A Survey On: Cheating Detection in Exams

Cheating in exams has become a serious issue these days. Exams play an important role in every student’s life. Cheating in exams has been a common problem all over the world. Manual cheating detection methods may not be completely successful, to stop cheating during examinations. Automating this process will help in detection with use of machine learning. The proposed method helps to make this process automated. So there is no need to wholly depend on manual method. Keywords: Machine Learning, automated, cheating detection.

Aplikasi Mobile Money Management Dengan Fitur Optical Character Recognition Menggunakan Framework React Native

Financial management is one of the important things in the process of achieving the financial goals of a person or an organization. Everyone has their own way to manages finances, this is dependent on the character and they goals. Financial management can be done conventionally, for example by manual method which is commonly done by write the expenses, income, and savings in a notebook. However, if the note must contain details of the transactions carried out, it can be considered less efficient. The use of Optical Character Recognition will be able to answer this problem, by taking a picture of the transaction, all transaction details will be recorded on the smartphone, and the user can validate the results obtained and save the record on the smartphone user. Users can also immediately see the total transactions made according to the selected time range without having to calculate each transaction made manually. The application will be designed using the react native framework which allows it to run on various platforms.

Which Three-Dimensional Printing Technology Can Replace Conventional Manual Method of Manufacturing Oral Appliance? A Preliminary Comparative Study of Physical and Mechanical Properties

Three-dimensional printing technology is widely being adopted in the manufacturing of oral appliances. The purpose of this study was to determine the most suitable method of manufacturing oral appliances by comparing the physical and mechanical properties of various 3D printing methods with the conventional method. Experimental groups consisted of six 3D-printed specimens via FDM, two polyjets, SLS, SLA, and DLP, and the milling methods. The control group consisted of an acrylic resin specimen made by the conventional manual method. The water absorption and solubility, color stability, flexural strength, and surface hardness were tested and statistically analyzed. The FDM, SLS, and DLP methods exhibited comparable water absorption and solubility with the control group, and only the SLA method exhibited significantly higher water solubility than the control group. In terms of the color stability, only the milling method met the requirements of the allowable clinical range. The FDM, SLA, and DLP methods exhibited comparable flexural strength with the control group. The surface hardness of the PJ-2, DLP, and milling methods was acceptable for replacing conventional manual method. Therefore, the most suitable method of manufacturing oral appliances among the experimental groups was the DLP method in terms of its water absorption and solubility, flexural strength, and surface hardness.

Demultiplexing Nanopore reads with LAST v2

This protocol is for a semi-manual method for read demultiplexing, as used after my presentation Sequencing DNA with Linux Cores and Nanopores to work out the number of reads captured by different barcodes. Input: reads as a FASTQ file, barcode sequences as a FASTA file Output: reads split into single FASTQ files per target [barcode] Note: barcode / adapter sequences are not trimmed by this protocol

Smart Identity Management System by Face Detection Using Multitasking Convolution Network

Identity management system in most academic and office environments is presently achieved primarily by a manual method where the user has to input their attendance into the system. The manual method sometimes results in human error and makes the process less efficient and time-consuming. The proposed system highlights the implementation and design of a smart face identification-based management system while taking into account both the background luminosity and distance. This system detects and recognizes the person and marks their attendance with the timestamp. In this methodology, the face is initially resized to 3 different sizes of 256, 384, and 512 pixels for multiscale testing. The overall outcome size descriptor is the overall mean for these characteristic vectors, and the deep convolution neural network calculates 22 facial features in 128 distinct embeddings in 22-deep network layers. The pose of the 2D face from −15 to +15° provides identification with 98% accuracy in low computation time. Another feature of the proposed system is that it is able to accurately perform identification with an accuracy of 99.92% from a distance of 5 m under optimal light conditions. The accuracy is also dependent on the light intensity where it varies from 96% to 99% under 100 to 1000 lumen/m2, respectively. The presented model not only improves accuracy and identity under realistic conditions but also reduces computation time.

