scholarly journals Malicious Attack Identification Using Deep Non Linear Bag-of-Words (FAI-DLB)

2021 ◽  
Author(s):  
E. Arul ◽  
A. Punidha ◽  
K. Gunasekaran ◽  
P Radhakrishnan ◽  
VD Ashok Kumar
Keyword(s):  
Machine Learning ◽  
Bag Of Words ◽  
Learning Approaches ◽  
Malicious Attack ◽  
Output Device ◽  
The Past ◽  
The World ◽  
Weak Points ◽  
Actual Meaning ◽  
Application Developers

Online media have flourished in modern years to connect with the world. Most of those stuff users share on blogs like facebook, twitter and many other are pessimistic or just middle spirited. Further, an increasingly professional anti - spyware technologies are dependent on Machine Learning(ML) technology to secure malicious consumers. Over the past few years, revolutionary learning approaches have yielded remarkable outcomes and have immediately generated photos, characters and text interpretations of dynamic weak points. The Purple consumer frequency makes the troll and attacker aim an enticing one. The users will learn the controversial topics and techniques used by malware from articles with ties to harmful material and bogus applications. It is essential to build and customize a lot of potential functionality in vulnerability and application developers around the world. To represent a public web firmware assault with deep logistic inference using Extreme Spontaneous Tree (FAI-DLB). A corresponding output device is named harmful or benign by training an FAI-DLB with different modulation clustered with such a normal or anomalous API. It was therefore equipped to locate a suspicious sequence in unidentified firmware of FAI Deep LB. The outcome demonstrates a good actual meaning of 96.25% and a low spyware assault of 0.03%.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dennie te Molder ◽  
Wasin Poncheewin ◽  
Peter J. Schaap ◽  
Jasper J. Koehorst
Keyword(s):  
Machine Learning ◽  
Plant Pathogens ◽  
De Novo ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Enabling Factors ◽  
Essential Step ◽  
The World ◽  
Genome Content

Abstract Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Results Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Conclusion The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

scholarly journals Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

2019 ◽  
Vol 10 (1) ◽  
pp. 46 ◽  
Author(s):  
Johannes Stübinger ◽  
Benedikt Mangold ◽  
Julian Knoll
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Home Team ◽  
Ensemble Strategy ◽  
The World ◽  
European Football ◽  
The Individual ◽  
Football Betting ◽  
Betting Odds

In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.

scholarly journals Detection of Novel Corona Virus Using Machine Learning and Image Recognition

2020 ◽  
Vol 6 (12) ◽  
pp. 394-397
Author(s):  
Dhruv Garg and Saurabh Gautam
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Recent Past ◽  
Great Precision ◽  
The Past ◽  
Corona Virus ◽  
The World ◽  
Affected Person ◽  
Precise Machine ◽  
Self Learning

In the recent past whole of the world has come to a standstill due to a novel airborne virus. The airborne nature of this disease has made it highly contagious which has led to a great number of people being infected very fast. This requires a new method of testing that is faster and more precise. Machine Learning has allowed us to develop sophisticated self-learning models that can learn from data being fed and decide on entirely new options. In the past we have used different Machine Learning algorithm to make models on different biomedical dataset to detect various kind of acute or chronic diseases. Here we have developed a model that successfully detects severe cases of Novel corona virus affected person with great precision.

scholarly journals Highlighting photonics: looking into the next decade

eLight ◽  
2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhigang Chen ◽  
Mordechai Segev
Keyword(s):  
Machine Learning ◽  
Quantum Computing ◽  
Topological Insulator ◽  
Science And Technology ◽  
Scientific Research ◽  
The Past ◽  
Emerging Trends ◽  
The World ◽  
Quantum Photonics ◽  
Young Researchers

