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2019 International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019

作者： Agrawal, Rohit Satapathy, Sachinandan bagla, Govind Rajakumar, K. VIT University B. Tech Computer Science and Engineering Vellore India

ISBN: (纸本)9781538693537

Detection of White blood Cell (WbC) cancer diseases like Acute Myeloid Leukemia (AML), Acute Lymphoblastic Leukemia (ALL), and Myeloma is a complex task in medical field because they are sudden in onset. Our proposed method consists of designing and developing an automated system which will assist the medical professionals in correctly diagnosing all the types and sub-types of this disease. In this paper, we have proposed a novel method in which we have taken microscopic blood images as an input image. A dataset of 100 images in which 62 training and 38 testing images is taken. After that we have converted the image to proper format (YCbCr) for segmentation. For segmenting, we have used the combination of Gaussian Distribution, Otsu Adaptive Thresholding and for clustering we have used K-Means method. Using Gray Level Co-occurrence Matrix (GLCM), the features are extracted and were used for classification using Convolutional Neural Network (CNN). The overall accuracy of the system obtained after processing is 97.3%. © 2019 IEEE.

关键词： Image segmentation

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blockchain based Identity Verification Model

Blockchain Based Identity Verification Model

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2019 International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019

作者： Malik, Gunit Parasrampuria, Kshitij Reddy, Sai Prasanth Shah, Seema NMIMS MPSTME Mumbai Campus B.Tech Computer Engineering India

ISBN: (纸本)9781538693537

blockchain is peer-to-peer decentralized system that provides security, reliability, authenticity, immutability and transparency. It is an expandable list of records called blocks which are linked using cryptography. blockchain by design, is resilient to modifications, it consists of an open distributed ledger that records all the transactions made between two parties which cannot be forged. This paper provides a unique model for document verification with use case of the government. Currently all issuing authorities are separate cells providing identity documents to a citizen. Due to this cellular nature document verification is a tedious and long process. The problem of falsifying documents is another issue within government bodies. We discuss the use of blockchain to improve efficiency and security. The purpose of this project is to develop a private permissioned blockchain network where an individuals official documents can be shared by government bodies, organizations and educational institutions. Using the blockchain technology, our platform achieves a decentralized system to share authenticated government documents between government bodies and private organizations without the need of a certain level of human intervention. With the concluding facts of this paper, it is observed that the process of document verification and issuing process's efficiency is drastically improved with our blockchain model. Along with this we reap benefits of security, reliability and transparency. © 2019 IEEE.

关键词： Smart contract

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A review on big data practices in healthcare

A review on big data practices in healthcare

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2019 IEEE International Conference on System, Computation, Automation and Networking, ICSCAN 2019

作者： Shah, Chinmay Shaikh, Maruf Shah, Dhruvesh Samdani, Krishna B-Tech Computer Engineering MPSTME NMIMS University Mumbai India Computer Engineering Department MPSTME NMIMS University Mumbai India

ISBN: (纸本)9781728115252

In recent years, there has been a rapid advancement in the healthcare sector. A major part of this development is possible due to the availability of electronic health records for the patients. Traditionally, the healthcare system used to consist of health records which were handwritten by doctors and other specialists and the medical history of the patients were filed up and they were kept either with the patients or at the hospital. This method not only required a huge amount of manual work and time but also resulted in extensive usage of papers. Thus, an enormous volume of data was generated that was physically present on paper. Simultaneously, with the advancements in the field of computer science, we are able to store the medical record as electrical health records. Also, with the help of clinical analytics, we now have various models and algorithms for storing and computing a huge amount of data and gain certain insights from them. This is known as big data. And healthcare, being an information-rich enterprise, a lot of big data applications can be used here. In this paper, we discuss the different frameworks that are used to store such data and, various methods used to predict certain diseases at an early stage. Such predictive diagnosis will help in cost reduction and can be lifesaving in certain cases. © 2019 IEEE.

关键词： Data mining

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Effective permeability of an immiscible fluid in porous media determined from its geometric state

arXiv

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arXiv 2022年

作者： Alzubaidi, Fatimah Mostaghimi, Peyman Niu, Yufu Armstrong, Ryan T. Mohammadi, Gelareh McClure, James E. berg, Steffen School of Minerals and Energy Resources Engineering University of New South Wales Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia National Security Institute Virginia Tech BlacksburgVA24061 United States Shell Global Solutions International B.V. Grasweg 31 Amsterdam1031HW Netherlands

based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure and wettability. For applications, fuel cells to underground CO2 storage, it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modelling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an Artificial Neural Network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. The developed ANN is based on a prescribed set of state variables based on physical insights that predicts the effective permeability of 4,500 unseen pore-scale geometrical states with R2 = *** Codes 76S05 (Primary), 76T06 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Pore structure

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Twitter Sentiments Analysis Using Machine Learninig Methods

Twitter Sentiments Analysis Using Machine Learninig Methods

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Emerging technology (INCET), International Conference for

作者： Lokesh Mandloi Ruchi Patel B.Tech Computer Science and Engineering Meidcaps University Indore India Dept. Computer Science and Engineering Medicaps University Indore India

ISBN: (数字)9781728162218

ISBN: (纸本)9781728162225

Analysis of sentiments is the method of deciding whether the sentiments in the text is positive, negative or neutral. It is also known as material polarity or mining of opinions. The growth and advancement in social media platforms engaged a huge number of users. Social media platform like twitter where users can post their tweets in 280 characters. because of the limited number of characters in tweets, it becomes easy for the sentiment analysis. On Twitter 550 millions of tweets are posted daily. Twitter also represents all age group people and also a fair representation of gender. Therefore, the sentiment analysis of twitter data becomes somewhat general sentiments of society. In this paper, we will compare various Machine Learning methods like the Naïve bayes Classification method, Support Vector Machine Classification Method and Maximum Entropy Classification method. We will see how sentiments analysis is done by this classification algorithm and what is the accuracy and precision in these cases.

