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CyberHelp: Sentiment Analysis on Social Media Data Using Deep Belief Network to Predict Suicidal Ideation of Students

CyberHelp: Sentiment Analysis on Social Media Data Using Dee...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Sakthi, U. Chen, Thomas M. Sathiyanarayanan, Mithileysh Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur TN Chennai India University of London School of Science and Technology City Department of Electrical and Electronic Engineering LondonEC1V 0HB United Kingdom Mit Square Research and Innovation London United Kingdom

ISBN: (纸本)9781665474511

Suicide is a very critical and important issue in modern society. Suicide is the third-leading cause of death for college and high school students. Social media allows students in the digital environment to share their suicidal ideas and thoughts with others. Accurate and early detection and prevention of suicidal ideation in students can save the students' lives. To identify the risk factor for suicidal attempts, a suitable method of analysing the suicidal behaviour of students using their sentiment text posted on social media can be used. This paper presents an optimized Dragonfly algorithm (DFA) using a Deep Belief Network (DBN) for the automatic detection of suicidal ideation in students. In our CyberHelp Solution, the proposed DFA-based DBN model analyses student social media data, predicts suicidal behavior, and treats students appropriately. The sentiment analysis performs automated categorization of online messages and makes accurate predictions of the student's suicidal behaviors. The dragonfly heuristic optimization algorithm is used for tuning the hyperparameter in the deep belief network. The proposed DFA-DBN technique has been implemented to predict suicidal ideation in students with a higher accuracy of 95.5% compared with other classification models. © 2023 IEEE.

关键词： Students

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Machine Learning-Based Dynamic Signal Switching: A Promising Solution to Traffic Congestion

Machine Learning-Based Dynamic Signal Switching: A Promising...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Kanchana Gopirajan PV Mahalakshmi B B. Vijaya Nirmala Department of Computing Technologies School of Computing SRM Institute of Science and Technology Katankulathur. Chennai Tamil Nadu India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Katankulathur. Chennai Tamil Nadu India Department of Computer Science and Engineering AMC Engineering College Bangalore

Today's growing urban population means increased revenue for individuals, translating into growing vehicles on the road. Nowadays, people use their private vehicles instead of public transportation, which results in a considerable number of personal vehicles that lead totraffic congestion everywhere. The ever-increasing number of vehicles and complex congestion leads to frequent traffic jams in major cities. In today's scenario, traditional traffic management methods have become redundant as the traffic volume has grown exponentially. The primary goal of this proposed work is to develop an algorithm that uses machine learning to control traffic effectively and ensure efficient traffic flow in the event of unforeseen events like accidents. The existing algorithm focuses on predicting shortterm traffic flow but ignores that traffic congestion may also occur due to accidents and emergencies. Our proposed model will consider all the above factors and use dynamic signal switching among lanes to predict the best and most efficient solution to reducing waiting time for vehicles and congestion on the road. To create an effective traffic flow management model with the least amount of road mayhem, our suggested model needs traffic data as input, which was then trained using various Machine Learning (ML) models.

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Cloud Based Smart Medical Recommendation System Using Pattern Recognition

Cloud Based Smart Medical Recommendation System Using Patter...

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Networking and Communications (ICNWC), International Conference on

作者： E. Sujatha S. Shanthasheela P.V. Gopirajan Department of Computer Science and Engineering Saveetha Engineering College (Autonomous)Thandalam Chennai Department of Computer Science and Engineering Velammal Institute of Technology Chennai Department of Computational Intelligence School of Computing SRM Institute of Science and TechnologyKattankulathur Chennai

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques.

关键词： Productivity Measurement Laboratories Process control Software Pattern recognition Task analysis

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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

作者： Kirubhakaran D. Karthikeswaran M. Arun R. Arun Chakravarthy J. Akshya M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A* algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry.

