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Development of IoT-based Scale Sensor to Measure AWD System Water Level in Crop Field 3

Development of IoT-based Scale Sensor to Measure AWD System ...

引用

3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Kabir, Md Shahriar Mahmud Niloy, Khalid Hossen, Md Imon Afrose, Sumaiya Hossain Mofazzol, Md. Ismail American International University-Bangladesh Dept. of Electrical & Electronics Engineering Bangladesh Uttara University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The paper introduces an innovative Internet of Things (IoT)-based scale sensor that measures the water level in the alternate wetting and drying (AWD) system and displays data in the mobile app to enable remote monitoring. The AWD system mainly consists of 30 cm of PVC pipe or bamboo with a diameter of 10-15 cm inserted into the paddy field, with a lower 20 cm buried in the soil and tiny holes at the bottom to allow field water to enter. The water level within the pipe indicates the field's water level, especially the underground water level. The AWD method optimizes water usage and reduces methane emissions by watering periodically and drying rice fields instead of maintaining continuous flooding. The current manual measurement process for water level needs to go to crop fields daily with a scale, which is difficult for continuous and remote monitoring. The developed scale sensor has IoT connectivity that removes the difficulties. The scale sensor works based on conductive rain sensors to precisely detect each centimeter rise or fall of water. The sensor consists of two open conductive layers that create a short circuit when the water level touches them by raising, triggering the microcontroller to register a voltage drop. The scale can measure up to 20 cm as it has 20 conductive layers, which fulfill the requirements. It has a 1 cm resolution, and in the test, it shows 95-99% accuracy. So, the sensor can effectively measure the water level in the AWD system, and further research is required to add an automatic irrigation system with the sensor so that the whole process can be controlled remotely. © 2024 IEEE.

关键词： Comparators (optical)

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

引用

2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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Predicting the Number of Fatalities in an Air Crash 8

Predicting the Number of Fatalities in an Air Crash

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Prajwala, T.R. Manoj, Nandana Kumar, Preethika Ajay Harshita, R. Dept. of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9798350334012

Air travel is one of the safest forms of travel today. The mortality rate in air travel is much lesser than that in automobile accidents. Nevertheless, aviation accidents have always been a huge cause of concern for people. Crashes could occur due to multiple factors like injury severity, weather condition, amateur built, make, etc. Air crashes could result in fatalities or serious injuries. The outcome of this research is to predict the number of fatalities when factors like the ones mentioned above have been provided. Air travel has a wealth of historical data available, and by analyzing patterns in flight accidents, there is potential to enhance the safety of air travel further, when compared to other modes of transport. This is essential as this can help the rescue team to respond accordingly. Three models namely, Gradient-boosting-regression, Random-Forest-Regressor, XGBoost have been implemented. After reviewing the results of all three models it has been found that Random-Forest comparatively better results. © 2023 IEEE.

关键词： Correlation methods

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Blockchain Unleashed: Exploring its Multifaceted Applications and the Dynamics of Proof-of-Work

Blockchain Unleashed: Exploring its Multifaceted Application...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Hassan, Inzimam Ul Ahmad Lone, Zeeshan Kaur, Inderdeep Kaur, Gaganjot Siya, Siya Priya, Simran Chandigarh University Dept. of Computer Science and Engineering Mohali India Vivekananda Global University Dept. of Computer Science and Engineering Jaipur India Chandigarh University Dept. of Cse Mohali India

ISBN: (纸本)9798350308167

In a range of sectors, such as banking, healthcare, and the Internet of Things, we have seen several data security and confidentiality issues. So, it is revealed that blockchain can provide a solution to the security problem. Thanks to the distributed database technology known as a blockchain, a trusted third party are not required for secure and transparent transactions between parties. The integrity of the chain is ensured by the cryptographic hash of each block in the chain, which makes each one tamper-proof. Blockchain technology has the potential to revolutionize a wide range of industries, from finance to healthcare, by providing a secure, efficient, and transparent method of storing and transmitting data and value. After reading the research on blockchain applications in different fields, we will highlight some of the major advantages we understand © 2023 IEEE.

