scholarly journals afpCOOL: An Accurate Tool for Antifreeze Protein Detection

2017 ◽  
Author(s):  
Morteza Eslami ◽  
Ramin Shirali-hossein-zade ◽  
Zeinab Takalloo ◽  
Ghasem Mahdevar ◽  
Abbasali Emamjomeh ◽  
...  
Keyword(s):  
Protein Detection ◽  
Freeze Tolerance ◽  
Support Vector ◽  
Discrimination Power ◽  
Current State ◽  
Comparison Results ◽  
Evolutionary Features ◽  
Benchmark Datasets ◽  
Cold Hardy ◽  
User Friendly

ABSTRACTVarious cold-adapted organisms produce antifreeze proteins (AFPs), which prevent to freeze of cell fluids by resisting the growth of the ice crystal. AFPs are currently being recognized in various organisms that are living in extremely low temperatures. AFPs have several important applications in increasing freeze tolerance of plants; maintain the tissue in frozen conditions and producing cold-hardy plants using transgenic technology. Substantial differences in the sequence and structure of the AFPs, pose a challenge for researcher to identify these proteins. In this paper, we proposed a novel method for identifying AFPs using support vector machine (SVM) by incorporating 4 types of features. Results on two benchmark datasets revealed the strength of the proposed method in AFP prediction. Also, according to the results on an independent test set, our method outperformed the current state-of-the-art methods. The further analysis showed the non-satisfactory performance of the BLAST in AFP detection: more than 62% of the BLAST searches have specificity less than 10% and there is no any BLAST search with sensitivity higher than 10%. These results reveal the urgent need for an accurate tool for AFP detection. In addition, the comparison results of the discrimination power of different feature types disclosed that evolutionary features and amino acid composition are the most contributing features in AFP detection. This method has been implemented as a stand-alone tool, namely afpCOOL, for various operating systems to predict AFPs with a user friendly graphical interface.AvailabilityafpCOOL is freely available at http://bioinf.modares.ac.ir:8080/AFPCOOL/page/afpcool.ispContactDr Zahiri [email protected]

scholarly journals A novel feature fusion based on the evolutionary features for protein fold recognition using support vector machines

2019 ◽  
Author(s):  
Mohammad Saleh Refahi ◽  
A. Mir ◽  
Jalal A. Nasiri
Keyword(s):  
Feature Extraction ◽  
Information Gain ◽  
Fold Recognition ◽  
Dimensional Structure ◽  
Support Vector ◽  
Protein Fold ◽  
Protein Fold Recognition ◽  
Evolutionary Features ◽  
Cross Covariance ◽  
Benchmark Datasets

AbstractProtein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting features from protein sequences and using a strong classifier. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physiochemical-based information to extract features. In recent years, Finding an efficient technique for integrating discriminate features have been received advancing attention. In this study, we integrate Auto-Cross-Covariance (ACC) and Separated dimer (SD) evolutionary feature extraction methods. The results features are scored by Information gain (IG) to define and select several discriminated features. According to three benchmark datasets, DD, RDD and EDD, the results of the support vector machine (SVM) show more than 6% improvement in accuracy on these benchmark datasets.

A Comprehensive Analysis of Image Forensics Techniques: Challenges and future Direction

2019 ◽  
Vol 13 ◽  
Author(s):  
Mohd Dilshad Ansari ◽  
Ekbal Rashid ◽  
S Siva Skandha ◽  
Suneet Kumar Gupta
Keyword(s):  
Image Forensics ◽  
Digital Era ◽  
Current State ◽  
Benchmark Datasets ◽  
Passive Authentication ◽  
Prime Objective ◽  
Future Direction ◽  
Source Of Information ◽  
Research Domain ◽  
Open Issues

Background: image forensics deal with the problem of authentication of pictures or their origins. There are two types of forensics techniques namely active and passive. Passive forgery is also known as blind forensics technique. In passive forgery, copy-move (cloning) image forensics is most common forgery technique. In this approach, an object or region of a picture is copied and positioned somewhere else in the same image. Active method used watermarking to solve picture genuineness problem. It has limitations like human involvement or particularly equipped cameras. To overwhelm these limitations, numerous passive authentication approaches have been developed. Moreover, both approaches do not require any prior information about the picture. Objective: The prime objective of this survey is to provide an inclusive summary as well as recent advancement, challenges and future direction in image forensics. In Today’s digital era the digital pictures and videos are having great impact on our life as well as society, as they became the important source of information. Though earlier it was very difficult to doctor the picture, nowadays digital pictures can be doctored easily with the help of editing tools and internet. These practices make pictures as well as videos genuineness deceptive. Conclusion: This paper presents the current state-of- the-art of passive (cloning) image forensics techniques, challenges and future direction of this research domain. Further, the major open issues in developing a robust cloning image forensics detector with their performance are discussed. Lastly, the available benchmark datasets are also discussed

scholarly journals Sampo-UI: A full stack JavaScript framework for developing semantic portal user interfaces

