Volume 10 | Issue 4

 Abstract

Ants are inevitable part of our ecosystem. They play many roles as decomposers, predators, pollinators, scavengers, soil engineers etc but they are least studied with respect to taxonomy, in India. In this regard, a study on diversity of ants was conducted in the Kizhakkumpuram region of Palakkad, Kerala. Study was carried out for 6 months from October 2020 to March 2021. All out-search method, pitfall and sifting method were employed for collection of ants. Ants of 20 different genera were obtained. Of that about 14 were identified up to species level. The Ants in the collection belonged to five different subfamilies Dolichoderinae, Formicinae, Myrmicinea, Ponerinae and Psuedomyrmicinae. More number of ant species belonged to Formicinae followed by Myrmicinae, Ponerinae, Dolichoderinae, Pseudomyrmicinea. Camponitini and Ponerini are the most abundant tribes. The high species diversity of ants can be attributed to the high diversity of plants and insects.

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 Keywords

Ant, Formicidae, Species Diversity, Biodiversity

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 Abstract

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 Keywords

Awareness, Low vision, Rehabilitation, Technical knowledge,Telerehabilitation.

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 Abstract

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 Keywords

Labeo rohita, Bifenthrin, 10% EC, synthetic pyrethroid, non-cyanogroup, Type I, Acute toxicity, static renewal, continuous flowthrough system (CFTS).

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Creative Commons Attribution 4.0 and The Open Definition

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 Keywords

SDGs, International Collaboration, SDG17, ASEAN, Indian Trade

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VATARAKTA, ASHWATTHA SIDDHA TAILA MATRA BASTI, KSHEERBASTI

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Wearing masks has become compulsory for everyone nowadays. Thus, existing face recognition systems have become inefficient. So we have decided to develop a deep learning model which can properly detect masked faces. We have used the MAFA dataset with 30, 811 Internet images and 35, 806 masked faces. Faces in the MAFA dataset have different orientations and occlusion degrees, and at least one part of the face is occluded by mask.We have used CNNs for masked face recognition. It comprises various convolutional layers, several pooling layers, non-linear layer and classification layer units. Deep CNN networks are typically trained on large labeled datasets like ImageNet to pick out general features which are appropriate into several detections and recognition jobs like image classification and verification, object detection, segmentation to texture identification.

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 Keywords

CNN-Convolutional Neural Network, Masked Face Recognition, Mask Detection, Occluded face detection.

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 Abstract

Switzerland has always been a pioneering force in the Watchmaking Industry, as much as to say that it has always enjoyed an unrivalled Monopoly when it comes to manufacturing and selling of fine watches. Swiss watches are known for their craftsmanship, precision and mechanical sophistication, created using skills which are rarely found beyond the Jura Mountains of the country, which ultimately helps Switzerland to capitalize from an international imitation Gap. However, there is a disproportionate inequality in the market share of the Swiss watch manufacturers to the level that just one brand controls approximately one-fourth of the total market share whereas all the other brands have to settle for a mere single digit market share. This arises the following questions: Does Brand Equity play a significant role in determining market share? Which is the more important determinant of brand equity- value proposition to a consumer or just smart marketing? What is the sweet spot in the market that can be capitalized on for revenue maximization? Etc. This paper attempts to expound on these questions.

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 Keywords

Watch industry, brand image

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As communication technology and e-commerce have improved, credit cards have become the most common mode of payment for both online and offline purchases. As a result, security in this system is expected to be very good to avoid fraudulent transactions. Each year, the number of fraudulent credit card data transfers rises. Researchers are also experimenting with unique approaches to detect and prevent such frauds in this direction. However, some strategies that can accurately and efficiently detect these scams are always needed. This study proposes a method for detecting credit card fraud using supervised and unsupervised learning approaches. The suggested neural network method outperforms existing methods such as Logistic Regression, Local Outlier Factor, Isolation Forest, and K-means clustering when compared. As the accuracy values of each model are nearly negligible because the dataset is imbalanced, we will compare the false-negative rate, recall, precision, and recall parameters of each model.

