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MACBT Algorithm: Two-Layer Security in WSN 2

MACBT Algorithm: Two-Layer Security in WSN

引用

2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Anand, Santosh Thilagaraj, T. Firdose, Seema Bharath, B.C. Dayananda Sagar College of Engineering Department of Information Science and Engineering Kumaraswamy Layout Karnataka Bangalore India Moodlakatte Institute of Technology Department of Computer Science and Engineering Moodalakatte Karnataka Kundapura India

ISBN: (纸本)9798350328202

WSN is deployed in several critical places to monitor specific parameters such as border monitoring systems and others. If any unauthorized devices are placed or inserted into this network, then it will lead to huge information loss or threat. Several works are done in this field of study, including fake node detection, encryption, malicious node detection, etc. With the help of physical and analytical redundancy (spatial redundancy) in the system, error recovery can be made if the sensor node gets corrupted (fake node) and still sends authentic data. Several methodologies are implemented on wireless sensor networks to improve security, such as Conditional Random Field, Hidden Markov models (HMMs), SDN gateway, the hybrid model combines blockchain technology with CNF and HMM. Recently, encryption techniques have been used in the implementation of blockchain technology to improve security. © 2024 IEEE.

关键词： Hidden Markov models

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Metrics Evaluation of Bell Pepper Disease Classification Using Deep Convolutional Neural Network (DCNN) 1

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2nd International Conference on Women Researchers in Electronics and Computing, WREC 2023

作者： Sowmiya, K. Thenmozhi, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9789819970773

ISBN: (纸本)9789819970766

Crop disease is one of the major issues in agricultural fields. It reduces the food production process and produces a huge economic loss for farmers in agricultural lands. Deep learning models are built for detecting and identifying diseases at an early stage to help the farmers and provide food security. In this paper, transfer learning approach is used to identify and classify the diseased portion of leaves in farming lands. The benchmark dataset was taken from the Internet (Kaggle) for bell pepper leaf for identifying the diseases such as pepper leaf spot, mosaic virus, blight, damping off disease, verticillium wilt, blossom end rot, yellowing leaves, curling leaves, and healthy leaves. Ten pre-trained models are built for recognizing diseases and compared to obtain better accuracy. Adam optimizer and binary cross-entropy is used in the model for calculating loss function. In this paper, experimental results are presented on bell pepper diseases for MobileNetV2 with better accuracy of 99.42%. The various performance metrics such as accuracy, precision, recall and F1-score, ROC curve are used to determine the accuracy of the model. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Tutorial on Automated Trading using API 23

Tutorial on Automated Trading using API

引用

6th ACM India Joint International Conference on Data science and Management of Data, CODS-COMAD 2023

作者： Chakole, Dr. Jagdish Kurhekar, Dr. Manish Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur India Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur Maharashtra Nagpur India

ISBN: (纸本)9781450397988

Automation has played a significant role in many domains, and stock market trading is not an exception. Even retail traders can automate his/her trading strategy using API. A computer program doing trading is known as Algorithmic Trading. It eliminates inefficiency due to human emotions. Trading logic that decides when to buy and sell a stock is generally term as a trading strategy. The availability of large trading data has made it possible to automate the generation of dynamic trading strategies. Speed and accuracy are very vital aspects of profitable stock market trading. Algorithmic trading is far superior to manual trading in terms of speed and accuracy. In this tutorial, we demonstrate the automation of predefined trading strategies using Python API. We will also demonstrate the generation of trading strategies using Reinforcement learning, Deep learning, and various other domains in computer science. Validating the performance of any predefined trading strategy on historical data plays a significant role in its live performance and it is known as backtesting. Backtesting is the key feature of Algorithmic Trading. We will demonstrate backtesting of the trading strategies using a computer program on the Indian and American stock market data. © 2023 ACM.

