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SARCASAM Analysis in Social Networks Using Deep Learning Algorithm

Procedia computer science 2025年 252卷 510-518页

作者： Priya M Vijaya kumar K Vennila P Prasanna M A Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam -611002 Lecturer Dept. of Computer Science and Engineering Women’s Engineering College Puducherry- 605008 Asst. Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam -611002 Asst. Professor Dept. of Computer Science and Engineering K.Ramakrishnan college of Technology Trichy -621112

Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one’s own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers’ opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner).

关键词： Data analytics Sarcasm Lexical dictionary N-gram Deep learning Text mining

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Real-Time Sign Language Recognition System for Physically Challenged Community Using Deep Learning Technique 8th

Real-Time Sign Language Recognition System for Physically Ch...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Devi, V. Anjana Bhuvaneswari, E. Tummala, Rama Krishna Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Associate Professor Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai India Accenture Solutions Pvt. Ltd Chennai India

ISBN: (纸本)9789819784219

Language disparities have always existed in human civilization. In their community, deaf individuals use sign languages to communicate with one another, but it can be difficult to do so when interacting with the wider population. The project comprises a real-time sign language recognition system that enables hearing-impaired individuals and persons who do not know sign language to effortlessly communicate. The most used sign language in South Asian nations is Indian sign language. Convolution neural networks, specifically Tensor Flow object detection, are utilized to assess and train the model on the images that are used. The transfer training approach is used to train the deep learning model with various hand movements. Loss is anticipated to be decreased. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Computational Overhead Analysis for Mitigation of Content Poisoning Attack in NDN-based IoT Body Area Networks

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Procedia computer science 2023年 230卷 257-263页

作者： M Priya S Castro K Sathesh Kumar N Kalaivani Assoc. Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam 611002 Asst. Professor Dept. of Information Technology Karpagam College of Engineering Coimbatore 641032 Asst. Professor Dept. of Electronics and Communication Engineering Vivekanandha College of Engineering for Women Tiruchengode 637205 Assoc. Professor Dept. of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore 641041

The Body Sensors Network has developed into a significant element of the Medical IoT. Massive data gathering from a resource-constrained network represents a laborious procedure in IoT equipped BSN/WBAN, creating a number of difficult problems. Conventional communication protocols may be used on IoT platforms without any alterations and provide impractical techniques for large-scale networks. The new architecture known as ICN has the potential to overcome these difficulties in huge data collection. According to this perspective, the proposed work indicates a CPA mitigation strategy for information centric WBANs.

关键词： Named Data Networks CP attacks, Efficiency, Overhead analysis

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Enhancing Network Lifetime of WSNs through the Implementation of a Modified Ant Colony Optimization Algorithm

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Procedia computer science 2023年 230卷 368-376页

作者： Malathi Murugesan Kanimozhi R Dharani K G Devi D Assoc. Professor Dept. of Electronics and Communication Engineering E. G. S. Pillay Engineering College Nagapattinam - 611002 Asst. Professor Dept. of Computer Science and Engineering Bharathiyar Institute of Engineering for Women Deviyakurichi Assoc. Professor Dept. of Electronics and Communication Engineering Akshaya College of Engineering & Technology Coimbatore Asst. Professor Dept. of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology

Energy efficient routing has been the mainstay of research in wireless sensor networks. With rising utility of WSNs in a broad range of applications, a significant volume of research contributions are targeted towards obtaining an energy efficient routing with a good quality of service (QoS) metric. In recent times, the utility of evolutionary algorithm for such multi-objective problems has been on the rising trend as evolutionary models closely mimic the real time scenario of any problem optimization. In this research work, a modified ant colony optimization (M-ACO) has been proposed and implemented for optimization in terms of energy conservation and thereby to extend the network lifetime of the network

关键词： WSN Intelligent Routing Energy Efficiency Modified Ant Colony Optimization Hierarchical protocols

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Optimizing Wireless Sensor Network Routing Through Memetic Algorithms: Enhancing Energy Efficiency and Data Reliability

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Procedia computer science 2023年 230卷 150-157页

作者： Aishwarya D Saranya S Sathiya A Samuel Manoharan J Asst. Professor/Dept. of Computer Science and Engineering School of Computing Sathyabama Institue of Science and Technology Chennai India Asst. Professor (SS)/ Dept. of Computer Science and Engineering Dr. N.G.P Institute of Technology Coimbatore India Research Scholar/Dept. of Electronics and Communication Engineering M. Kumarasamy College of Engineering Karur India Professsor/Dept. of Electronics and Communication Engineering Sir Issac Newton College of Engineering and Technology Nagapattinam India