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 Keywords

Machine learning Logistic regression Isolation Forest Local Outlier Factor K means clustering Neural Networks.

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 Abstract

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Raspberry Pi, Smart Mirror, Internet of Things, smart home technology, speech interaction

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Mental health, Mental Illness and Demon possession.

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The use of gestures as means to convey information is an important part of human communication. The hand gesture recognition are important methods for nonverbal communication for human beings. The non-verbal communication is not only used for physically challenged person but also it can be efficiently used for various applications such as 3D gaming, aviation, surveying, etc. To solve the difficulties faced by hearing impairment, it is necessary to develop a system which translates the sign language into text which can easily be recognized by the impaired people. A system is developed for hand gesture recognition in mat lab by using an artificial neural network.

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Gesture Recognition, Artificial Neural Network, Feature Extraction

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 Abstract

Optimization of piston material using Artificial intelligence in CATIA Software performs. These algorithm based on Data collection and Simulink modelling orientated. Our work collect some material properties for different Terms and conditions than collects data performs ANOVA. Our observation Analysis of some parameters Like as MSE (Mean square error), Accuracy and Regression with our observation improve Accuracy and Reduction of Material detection Accuracy parameters.

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MSE, ANN, training algorithm, neural network layers, Different Mechanical properties

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The Right to Education has become one of the reformative legislations in the Indian education system, though it promotes the children education from the age of 6 to 14 whereas the turnout ratio of the girl children education is comparatively less. The reservation of the children in schools especially in the private school become the challenging task for the middle- and lower-income group because of the socio-economic factors which makes the children to be deprived of their fundamental Right guaranteed under Article 21 of the constitution of India. The quality education, experienced teachers, good infrastructure and implementation of the plans and policies of the legislation could be considered to be areas of concern. National Education policy is the recent development in the education system which focuses on the holistic approach of the education system but there is need to have policies and programs to make the education policy effective only when the implementation of legislation is done in the true spirit, this research focuses on various issues and the challenges in implementing the RTE Act 2009.

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Educational Development, Fundamental Right, Academic Integrity,Administration, Universalisation of education

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Abstract: . Introduction: In terms of reducing health care resource,complication,length of stay clinical pathway have proven to be simple and effective by evaluating periodically1.Majority of post craniotomy have complications due to prolonged hospital stay and mechanical ventilation2.Thirty percent of patients among post craniotomy have at-least one neurological or medical complication3. Objective of the study : To compare the outcome among patients undergone craniotomy with clinical pathway and without the clinical pathway Methodology A quasi-experimental design is used in this study with quantitative approach, study is conducted in a selected hospital of Navi Mumbai, included all the patients undergoing craniotomy surgeries and are available during study. 60 samples selected for the study, clinical care pathway was implemented on prospective group(n=30) and not implemented on retrospective group(n=30). Outcome is compared among both the groups. Results: Analysis and comparison done using in comparison of retrospective (without clinical pathway,n=30) and prospective (with clinical pathway,n=30) using independent t-test.Prospectively there was reduction in length of stay (p=0.008)and duration of definitive airway (p=0.044).Medical complications, Surgical site infection (p=0.043) and pulmonary complication (p=0.016) has significant difference and Neurological complication shows no different ,other complications (p=0.022) like bedsore,CLABSI,CAUTI and sepsis was decreased.Readmission(p=0.043) was also significantly reduced in prospective. Conclusion : Patients outcome after implementation of clinical pathway shows reduction in length of stay,complication, and readmission this will benefit the hospital ,patient and staffs.Nursing staffs will gain the knowledge regarding care of post craniotomy patient in critical care unit through this study .Only few parameters were analyzed in the study.