关键词： Sentiment analysis Twitter Machine learning Feature extraction Support vector machines Entropy

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Internet of things (IoT) applications in dentistry

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Current Medicine Research and Practice 2020年第2期10卷 80-81页

作者： Abid Haleem Mohd Javaid Ibrahim Haleem Khan Department of Mechanical Engineering Jamia Millia Islamia New Delhi India B. Tech (Computer Engineering) Jamia Hamdard New Delhi India

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bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model

Bayesian Optimization with Stacked Sparse Autoencoder based ...

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International Conference on Smart Systems and Inventive technology (ICSSIT)

作者： Irfan Abdul Karim Shaikh P. Vamsi Krishna Swagat Gourav biswal A. Sasi Kumar S. baranidharan Kamlesh Singh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Hyderabad Telangana India B. Tech Department of Electrical Engineering Odisha University of Tecnology and Research Bhubaneswar Orissa India Department of Cloud Technology & Data Science (Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore) Institute of Engineering & Technology Srinivas University Dakshina Kannada Karnataka India Department of MBA and Research Center Presidency College (Autonomous) Bangalore Karnataka India Department of Computer Science and Engineering Graphic Era Hill University Dehradun Campus Dehradun India

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (bOSSAE-CPP) model. The major intention of the bOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the bOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the bO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the bOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the bOSSAE-CPP technique.

关键词： Training Government policies Online banking Social networking (online) Legislation Predictive models Cryptocurrency

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AI-Driven Autonomous Robots for Search and Rescue Operations in Disaster Zones

AI-Driven Autonomous Robots for Search and Rescue Operations...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Meenakshi S. Pavaimalar Rama Prabha K. P S. Ravi N. Kumaran S. b. Priya Department of Artificial Intelligence & Data Science Nitte Meenakshi Institute of Technology Nitte University Bangalore India Department of Information Technology V. S. B. Engineering College Karur Tamilnadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Search and rescue (SAR) operations designed to save lives in disaster zones face difficulties executing effectively because of dangerous surroundings and challenging terrain features. An AI-controlled robotic system exists to deal with existing challenges. The system combines a reinforcement learning-based navigation system that integrates multiple sensors including LiDAR with thermal cameras and RGb sensors for detecting victims to work with decentralized task distribution frameworks. Real-time simulations tested disaster navigation datasets showing an overall detection accuracy rate of 95.6%, navigation accuracy at 92.8%, while obstacle avoidance performed at 97.2%. This deployed system achieved 3.2-minute average response times and processed requests with 85.4% resource efficiency. The system proves superior performance over existing SAR methods while demonstrating speed enhancement, accuracy improvement, and autonomous operational value in SAR operations. Future disaster response will change thanks to AI-powered robotics because this technology enables scalable efficient reliable solutions for future deployments.

关键词： Accuracy Navigation Disasters Transforms Thermal sensors Sensor systems Time factors Collision avoidance Robots Optimization

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SD-measure: a social distancing detector

arXiv

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arXiv 2020年

作者： Gupta, Savyasachi Kapil, Rudraksh Kanahasabai, Goutham Joshi, Shreyas Srinivas Joshi, Aniruddha Srinivas B.Tech Department of Computer Science and Engineering National Institute of Technology Warangal Telangana506004 India

The practice of social distancing is imperative to curbing the spread of contagious diseases and has been globally adopted as a non-pharmaceutical prevention measure during the COVID-19 pandemic. This work proposes a novel framework named SD-Measure for detecting social distancing from video footages. The proposed framework leverages the Mask R-CNN deep neural network to detect people in a video frame. To consistently identify whether social distancing is practiced during the interaction between people, a centroid tracking algorithm is utilised to track the subjects over the course of the footage. With the aid of authentic algorithms for approximating the distance of people from the camera and between themselves, we determine whether the social distancing guidelines are being adhered to. The framework attained a high accuracy value in conjunction with a low false alarm rate when tested on Custom Video Footage Dataset (CVFD) and Custom Personal Images Dataset (CPID), where it manifested its effectiveness in determining whether social distancing guidelines were practiced. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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A Review on Detecting Phishing URLs using Clustering Algorithms 6

A Review on Detecting Phishing URLs using Clustering Algorit...

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6th IEEE International Conference on Advances in Computing, Communication and Control, ICAC3 2019

作者： Mondal, Shaheen Maheshwari, Diksha Pai, Nilima biwalkar, Ameyaa B. Tech. Computer Engineering Mukesh Patel School of Technology Management and Engineering NMIMS India Mukesh Patel School of Technology Management and Engineering NMIMS Dept. of Computer Engineering India

ISBN: (纸本)9781728123868

Phishing is a kind of a social engineering attack. The attacker poses as a legitimate entity and communicates with the victim through some mode of communication. The user is prompted to open a link which has been designed to look similar to a legitimate website, or is prompted to relay sensitive credentials over the phone. The attacker steals the users' information to perform identity theft, account hijacking, etc. In this paper, we focus on URL based phishing attacks. Most of the solutions that we investigated focused more on the classification algorithms rather than clustering. Our aim is to experiment and compare the results of both of these types of algorithms. The main premise of our approach is a hybrid machine learning model comprising of two steps-checking with a blacklist and whitelist, and heuristics based detection, to increase the accuracy of the proposed algorithm. © 2019 IEEE.

关键词： Machine learning

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