关键词： Automation

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Real estate price prognostication through machine learning models

Real estate price prognostication through machine learning m...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Vestly, Doddamani John Thangallapelly, Neelima Manish, Gandra Sai Hariharan, Shanmugasundaram Kukreja, Vinay Venkateswara Reddy, H. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350348460

In the dynamic realm of real estate, house prices became the crucial part to encounter the set of hurdles to make lives better. Through meticulous feature engineering, which includes advanced data cleansing and feature manipulation, coupled with robust machine learning techniques, our model offers a nuanced understanding of valuation dynamics. The optimization process involves hyper parameter tuning and cross-validation, employing cutting-edge methodologies to extract latent patterns and yield meaningful insights from the underlying data. Leveraging algorithms of supervised learning algorithms like linear regression and K-fold, chosen for their ability to discern intricate patterns within diverse datasets, our research pioneers a transformative approach to real estate valuation. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and R-squared were used to ensure a robust and accurate predictive framework which were promising © 2024 IEEE.

关键词： Algorithms Data science Feature engineering Machine Learning Predictive Modeling Real Estate Valuation

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Ventricular Vision: AI-Driven Heart Disease Detection with CNN-SVM-Based Classification

Ventricular Vision: AI-Driven Heart Disease Detection with C...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Gupta, Richa Garg, Navin Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

This study builds upon the CNN-SVM model as a sound diagnostic method for identifying five distinct heart diseases. We utilize the effectiveness of the deep learning procedures of various CNNs and the ability of the SVMs, which, in the end, might be customized and optimized to solve particular cardiac diseases. Two classifications examined are atrioventricular block, arrhythmia, bradycardia, tachycardia, second-degree heart block, and annexation. The appraisal of CNN-SVM model performance accomplished through various parameters was detailed. The precision of the correct classes arrayed from 94.89% to 97.00%, the recall ranged between 93.70% and 98.91%, and the F1-scores were also high, extending from 94.87% to 97.25%. A significant enhancement in the precision and accuracy of the model was registered, which demonstrated statistics close to 0.99 for Class 1, 0.98 for Classes 2 and 3, and 0.99 for Classes 4 and 5, highlighting its high performance. Various types of averages were used to evaluate the model's effectiveness. The superiority of the macro average in accuracy, recall, and F1-score was demonstrated. It is calculated for all classes equally. The results were 96.14%, 96.20%, and 96.16%. We applied the weighted average (taking into account class imbalance) and got 96.15% for the straight as well as for the recall and the F1-score. The micro average, which selects the best from among all classes with identical scores, is kept at 96.15% for all tips, showing a well-suited classifier. © 2024 IEEE.

关键词： Convolutional neural networks

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Flask Powered Heart Health Predictor Using Machine Learning Algorithms 2

Flask Powered Heart Health Predictor Using Machine Learning ...

引用

2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Bhavani, Dhanunjay Reddy, Kattekommula Farid Kumar Ananthula, Sai Manish Soundararajan, Surendhar Shanmugasundaram, Hariharan Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383652

Heart Health Predictor Using Flask is an innovative web application that integrates Flask as its front end and Python as its back end. Ideal for healthcare professionals and individuals, it forecasts heart disease risk through a seamless fusion of Flask for data input and Python for machine learning. With cardiovascular disease claiming a life every minute, automating prediction becomes crucial. In medical care, information science manages vast data, addressing the complexity of coronary disease prediction. Utilizing a cardiac disease dataset from the UCI AI repository. The research presented here employs AI algorithms - Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests - to predict and categorize the likelihood of coronary sickness. The study, providing a relative report on algorithm performance, reveals Light GBM's exceptional accuracy of 90%, making it a promising solution for enhancing predictive capabilities in heart health. © 2024 IEEE.

关键词： Diseases

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Enhancing the Resolution of ChestX-ray Images with SRGAN and Sub-Pixel CNN 7

Enhancing the Resolution of ChestX-ray Images with SRGAN and...