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 Keywords

Clinical care pathway,Neurology surgery,Post craniotomy ,Complications of craniotomy

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The network attacks detection become the prime security problems in the day today life. As the use of computing resources is increased, cyberpunks are planning new tactics of network attacks. Many techniques have been invented to detect these attacks which are based on data mining and machine learning approaches. Many clustering methods have been used to detect network intrusions. These intrusions detection methods have been applied on various IDS datasets. UNSW-NB15 is the newest dataset which contains different modern attack types and normal activities. In this paper, we have proposed unsupervised machine learning algorithms for anomaly based detection on reduced UNSW NB15 dataset.

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 Keywords

UNSW NB-15, Machine Learning, Low-High Distance, Low-High Split Clustering Algorithm.

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The Chalcogenide compounds of group V-VI-VII have attracted the minds of researchers due to their multifunctional properties. They exhibit many rare combinations of strongly coupled photoconductive, semiconductive, Pyroelectric and ferroelectric properties which have been subjects of much recent interest. SbSI is one of most extensively studies compound of this series. It is expected that introduction of impurities may lead to some constructive changes in the properties of the compound. Therefore, synthesis of doped SbSI and SbTeI was carried with solid state thermal reaction technique. Tin and Indium were the two dopants introduced in place of Antimony and Sulfur. Composition of the elements present in each of these materials was examined by EDAX. The electrical conductivities of the doped materials were compared with the pure SbSI material. The dielectric constants of the samples were calculated using the capacitance of the material.

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SbSI, SbTeI, Tin, Indium, doped material, EDAX, Dielectric Constant.

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 Abstract

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Keyword Extraction, Natural language processing, Text Summarization

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In this paper, we propose a single transmit and multi transmit fuzzy queuing models with single and multiple servers. Here we consider the arrival rate to follow pentagonal, heptagonal fuzzy numbers. Based on that we execute the characteristic of queuing model by applying ranking technique..

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Fuzzy number, arrival rate, service rate, servers

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Today, thanks to the internet connection, the borders are disappearing and accessing information is more comfortable. Besides desktop applications, numbers of web-based applications which instant changes can be seen by all users are increasing day by day. The diversity of web-based applications that are currently used in presenting spatial information to users is also progressing. Using open source libraries, developers can develop web applications for their own purposes. Three dimensional (3D) visualization on the web is a commonly used approach in geographic information systems (GIS) applications. In this article, a 3D web application is developed using Cesium open source JavaScript library. Vector data layers containing attribute data on global, country and city scales are visualized on the web application. Development of the application was based on open source software tools such as Map Server for the GIS functions, PostgreSQL and Post GIS for the data management and HTML, PHP and JavaScript as programming languages. In addition, background processes are used in an innovative manner to handle the time consuming and computational costly procedures of the application.

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 Keywords

Web applications, Open-source, Geographic Information Systems (GIS), software development remote sensing, Open-source library.

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Creative Commons Attribution 4.0 and The Open Definition

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Deep learning has evolved into a powerful machine learning technology that incorporates multiple layers of features or representations of data to get cutting-edge results. Deep learning has demonstrated outstanding performance in a variety of fields, including picture classification, segmentation, and object detection. Deep learning approaches have recently made significant progress in fine-grained picture categorization, which tries to discriminate subordinate-level categories. Due to strong intra-class and low inter-class variance, this task is highly difficult. Object detection and the identification of pedestrians is critical in autonomous driving applications. In real-time applications, approaches based on Convolutional Neural Networks have shown significant increases in accuracy and decision speed. In this research, authors present various state of the art deep learning algorithms i.e., VGG-16, VGG19, DenseNet-121, InceptionV3 and customized 3 layers CNN model for object detection. Model adopted is trained and validated on self-made five class of furniture dataset. After extensive experiments, highest accuracy obtained was 99.89% with VGG-19.

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 Keywords

Object Detection, Object classification, Convolutional Neural Network, VGG, Confusion Matrix, Accuracy

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