Overview of Follicular Extraction

Follicular unit extraction (FUE), now named as follicular unit excision, is one of the methods of harvesting hair follicles from the donor area for implanting in the recipient area. The occipital scalp area is the most common donor area, but nonscalp donor areas like beard, chest, and other hairy body parts can be used as donor hair follicle area. The extraction of the hair follicle leaves a tiny circular scar over the donor area. Over the past 20 years, various devices for FUE have been developed, starting from manual, simple motorized to highly advanced motors with rotation, oscillation, and vibration. Similarly, different types of punch are used: dull, sharp, ultrasharp, serrated, hybrid and specially designed punch blade for long hair follicles harvesting in various diameters from 0.7 mm to 1.1 mm. The follicles can be harvested either by manual method or by motorized method.

Fast Anther Dehiscence Status Recognition System Establishing by Deep Learning to Screen Heat Tolerant Cotton

BackgroundCotton is one of the most economically important crops in the world. The fertility of male reproductive organs is a key determinant of cotton yield. The anther dehiscence or indehiscence directly determine the probability of fertilization in cotton. Thus, the rapid and accurate identification of cotton anther dehiscence status is important for judging anther growth status and promoting genetic breeding research. The development of computer vision technology and the advent of big data have prompted the application of deep learning techniques to agricultural phenotype research. Therefore, two deep learning models (Faster R-CNN and YOLOv5) were proposed to detect the number and dehiscence status of anthers. ResultThe single-stage model based on YOLOv5 has higher recognition efficiency and the ability to deploy to the mobile end. Breeding researchers can apply this model to terminals to achieve a more intuitive understanding of cotton anther dehiscence status. Moreover, three improvement strategies of Faster R-CNN model were proposed, the improved model has higher detection accuracy than YOLOv5 model. We have made four improvements to the Faster R-CNN model and after the ensemble of the four models, R2 of “open” reaches 0.8765, R2 of “close” reaches 0.8539, R2 of “all” reaches 0.8481, higher than the prediction result of either model alone, and can completely replace the manual counting method. We can use this model to quickly extract the dehiscence rate of cotton anther under high temperature (HT) condition. In addition, the percentage of dehiscent anther of randomly selected 30 cotton varieties were observed from cotton population under normal conditions and HT conditions through the ensemble of Faster R-CNN model and manual observation. The result showed HT varying decreased the percentage of dehiscent anther in different cotton lines, consistent with the manual method. ConclusionsThe deep learning technology first time been applied to cotton anther dehiscence status recognition instead of manual method to quickly screen the HT tolerant cotton varieties and can help to explore the key genetic improvement genes in the future, promote cotton breeding and improvement.

Design and Development of an Electronic Sieving for Sand Separation using Node MCU System

Sand sieving is now considered one of the essential needs in the construction industry. Where businesses collaborate to find the best and highest-quality methods for extracting pure sand suitable for construction. These businesses always require high-quality machines to complete the process flawlessly. This is also to prove its market power and guarantee its products. This research talks about the various mechanisms for designing and manufacturing sand sieving. The sand sieving process expresses the filtering of sand from the rest of the components such as stones or gravel. The literature department studied ten different studies in the design and manufacture of sand sieving machine in different ways. Where these methods vary between using the engine and electricity and using the primitive manual method. After performing these machines several tests and evaluation of the process, it was found that the engine speed affected the energy consumed to sift the sand. Also, the sieve holes are affected by the size of the sifted sand. Where sieves are manufactured in different sizes to suit the size of the sand to be purified. On the other hand, this article contains the future recommendation of the machine to avoid errors and give effective results as needed.

Do forests ‘fall silent’ following aerial applications of 1080 poison? Development and application of bird monitoring methods using automated sound recording devices

