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A Multi-syndrome Pathology for Breast Cancer through Intelligent Learning

IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： C.N. Vanitha S. Malathy Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Perundurai - 638 060

The field of data analytics dealt with various methods to analyze the available data and help us to drawn into a point of conclusion about that information. Machine will learn the behavior based on the algorithms applied on those data and provide an appropriate conclusion. When these kinds of data analytic metrics applied to medical diagnosis various symptoms obtained from the patients and then the disease could be detected effectively. When certain optimization methods are applied on the data, the overall efficiency of the system will be improved considerably. To meet the challenges in diagnosing breast cancer based on clinical record sources, different symptoms in descriptions, clinical symptoms, a novel method, which consists of choosing the suitable features, multi-class functions, and multi-label parameters, has been proposed. The proposed work will be implemented as two steps such as discriminative symptoms selection and multi-syndrome learning. Public Breast Cancer Wisconsin (Diagnostic) Data Set has been taken for implementation. The breast cancer data set utilized for this work comprises 699 tumour samples. In that 458(65.5 %) samples belong to Benevolent (non-cancer) tumours and 241(34.5%) belongs to malevolent(cancer) tumours. The overall verisimilitude is 95.23% which will be improved to a greater extent when compared to the existing schemes.

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Optimizing Wireless Multimedia Sensor Networks Path Selection using Resource Levelling Technique in Transmitting Endoscopy Biomedical data

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： C.N. Vanitha S Malathy Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Perundurai - 638 060

The urge to transmit especially biomedical data rapidly is essential to save human life. Every user demands for high speed communication with no delay. But the network suffers from huge delay which degrades the performance of the network and it also dissatisfies the user expectation. To meet the user needs, the proposed work mainly focuses on increasing the speed and minimizing the delay in communication using Internet applied in Things (IoT) and Wireless Multimedia Sensor Networks. It can be achieved by avoiding over allocation of resources in the network i.e., resource level balancing and planning the transmission. These two have greater impact in upgrading the performance of network. This paper introduces a unique methodology for Wireless Multimedia Sensor Networks and end-device assisted system. The transmission time can reduced by using the concept of probability estimation for crashing the interpretative path. It extends the battery level of the motes by enhancing the pace and minimizing the energy level during transmission process. The Crashing Interpretative path protocol is simulated against other literature method. The simulation results shows that the CCP provides better efficiency in terms of transmission time, the lifetime of sensors for biomedical data transmission. This methodology is greatly helpful to save precious human life.

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Real-time GB pattern convolution neural network-based brain image classification

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AIP Conference Proceedings 2024年第1期3075卷

作者： Amarendra Reddy Panyala M. Baskar Research Scholar Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu 603 203 India Assistant Professor Department of Information Technology MLR Institute of Technology Hyderabad Telangana 500043 India Associate Professor Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamil Nadu 603 203 India

Medical image classification has been identified as a critical task in several medical solutions. Brain image classification has been identified as a challenging task that has been approached with different methods. The existing methods use different features like shape, texture, and gray to classify brain images. However, the methods need to improve performance in categorizing the brain image and detecting the presence of tumors. An efficient GB Pattern Convolution Neural Network (GBP-CNN) is presented to handle this issue. The method involves preprocessing the brain image given with Adaptive Feature Distribution Normalizer (AFDN) algorithm. The Grey Canvas Segmentation (GCS) technique was used to segment the normalized image. The feature vector is created by extracting the binary and grayscale patterns from the image that has been segmented. A convolution neural network is trained to recognize the features that were extracted. Two convolution layers in the CNN’s design are used to convolve the image’s features. Additionally, a pooling layer is being incorporated into the network’s design, which reduces the data to a single dimension. The neuron predicts support values for several attributes during the assessment stage. The approach then calculates the Class-Based Fitness value (CBFV) in order to categories the image against various tumour classes. The proposed GBP-CNN algorithm improves the performance of brain image classification with less time complexity.

