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Predicting and Navigating Environmental Transformations using Machine Learning 2

Predicting and Navigating Environmental Transformations usin...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Lakshin Archana, A. Nath, Keshab Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam686635 India

In today's world, as ecological challenges continue to rise, the importance of environmental monitoring and conservation cannot be overstated. These efforts play a vital role in countering the adverse impacts of human activities on our planet. The development of predictive models that can accurately predict the results of different conservation measures is essential for the success of these conservation efforts. This paper's objective is to create a Machine Learning (ML) model that predicts how environmental changes can unfold. This prediction helps in proactive monitoring and conservation planning, considering factors like deforestation, air pollution, sea level rise, temperature shifts, and forest preservation. The model relies on machine learning techniques, including AdaBoost, ETS, Prophet, SARIMA, LightGBM, and TL-BLSTM models, to analyze data, uncover trends, and inform decision-making. By offering a thorough assessment of the effects of conservation approaches, this model aids in shaping well-informed decisions regarding the distribution of resources. As a result, this process strengthens initiatives designed to protect biodiversity and the Earth's natural resources. Predicting changes in environmental variables is the main goal of this research, which will improve effective environmental management. In order to draw conclusions that are meaningful, this study compares several models for each of the four crucial factors. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sea level

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Social Distance and Face Mask Detection using vision-based sensors and IP cameras 3

Social Distance and Face Mask Detection using vision-based s...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Vineela, A. Devi, G. Lavanya Deekshit, G. Vinay GMR Institute of Technology Department of Computer Science and Engineering Rajam India Department of Computer Science and System Engineering Vishakhapatnam India

ISBN: (纸本)9781665494007

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

关键词： computer vision

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Leveraging Sentiment Analysis and Emotion Detection for Mental Health Insights

Leveraging Sentiment Analysis and Emotion Detection for Ment...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Jose, Joysh Jayaron Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391213

This paper aims to establish a comprehensive analysis of mental health patient reviews using various NLP models, to derive insights and propose improvements that will lead to the enhancement of various mental health facilities. These NLP techniques were further used to categorize key themes such as doctor behaviour, communication and cost. A thorough correlation analysis was also conducted, understanding the relationship between various themes and how their frequency affects the overall patient satisfaction. The findings indicate that attentive listening, respectful staff behavior, and affordable services are critical to improving patient satisfaction. While speaking of mental health is still a taboo in various places, it is important to understand the frequency of the rising mental health related cases such as depression, anxiety, stress and social stigma associated with it. It is indeed much more important to provide better resources and lend understanding hand to the unaddressed mental health problems that are prevalent in various countries and places. © 2024 IEEE.

关键词： Electronic health record

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Enhanced Fake News Detection With Natural Language Processing

Enhanced Fake News Detection With Natural Language Processin...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Shrivastava, Sakshi Singh, Pushpa Kumar, Pankaj Gl Bajaj Institute Of Technology And Management Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350366846

The ubiquitous use of social media impacts the social life of humans. Fake news negatively affects a person's behavior. Fake news detection in social media has been a challenging problem for the last decade. Researchers are searching for accurate methods to detect fake news as early as possible. This paper discusses two NLP methods, LSTM, and bi-directional LSTM, that detect fake news. This paper uses a fake news challenge dataset, categorized into four stance labels: agree, disagree, discussed, and unrelated. Experimental results showed LSTM model received 92% accuracy in testing time and 95% accuracy in training time. On the extension of the method, Bi- directional LSTM achieved 95% accuracy in testing time and 98% accuracy in training time. © 2024 IEEE.

关键词： Natural language processing systems

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Guarding the Beats by Defending Resource Depletion Attacks on Implantable Cardioverter Defibrillators 1

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10th International Conference on Mathematics and Computing, ICMC 2024

作者： Mitra, Anisha Chowdhury, Dipanwita Roy Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (数字)9789819720699

ISBN: (纸本)9789819720682

Implantable Medical Devices (IMDs) have revolutionized the treatment of critical diseases. However, the increasing reliance on these life-saving devices’ wireless functionality has made them vulnerable to cyber-attacks. Implantable Cardioverter Defibrillator (ICD) has emerged as a leading IMD owing to the worldwide surge in cardiac diseases. Given the resource-constrained ICD environment, there’s a pressing need to develop tailored security measures for protection, moving beyond traditional approaches. In this paper, we present resource depletion attack scenarios in an ICD environment where attackers can exploit the ICD’s wireless connectivity function. We propose some comprehensive approaches to mitigate such attacks, offering a significant step forward in safeguarding the well-being of patients. This research contributes to the ongoing efforts to secure the Internet of Medical Things (IoMT) ecosystem and underscores the importance of cyber-security in modern healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Defibrillators

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Advancing Healthcare Accessibility: Development of an AI-Driven Multimodal Chatbot 4

Advancing Healthcare Accessibility: Development of an AI-Dri...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Garimella, Bala Subrahmanyam Garlapati, Hari Sharan Choul, Sriharini Cherukuri, Rajesh Lanke, Pallavi B v Raju Institute of Technology Dept.Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350349900

In today's rapidly evolving healthcare sector, the need for efficient and accessible medical assistance is more crucial than ever. With the advancements in Artificial intelligence (AI), healthcare chatbots have emerged as a promising solution to address this need. These chatbots are capable of providing personalized medical information, scheduling appointments, and offering support to patients, thereby enhancing healthcare accessibility and patient engagement. The purpose of this project is to develop an advanced AI-powered healthcare chatbot that builds upon existing technologies to bring significant improvements in patient interaction and healthcare delivery. By leveraging the capabilities of Natural Language Processing (NLP) algorithms and deep learning techniques, the project aims to enhance the chatbot's functionality by integrating multimodal interaction features, enabling users to interact via text, voice, etc and provide more accurate and relevant responses to user queries. Additionally, the project seeks to create a comprehensive patient portal where users can securely access their medical records, reports, appointments, and engage with the chatbot for personalized assistance. © 2024 IEEE.

