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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms 5

Successful Integration of IoT and Blockchain Technologies Us...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Calarany, C. Indumathy, M. Senthilraja, P. Suganya, D. Gopi Vardhan, V. Rahul, Yelisetty Shanmukh Tagore Government Arts and Science College Department of Computer Science Puducherry India Rajiv Gandhi College of Engineering & Technology Department of Information Technology Kirumampakkam India K.S.Rangasamy College of Technology Department of Computer Science Tamil Nadu India Rajiv Gandhi College of Engineering & Technology Department of Computer Science Kirumampakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction. © 2024 IEEE.

关键词： IoT MML RNN

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Attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays 1

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Karkera, Thejas Singh, Chandra Bekal, Anush Shetty, Shailesh Prajwal, P. Department of Electronics Communication Engineering Sahyadri College of Engineering and Management Mangalore India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

Covid-19 pandemic is spreading across the world at a breakneck pace. As of September 2022, JHU CSSE COVID-19 data and Our World in data have recorded up to 5.2 M deaths due to Covid-19. One of the essential steps of fighting this pandemic is to detect the disease early. Studies show some abnormalities in the chest X-rays of the Covid-19 patients, and these features help classify the covid-19 positive patients from the negative patients. In recent years in the field of medical x-rays, Deep Learning models have proven to have the ability to learn and identify features that a trained person can only identify. Due to the exponential increase in the covid-19 cases, the waiting time for covid results has increased, resulting in the late diagnosis of patients for Covid-19. Classifying a patient as Covid-19 positive for a genuinely covid positive patient is as essential as classifying a patient Covid-19 negative who is genuinely covid-negative. Here we present a detailed quantitative analysis on the performance of state-of-the-art models like ResNet-50, Dense-Net, Mobile-Net-V2, and MNAS-Net on classifying patients for Covid positive, Pneumonia positive, and Normal by evaluating Accuracy, Precision, Recall, and Jaccard Index. We propose a new deep neural network classification model for low-end devices that uses two Attention mechanisms. Figure 1. shows the attention map with confidence percentage for each classes. Our model uses fewer parameters and FLOPS than other state-of-the-art models and recorded a 2% increase in Accuracy and other evaluation parameters. The dataset used for implementation is a public dataset COVID-dataSET and NIH Chest X-ray from kaggle. It has over 1000 images of Covid Chest X-ray, Pneumothrax, Mass, Pneumonia, Cardiomegaly, Nodule, Effusion, Atelectasis and inflitration. All the model implementations are implemented on PyTorch. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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Diabetic Retinopathy Severity Detection an Automated Tool 1

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World Conference on Information systems for business Management, ISBM 2023

作者： Rao, Supriya B. Shetty, S. Shailesh Padmanayana Thanmayee, S. Divya Department of AI&ML New Horizon College of Engineering Bangalore India Department of CS and Business System Srinivas Institute of Technology Mangalore India Department of CSE Srinivas Institute of Technology Mangalore India Department of Data Science Srinivas University Institute of Engineering and Technology Mangalore India Department of CSE AJ Institute of Engineering and Technology Mangalore India

ISBN: (数字)9789819986125

ISBN: (纸本)9789819986118

Diabetics start to occur when there is not enough insulin secretion in the body. Diabetes is the primary cause of diabetic retinopathy in patients, which leads to vision impairment. The main reason for vision impairment is that lesions get accumulated in the retina. Early detection of diabetic retinopathy using the fundus images surely helps in faster recovery of the patient. Manual detection of the severity of diabetic retinopathy requires time, which may cause a delay in cure. To automate the process of severity detection, a convolutional neural network (CNN) is developed and presented, which is capable to predict the severity of the diabetic retinopathy in the fundus images. A model developed can learn the features of the retinal images and predict either of the five stages of diabetic retinopathy that is non-proliferative diabetic retinopathy, mild non-proliferative diabetic retinopathy, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy, and proliferative diabetic retinopathy. Images were enhanced using various methods. After conducting tests on the model using random test data, the model proved to be reliable resulting in 95.5% specificity and 93.5% sensitivity. With this specificity and sensitivity, our model is reliable when deployed to the real world where the doctors in the medical field can rely on in detecting the presence of diabetic retinopathy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive Intrusion Detection in Cyber-Physical systems using Reinforcement Learning-Based Autoencoders 2

Adaptive Intrusion Detection in Cyber-Physical Systems using...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Rajathi, N. Divya, S. Anitha Elavarasi, S. Saritha, G. Jeya ramya, V. Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

The increasing integration of Cyber-Physical systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection system (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments. © 2024 IEEE.

关键词： Network intrusion

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Deriving Pipeline for Emergency Services Using Natural Language Processing Techniques 11th

Deriving Pipeline for Emergency Services Using Natural Langu...

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Anand, Akshat Rajeswari, D. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India

ISBN: (纸本)9789811988240

Communications have a crucial role in times of crisis, particularly in times of emergency. Whenever a region is affected by any disaster, social media sites such as Twitter, etc., are a great way to get information out to those who need it the most. Our paper suggests that in times of crisis, when people are worried about finding the right emergency contacts or are experiencing a transformation in their psychological response, they should manually enter their experience into an app to obtain contact information for the nearest SOS. Alternatively, the proposed application suggests scraping hashtags from around the world with web scraping scripts to retrieve geo-referenced tweets in real time. In order to gather accurate data, Twitter's API or some third-party application can be used in the development of this software. When there is a major crisis, social media user behavior plays a vital role, owing to its efficiency and high reliability with swift data dissemination. Anxiety over finding the right emergency team contact may cause people to lose their cool and become unclear about which authority they should call when an emergency occurs. Eventually, our application bridges the gap between clients in need of help and the approved agency that can really provide that support in different circumstances. In the case of, for example, a lost child or a flooded area, our application helps you get in touch with an organization that can assist you in dealing with the situation. We have used CNN-1-D + LSTM, CNN-1-D + Bi-LSTMs, and CNN-1-D + LSTM + Glove-100-D to get maximum accuracy for the classification model. It is possible to use the prototype solution in a novel way to organize immediate assistance with authorities in comprehensive administration, while integrating Twitter or other social platforms into the application can make it a feasible platform for preventive intervention and catastrophe management. © 2023, The Author(s), under exclusive license to Springer Nature