Semantic Web ◽  
2021 ◽  
pp. 1-16
Author(s):  
Esko Ikkala ◽  
Eero Hyvönen ◽  
Heikki Rantala ◽  
Mikko Koho
Keyword(s):  
User Interfaces ◽  
Linked Data ◽  
State Of The Art ◽  
Software Framework ◽  
End User ◽  
Faceted Search ◽  
Software Developer ◽  
Current State ◽  
Knowledge Graphs ◽  
User Friendly

This paper presents a new software framework, Sampo-UI, for developing user interfaces for semantic portals. The goal is to provide the end-user with multiple application perspectives to Linked Data knowledge graphs, and a two-step usage cycle based on faceted search combined with ready-to-use tooling for data analysis. For the software developer, the Sampo-UI framework makes it possible to create highly customizable, user-friendly, and responsive user interfaces using current state-of-the-art JavaScript libraries and data from SPARQL endpoints, while saving substantial coding effort. Sampo-UI is published on GitHub under the open MIT License and has been utilized in several internal and external projects. The framework has been used thus far in creating six published and five forth-coming portals, mostly related to the Cultural Heritage domain, that have had tens of thousands of end-users on the Web.

scholarly journals Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

2021 ◽  
Author(s):  
Jia-Bin Zhou ◽  
Yan-Qin Bai ◽  
Yan-Ru Guo ◽  
Hai-Xiang Lin
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Twin Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Intuitionistic Fuzzy ◽  
Benchmark Datasets ◽  
The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

ARESIBO HORIZON 2020 EUROPEAN RESEARCH PROJECT – ENRICHED SITUATION AWARENESS FOR BORDER SURVEILLANCE

2021 ◽  
Vol 17 (1) ◽  
pp. 247-255
Author(s):  
Konstantinos CHARISI ◽  
Andreas TSIGOPOULOS ◽  
Spyridon KINTZIOS ◽  
Vassilis PAPATAXIARHIS
Keyword(s):  
Augmented Reality ◽  
Situation Awareness ◽  
User Interfaces ◽  
Border Security ◽  
Multiple Sources ◽  
Horizon 2020 ◽  
Research And Innovation ◽  
Current State ◽  
Available Information ◽  
User Friendly

Abstract. The paper aims to introduce the ARESIBO project to a greater but targeted audience and outline its main scope and achievements. ARESIBO stands for “Augmented Reality Enriched Situation awareness for Border security”. In the recent years, border security has become one of the highest political priorities in EU and needs the support of every Member State. ARESIBO project is developed under HORIZON 2020 EC Research and Innovation program and it is the joint effort of 20 participant entities from 11 countries. Scientific excellence and technological innovation are top priorities as ARESIBO enhances the current state-of-the-art through technological breakthroughs in Mobile Augmented Reality and Wearables, Robust and Secure Telecommunications, Robots swarming technique and Planning of Context-Aware Autonomous Missions, and Artificial Intelligence (AI), in order to implement user-friendly tools for border and coast guards. The system aims to improve the cognitive capabilities and the perception of border guards through intuitive user interfaces that will help them acquire an improved situation awareness by filtering the huge amount of available information from multiple sources. Ultimately, it will help them respond faster and more effectively when a critical situation occurs.

scholarly journals Virtual Reality Training Applications in Industry - Towards a User-friendly Application Design

2021 ◽  
pp. 59-80
Author(s):  
Benjamin Knoke ◽  
◽  
Moritz Quandt ◽  
Michael Freitag ◽  
Klaus-Dieter Thoben
Keyword(s):  
Virtual Reality ◽  
Technology Acceptance ◽  
User Interfaces ◽  
Interface Design ◽  
Design Guidelines ◽  
Electrical Safety ◽  
Industrial Training ◽  
Current State ◽  
Vr Training ◽  
User Friendly

The purpose of this research is to aggregate and discuss the validity of challenges and design guidelines regarding industrial Virtual Reality (VR) training applications. Although VR has seen significant advancements in the last 20 years, the technology still faces multiple research challenges. The challenges towards industrial VR applications are imposed by a limited technological maturity and the need to achieve industrial stakeholders' technology acceptance. Technology acceptance is closely connected with the consideration of individual user requirements for user interfaces in virtual environments. This paper analyses the current state-of-the-art in industrial VR applications and provides a structured overview of the existing challenges and applicable guidelines for user interface design, such as ISO 9241-110. The validity of the identified challenges and guidelines is discussed against an industrial training scenario on electrical safety during maintenance tasks.

scholarly journals Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

2020 ◽  
Vol 34 (04) ◽  
pp. 4844-4851
Author(s):  
Fanghui Liu ◽  
Xiaolin Huang ◽  
Yudong Chen ◽  
Jie Yang ◽  
Johan Suykens
Keyword(s):  
State Of The Art ◽  
Sampling Strategy ◽  
Prediction Performance ◽  
Current State ◽  
Kernel Approximation ◽  
Sampling Process ◽  
New Strategy ◽  
The Matrix ◽  
Benchmark Datasets ◽  
Random Fourier Features

