scholarly journals Methods for the Detection and Quantification of Micro and Nanoplastics- A Review

2021 ◽  
pp. 39-51
Author(s):  
S. Priyanka ◽  
G. A. Pallavi ◽  
Nayak N. Swathi ◽  
Jawali D. Ashita
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Infrared Spectroscopy ◽  
Review Paper ◽  
Low Cost ◽  
Synthetic Polymers ◽  
Learning Approaches ◽  
The Past ◽  
Plastic Materials ◽  
Detection And Quantification

Over the past 35 years, synthetic or semi-synthetic polymers called “plastics” have been widely used across multiple fields due to their low cost, versatility, durability. Plastics have proved to be a boon to mankind. However, overuse of non- biodegradable plastics comes with its own downsides. Despite constant efforts to reuse and recycle plastics, these polymers substantially contribute towards the accumulation of debris hazardous to the environment. Plastic materials are slowly broken into fragments of micro- and nano plastics due to aging and weathering. Micro- and nano plastics were found capable of entering the food chain and hence are viewed as threats. This review paper revolves around methods used for the detection and quantification of micro- and nano plastics. Detection of micro- and nano plastics using methods like Raman spectroscopy, Infrared Spectroscopy, SERS, MALDI-TOF, and machine learning approaches are discussed here. The research efforts carried out in this article aims to further facilitate the R&D initiatives of Jozbiz Technologies.

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 A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2514
Author(s):  
Tharindu Kaluarachchi ◽  
Andrew Reis ◽  
Suranga Nanayakkara
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Review Paper ◽  
Learning Systems ◽  
Learning Approaches ◽  
Application Development ◽  
Research Gaps ◽  
Domain Experts ◽  
Working Definition ◽  
Real World Application

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

scholarly journals Chitosan-Based Nanocomposite Polymeric Membranes for Water Purification—A Review

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2091
Author(s):  
Angela Spoială ◽  
Cornelia-Ioana Ilie ◽  
Denisa Ficai ◽  
Anton Ficai ◽  
Ecaterina Andronescu
Keyword(s):  
Water Purification ◽  
Antioxidant Activities ◽  
Low Cost ◽  
Synthetic Polymers ◽  
Water Systems ◽  
Polymeric Membranes ◽  
Wastewater Purification ◽  
Toxic Compounds ◽  
The Past ◽  
Materials Used

During the past few years, researchers have focused their attention on developing innovative nanocomposite polymeric membranes with applications in water purification. Natural and synthetic polymers were considered, and it was proven that chitosan-based materials presented important features. This review presents an overview regarding diverse materials used in developing innovative chitosan-based nanocomposite polymeric membranes for water purification. The first part of the review presents a detailed introduction about chitosan, highlighting the fact that is a biocompatible, biodegradable, low-cost, nontoxic biopolymer, having unique structure and interesting properties, and also antibacterial and antioxidant activities, reasons for using it in water treatment applications. To use chitosan-based materials for developing nanocomposite polymeric membranes for wastewater purification applications must enhance their performance by using different materials. In the second part of the review, the performance’s features will be presented as a consequence of adding different nanoparticles, also showing the effect that those nanoparticles could bring on other polymeric membranes. Among these features, pollutant’s retention and enhancing thermo-mechanical properties will be mentioned. The focus of the third section of the review will illustrate chitosan-based nanocomposite as polymeric membranes for water purification. Over the last few years, researchers have demonstrated that adsorbent nanocomposite polymeric membranes are powerful, important, and potential instruments in separation or removal of pollutants, such as heavy metals, dyes, and other toxic compounds presented in water systems. Lastly, we conclude this review with a summary of the most important applications of chitosan-based nanocomposite polymeric membranes and their perspectives in water purification.

scholarly journals Ice Detection on Aircraft Surface Using Machine Learning Approaches Based on Hyperspectral and Multispectral Images