关键词： Deep learning

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Vehicular Fog Computing through Nash Equilibrium in Game Theory

Vehicular Fog Computing through Nash Equilibrium in Game The...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Shukla, Aditya Agarwal, Mayank Harnesha, Jenit Bandyopadhyay, Anjan Kalinga Institute of Industrial Technology Bhubaneswar Computer Science & Engineering Odisha Bhubaneswar751012 India

ISBN: (纸本)9798350361537

In the fast-changing environment of intelligent transportation systems, vehicular fog computing (VFC) is emerging as a critical technology for real-time data processing at the network's edge. This study describes a novel way to optimize VFC resources and services using the strategic perspective of Nash Equilibrium in game theory. We find equilibrium solutions that reconcile individual utility maximization with collective resource efficiency by modeling the interactions between autonomous vehicles and fog nodes as a non-cooperative game. Our suggested system not only improves computational offloading performance but also ensures fairness and stability in the vehicular network. We illustrate the efficacy of our optimization strategy by conducting thorough theoretical analysis and extensive simulations, opening the way for a more resilient and adaptive VFC ecosystem. © 2024 IEEE.

关键词： Fog computing

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Early recognition of Alzheimer’s disease using machine learning

Early recognition of Alzheimer’s disease using machine lear...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Nagaraj, Prajwal Koundinya, Anjan K. Thippeswamy, G. Computer Science and Engineering B.M.S Institute of Technology and Management Bangalore India

ISBN: (纸本)9781032669663

Alzheimer’s Disease (AD), a neurological disorder, is the most common cause of dementia in those over the age of AD must be precisely and soon identified in order to halt the progression of this lethal condition. This article focuses on a machine learning method for MRI images to identify The condition Alzheimer’s. The brain’s hippocampus region is the fundamental objective of the recommended tactic. Use a Gray Level Concidence Matrix to extract the hippocampus region’s textural characteristics, including entropy, uniformity, energy, contrast, correlation, and variance (GLCM). Using moment invariants, area and form elements are retrieved. A classifier is utilised to identify distinct phases of AD using error propagation (EBP) of an artificial neural network. An average accuracy of 86.8% is offered by the proposed technique. © 2024 The Author(s).

关键词： Neural networks

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An Efficient IoT-Blockchain Sharding Technique using Frequently Transacting Sender and Receiver Information 9

An Efficient IoT-Blockchain Sharding Technique using Frequen...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Pramod Jain, S.A. Yashaswini, H.M. Salvi, Sanket Dashputre, Nupur Tyagi, Saanya Panchal, Krishna Nitte Meenakshi Institute of Technology Department of Information Science And Engineering Karnataka Bangalore India Dr. Ambedkar Institute of Technology Department of Computer Science And Engineering Karnataka Bangalore India Mit World Peace University School of Computer Engineering Technology Maharashtra 411038 India

ISBN: (纸本)9798350394474

Distributed systems have become an integral part of many modern applications, processing large amounts of data and serving large numbers of users simultaneously. However, as these systems grow in size and complexity, it becomes increasingly difficult to ensure optimal performance and scalability. This scenario is more common for Internet of Things (IoT) applications. In this research, the problem of limited scalability of IoT frameworks is addressed by proposing a blockchain-integrated new sharding algorithm. The algorithm is based on analyzing transaction records of frequently traded sender-receiver pairs. Our goal is to improve the overall performance of the system and improve its ability to handle higher volumes of transactions. By analyzing a real dataset, we observed a significant reduction in overall execution time by about 10%, demonstrating the effectiveness of our proposed sharding algorithm. Additionally, we found that the algorithm continued to improve the transaction per second rate (TPS) as the number of tasks processed increased. These results highlight the potential of our proposed method for optimizing the efficiency and scalability of distributed systems. Further investigation and analysis across different datasets and system configurations are recommended to validate the generalizability and effectiveness of our approach. © 2024 IEEE.

关键词： Scalability

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Advancing Image Forgery Detection: A CNN-Based Approach 8

Advancing Image Forgery Detection: A CNN-Based Approach

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rakshitha, Sai Nithyashree, H.V. Rakshitha, S.L. Shravan, Motamarri Sai Uttej, K Sai Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

In the digital age, image forgery is a significant concern due to advanced editing tools. This study addresses the need for reliable forgery detection, focusing on copy-move, splicing, and retouching techniques. Using Convolutional Neural Networks (CNNs) known for learning hierarchical image features, the research developed a robust detection system. The CNN model, trained on a diverse dataset which has over 10,000+ images and it is split into test and train data for better classification results, detected subtle inconsistencies indicative of manipulation, achieving a peak accuracy of 94% over 50 training epochs. Test cases showed the system effectively detected forged images and identified forgery types, though it had shortcomings with invalid inputs. The CNN architecture employed convolutional and max-pooling layers with specific hyperparameters to enhance feature extraction. This paper offers a valuable tool for digital forensics experts, law enforcement, and journalists to ensure the authenticity of visual content. © 2024 IEEE.

关键词： Convolutional neural networks

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