scholarly journals Malaria Parasite Diagnosis Using Computational Techniques: A Comprehensive Review

2021 ◽  
Vol 2107 (1) ◽  
pp. 012031
Author(s):  
Wan Azani Mustafa ◽  
Hiam Alquran ◽  
Muhammad Zaid Aihsan ◽  
Mohd Saifizi ◽  
Wan Khairunizam ◽  
...  
Keyword(s):  
High Performance ◽  
Malaria Diagnosis ◽  
Paper Analysis ◽  
Computational Techniques ◽  
Detection Techniques ◽  
Blood Smears ◽  
Morphological Approach ◽  
Malaria Death ◽  
Serious Disease ◽  
Death Cases

Abstract Malaria is a very serious disease that caused by the transmitted of parasites through the bites of infected Anopheles mosquito. Malaria death cases can be reduced and prevented through early diagnosis and prompt treatment. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. Currently, microscopy-based diagnosis remains the most widely used approach for malaria diagnosis. The development of automated malaria detection techniques is still a field of interest. Automated detection is faster and high accuracy compared to the traditional technique using microscopy. This paper presents an exhaustive review of these studies and suggests a direction for future developments of the malaria detection techniques. This paper analysis of three popular computational approaches which is k-mean clustering, neural network, and morphological approach was presented. Based on overall performance, many research proposed based on the morphological approach in order to detect malaria.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals Measuring the Performance of Computer Vision Systems in Malaria Studies

2020 ◽  
Vol 16 (11) ◽  
pp. 120
Author(s):  
Juan Valentín Lorenzo-Ginori ◽  
Lyanett Chinea-Valdés ◽  
Niurka Mollineada-Diogo ◽  
Alfredo Meneses-Marcel
Keyword(s):  
Computer Vision ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Malaria Diagnosis ◽  
Statistical Measures ◽  
Blood Smears ◽  
Minimum Number ◽  
Microscopy Analysis ◽  
Computer Vision Systems ◽  
Very High

Digital image processing-computer vision (DIP-CV) systems are used to automate malaria diagnosis through microscopy analysis of thin blood smears. Some variability is observed in the experimental design to evaluate the statistical measures of performance (SMP) of such systems. The objective of this work is assessing good practices when using SMP to evaluate DIP-CV systems for malaria diagnosis. A mathematical model was built to characterize diagnosis using DIP-CV systems and used to obtain curve families showing the relationships among various SMP of these systems, both using theoretical equations and computer simulation. Curve families showing (a) the relationships among the minimum number of positive erythrocytes (RBCs) to be observed, the per object (RBC) sensitivity and the probability to detect at least one positive, (b) per specimen sensitivity vs. total number of RBCs observed for a typical per object sensitivity and a range of parasite densities (c) per object positive predictive value vs. per object specificity for a typical per object sensitivity and various parasite densities. When determining the per specimen sensitivity, the parasite density <em>p</em> showed to have more influence on the number of RBCs that must be analyzed than the per object sensitivity. Measuring <em>p</em> accurately depends heavily upon the per object positive predictive value of the classifier. For low <em>p</em> values, this would require very high per object specificity and a high enough value of observed RBCs to measure this accurately.

Modeling of Steelmaking Processes

2016 ◽  
pp. 369-421
Author(s):  
Seppo Louhenkilpi ◽  
Subhas Ganguly
Keyword(s):  
Statistical Learning ◽  
Quantum Chemical ◽  
High Performance ◽  
Process Model ◽  
Computational Models ◽  
Computational Techniques ◽  
Computational Process ◽  
Steel Making ◽  
Steel Converter ◽  
Steelmaking Technology

In the field of experiment, theory, modeling and simulation, the most noteworthy progressions applicable to steelmaking technology have been closely linked with the emergence of more powerful computing tools, advances in needful software's and algorithms design, and to a lesser degree, with the development of emerging computing theory. These have enabled the integration of several different types of computational techniques (for example, quantum chemical, and molecular dynamics, DFT, FEM, Soft computing, statistical learning etc., to name a few) to provide high-performance simulations of steelmaking processes based on emerging computational models and theories. This chapter overviews the general steps and concepts for developing a computational process model including few exercises in the area of steel making. The various sections of the chapter aim to describe how to developed models for various issues related to steelmaking processes and to simulate a physical process starts with the process fundaments. The examples include steel converter, tank vacuum degassing, and continuous casting, etc.

