ALGORTHMIC APPROACHES IN DATA MINING

Data mining may be a process which finds useful patterns from great deal of knowledge . The paper discusses few of the info mining techniques, algorithms and a few of the organizations which have adapted data processing technology to enhance their businesses and found excellent results. Research on data processing has successfully provided the use of various tools, methods, methods and approaches for various purposes and problem solving.data processing has become an integral a part of many application domains like data ware housing, predictive analytics, business intelligence, bio-informatics and decision support systems. Prime objective of knowledge mining is to effectively handle large scale data, extract actionable patterns, and gain insightful knowledge. data processing is a component and parcel of data discovery in databases (KDD) process. Success and improved deciding normally depends on how quickly one can discover insights from data. These insights may not be able to execute optimal actions, they may be used in operational processes and may even predict future behavior.This paper presents an summary of varied algorithms necessary for handling large data sets. These algorithms define the various structures and methods implemented to handle large data.The review also discusses the overall strengths and limitations of those algorithms. This paper can quickly guide or be an eye fixed opener to the info mining researchers on which algorithm(s) to pick and apply in solving the issues they're going to be investigating

MACHINE LEARNING – OVERVIEW

Given the uncommon accessibility of information and registering assets, there is broad recharged revenue in applying information driven AI strategies to issues for which the advancement of traditional designing arrangements is tested by displaying or algorithmic inadequacies. This instructional exercise style paper begins by tending to the inquiries of why and when such strategies can be helpful. It at that point gives an elevated level prologue to the nuts and bolts of administered and solo learning. For both directed and unaided picking up, epitomizing applications to correspondence networks are talked about by recognizing undertakings completed at the edge and at the cloud fragments of the organization at various layers of the convention stack, with an accentuation on the actual layer. List Terms Machine learning, directed learning, unaided learning, correspondence organizations, remote interchanges

Poly-Gamma-Glutamic Acid (γ-PGA)-based encapsulation of Adenovirus to evade neutralizing antibodies.

In recent years, there has been an increasing interest in oncolytic adenoviral vectors as an alternative anticancer therapy. The induction of an immune response can be considered as a major limitation of this kind of application. Significant research efforts have been focused on the development of biodegradable polymer poly-gamma-glutamic acid (γ-PGA)-based nanoparticles used as a vector for effective and safe anticancer therapy, owing to their controlled and sustained-release properties, low toxicity, as well as biocompatibility with tissue and cells. This study aimed to introduce a specific destructive and antibody blind polymer-coated viral vector into cancer cells using γ-PGA and chitosan (CH). Adenovirus was successfully encapsulated into the biopolymer particles with an encapsulation efficiency of 92% and particle size of 485 nm using the ionic gelation method. Therapeutic agents or nanoparticles (NPs) that carry therapeutics can be directed specifically to cancerous cells by decorating their surfaces using targeting ligands. Moreover, in vitro neutralizing antibody response against viral capsid proteins can be somewhat reduced by encapsulating adenovirus into γ-PGA-CH NPs, as only 3.1% of the encapsulated adenovirus was detected by anti-adenovirus antibodies in the presented work compared to naked adenoviruses. The results obtained and the unique characteristics of the polymer established in this research could provide a reference for the coating and controlled release of viral vectors used in anticancer therapy.This work was funded by the Ministry of Higher Education and Scientific Research (Iraq). This work was also partially funded by the Research Investment Fund, University of Wolverhampton (Wolverhampton, United Kingdom) and the Italian Ministry of University and Research (MIUR)

Maternal effects on anogenital distance in a wild marmot population

Peer reviewedPublisher PD

DESIGN AND FABRICATION OF VERTICAL AXIS WIND MILL POWER GENERATION

approaches are needed to reduce growth in blade mass with blade length. With design focus onturbine mass and cost for given performance, need arises for passive and active techniques to control theflow and the loads on the blades/turbine. To maximize the overall system benefits of these techniques,load control should be included from the onset. This research uses a optimization technique using themicro-tabs and different materials like Steel , aluminum , GFRP composites is used to reduce suchweight. The well-defined model of wind blade is created and this will undergo for the analysis by ansyssoftware, and the results will compare with that of basic materials like steel and existing design. Also toreduce the weight optimization via making ribsalso is used to modify the flow characteristics and cangive the controlled flow of wind which in fact to increases the aerodynamic efficiency by means ofcomposite materials. The thin airfoil blade is designed and performed with Ansys for the differentmaterials. With prototype fabrication of the wind blade was carried out. Due to this smart material likeglass fibre reinforced plastic with aluminum the rotor torque will increased. With this effect the overallwind mill performance will increase

Implementation of Improved Method on Embedded Surveillance System with Reduced Power Usage

In this project design and implement a home embedded surveillance system with ultra-low alert power. Traditional surveillance systems suffer from an unnecessary waste of power and the shortcomings of memory conditions in the absence of invasion. In this design we pressure sensors as the alert group in windows and doors where an intruder must pass through. These low-power alert sensors wake up the MCU (Micro Controller Unit) which has power management for the ultrasonic sensors and PIR sensors indoors. This state transition method saves a large number of sensors required for the alert power. We also use the Majority Voting Mechanism (MVM) to manage the sensor groups to enhance the probability of multiple sensors sensing. After the MCU sends the sensor signals to the embedded system, the program starts the Web camera. Our sensing experiment shows that we reduce the system’s power consumption Keywords: Embedded Surveillance System, PIR Sensor, Ultrasonic Sensor, Low-PowerStat

COMPUTATIONAL SCREENING AND MOLECULAR DOCKING OF LICHEN SECONDARY METABOLITES AGAINST SEVERE ACUTE RESPIRATORY SYNDROME-COV-2 MAIN PROTEASE AND SPIKE PROTEIN

Objective: At present, the coronavirus disease (COVID)-19 pandemic is increasing global health concerns. This coronavirus outbreak is caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2. Since, no specific antiviral for treatment against COVID-19, so identification of new therapeutics is an urgent need. The objective of this study is to the analysis of lichen compounds against main protease and spike protein targets of SARS-CoV-2 using in silico approach. Methods: A total of 108 lichen compounds were subjected to ADMET analysis and 14 compounds were selected based on the ADMET properties and Lipinski’s rule of five. Molecular docking was performed for screening of selected individual lichen metabolites against the main protease and spike proteins of SARS-CoV-2 by Schrodinger Glide module software. Results: Among the lead compounds, fallacinol showed the highest binding energy value of −11.83 kcal/mol against spike protein, 4-O-Demethylbarbatic acid exhibited the highest dock score of −11.67 kcal/mol against main protease. Conclusion: This study finding suggests that lichen substances may be potential inhibitors of SARS-CoV-2

DeepPep: Deep proteome inference from peptide profiles

Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability (AUC of 0.80±0.18, AUPR of 0.84±0.28) in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/

Quinoxaline: Z′ = 1 form

A new Z′ = 1 crystal structure of quinoxaline (or 1,4-diaza­naphthalene), C8H6N2, with one-fifth the volume of the earlier known Z′ = 5 structure was obtained by means of an in situ cryocrystallization technique