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An Efficient Machine Learning Techniques Based on IoT for Effective Load Forecasting

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

作者： K. Rameshwaraiah Sankarpu Sateesh Kumar K. Srinivasa Babu T. Madhu Professor Computer Science and Engineering Assistant Professor Computer Science and Engineering Nalla Narasimha Reddy Educational Society's Group of Institutions RGUKT – Srikakuram A.P

Internet of Things (IoT) networks (IoT) are computer networks which have an acute IT protection problem and in particular a computer attack detection problem. In order to solve this issue, the paper recommends the combination of machine learning approaches and concurrent data processing. The framework is developed and a new approach to the combination of the main classifiers intended for attacks on IoT networks. In which the accuracy ratio to the training time is the integral measure of efficacy, the problem classification statement is developed. We recommend the use of the data processing and multithreaded mode provided by Spark to accelerate the speed of training and testing. In addition, a technique is suggested for preprocessing data set, which results in a large reduction in the sample length. An experimental examination of the proposed approach reveals that the precision of IoT networks' attack detection is 100 percent and the processing speed of data sets increases proportionally to the number of parallel threads.

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Modified Chorological Coded Data Compression Methodology for Wireless Sensor Networks

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Journal of Physics: Conference Series 2021年第1期1770卷

作者： S. Jancy Suji Helen Mercy Paul Selven A. Viji Amutha Mary M.D Antopraveena M.S. Roobini Assistant Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology ChennaiIndia Associate Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Wireless Sensor Network (WSN) is a fast evolving current technology which is being employed in various applications. Despite its wide usage, WSNs have a few challenges to overcome to be called as an ideal technology. Some of the challenges are battery life time, memory storage and deployment issues. Batteries are the primary source of power supply to WSN and one of the major challenges is the energy constraint. This paper aims to propose a few techniques to better the energy efficiency of the sensor networks by saving sensor energy using data compression methodology. In this paper, a modified chorological coded data compression methodology is proposed (MCDC). This algorithm deals with assigning the sequence value to the given input information. If the assigned sequence value is a double digit number, it is converted in to single digit number. Double digit numbers and single digit numbers are combined. Separate location tables are generated for all double digit and single digit numbers. This procedure continues until all the sequence values are changed in to single digit number. Then the final single digit is assign with an equivalent Sequence Code (SC). MCDC algorithm is compared with DELTA compression and RUNLENGTH compression and a better compression ratio was achieved when compared with DELTA compression and RUNLENGTH compression algorithms.

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Real Time Fire detection and Localization in Video sequences using Deep Learning framework for Smart Building

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Journal of Physics: Conference Series 2021年第1期1916卷

作者： P Sridhar R R Sathiya Assistant Professor Ece Sri Ramakrishna Engineering College Coimbatore Assistant Professor Department of Computer Science and Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

This work presents autonomous electrical fire-detection and localization using computer vision based techniques. The proposed work uses YOLO v2 to extract the electrical fire features more effectively than other conventional and machine learning approaches. This working model is tested on commercial and residential building as well as indoor and outdoor environments. This framework has achieved high detection accuracy and low false alarm rate. Besides, the proposed frame work can be used for early real-time electrical fire detection in surveillance videos and we present experimental results for electrical fire localization in CCTV footage using the deep learning architecture proposed in this work.