MULTIBEAM GPU TRANSIENT PIPELINE FOR THE MEDICINA BEST-2 ARRAY

2013 ◽  
Vol 02 (01) ◽  
pp. 1350008 ◽  
Author(s):  
A. MAGRO ◽  
J. HICKISH ◽  
K. Z. ADAMI
Keyword(s):  
Event Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Data Transfer ◽  
Digital Signal ◽  
Dispersion Measure ◽  
Radio Telescopes ◽  
Arrival Times ◽  
Detection Techniques ◽  
Graphics Processing

Radio transient discovery using next generation radio telescopes will pose several digital signal processing and data transfer challenges, requiring specialized high-performance backends. Several accelerator technologies are being considered as prototyping platforms, including Graphics Processing Units (GPUs). In this paper we present a real-time pipeline prototype capable of processing multiple beams concurrently, performing Radio Frequency Interference (RFI) rejection through thresholding, correcting for the delay in signal arrival times across the frequency band using brute-force dedispersion, event detection and clustering, and finally candidate filtering, with the capability of persisting data buffers containing interesting signals to disk. This setup was deployed at the BEST-2 SKA pathfinder in Medicina, Italy, where several benchmarks and test observations of astrophysical transients were conducted. These tests show that on the deployed hardware eight 20 MHz beams can be processed simultaneously for ~640 Dispersion Measure (DM) values. Furthermore, the clustering and candidate filtering algorithms employed prove to be good candidates for online event detection techniques. The number of beams which can be processed increases proportionally to the number of servers deployed and number of GPUs, making it a viable architecture for current and future radio telescopes.

QUANTIFICATION OF XYLOPIC ACID AND CHROMATOGRAPHIC FINGERPRINT EVALUATIONS OF THE DRIED FRUITS OF XYLOPIA AETHIOPICA FROM FOUR AFRICAN COUNTRIES

2018 ◽  
Vol 15 (3) ◽  
pp. 18
Author(s):  
Raphael N. Alolga ◽  
Assogba G. Assanhou ◽  
Vitus Onoja
Keyword(s):  
High Performance ◽  
Solid Phase ◽  
Fungal Infections ◽  
Active Principle ◽  
Average Content ◽  
African Countries ◽  
Chromatographic Fingerprint ◽  
Dried Fruits ◽  
Detection Techniques ◽  
Xylopia Aethiopica

Dunal) A. Rich, (herein called XYA), family Annonaceae, commonly known as “Guinea pepper”, “Ethiopian pepper” or “Negro pepper”, are widely used in traditional African medicines to treat a wide array of diseases including malaria, fungal infections, rheumatism, arthritis, etc. Scientific investigations have ascribed the following activities to the fruits of XYA; anti-diabetic, anti-inflammatory, antimicrobial, antiplasmodial, analgesic, anti-nociceptive, anti-proliferative, spermatogenic and neuropharmacological effects. The main active principle reported is xylopic acid (XA), a kaurene diterpene. This study aimed to develop and validate a simple HPLC/UV (high performance liquid chromatography – ultraviolet detection) analytical method for the quantification of XA that can be reproduced in poor-resource settings where advanced analytical detection techniques such as HPLC-MS are unavailable. Materials and Methods: Thus in this study, a simple C18 solid-phase extraction (SPE) column-pretreatment ─ HPLC/UV analytical procedure was developed for the quantification of XA in the dried fruits of XYA from four African countries, Benin, Cameroon, Ghana and Nigeria. The samples of XYA from the four countries were assessed for similarities using chromatographic fingerprinting. Results: The HPLC method was validated for linearity, limits of detection and quantification, precision and accuracy. The samples of XYA from Cameroon were found to have the highest average content of XA while those from Benin had the lowest average content of XA. Conclusion: Using the chromatographic fingerprint evaluation, the similarities of the samples from the four countries to the reference chromatogram was in the order: Benin > Cameroon > Nigeria > Ghana. Key words: Xylopia aethiopica, xylopic acid, C18

scholarly journals Critical Review of Synthesis, Toxicology and Detection of Acyclovir