AbstractLet there be light–to change the world we want to be! Over the past several decades, and ever since the birth of the first laser, mankind has witnessed the development of the science of light, as light-based technologies have revolutionarily changed our lives. Needless to say, photonics has now penetrated into many aspects of science and technology, turning into an important and dynamically changing field of increasing interdisciplinary interest. In this inaugural issue of eLight, we highlight a few emerging trends in photonics that we think are likely to have major impact at least in the upcoming decade, spanning from integrated quantum photonics and quantum computing, through topological/non-Hermitian photonics and topological insulator lasers, to AI-empowered nanophotonics and photonic machine learning. This Perspective is by no means an attempt to summarize all the latest advances in photonics, yet we wish our subjective vision could fuel inspiration and foster excitement in scientific research especially for young researchers who love the science of light.

scholarly journals Deep-Droid: Deep Learning for Android Malware Detection

2020 ◽  
Vol 9 (12) ◽  
pp. 122-125
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Learning Approaches ◽  
Android Malware ◽  
Detection Systems ◽  
Learning Framework ◽  
The Past ◽  
Android Malware Detection ◽  
Android Os

Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. Machine learning approaches have been showing promising results in classifying malware where most of the method are shallow learners like Random Forest (RF) in recent years. In this paper, we propose Deep-Droid as a deep learning framework, for detection Android malware. Hence, our Deep-Droid model is a deep learner that outperforms exiting cutting-edge machine learning approaches. All experiments performed on two datasets (Drebin-215 & Malgenome-215) to assess our Deep-Droid model. The results of experiments show the effectiveness and robustness of Deep-Droid. Our Deep-Droid model achieved accuracy over 98.5%.

scholarly journals Illuminating Elite Patches of Chemical Space

2020 ◽  
Author(s):  
Jonas Verhellen ◽  
Jeriek Van den Abeele
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
State Of The Art ◽  
Chemical Space ◽  
Search Space ◽  
Robot Design ◽  
Soft Robot ◽  
Learning Approaches ◽  
High Performing ◽  
The Past

In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [Jensen, Chem. Sci., 2019, 12, 3567-3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [Mouret et al., IEEE Trans. Evolut. Comput., 2016, 22, 623-630], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches.

scholarly journals VolleyJump: Uma aplicação para a análise de saltos no voleibol de praia

2018 ◽  
Author(s):  
Renan Bandeira ◽  
Fernando Trinta ◽  
João Gomes ◽  
Marcio Maia ◽  
Alexandre Araripe
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Learning Strategies ◽  
High Performance ◽  
Professional Sports ◽  
Jump Height ◽  
The Past ◽  
The World ◽  
Main Factors

Professional sports are increasingly dependents of technological resources given the remarkable level of competitiveness faced by high performance athletes. With such resources, it is possible to analyze matches, avoid mistakes that may be committed by the referee or even to analyze the athletes’ performance. One of these sports is beach volleyball, one of most popular sports in Brazil. In the past 12 years, the Brazilian volleyball teams has been always among the best teams in the world. The athletes’ performance during the jump movement is one of the main factors that one team needs to improve to be successful because it is the movement that is most performed during a volleyball match. There are some approaches that study the jump movement in order to calculate its height and give evidences to improve it. Nevertheless, these solutions are expensive and are not viable to athletes with no sponsorship. Having this in mind, this works presents VolleyJump, an application created to analyze beach volleyball athlete jumps using machine learning strategies to calculate the jump height and classify it as an attack or block jump. Results show that VolleyIoT makes possible to analyze athletes’ jumps using mobile devices sensors, helping them to focus on their trainning to improve its technique.

scholarly journals Illuminating Elite Patches of Chemical Space

2020 ◽  
Author(s):  
Jonas Verhellen ◽  
Jeriek Van den Abeele
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
State Of The Art ◽  
Chemical Space ◽  
Search Space ◽  
Robot Design ◽  
Soft Robot ◽  
Learning Approaches ◽  
High Performing ◽  
The Past

In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [Jensen, Chem. Sci., 2019, 12, 3567-3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [Mouret et al., IEEE Trans. Evolut. Comput., 2016, 22, 623-630], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches.

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