关键词： banking finance Blockchain security integrity tamper-proof

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Balancing Safety and Security in Autonomous Driving Systems: A Machine Learning Approach with Safety-First Prioritization 40

Balancing Safety and Security in Autonomous Driving Systems:...

引用

40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Hasani, Afshin Nia, Mehran Alidoost Atani, Reza Ebrahimi University of Guilan Dept. of Computer Engineering Rasht Iran Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9798331532819

In the era of autonomous vehicles (AVs), ensuring the safety and security of the system is an ongoing challenge, particularly when faced with increasingly cyber-Attacks such as GPS spoofing and man-in-The-middle. This paper presents a novel reinforcement learning (RL)-based framework that prioritizes safety over security concerns in AVs, ensuring that life-critical tasks such as collision avoidance and speed control are always maintained, even in the presence of security threats. Our system leverages the MAPE-K loop to dynamically adapt to changing conditions, using RL in the Analyzing phase to make real-Time decisions that maximize cumulative safety rewards while addressing security risks that directly affect operational integrity. We demonstrate how our RL-based self-Adaptive system mitigates security threats while maintaining safe driving behaviors through various simulations and experiments. The proposed system shows significant improvements in collision avoidance and threat detection, ultimately ensuring that the AV's safety is preserved in even the most challenging scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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Electricity Generation From Food Waste Using IOT 3

Electricity Generation From Food Waste Using IOT

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Arunkumar, M. Agalya, S. Bhuvanaeshwari, M. IFET College of Engineering Dept. of Information Technology Villupuram India Surya Group of Institutions Dept. of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350372120

One inventive solution is to turn food waste into power using the Internet of Things (IoT) to meet the growing need for sustainable energy sources. It turns thermal energy from trash into electricity by utilizing thermoelectric devices and the Seebeck effect. Real-time monitoring and control are made possible with IoT integration. By improving the extraction of energy from liquid or semi-solid waste, this technique reduces waste and promotes a circular energy economy. The system makes use of hardware components such as transformers and voltage regulators. The outcomes of experiments demonstrate its efficacy and possible role in renewable energy. This creative idea, which provides a sustainable route ahead for the production of power from food waste, highlights the importance of IoT in improving waste-to-energy systems. © 2024 IEEE.

关键词： Internet of things

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Enhanced Rainfall Prediction: Leveraging Ensembling Models for Maximum Forecasting Performance 1

Enhanced Rainfall Prediction: Leveraging Ensembling Models f...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dasari, Krishna Kumar Nuthanakanti, Bhaskar Raju, K.Srujan Madhusudhan, Mudimela Nazare, Sanjana S Raji Reddy, Avala Cmr Technical Campus Dept. Computer Science and Engineering Telangana Hyderabad India Cmr Dept. of Mechanical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350367720

Many important fields rely on accurate rainfall predictions, including agriculture, water resource management, and emergency preparation. The many nonlinear interactions included in weather data are notoriously difficult for traditional approaches to grasp. Agriculture, water resource management, and disaster preparation are just a few of the many sectors that might benefit greatly from accurate rainfall predictions. Because meteorological data is both complicated and dynamic, traditional methods often get it wrong. Recent years have seen encouraging results in the use of machine learning (ML) approaches, especially ensemble models, to enhance the precision and consistency of precipitation forecasts. This research provides a new method for predicting precipitation using an ensemble of ML models. The ensemble model EERP algorithm combines the predictions of numerous base learners to create a more reliable and accurate forecast. Decision trees, random forests, gradient boosting machines, neural networks, and other ML approaches serve as the ensemble's base learners. Assess the effectiveness of the proposed approach and then compare it with some existing classifiers in which the proposed classifier gave the best accuracy when compared with the other i.e., 96 %. © 2024 IEEE.