关键词： Industries Adaptation models Heuristic algorithms Real-time systems Path planning Vehicle dynamics Collision avoidance Autonomous vehicles Optimization Logistics

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Bayesian Network-based Active Learning for Ontology-based Video Retrieval using Hidden Markov Logic Model with Edge Histogram Descriptor

Bayesian Network-based Active Learning for Ontology-based Vi...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： B. Sathiyaprasad Sreeji. S B. Satheeshkumar D. Menaka K. Babu G. Maria Jones Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai School of Computing Science and Engineering Galgotias University Greater Noida UP India Department of Computational Intelligence SRM Institute of Science and Technology Katankulathur Chennai India

A large amount of data is collected, analysed, and used on a regular basis by a large number of people for web pages, books, journals, and files which make up a significant portion of the data on the Internet. The burden on significant content-based retrieval and other applications is increasing as the volume of surveillance video data grows. Therefore to reduce the burden during retrieval and to solve the problem of video surveillance an ontology based video retrieval by feature extraction and classification techniques is proposed. In the Initial step the examined form of description files in OWL (Web Ontology Language is fed as input. Then the video pre-processed and segmented before extracting the features. The features of video has been extracted using "Hidden Markov Logic Model With Edge Histogram Descriptor (HMLM_EHD)" in which the spatio based features are extracted with Shape-based features that define object shapes in image is extracted from object contours or regions. After the extraction of features, classification is done using Bayesian network based active learning. The retrieval subsystem evaluates the provided query, performs similarity matching operations (which can be done using the correlation Distance Algorithm), and finally provides the result to the end user for these categorised video frames. The experimental results shows retrieved video frames and their parametric analysis in terms of accuracy of 94%, precision of 85%, recall of 71 % and F-1 score of 73%.

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MRI Image Processing and Segmentation on Brain Tumor: A Survey

MRI Image Processing and Segmentation on Brain Tumor: A Surv...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Jayaraj Ramasamy Sreekumar Narayanan Gopirajan PV Ravikumar RN Department of Computer Science Botho University Botswana Africa Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Engineering Marwadi University Rajkot India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Image segmentation is a critical step in many clinical applications and is crucial in computer-assisted diagnosis. Segmentation of brain is the technique of automatically recognizing malignant brain tissues and labeling them depending on tumor kind. Manual tumor segmentation using brain MRI was time consuming and prone to inaccuracy. In brain MRI research, image segmentation is commonly used to detect and interpret anatomical features, analyze changes to the brain, identify sick areas, organize therapy and image guided surgery. Over the last few decades, different segmentation methods were developed and published in the research, with varying degrees of detail and difficulty. This study will focus on the most common segmentation methods. To begin addressing the complex details and challenges of the MRI Brain issue, we have introduced fundamental image preprocessing stages and then we have performed Image segmentation.

关键词： Surveys Support vector machines Image segmentation Magnetic resonance imaging Brain tumors Surgery Watersheds Artificial intelligence Random forests Cancer

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Attention Based Generative Adversarial Networks to Augment Image Data for Rapid Training

Attention Based Generative Adversarial Networks to Augment I...

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International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production Computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

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Intervention of Digital Twin for Smart Monitoring of Battery Management System 14

Intervention of Digital Twin for Smart Monitoring of Battery...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Joshi, Kapil Gupta, Amit Kaushik, Vishal Anandaram, Harishchander Gupta, Himanshu Kumar, Anil Uttaranchal University Uttaranchal Institute of Technology Department of Cse Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Upes School of Computer Science Dehradun India Amrita Vishwa Vidyapeetham Centre for Computational Engineering and Networking Tamil Nadu Coimbatore India Coer University Roorkee India Dit University School of Computing Dehradun India

ISBN: (纸本)9798350335095

The use of digital twins (DT) has made important improvements not just from the beginning of the process of automobile development through the final phases of vehicle building, but additionally during its usage, collecting essential information from its everyday activities to make traveling more joyful, relaxed, and secure. This study organizes the DT development history, fundamental principles, and essential technologies, as well as summarizing current research methodologies and problems in battery simulation, state estimate, remaining useful life (RUL) projection, battery protection, and management. In addition, study provide methods for batteries digital simulation, real-time status calculation, variable charging supervision, dynamic heat leadership, and energetic leveling regulation in the smart battery management system (BMS) using DT. The study also provides prospects for DT growth in the industry of battery. Lastly, the article concludes with an examination of the technological economic effects of DT technology, which will revolutionize conventional automobile manufacturing, as well as the barriers to continued growth. © 2023 IEEE.

