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

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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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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An integrated and Dynamic Wireless Intrusion Exposure Solutions based on Neural Network

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

作者： S.L. Jany Shabu J. Refonaa Sardar Maran S. Dhamodaran Vedanarayanan Assistant professor Dept. of Computer Science Sathyabama Institute of Science and Technology Chennai Assistant professor Dept. of Electronics & Telecommunication Engineering Sathyabama Institute of Science and Technology Chennai

Intrusion Exposure Solutions in wireless can be categorized into Outlier Intrusion Detection and Exploitation Intrusion Detection. Outlier Intrusion Detection consider the unusual identified wireless attacks. Misuse Intrusion Detection method is the identification of the known attacks. In this proposed solution, present a half breed wireless intrusion Exposure Model. To execute the wireless intrusion Exposure Model, we planned a straightforward lightweight operator. The proposed specialist identifies the most decimating and genuine assaults; Man The Middle and Denial of Provision; with the base chosen highlight set. To assess our proposed Exposure and its operator, we gather a total informational index utilizing open source assault generator virtual products. In the proposed system features are analyzed manually; it extracts the features from the raw dataset. The proposed system utilizes the LSTM systems to deal with the successive idea of the PC organize information. We dole out an edge esteem dependent on cross-approval to order whether the approaching system information succession is irregular or not. In addition, the proposed system can chip away at both stable and adjustable length information arrangement and works productively for unexpected and capricious system assaults. We at that point additionally utilize the unaided rendition of the Long Short Term Memory and Neural Networks.

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Mitigating post-harvest losses through IoT-based machine learning algorithms in smart farming

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AIP Conference Proceedings 2024年第1期3161卷

作者： R. Rajesh Kanna T. Mohana Priya V. Sivakumar Chandrasekharan Nataraj Jishamol Thomas Assistant Professor Department of Computer Science CHRIST (Deemed to be University) Bangalore India Associate Professor School of Computing Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Senior Lecturer School of Engineering Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia Research Scholar Department of Sociology and Social Work CHRIST (Deemed to be University) Bangalore India

This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem.

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A System to automate the development of anomaly-based network intrusion detection model

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Journal of Physics: Conference Series 2021年第1期2089卷

作者： B Padmaja K Sai Sravan E Krishna Rao Patro G Chandra Sekhar Associate Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India Student Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India Assistant Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India

Cyber security is the major concern in today's world. Over the past couple of decades, the internet has grown to such an extent that almost every individual living on this planet has the access to the internet today. This can be viewed as one of the major achievements in the human race, but on the flip side of the coin, this gave rise to a lot of security issues for every individual or the company that is accessing the web through the internet. Hackers have become active and are always monitoring the networks to grab every possible opportunity to attack a system and make the best fortune out of its vulnerabilities. To safeguard people's and organization's privacy in this cyberspace, different network intrusion detection systems have been developed to detect the hacker's presence in the networks. These systems fall under signature based and anomaly based intrusion detection systems. This paper deals with using anomaly based intrusion detection technique to develop an automation system to both train and test supervised machine learning models, which is developed to classify real time network traffic as to whether it is malicious or not. Currently the best models by considering both detection success rate and the false positives rate are Artificial Neural Networks(ANN) followed by Support Vector Machines(SVM). In this paper, it is verified that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperforms support vector machine (SVM) technique while classifying network traffic as harmful or harmless. Initially to evaluate the performance of the system, NSL-KDD dataset is used to train and test the SVM and ANN models and finally classify real time network traffic using these models. This system can be used to carry out model building automatically on the new datasets and also for classifying the behaviour of the provided dataset without having to code.

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