关键词： Prediction models

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Learning to Communicate Using Action Probabilities for Multi-Agent Cooperation

Learning to Communicate Using Action Probabilities for Multi...

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2023 IEEE International Conference on Agents, ICA 2023

作者： Bai, Yidong Sugawara, Toshiharu Waseda University Dept. Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350312782

While communication is an essential tool for cooperation in multi-agent systems (MAS), existing approaches generally assume high-dimensional messages genereated by deep networks, which are uninterpretable for humans and require expensive transmission and complex encoding/decoding networks in agents. Uninterpretability may in turn raise reliability and security issues for the systems. To achieve low-dimensional and interpretable communication, we demonstrate that each agent's action probabilities can be used as messages, inspired by the fact that humans often share likely actions during collaboration. Our proposed method, communication based on action probabilities (CAP), is a simple yet effective communication architecture for coordination and collaboration in MAS, which facilitates our understanding of the agents' learned coordinated and cooperative behaviors. Moreover, because messages can be generated by the actor network, which is usually implemented in deep reinforcement learning agents, we can eliminate the requirement of extra message-generator networks. Our experiments show that CAP can achieve comparable performance to those of the state-of-the-art methods, with quicker convergence, simpler network structures and better interpretability. © 2023 IEEE.

关键词： Reinforcement learning

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Shanketik: Revolutionizing Communication for the Deaf and Mute - A Reinforcement Learning-Enhanced Bangla AAC Application 26

Shanketik: Revolutionizing Communication for the Deaf and Mu...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Monalisa, Natasha Tanzila Himi, Shinthi Tasnim Parvin, Kohinur Uddin, Mohammad Shorif Jahangirnagar University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

This research addresses the communication challenges faced by the deaf and mute community, leveraging advanced technology to enhance augmentative and alternative communication (AAC). In light of the limitations of existing methods, we propose a novel approach, 'Shanketik', that integrates Reinforcement Learning (RL) into an AAC mobile application in Bangla language. This system bridges the gap in accessible communication tools, particularly in languages like Bangla, where options are limited. The 'Shanketik' system functions as an agent, interacting with users through symbols in AAC. It incorporates a dynamic feedback loop, learning from positive and negative user responses. The system's core architecture involves the innovative use of the Deep Q-Network (DQN) algorithm, a fusion of Q-learning and deep neural networks. The 'Shanketik' application not only refines text and speech generation for accuracy and contextual relevance but also offers a user-friendly interface, ensuring navigation flexibility and enhanced efficiency in usability. The research presents a significant advancement in AAC technology, marking a transformative step toward inclusive communication for the deaf and mute community. © 2023 IEEE.

关键词： Augmented and Alternative Communication (AAC) Bangla Language Processing Human-computer Interaction Mobile Application Reinforcement Learning

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Hybrid ANN-Based Model for Lung Cancer Disease Prediction 3

Hybrid ANN-Based Model for Lung Cancer Disease Prediction

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Paul, Banibrata Mir, Tawseef Ahmad Murugan, Stalin Alliance University Dept. of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350317022

Currently, lung cancer ranks as the world's leading cause of cancer-related fatalities in both men and women. The primary cause of lung cancer may be attributed to smoking. Although lung cancer can develop in any part of the lung, 90% to 95% of cases are believed to start in the epithelial cells that line the larger and smaller airways (bronchi and bronchioles). This study's main goal is to identify lung cancer by employing artificial neural networks with scaled conjugate gradient back propagation and k-fold cross validation. We have utilized the Lung Cancer dataset through kaggle. data from 1000 lung cancer patients, ages 14 to 73, is used to train the network. How many neurons are in the hidden layer affects how accurate the results are. The proposed system uses 11 input attributes to assess the presence and absence of lung cancer during testing, yielding a minimum accuracy percentage of 83.1429% and a maximum accuracy percentage of 100%. © 2023 IEEE.

关键词： Artificial Neural Network Back propagation Lung Cancer Lung cancer dataset Scaled Conjugate Gradient Algorithm

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