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-of-the-art method that uses the leverage weighted scheme (Li et al. 2019), our new strategy is simpler and more effective. It uses kernel alignment to guide the sampling process and it can avoid the matrix inversion operator when we compute the leverage function. Given n observations and s random features, our strategy can reduce the time complexity for sampling from O(ns2+s3) to O(ns2), while achieving comparable (or even slightly better) prediction performance when applied to kernel ridge regression (KRR). In addition, we provide theoretical guarantees on the generalization performance of our approach, and in particular characterize the number of random features required to achieve statistical guarantees in KRR. Experiments on several benchmark datasets demonstrate that our algorithm achieves comparable prediction performance and takes less time cost when compared to (Li et al. 2019).

scholarly journals BETTER ALTERNATIVES FOR STEPWISE DISCRIMINANT ANALYSIS

2015 ◽  
Vol 1 (311) ◽  
Author(s):  
Katarzyna Stąpor
Keyword(s):  
Feature Selection ◽  
Discriminant Analysis ◽  
Tabu Search ◽  
Stepwise Discriminant Analysis ◽  
Selection Methods ◽  
Discrimination Power ◽  
Statistical Software ◽  
Software Packages ◽  
Benchmark Datasets

Discriminant Analysis can best be defined as a technique which allows the classification of an individual into several dictinctive populations on the basis of a set of measurements. Stepwise discriminant analysis (SDA) is concerned with selecting the most important variables whilst retaining the highest discrimination power possible. The process of selecting a smaller number of variables is often necessary for a variety number of reasons. In the existing statistical software packages SDA is based on the classic feature selection methods. Many problems with such stepwise procedures have been identified. In this work the new method based on the metaheuristic strategy tabu search will be presented together with the experimental results conducted on the selected benchmark datasets. The results are promising.

scholarly journals Facial Expression Recognition using SVM with CNN and Handcrafted Features

2019 ◽  
Vol 8 (4) ◽  
pp. 3570-3574
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Vital Role ◽  
Working Environment ◽  
Support Vector ◽  
Expression Recognition ◽  
Shopping Malls ◽  
Benchmark Datasets ◽  
Mind Set

The facial expression recognition system is playing vital role in many organizations, institutes, shopping malls to know about their stakeholders’ need and mind set. It comes under the broad category of computer vision. Facial expression can easily explain the true intention of a person without any kind of conversation. The main objective of this work is to improve the performance of facial expression recognition in the benchmark datasets like CK+, JAFFE. In order to achieve the needed accuracy metrics, the convolution neural network was constructed to extract the facial expression features automatically and combined with the handcrafted features extracted using Histogram of Gradients (HoG) and Local Binary Pattern (LBP) methods. Linear Support Vector Machine (SVM) is built to predict the emotions using the combined features. The proposed method produces promising results as compared to the recent work in [1].This is mainly needed in the working environment, shopping malls and other public places to effectively understand the likeliness of the stakeholders at that moment.

Current state of cold hardiness research on fruit crops

1997 ◽  
Vol 77 (3) ◽  
pp. 399-420 ◽  
Author(s):  
Pauliina Palonen ◽  
Deborah Buszard
Keyword(s):  
Cold Hardiness ◽  
Cultural Practices ◽  
Limiting Factor ◽  
Screening Methods ◽  
Fruit Crops ◽  
Factors Affecting ◽  
Fruit Species ◽  
Current State ◽  
Deep Supercooling ◽  
Cold Hardy

This article gives an overview of the current state of cold hardiness research in fruit crops by reviewing the recently published studies on cold hardiness of both tree fruit and berry crops. Topics discussed include cold hardiness of fruit species, cultivars and different plant organs, biophysical and biochemical aspects of hardiness, evaluation of hardiness, as well as endogenous, cultural and environmental factors affecting cold hardiness in these species. Lack of cold hardiness is a major limiting factor for production of fruit crops in many regions of the world and improved cold hardiness one of the major objectives in numerous breeding programs and research projects. Screening cultivars or selections for cold hardiness is commonly done, and different methods applied to the evaluation of hardiness are discussed. The physical limit of deep supercooling may be a restricting factor for expanding the production of some fruit crops, such as Prunus species and pear. As for biochemical aspects, a relationship between carbohydrates and cold hardiness is most commonly found. Studies have also been made on different hardiness modifying cultural factors including rootstock, crop load, raised beds and application of growth regulators. The latter seems promising for some species. Cold hardiness is an extremely complex phenomenon and understanding different mechanisms involved is critical. Since hardiness is, however, primarily affected by genotype, developing cold-hardy fruit cultivars and effective screening methods for hardiness are essential. Finally, cultural practices may be improved to further enhance hardiness. Key words: Berries, cold hardiness, fruits, small fruits, stress, winter hardiness

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