Memetic Algorithm Based Routing in Wireless Sensor Networks (WSNs) is an innovative approach for optimizing data transmission routes within a network of interconnected sensors. Traditional routing algorithms often fail to consider the dynamic and resource-constrained nature of WSNs, resulting in inefficient energy utilization and decreased network lifetime. By incorporating principles of natural selection, crossover, and mutation from genetic algorithms, along with local search methods, memetic algorithms offer a more adaptive solution. This research aims to implement a memetic algorithm to find the optimal routing paths while minimizing energy consumption and maximizing data delivery ratio. Preliminary results indicate a significant improvement in the network's overall performance metrics, including a longer lifespan and higher data reliability, compared to traditional routing methods.

关键词： Wireless Sensor Networks Energy Efficient Routing Memetic Computing Evolutionary algorihtms

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Satellite Image Classification using CNN with Particle Swarm Optimization Classifier

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Procedia computer science 2024年 233卷 979-987页

作者： Vidhya S Balaji M Kamaraj V Asst. Professor/Dept. of Computing Technologires School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Asso. Professsor/Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Professor /Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai

Disaster relief, police work, and environmental monitoring all benefit from satellite images. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satellite images. The results of this classifier model are better than those of existing approaches.

关键词： Deep Learning Convolutional Neural Network Particle Swarm Optimization Satellite Image Classifier Satellite Images

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Heart Rate Anomaly Detection in Healthcare Using Elliptic Envelope and Local Forest

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Procedia computer science 2025年 258卷 1677-1687页

作者： Chandralekha E S. Vinodhini Kandasamy V Rama P Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India-600062 Assistant Professor Department of Information Technology Velammal Engineering College Chennai Tamilnadu India-600062 Assistant Professor Panimalar Engineering College Chennai Tamilnadu India – 600123 Assistant Professor Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India-603203

Medical professionals use Anomaly Detection(AD) to identify patients with potential health problems in the heart rate data (HRD), an essential metric related to cardiovascular health. In this research, the most effective AD techniques were identified in detecting unusual variations of the heart rate pattern. Synthetic HRD data with anomalous rates of 15%, 20%, and 25% was generated and standardized it using min-max scaling for consistency among the datasets. Normal HR was based on function with a mean of 70 and a standard deviation of 5 while anomalous values were generated with a mean of 120 and a standard deviation of 10. Among the evaluated models (Elliptic Envelope, one-class SVM, Local Outlier Factor, and Isolation Forest), the Elliptic Envelope (EE) and Isolation Forest (IF) models obtain the best scores in terms of recall and precision – 0.92 and 0.89, respectively. Overall, the results of this research indicate that equally robust AD models should be implemented within clinical systems and wearable devices that serve as a digital space for early cardiac risk identification, prompting adequate intervention and, as a result, health improvement.

关键词： Anomaly Detection Heart Rate Data Isolation Forest Local Outlier Factor One-Class SVM Elliptic Envelope

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Bridging the Skill Gap Between Academia and Industry in Punjab: A Case Study 20

Bridging the Skill Gap Between Academia and Industry in Punj...

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Proceedings of the 12th International Conference on Education Technology and computers

作者： Gulam B. Mustafa Kadhiravan G Venkatesh Vijay C Malathi S Subburaj S Ashwan Paranthaman UG Scholar at Panimalar Engineering College India Senior Lecturer Computer EngineeringBirmingham City University UK Professor Computer Science Engineering Panimalar Engineering College India Asst. Professor Computer Science Engineering Panimalar Engineering College India Associate Professor Panimalar Medical College Hospital & Research Institute India

ISBN: (纸本)9781450388276

Over the past years, the number of students going abroad for higher studies and settling abroad has increased a lot in northern states of India mainly in Punjab whereas it's significantly less in south India. The purpose of this research is to find the reasons for the brain drain also analyzing the strategies used by south Indian universities so that those ideas can be implemented in Punjab so that the brain drain can be reduced. Using a credits composition analysis for two universities, this study analyzed that the college in Punjab had more credits towards practical and college in South India had more credit composition towards theory. This analysis gave us a result that panimalar engineering college has found a sweet spot in the composition of credits along with some other factors. Using a comparative analysis of various factors for various south Indian Universities along with Punjab University, we have found out that there are many other factors that also influence's the brain drains along with Industry Institute Relationship. We have found out the methodologies used by south Indian universities to prevent brain drain. This study definitively answers the question regarding reasons for brain drain in Punjab along with the solutions to overcome it. Further Detailed studies are needed along with the implementation of these ideas to develop preventative measures.