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Reddy, K. Lokeshwar Sudharson, S. Natarajan, B. Theetchenya, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science And Engineering Chennai India Computer Science And Engineering Vellore Institute of Technology Chennai India Computer Science And Engineering Sona College of Technology Salem India

ISBN: (纸本)9798350371406

A crucial diagnostic tool for respiratory and heart disorders is chest X-ray imaging. However, the limited resolution of acquired images can hinder accurate interpretation and subsequent treatment decisions. To address this challenge, we employ deep learning-based super-resolution techniques, specifically SRGAN and ESPCN, to enhance the chest X-ray images. In this study, we proposed a modified ESPCN and present our findings on the application of SRGAN and ESPCN for enhancing chest Xray images. The enhanced image resolution enables radiologists to visualize fine details and subtle abnormalities that were previously difficult to discern. This enhancement significantly improves image interpretation and diagnostic accuracy. Moreover, our findings demonstrate the potential of super-resolution techniques for medical education and training. Through extensive experimentation, we saw significant visual improvements, as evidenced by PSNR(peak signal-to-noise ratio) of SRGAN and proposed ESPCN are 40.46 and 40.17, and achieved SSIM values 0.84 and 0.71 respectively. In conclusion, our study showcases the efficacy of SRGAN and ESPCN in enhancing chest X-ray imaging. By improving image quality and diagnostic accuracy, these techniques have the potential to revolutionize medical diagnostics, leading to better patient outcomes and a more effective healthcare system. © 2023 IEEE.

关键词： Image resolution

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Design and Implementation of Solar Powered Smart Irrigation Using IoT 8

Design and Implementation of Solar Powered Smart Irrigation ...

引用

8th IEEE International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Santhosh Kumar, C. Brindasri, S. Krishnamoorthy, J. Vidhya, B. Saranya, G. Madhan, K. Er.Perumal Manimekalai College of Engineering Department of Computer Science and Engineering Hosur India Hindustan Institute of Technology and Science Department of Computer Science and Engineering India Sri Krishna College of Technology Department of ECE Coimbatore India Mailam Engineering College Department of Computer Science and Engineering Mailam India

ISBN: (纸本)9798350347791

The farming is one concerning the indispensable assets due to the fact of advent of ingredients but assumes sizeable portion about the economic regulation about each and every United States on America via which include after Today, coast owners hold a snack troubles within agribusiness appropriate according to the fact of non-appearance about downpours after shortage about water. So, between that task, we wish raise external computerized wind law case due to the fact bank lords as like intention maintain time, value then after on Manual inclusion is wished due to the fact the farmland strategies. By construction use over soil humidity sensor degrees involving ground dampness/mugginess may additionally keep checked. At some also is an harmony the trade yet sends signal in conformity over the microcontroller or relying upstairs that the cloud provision framework works. Robotized cloud government litigation uses valves in accordance in accordance with Engines work continue to be robotized effectively by using construction use of regulators yet no assistance over guide intervention among accordance about flirt apparatus ON then OFF. © 2023 IEEE.

关键词： Internet of things

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. Applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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The Federated Fielder: CNNs and the Future of Cricket Umpire Analysis

The Federated Fielder: CNNs and the Future of Cricket Umpire...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Chaudhary, Preeti Joshi, Kireet Jain, Vishal Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

As we move into a landscape where sports analysis is becoming more critical, the demand for action recognition systems that are real-time and high-accuracy is also increasing. It is vital in complex cricket games where umpire signals have taken center stage in decision-making. This research suggests a new way of discovering the invisible actions of cricket umpires. The method of FLAN associated with CNNs for the confident detection of two types of umpire signals will enable strides in automating umpiring in tennis. The very point of our solution is that five different clients use it. Each client gives us information about its climate characteristics that can be used in a compact universal model that does not need to be updated after every client response. The application of this option preserves the confidentiality of data that must be privately protected. We give a detailed breakdown of the aggregated outcomes, using macro, Micro, and weighted averages as an example, by collecting local data insights and improving the overall model. In the presence of five direct customers, the on-site trial resulted in macro averages varying between 83.99% and 90.31%. The same study also produced micro averages ranging from 83.99% to 90.28%, and weighted averages tended to match macro averages. This connotes the reliability and dependability of our system. Throughout the development, federated averaging became a solid method for enhancing a global dataset through local discrepancies of input data for the improvement of model accuracy altogether with secure privacy. © 2024 IEEE.

关键词： Cricket (Sports)

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