关键词： Blockchain

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Analysis of Panoramic Images using Deep Learning For Dental Disease Identification 3

Analysis of Panoramic Images using Deep Learning For Dental ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Mallick, Mohamed Midhun Kumar, A. Govindaraju, Sakthi Kumar, A. Suresh Kandasamy, Manivel Anitha, P. Rathinam Group of Institutions Coimbaotore India Indian Biological Data Center Center for Biotechnology Database Engineer Faridabab India Galogotias University School of Computing Science and Engineering Delhi NCR India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Karnavati University Unitedworld School of Computational Intelligence Gujarat India Oasys Institute of Technology Trichy India

ISBN: (纸本)9781665462167

Dental caries is a distinct disease strategy caused by the metabolic action of dental biofilm, which continually demineralizes lacquer and dentine. Its commonly known as tooth rot, is one of the most prevalent and long- lasting dental illnesses, affecting people at all stages of their lives. There are a few different sorts of dental x-rays, each of which records a different perspective on dental living systems for caries detection and has the highest symptomatic accuracy, whereas panoramic radiography has the lowest Despite this, Bitewing has a few downsides, such as patient unease. Because of the lack of knowledge among administrators, Bitewing imaging frequently results in increased patient radiation exposure as a result of the need for image retakes. On the other hand, panoramic x-ray scans, also known as Orthopantomogram (OPG), are commonly utilized to capture the whole mouth with a small amount of radiation. OPG has a low radiation content, is simple to use, takes less time, and provides exceptional patient comfort. In this way, when compared to intraoral frameworks, pediatric, impaired, and senior people would profit greatly from panoramic imaging frameworks. This research work explores panoramic photos to detect accurate dental disease using deep learning. As a result, most patients can benefit from panoramic imaging, which covers a large portion of the maxillofacial region, including all of the teeth. © 2023 IEEE.

关键词： Image enhancement

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Improved Metaheuristics with Deep Autoencoders for COVID-19 Detection and Classification 2

Improved Metaheuristics with Deep Autoencoders for COVID-19 ...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Assudani, Manish K Nirmala, B. Kanimozhi, N. Chandre, Shanker Sunalini, K.K. Gopatoti, Anandbabu G H Raisoni University Department of Computer Science and Engineering Maharashtra Amravati India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Department of Computer Science and Engineering Chennai India School of Computing SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SR University Department of Computer Science & Artificial Intelligence Telangana Warangal India Koneru Lakshmaiah Education Foundation Department of English Andhrapradesh Guntur India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350397635

A healthcare monitoring system requires the support of current technologies like big data, artificial intelligence (AI), and machine learning (ML), particularly during the outbreak of COVID19. This pandemic caused millions of death worldwide. ML and AI are used to detect and combat COVID19 in the initial phase. Deep learning refers to a subdivision of ML that utilizes NN for making predictions and learning from data. In the context of COVID19 detection, deep learning (DL) methods can be trained for examining medical images like CT scans and chest X-rays and identify the presence of virus. This study presents an improved whale optimization algorithm with deep autoencoder (IWOA-DAE) algorithm for COVID-19 detection and classification. The presented IWOA-DAE technique makes use of DL model with metaheuristic hyperparameter optimizer for COVID-19 classification. To accomplish this, the IWOA-DAE technique employs bilateral filtering (BF) process to eliminate noisy data. Next, the IWOA-DAE technique uses NASNetLarge model for feature vector generation purposes. For COVID-19 detection, the IWOA-DAE technique uses deep autoencoder (DAE) model with IWOA based optimal hyperparameter adjustment process. To highlight the improved performance of the IWOA-DAE algorithm, a wide-ranging simulation analysis takes place. The simulation outcomes highlighted the enhanced performance of the IWOA-DAE technique over other methods. © 2023 IEEE.

关键词： COVID-19

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