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 45
Author(s):  
Maria Angela Musci ◽  
Luigi Mazzara ◽  
Andrea Maria Lingua
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Critical Role ◽  
Operating Time ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Ice Detection ◽  
Under Construction ◽  
Hyperspectral Camera

Aircraft ground de-icing operations play a critical role in flight safety. However, to handle the aircraft de-icing, a considerable quantity of de-icing fluids is commonly employed. Moreover, some pre-flight inspections are carried out with engines running; thus, a large amount of fuel is wasted, and CO2 is emitted. This implies substantial economic and environmental impacts. In this context, the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of Ice) aims to provide innovative tools to identify the ice on aircraft and improve the efficiency of the de-icing process. The project includes the design of a low-cost UAV (uncrewed aerial vehicle) platform and the development of a quasi-real-time ice detection methodology to ensure a faster and semi-automatic activity with a reduction of applied operating time and de-icing fluids. The purpose of this work, developed within the activities of the project, is defining and testing the most suitable sensor using a radiometric approach and machine learning algorithms. The adopted methodology consists of classifying ice through spectral imagery collected by two different sensors: multispectral and hyperspectral camera. Since the UAV prototype is under construction, the experimental analysis was performed with a simulation dataset acquired on the ground. The comparison among the two approaches, and their related algorithms (random forest and support vector machine) for image processing, was presented: practical results show that it is possible to identify the ice in both cases. Nonetheless, the hyperspectral camera guarantees a more reliable solution reaching a higher level of accuracy of classified iced surfaces.

scholarly journals MLCA—A Machine Learning Framework for INS Coarse Alignment

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6959
Author(s):  
Idan Zak ◽  
Reuven Katz ◽  
Itzik Klein
Keyword(s):  
Machine Learning ◽  
Field Experiments ◽  
Inertial Sensors ◽  
Initial Conditions ◽  
Low Cost ◽  
Navigation Systems ◽  
Learning Approaches ◽  
Alignment Procedure ◽  
Navigation Solution ◽  
Learning Framework

Inertial navigation systems provides the platform’s position, velocity, and attitude during its operation. As a dead-reckoning system, it requires initial conditions to calculate the navigation solution. While initial position and velocity vectors are provided by external means, the initial attitude can be determined using the system’s inertial sensors in a process known as coarse alignment. When considering low-cost inertial sensors, only the initial roll and pitch angles can be determined using the accelerometers measurements. The accuracy, as well as time required for the for the coarse alignment process are critical for the navigation solution accuracy, particularly for pure-inertial scenarios, because of the navigation solution drift. In this paper, a machine learning framework for the stationary coarse alignment stage is proposed. To that end, classical machine learning approaches are used in a two-stage approach to regress the roll and pitch angles. Alignment results obtained both in simulations and field experiments, using a smartphone, shows the benefits of using the proposed approach instead of the commonly used analytical coarse alignment procedure.

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%.

scholarly journals Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets

2021 ◽  
Vol 7 ◽  
pp. e670
Author(s):  
Marcio Dorn ◽  
Bruno Iochins Grisci ◽  
Pedro Henrique Narloch ◽  
Bruno César Feltes ◽  
Eduardo Avila ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sampling Methods ◽  
Complete Blood Count ◽  
Low Cost ◽  
Class Imbalance ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Comparison Of The Results ◽  
Aid Decision

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil’s case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms.

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%.

A systematic survey on deep learning and machine learning approaches of fake news detection in the pre- and post-COVID-19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajshree Varma ◽  
Yugandhara Verma ◽  
Priya Vijayvargiya ◽  
Prathamesh P. Churi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Low Cost ◽  
Online Communication ◽  
Future Research ◽  
Learning Approaches ◽  
Fake News ◽  
Content Type ◽  
Human Ability ◽  
Detection Technologies

PurposeThe rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.Design/methodology/approachThe detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.FindingsThe paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.Originality/valueThe study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

Sign in / Sign up

Export Citation Format

Share Document