关键词： Emergency services

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Application of data Analysis and Visualization Tools for U.S. Renewable Solar Energy Generation, Its Sustainability Benefits, and Teaching In engineering Curriculum

Application of Data Analysis and Visualization Tools for U.S...

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2024 ASEE Annual Conference and Exposition

作者： Radhakrishnan, Ben D. Altamirano, Nelson Jaurez, James J. Department of Engineering Data and Computer Sciences College of Business Engineering and Technology National University San DiegoCA United States Department of Finance Economics Marketing and Accounting College of Business Engineering and Technology National University San DiegoCA United States Department of Computer Science & Cyber Security College of Business Engineering and Technology National University San DiegoCA United States

The advancement of data Analysis technologies with visualization has gained significant ground in the industries and they are also gaining ground in higher education curriculum. This research will focus on the application of these techniques to the energy industry - in particular, solar renewable energy generation in all the States in the United States (US). One strong opposing action against the progression of climate change is the use of renewable energy. The objective of the research is to develop a case study on renewable solar energy and its impact on abating or preventing CO2 emissions to help reduce the severe impacts of climate change in the United States and demonstrate how it supports the 3Es of sustainability at the same time. This research paper will specifically explore the past production of solar energy in all the states in the US, and with the use of data analysis tools will predict the production to the year 2030. The reduction of CO2 emissions with the use of renewable solar energy is in direct support of the three elements of sustainability, namely the 3Es: Environment, Economics, and Equity (or social justice). This research will quantify the past benefits already realized in all these three areas for solar energy, and project them up to 2030. Cluster analysis technique will be applied to solar generation across all US States to identify group(s) at distinct levels of production. This can help States to follow the leading State(s) policy and process to increase their solar generation and thus help manage climate change. Solar energy generation challenges and recycling issues of solar equipment will also be addressed. This case study approach will fill a gap that currently exists in engineering education when it comes to exploring renewable energy and its sustainability benefits with modern data analysis tools. This research will use publicly available data sources (e.g., NREL, EIA), ubiquitous Excel, and open-source data analytics and prediction

关键词： Curricula

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A Novel Energy Efficient Sensor data Encryption in Fog Based IoT Networks

A Novel Energy Efficient Sensor Data Encryption in Fog Based...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Manicka Raja, M. Manoj Kumar, S. Kiruba, B. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350319088

Smart devices equipped with embedded systems, including CPUs, sensors, and communication hardware, utilize web connectivity to gather and relay data from their environment, forming a segment of the Internet of Things network. Sensors in an IoT network are resource-constrained devices, but traditional data security techniques use complicated security mechanisms with long processing and reaction times, which reduce the network's overall lifespan. As a consequence, we proposed an EELWEP (energy efficient light weight encryption process) system to keep the data acquired by each sensor node private. When using this strategy, the secret key is exchanged using the Diffie-Hellman method using the Pretty Good Privacy (PGP) program, and a large portion of the operations is carried out via the use of symmetric cryptography. The energy usage and calculation time on the sensor network are significantly reduced as a result of this method. The suggested system is simulated, and their results are analysed using a variety of parameters in comparison to existing benchmark schemes. Comparing the suggested technique to the current approaches reveals that it outperforms the alternatives in the vast majority of situations studied. © 2023 IEEE.

关键词： Internet of things

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Empirical Assessment and Detection of Suicide Related Posts in Twitter using Artificial Intelligence enabled Classification Logic 2

Empirical Assessment and Detection of Suicide Related Posts ...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Ravishankar, T. Nadana Kumar, Ata. Kishore Venkatesh, J. Prabhu, M.Ramkumar Bhargavi, V. Sharmila Muthamilselvan, S. Srm Institute of Science and Technology Department of Data Science and Business System Kattankulathur India School of Engineering Department of ECE Tirupati India Chennai Institute of Technology Department of Cse Chennai India Peri Institute of Technology Department of ECE Chennai India R.M.K. Engineering College Department of Cse Chennai India Sri Sairam Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350315905

The identification of suicidal thoughts in online social networks is an expanding field of study fraught with major challenges. Recent studies have shown that the readily available data, dispersed over many online life phases, contains useful clues for accurately identifying persons with suicidal intentions. The primary challenge in preventing suicide is learning to recognize and respond appropriately to the sometimes-confusing risk factors and warning indications that may precipitate an attempt. Indicators useful for diagnosing people with suicide thoughts can be found in publicly available material shared over social media platforms, according to recent studies. Understanding and recognizing the myriad risk factors and warning symptoms that may precede a suicide attempt is the primary difficulty in this area of public health. In this research, we developed a benchmark for multi-class categorization using machine learning models. We used a majority classifier, a frequency-based technique, and two deep learning models as our models. Both deep learning models outperformed the majority and the word frequency classifier, with results that were very comparable. These classification results are on par with the state-of-the-art on similar problems and, in most cases, with human results. © 2023 IEEE.

关键词： Support vector machines

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