<p>Electronic bioacoustic techniques are providing new and effective ways of monitoring birds and have a number of advantages over other traditional monitoring methods. Given the increasing popularity of bioacoustic methods, and the difficulties associated with automated analyses (e.g. high Type I error rates), it is important that the most effective ways of scoring audio recordings are investigated. In Chapter Two I describe a novel sub-sampling and scoring technique (the ‘10 in 60 sec’ method) which estimates the vocal conspicuousness of bird species through the use of repeated presence-absence counts and compare its performance with a current manual method. The ‘10 in 60 sec’ approach reduced variability in estimates of vocal conspicuousness, significantly increased the number of species detected per count and reduced temporal autocorrelation. I propose that the ‘10 in 60 sec’ method will have greater overall ability to detect changes in underlying birdsong parameters and hence provide more informative data to scientists and conservation managers. It is often anecdotally suggested that forests ‘fall silent’ and are devoid of birdsong following aerial 1080 operations. However, it is difficult to objectively assess the validity of this claim without quantitative information that addresses the claim specifically. Therefore in Chapter Three I applied the methodological framework outlined in Chapter Two to answer a controversial conservation question: Do New Zealand forests ‘fall silent’ after aerial 1080 operations? At the community level I found no evidence for a reduction in birdsong after the 1080 operation and eight out of the nine bird taxa showed no evidence for a decline in vocal conspicuousness. Only one species, tomtit (Petroica macrocephala), showed evidence for a decline in vocal conspicuousness, though this effect was non-significant after applying a correction for multiple tests. In Chapter Four I used tomtits as a case study species to compare manual and automated approaches to: (1) estimating vocal conspicuousness and (2) determine the feasibility of using an automated detector on a New Zealand passerine. I found that data from the automated method were significantly positively correlated with the manual method although the relationship was not particularly strong (Pearson’s r = 0.62, P < 0.0001). The automated method suffered from a relatively high false negative rate and the data it produced did not reveal a decline in tomtit call rates following the 1080 drop. Given the relatively poor performance of the automated method, I propose that the automatic detector developed in this thesis requires further refinement before it is suitable for answering management-level questions for tomtit populations. However, as pattern recognition technology continues to improve automated methods are likely to become more viable in the future.</p>

Do forests ‘fall silent’ following aerial applications of 1080 poison? Development and application of bird monitoring methods using automated sound recording devices

<p>Electronic bioacoustic techniques are providing new and effective ways of monitoring birds and have a number of advantages over other traditional monitoring methods. Given the increasing popularity of bioacoustic methods, and the difficulties associated with automated analyses (e.g. high Type I error rates), it is important that the most effective ways of scoring audio recordings are investigated. In Chapter Two I describe a novel sub-sampling and scoring technique (the ‘10 in 60 sec’ method) which estimates the vocal conspicuousness of bird species through the use of repeated presence-absence counts and compare its performance with a current manual method. The ‘10 in 60 sec’ approach reduced variability in estimates of vocal conspicuousness, significantly increased the number of species detected per count and reduced temporal autocorrelation. I propose that the ‘10 in 60 sec’ method will have greater overall ability to detect changes in underlying birdsong parameters and hence provide more informative data to scientists and conservation managers. It is often anecdotally suggested that forests ‘fall silent’ and are devoid of birdsong following aerial 1080 operations. However, it is difficult to objectively assess the validity of this claim without quantitative information that addresses the claim specifically. Therefore in Chapter Three I applied the methodological framework outlined in Chapter Two to answer a controversial conservation question: Do New Zealand forests ‘fall silent’ after aerial 1080 operations? At the community level I found no evidence for a reduction in birdsong after the 1080 operation and eight out of the nine bird taxa showed no evidence for a decline in vocal conspicuousness. Only one species, tomtit (Petroica macrocephala), showed evidence for a decline in vocal conspicuousness, though this effect was non-significant after applying a correction for multiple tests. In Chapter Four I used tomtits as a case study species to compare manual and automated approaches to: (1) estimating vocal conspicuousness and (2) determine the feasibility of using an automated detector on a New Zealand passerine. I found that data from the automated method were significantly positively correlated with the manual method although the relationship was not particularly strong (Pearson’s r = 0.62, P < 0.0001). The automated method suffered from a relatively high false negative rate and the data it produced did not reveal a decline in tomtit call rates following the 1080 drop. Given the relatively poor performance of the automated method, I propose that the automatic detector developed in this thesis requires further refinement before it is suitable for answering management-level questions for tomtit populations. However, as pattern recognition technology continues to improve automated methods are likely to become more viable in the future.</p>