Molecules ◽  
2021 ◽  
Vol 26 (21) ◽  
pp. 6566
Author(s):  
Yan-Ping Wei ◽  
Liang-Yuan Yao ◽  
Yi-Yong Wu ◽  
Xia Liu ◽  
Li-Hong Peng ◽  
...  
Keyword(s):  
Liquid Chromatography ◽  
High Performance ◽  
Electrochemical Sensors ◽  
Antiviral Drug ◽  
Preparation Methods ◽  
Detection Techniques ◽  
Toxicological Studies ◽  
Liquid Chromatography Tandem Mass ◽  
Past Life ◽  
Flow Injection Chemiluminescence

Acyclovir (ACV) is an effective and selective antiviral drug, and the study of its toxicology and the use of appropriate detection techniques to control its toxicity at safe levels are extremely important for medicine efforts and human health. This review discusses the mechanism driving ACV’s ability to inhibit viral coding, starting from its development and pharmacology. A comprehensive summary of the existing preparation methods and synthetic materials, such as 5-aminoimidazole-4-carboxamide, guanine and its derivatives, and other purine derivatives, is presented to elucidate the preparation of ACV in detail. In addition, it presents valuable analytical procedures for the toxicological studies of ACV, which are essential for human use and dosing. Analytical methods, including spectrophotometry, high performance liquid chromatography (HPLC), liquid chromatography/tandem mass spectrometry (LC-MS/MS), electrochemical sensors, molecularly imprinted polymers (MIPs), and flow injection–chemiluminescence (FI-CL) are also highlighted. A brief description of the characteristics of each of these methods is also presented. Finally, insight is provided for the development of ACV to drive further innovation of ACV in pharmaceutical applications. This review provides a comprehensive summary of the past life and future challenges of ACV.

scholarly journals Biomimetic Kinetic Shading Facade Inspired by Tree Morphology for Improving Occupant’s Daylight Performance

2021 ◽  
Vol 8 (1) ◽  
pp. 65-85
Author(s):  
Seyed Morteza Hosseini ◽  
◽  
Fodil Fadli ◽  
Masi Mohammadi ◽  
◽  
...  
Keyword(s):  
Modular Forms ◽  
High Performance ◽  
Morphological Changes ◽  
Relevant Literature ◽  
Base Case ◽  
Literature Study ◽  
Tree Morphology ◽  
Morphological Approach ◽  
Daylighting Simulation ◽  
Daylight Performance

Many recent studies in the field of the kinetic façade developed the grid-based modular forms through primary kinetic movements which are restricted in the simple shapes. However, learning from biological analogies reveals that plants and trees provide adjustable daylighting strategies by means of multilayered and curvature morphological changes. This research builds on a relevant literature study, observation, biomimicry morphological approach (top-down), and parametric daylighting simulation to develop a multilayered biomimetic kinetic façade form, inspired by tree morphology to improve occupants’ daylight performance. The first part of the research uses a literature review to explore how biomimicry influences the kinetic façade’s functions. Then, the study applies the biomimicry morphological approach to extract the formal strategies of tress due to dynamic daylight. Concerning functional convergence, the biomimicry principles are translated to the kinetic façade form configuration and movements. The extracted forms and movements are translated into the design solutions for the kinetic façade resulting in the flexible form by using intersected-multilayered skin and kinetic vectors with curvature movements. The comprehensive annual climate-based metrics and luminance-based metric simulation (625 alternatives) confirm the high performance of the bio-inspired complex kinetic façade for improving occupants’ daylight performance and preventing visual discomfort in comparison with the simple plain window as the base case. The kinetic façade provides daylight performance improvement, especially the best case achieves spatial Daylight Autonomy, Useful Daylight Illuminance, and Exceed Useful Daylight Illuminance of 50.6, 85.5, 7.55 respectively.

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