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computer Vision based Early Electrical Fire-detection in Video Surveillance oriented for Building environment

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Journal of Physics: Conference Series 2021年第1期1916卷

作者： P Sridhar R R Sathiya Assistant Professor ECE Sri Ramakrishna Engineering College Coimbatore Assistant Professor Department of Computer Science and Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

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A Review of Different Approaches for Detecting Emotion from Text

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IOP Conference Series: Materials science and engineering 2021年第1期1110卷

作者： Ashritha R Murthy K M Anil Kumar Assistant Professor Department of Computer Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University Associate Professor Department of Computer Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University

Emotion detection and analysis is one of the challenging and emerging issues in the field of natural language processing (NLP). Detecting an individual's emotional state from textual data is an active area of study, along with identifying emotions from facial and audio records. The study of emotions can benefit from many applications in various fields, including neuroscience, data mining, psychology, human-computer interaction, e-learning, information filtering systems and cognitive science. The rich source of text available in the Social media, blogs, customer review, news articles can be a useful resource to explore various insights in text mining, including emotions. The purpose of this study is to provide a survey of existing approaches, models, datasets, lexicons, metrics and their limitations in the detection of emotions from the text useful for researchers in carrying out emotion detection activities.

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Implementation of station vigilance using RF for railway passengers

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AIP Conference Proceedings 2023年第1期2523卷

作者： S. Mahaboob Basha A. Arun T. D. Subha V. Bhuvaneswari D. KalaiSelvi J. Navin Sankar 1Assistant Professor Department of Electronics and Communication Engineering R.M.K. Engineering College R.S.M Nagar Kavaraipettai Tamil Nadu India 2Assistant Professor Department of Computer Science and Engineering SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu India 3Assistant Professor Department of Electronics and Communication Engineering R.M.K. Engineering College R.S.M Nagar Kavaraipettai Tamil Nadu India 4Assistant Professor Department of Electronics and Communication Engineering SRM Institute of Science and Technology City Campus Vadapalani Chennai Tamil Nadu India 5AP Department of Electronics and Instrumentation Engineering R.M.D. Engineering College R.S.M Nagar Kavaraipettai Tamil Nadu India 6Application Engineer Entuple Technologies Pvt Ltd Bangalore Karnataka India

The main aim of this work is to make the passengers to reach the station in the particular time. The microcontroller is used for prediction alert purpose. Nowadays frequently, during night time people are sleeping in trains and miss their destination station are occurred. Hence, the valuable time is absent. Therefore, using AT89C52 microcontroller, RF controller and RF receiver module the project was designed to avoid such mistakes to alert the passengers and to wake up at the correct station by getting vibration automatically. The objective of this project is to use 89C52 microcontroller to alert the passengers about the station which he/she want to reach mechanically, using RF modules. In front of, 5Km ahead of the station may have transmitter tag to transmit the zone computing by RF signals. A receiver module placed in the seat to get the zone information, by fixing a watch which is to be used to alert the passenger with small vibration to awake.

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Multiple Watermarking Scheme for Video & Image for Authentication & Copyright Protection

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IOP Conference Series: Materials science and engineering 2021年第1期1131卷

作者： Neha Janu Ankit Kumar Pankaj Dadheech Gajanand Sharma Ashutosh Kumar Linesh Raja Associate Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Science & Engineering JECRC University Jaipur Rajasthan India Assistant Professor Department of Computer Applications Manipal University Jaipur Rajasthan India

Watermarking has been used frequently to authenticate the accuracy and security of the image and video files. In the world of computer technology, several watermarking strategies have developed during the past 20 years. Integrate a picture of identity that is not always concealed, such that no detail is not possible to delete. A monitoring code can also be used to deter unauthorized recording equipment. Another application is the watermark copyright control, which works at stopping the creator of the image from stealing photos unlawfully. A watermark is a promising option for the copyright transcendence of multi-media files, as embedded messages are still included. Due to the limits of fidelity, a watermark may be implemented in a small multimedia data space. There is no proof that Automated Watermarking technologies will fulfil the ultimate purpose of the cleaners of all sorts of copyright security operations to gather knowledge from the data they obtain. Relevant situations may be deemed more fairly expected with the usage of automated copyright marking technologies. A perfect device will not be able to add a digital watermark without the limit, which does not supply the whole object with details. In this work, a modern technique for watermarking includes injecting two or more messages or photographs into a single picture for protection purposes and repeating a similar procedure for N-frames for authentication in the film.

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