关键词： Curriculum Employability Employability skills Higher education Strategies or approaches

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UGVs for Agri Spray with AI assisted Paddy Crop disease Identification

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Procedia computer science 2023年 230卷 70-81页

作者： K. Sujatha T. Kalpalatha Reddy N.P.G. Bhavani R.S. Ponmagal V. Srividhya N. Janaki Professor EEE/ECE Department Dr. MGR Educational and Research Institute Chennai-600095 India Asso.Professor ECE Department Saveetha Institute of Medical Sciences Chennai India Professor CSE Department SRM Institute of Science and Technology Kattankolathur India Asso.Professor EEE Department Meenakshi College of Engineering Chennai India Asst. Prof Vels Institute of Science Technology and Advanced Studies Chennai India

This research work involves data access and control of Unmanned Ground Vehicles (UGVs) which is designed for health monitoring of paddy crops as an exception from traditional monitoring system. The proposed scheme of autonomous navigation of drones is planned to be carried out as per the six milestones for disease detection and control in paddy crops. Drones serve as Unmanned Robotic Vehicles (URV) capable of performing desired tasks in unstructured, uncertain and potentially hostile environments and are remotely-operated without human intervention. URVs function completely as autonomous entities in diversified environments. Current UGVs adhere to different levels of automaticity. Typically the vehicle follows high level waypoints spaced for few hundred meters of distance to provide monitoring of agricultural fields and early detection of the various diseases that may occur in the paddy fields in a polyhouse. To increase the vehicle's abilities, tracking efficiency, obstacle avoidance, path planning or lead and follow up augmented control with Fuzzy Logic Controller (FLC) is incorporated. The distributed autonomous system for information gathering related to the paddy crops in polyhouse is enabled using different sensors, which is a data-intensive task. To increase the robustness of the system, fuzzy controllers are proposed to control the navigation of the proposed UGV in “All terrain conditions”. They are needed to offer problem specific heuristic control knowledge for the Inference Engine Design which occurs due to imprecision and uncertainty of the sensor readings. It also requires low computation time which favours the polyhouse situations. The navigation of UGV and the FLC action will in turn depend on “All terrain traversability” and “dead zone” monitoring. The proposed UGV model is capable of measuring the parameters associated with the paddy crops inside a polyhouse. The various diseases in paddy crops are False Smut (FS), Sheath Blight (SB), Rice Blast (RB

关键词： Median filtering Improved Canny Algorithm Convolutional Neural Networks (CNN) Paddy Crop Disease Identification Unmanned Ground Vehicle

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IoT based Power and Water Theft Detection in Urban Cities

IoT based Power and Water Theft Detection in Urban Cities

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Praful V. Nandankar G.N.R. Prasad M. Lakshminarayana Mr. Doddabasappa N G. Satish Ramya Maranan Assistant Professor Department of Electrical Engineering Government College of Nagpur Maharashtra India Sr. Asst. Professor Department of Master of Computer Application Chaitanya Bharathi Institute of Technology (A) Hyderabad India Assistant Professor Department of Medical Electronics Engineering M S Ramaiah Institute of Technology Karnataka India Assistant Professor School of Electrical and Electronic Engineering REVA University Bengaluru Karnataka India Associate Professor Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Ibrahimpatnam Hyderabad Telangana India Assistant Professor Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

Many people in metropolitan areas have experienced problems with energy and water shortages. Some people used modern technologies and advancements to theft water and power illegally. Because the planet absolutely needs both water and power. Currently, the Internet of Things (IoT), smart meters, smart pipelines, and smart homes are being heavily integrated to replace traditional systems for detecting theft activities. This can be digitizing both the meter readings and the data that has been gathered. Wireless technology may be used to collect and analyze the data, which helps to reduce the need for physical labor. In this project, we suggested creating an IoT-based system for water and power theft detection. The proposed project aims to design a system to monitor the amount of power and water used by the people and indicates if water and electricity theft activities happen. This research study offers a user interface system to help people to monitor whether anyone is stealing their water or power. This study employs a hardware like voltage sensors, current sensors, flow sensors, and additionally, the Arduino IoT cloud to enable remote control and monitor the water and power usage.

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