compared to pair-based losses in terms of convergence speed. However, proxies that are assigned to different classes may end up being closely located in the embedding space and hence having a hard time to distinguish between positive and negative items. Alternatively, they may become highly correlated and hence provide redundant information with the model. To address these issues, we propose a novel approach that introduces Soft Orthogonality (SO) constraint on proxies. The constraint ensures the proxies to be as orthogonal as possible and hence control their positions in the embedding space. Our approach leverages Data-Efficient Image Transformer (DeiT) as an encoder to extract contextual features from images along with a DML objective. The objective is made of the Proxy Anchor loss along with the SO regularization. We evaluate our method on four public benchmarks for category-level image retrieval and demonstrate its effectiveness with comprehensive experimental results and ablation studies. Our evaluations demonstrate the superiority of our proposed approach over state-of-the-art methods by a significant margin.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-06-22
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: Effect of vinasse (condensed molasses solubles) on performance and meat chemical composition of Holstein male calves

Zali, Abolfazl / Eftekhari, Mahdi / Fatehi, Farhang / Ganjkhanlou, Mahdi

Italian journal of animal science. 2017 July 3, v. 16, no. 3

2017

Abstract: Twenty-four Holstein male calves (BW = 320 ± 48kg) were used to evaluate the effects of vinasse supplementation on growth, carcase and meat chemical composition and total-tract digestibility in a randomised complete block design. The calves were divided ... ...

Abstract	Twenty-four Holstein male calves (BW = 320 ± 48kg) were used to evaluate the effects of vinasse supplementation on growth, carcase and meat chemical composition and total-tract digestibility in a randomised complete block design. The calves were divided into four groups and allocated to four diets: a maize/barley-based diet with no added vinasse (C); a diet containing 5% (DM basis) vinasse (LV); a diet containing 10% (DM basis) vinasse (MV) and a diet containing 15% (DM basis) vinasse (HV). Amount of feed offered was recorded daily and the calves were weighed monthly and slaughtered after 4 months of trial. Dry matter intake was not affected significantly by treatments. Calves fed with C and LV diets had higher live slaughter weight, ADG, longissimus muscle area and lower feed efficiency than calves fed the MV and HV diets (p < .001). Digestibility of OM, EE and NDF were not different between C and LV diets (p > .05), but it was decreased as the level of vinasse increased to the level of 15% in HV diet (p < .05). No differences were detected in the NH₃–N and molar proportion of rumen VFAs except for propionate, in which calves were fed the C and LV diets had higher concentration of propionate and total VFAs compare to those fed the MV and HV diets (p < .05). These results showed that vinasse can be included in the growing calves ration around 5% without adverse effects and would promote carcase composition.
Keywords	Holstein ; carcass composition ; diet ; digestibility ; dry matter intake ; feed conversion ; longissimus muscle ; males ; meat ; molasses ; propionic acid ; slaughter weight ; vinasse
Language	English
Dates of publication	2017-0703
Size	p. 515-520.
Publishing place	Taylor & Francis
Document type	Article
Note	NAL-light
ZDB-ID	2408994-1
ISSN	1828-051X ; 1594-4077
ISSN (online)	1828-051X
ISSN	1594-4077
DOI	10.1080/1828051X.2017.1298407
Database	NAL-Catalogue (AGRICOLA)

Order via subito

Book ; Online: DISCERN

Hassani, Ali / Iranmanesh, Amir / Eftekhari, Mahdi / Salemi, Abbas

Diversity-based Selection of Centroids for k-Estimation and Rapid Non-stochastic Clustering

2019

Abstract: One of the applications of center-based clustering algorithms such as K-Means is partitioning data points into K clusters. In some examples, the feature space relates to the underlying problem we are trying to solve, and sometimes we can obtain a ... ...

Abstract	One of the applications of center-based clustering algorithms such as K-Means is partitioning data points into K clusters. In some examples, the feature space relates to the underlying problem we are trying to solve, and sometimes we can obtain a suitable feature space. Nevertheless, while K-Means is one of the most efficient offline clustering algorithms, it is not equipped to estimate the number of clusters, which is useful in some practical cases. Other practical methods which do are simply too complex, as they require at least one run of K-Means for each possible K. In order to address this issue, we propose a K-Means initialization similar to K-Means++, which would be able to estimate K based on the feature space while finding suitable initial centroids for K-Means in a deterministic manner. Then we compare the proposed method, DISCERN, with a few of the most practical K estimation methods, while also comparing clustering results of K-Means when initialized randomly, using K-Means++ and using DISCERN. The results show improvement in both the estimation and final clustering performance.
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	006
Publishing date	2019-10-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: High dimensionality reduction by matrix factorization for systems pharmacology.

Mehrpooya, Adel / Saberi-Movahed, Farid / Azizizadeh, Najmeh / Rezaei-Ravari, Mohammad / Saberi-Movahed, Farshad / Eftekhari, Mahdi / Tavassoly, Iman

Briefings in bioinformatics

2021 Volume 23, Issue 1

Abstract: The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of ... ...

Abstract	The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.
MeSH term(s)	Algorithms ; Humans ; Neoplasms/drug therapy ; Neoplasms/genetics ; Network Pharmacology
Language	English
Publishing date	2021-12-10
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbab410
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 6262: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
ab Jg. 2022: Lesesaal (EG)

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

To top

Full text online

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito