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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Katyani Singh Avi Seth Harshit Singh Sandhu Krishna Samdani B.Tech Computer Engineering MPSTME NMIMS University Computer Engineering Department MPSTME NMIMS University

Image enhancement is the most important field in image research and processing. The main aim here is to artificially enhance the photo quality and visual appearance. Many photos end up with unusually high amount of noise and grain due to poor sensor quality and control, or with low sharpness. It is expensive and sometimes impossible to increase the resolution of the sensors (even counter intuitive at times) inside the cameras. Convolutional Neural Networks (CNN) rely on image structure and the texture to serve as differentiating features. In this paper, we do a literature review of CNN based techniques for Image denoising, Image super resolution, contrast enhancement, improving images in low light conditions, and then propose a method utilizing the above mechanisms, and our own devised system which uses nearest neighbor classification to rebuild images, along with an HDR photography approach similar to Google RAISR's.

关键词： Cameras Image resolution Convolutional neural networks Image enhancement Image color analysis Google Adaptation models

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Android Malware Detection Amid COVID-19

Android Malware Detection Amid COVID-19

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Rohit Srivastava R.P. Mishra Vivek Kumar Himanshu Kumar Shukla Neha Goyal Chandrabhan Singh Computer Science and Engineering F.O.E.T. University Lucknow India Electronics and Communication Dr R.M.L. Awadh University Ayodhya India Computer Science and Engineering Teerthanker Mahaveer University Moradabad India Computer Science and Engineering K.M. C. Language University Lucknow India Computer Science and Engineering B.N. College of Engg. and Tech. Lucknow India

ISBN: (数字)9781728189086

ISBN: (纸本)9781728189093

Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious.

关键词： COVID-19 XML Static analysis Machine learning Feature extraction Malware Smart phones

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SOUL: Simulation of Objects in Unity for Learning

SOUL: Simulation of Objects in Unity for Learning

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International Conference on Intelligent Communication and Computational techniques (ICCT)

作者： Lavina Nagpal Meghna Jaglan Anuraj Kathait Aakil Mathur Abhishek Vichare B.Tech Computer Engineering MPSTME NMIMS University Mumbai India

This paper explores the realms of creating a user-friendly and highly interactive environment for e-learning. It offers an in depth explanation about how a web based application along with Unity can be built to allow the user to learn myriad of subjects with ease. The end user can access a farrago of e-learning modes such as basic mode, game mode, fast track mode, and certification mode for a wide range of concepts and their topics. The paper gives detailed information about the requirement specifications for building such an e-learning portal and its proposed architecture and flow diagrams. It then further explains how to build these modes in Unity using C# to write the scripts and then embed them into a web application subsequently. The paper concludes by offering critical analysis on developing such an e-learning application and the benefits of each mode.

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Variant component principal linear reduction for prediction of hypothyroid disease using machine learning

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Journal of Physics: Conference Series 2021年第1期1767卷

作者： M. Shyamala Devi P. Swathi Ayesha Jahangir A. Dhanunjai Sai Kumar A. Naga bhushan Naidu M. Raghuram Manas Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Third Year B.Tech Students Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

With the tremendous technological growth, the world is shifted to adapt the different food and life style by the people that results in the improper working of the body organs. The change in the food habits leads to a major problems that we face in the current scenario is the presence of hypothyroid in the body. The likelihood of hypothyroid still ruins as a challenging issue due to the uncertainty of proper symptoms. With this background, the machine learning can be used towards health care scenarios for the prediction of disease based on the patients past history. This paper focus on predicting the existence of hypothyroid with respect to the patients' medical parameters. The hypothyroid patient dataset is taken from the UCI Metadata repository with 24 columns and 3163 unique patient's records is used for the experimentation of hypothyroid with the following contributions. Firstly, the hypothyroid dataset from UCI machine repository is subjected with the data processing and exploratory analysis of the dataset. Secondly, the unrefined data set is fixed with different classifier algorithm to find the presence of hypothyroid and to examine the efficiency metrics before and after feature scaling. Thirdly, the data is processed to PCA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Fourth, the data is processed to LDA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Experimental results show that the Kernel Support Vector Machine classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced PCA dataset. Similarly, the Logistic Regression, Kernel Support Vector Machine and Gaussian Naïve bayes classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced LDA dataset.

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Annotation and Retrieval of Retinal Images using Support Vector Machine with Active Learning

Annotation and Retrieval of Retinal Images using Support Vec...

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2019 IEEE International Conference on Intelligent techniques in Control, Optimization and Signal Processing, INCOS 2019

作者： Hephzi Punithavathi, I.S. Ganeshkumar, P. Duraipand, P. Shadan College of Engg. Tech Department of Computer Science Engineering Hyderabad India Anna University Regional Campus Department of Information Technology Coimbatore India J.B.Institute of Engg. Technology Department of Electrical Electronics Engineering Hyderabad India

ISBN: (纸本)9781538695425

in the medical field, many images are acquired using different modalities like X-ray, Computed Tomography and Magnetic Resonance Imaging. Organizing these images and retrieving them is a critical task. in this paper, a novel retinal image retrieval and automatic annotation of ten different retinal diseases is presented and is comprised of three steps namely, feature extraction, automatic annotation and retrieval of similar images. in feature extraction, visual features like texture, color and shape were derived from the images and stored in a database. The retinal images are affected by various diseases and have to be identified easily and quickly for better treatment. For automatic annotation, we have considered ten different retinal diseases and annotated the retinal image with its corresponding disease using Support Vector Machine (SVM) with Active Learning (AL) and discriminative based automatic image annotation. in retrieval of images, bray Curtis distance measure was used to retrieve the similar images by comparing the feature values of images stored in the database. Our proposed system has achieved a precision of 82% and specificity of 96.3%. © 2019 IEEE.

关键词： Support vector machines

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Activation Layers Implication of CNN Sequential Models for Facial Expression Recognition

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IOP Conference Series: Materials Science and engineering 2021年第1期1074卷

作者： M. Shyamala Devi Ankita Sagar Karan Thapa Mreenmoy Hazarika P. Swathi Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial Expression Recognition is the critical part in the human emotional detection in the field of image processing. The application tends to soft or hard real time application based on the power of expression detection of idle image or videos. The Social communication with any object can be done by verbal or non-verbal format. Expression and Emotion detection completely rely on the non-verbal communication and facial expression. Machine Learning play an important role in the recognition of facial expression. An attempt is made in this paper to analyse the performance of Convolutional neural network models with diverse activation layer for the performance ***, the facial expression dataset is extracted from the website http://***/ckagree/, http://***/database/faces/ is subjected with the data processing. Secondly, the data analysis is done for the distribution of expression image in the training and testing dataset. Thirdly, the facial expression images are detected with HAAR cascade and then the images are cropped with (350, 350). Fourth, the facial expression images is applied with normalized and the bottleneck features are created for training and testing data. Fifth, the training dataset is fitted with convolutional sequential neural network models with various activation layers like Sigmoid, Elu, Relu, Selu, Tanh, Softsign and Softplus. Sixth, the performance analysis is done with loss and accuracy for all the epoch of all CNN models for all the activation layers. Experimental results show that CNN sequential model with Relu activation layer is found to have the accuracy of 100%.

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Deep learning for optimal deployment of UAVs with visible light communications

Deep learning for optimal deployment of UAVs with visible li...

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作者： Wang, Yining Chen, Mingzhe Yang, Zhaohui Luo, Tao Saad, Walid Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical Engineering Princeton University PrincetonNJ08544 United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Hong Kong Department of Informatics Centre for Telecommunications Research King's College London LondonWC2B 4BG United Kingdom Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech BlacksburgVA24061 United States

In this paper, the problem of dynamical deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities for optimizing the energy efficiency of UAV-enabled networks is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Since ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem which jointly optimizes UAV deployment, user association, and power efficiency while meeting the illumination and communication requirements of users. To solve this problem, an algorithm that combines the machine learning framework of gated recurrent units (GRUs) with convolutional neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. Given the prediction of illumination distribution, the original nonconvex optimization problem can be divided into two sub-problems and is then solved using a low-complexity, iterative algorithm. Then, the proposed algorithm enables UAVs to determine the their deployment and user association to minimize the total transmit power. Simulation results using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 68.9% reduction in total transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution and user association. © 2002-2012 IEEE.

关键词： Energy efficiency

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A Statistical Analysis On Covid-2019 To Distinguish between Myths And Facts With Data Visualization

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IOP Conference Series: Materials Science and engineering 2021年第1期1022卷

作者： Karthik Konar Nirali Kabli MCA Student Dept of Computer Engineering NMIMS Mukesh Patel School Of Technology Management & Engineering Vile Parle(West) Mumbai B. Tech Integrated Student Dept of Computer Engineering NMIMS Mukesh Patel School Of Technology Management & Engineering Vile Parle(West) Mumbai

Covid-2019 pandemic which has become the world's worst nightmare, with no cure in sight and a lot of misinformation available on the internet, one can have a very tough time in separating fact from fiction. From non-veg meats, garlic water and weird self test, one will find a lot of myths related to coronavirus infection. Therefore to separate myths from facts we conducted a survey to know what actually people think about the virus. The main objectives of our survey was:(1)To describe the level of knowledge of the participants regarding COVID-19.(2)To determine participants' point of view related to the COVID-19 disaster.(3)Provide useful information to the government of India.(4)To distinguish between myths and facts related to COVID-19 Disaster.

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Extracting Facial Features in Filter Modified Images using Neural Networks

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IOP Conference Series: Materials Science and engineering 2021年第1期1074卷

作者： M Sumithra M Shyamala Devi A Poornima Shatadru Sarkar Harish Patel Preethi Mahankali boggula Navya Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial identification plays a vital role in this digital era. Recent advancements in technology provide various filtering effects while capturing the images. Extracting the facial features from the filter modified image becomes challenging. To identify the important facial attribute values from those images, special features extraction techniques needs to be applied. Facial points are the key factors to recognize the face. When these factors are altered, facial detection becomes crucial. Initially apply the image pre-processing techniques to reduce the noise level in the images then perform feature selection. To overcome the curse of dimensionality, Occam's razor approach is followed to limit the selection of attributes. In different stages the features are going to be filtered by replacing the weaker attributes. Later Pearson Correlation, a filter based correlation technique is applied to evaluate the deviated numerical value from the target. The deviated features can be treated with Lasso effect to suppress the poor weighted features. Finally through recursive feature elimination, highest influencing attribute factor can be identified. In this approach some of the facial feature which is used in training the neural network can be discarded based on the deviation rates. In terms of accuracy the proposed system can recognize the dataset with 70% of accuracy with filter effected images.

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Epoch interrogation for skin cancer detection using convolutional neural network models

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IOP Conference Series: Materials Science and engineering 2021年第1期1074卷

作者： A Poornima M Shyamala Devi M Sumithra Mullaguri Venkata bharath Swathi S Sathishkumar K Yogesh Shubham Santosh Upadhyay Nitesh Kumar Sah Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Skin cancer is very important notable disease and it is probable to everyone nowadays, it flourishes on the area of body where it exposed to ultraviolet rays. It leads anomalous gain in skin cells. It initiate on various parts of body like face, hand and bottoms of the feet as cautious hole or spot. The initial investigation of anomalous gain is essence to cure the disease at early stage, and it still remains a feasible challenge in the scientific improvements. From the analysis, this paper endeavour to inspect the category of disease with the following improvements. Initially, the skin dataset from ISIC machine archive is utilized for image processing. Secondly, the values of dataset images are normalized by dividing all the RGb values by 255. Thirdly, keras sequential API is used to add one layer at a time, initiating from the input. The CNN can extract the features that are useful for classifying the image, by using the kernel filter matrix. MaxPool reduce the computational cost by down-sampling the image, and the relu activation function is implemented to provide non linearity to the network. The flatten layer is utilized to remodel the final feature maps into 1D vector. CNN model provides accuracy of 94.83% with 3297 images and ResNet 50 model has attained accuracy of 90.78% due to less number of images used for classification. AlexNet model has attained accuracy of 81.8% with 1300 images and GoogleNet V3 inception has attained accuracy of 96% with 3374 images. Finally Vgg16 model has attained accuracy of 97.3% with 5636 samples.

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