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Optimal Deep Learning Models for Post-Flood House Detection in Drone Imagery

Optimal Deep Learning Models for Post-Flood House Detection ...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Jain, Swati Sushmitha, M. Einstein, A Manish Mahapatra, Ansuman National Institute of Technology Puducherry Dept. of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Floods remain a persistent threat to human lives and infrastructure. Identifying flood-impacted houses in drone imageries will support the rescue teams to prioritize rescue operations in highly affected areas. This study investigates the performance of various deep-learning detection models for post-flood house detection using drone imagery. Our research aims to identify the most suitable model and optimized parameters for accurately and efficiently identifying flood-affected houses, thereby facilitating timely disaster response and recovery efforts. The paper explores various state-of-the-art architectures and provides a thorough performance evaluation. © 2024 IEEE.

关键词： Flood damage

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Transformer-Based Self-Supervised Imputation for Medical Data 4

Transformer-Based Self-Supervised Imputation for Medical Dat...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Singh, Aryan Kumar Saikia, Arpit Baro, Pranita Borah, Malaya Dutta National Institute of Technology Silchar Dept. of Computer Science and Engineering Cachar India

ISBN: (纸本)9798350391183

Dealing with missing data is necessary to develop a solid predictive medical research model. This work presents a hybrid dynamic imputation method devoted to addressing the challenge of missing data, specifically focusing on medical datasets. In this work, we investigate the incompleteness of the dataset using a sophisticated measure called the Incompleteness Factor. This metric is used to guide a hybrid dynamic approach. A novel Transformer-based Self-Supervised imputation method is employed to learn complex data dependencies to impute missing data. This paper introduces one potential solution to obtain model-level accuracy increase for tasks like disease prediction, patient prognosis, etc., over medical datasets. The proposed method aims to remove the measurement biases caused by incompleteness and preprocess the data to enhance the credibility and clinical relevance of the experimental results. © 2024 IEEE.

关键词： Self-supervised learning

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Cloud-Based Water Parameter Monitoring for Smart Fisheries

Cloud-Based Water Parameter Monitoring for Smart Fisheries

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Bahadur, Arshad Mohammad Sakib, Najmus Moinuddin, Kazi Md. Hussein Islam, Tanzimul Islam, A.K.M. Muzahidul United International University Dept. of Computer Science & Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350364866

The Cloud-based Water Parameter Monitoring System leverages cloud and IoT technologies for the precise monitoring of water quality across various water bodies. In Bangladesh, fish is a vital dietary component, consumed more frequently and in larger quantities than any other animal-source food. The fisheries sector greatly contributes to GDP, employment, and it is the second-largest export product of the country. However, pollution and inadequate real-time water quality monitoring significantly impact the industry, costing thousands of crores. This research involved collecting water parameter data from different water bodies using specialized sensors connected to Arduino devices. The data was processed and stored on an edge server before being periodically sent to a cloud server. We developed a user-friendly web application and integrated an alert system to notify farm workers when parameters deviate from acceptable ranges. The goal is to enhance sustainable fish farming practices by improving operational efficiency, reducing environmental impact, and ensuring optimal fish health through IoT, Machine Learning techniques, and Cloud technologies. © 2024 IEEE.

关键词： Fish

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Pruning Based Improved Pedestrian Detection in Video Surveillance 11

Pruning Based Improved Pedestrian Detection in Video Surveil...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Sharma, Suchi Diwakar, Manoj Gangodkar, Durgaprasad Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350350357

Real-time object identification applications are using methods for image processing more and more as a result of advancements in computer technology. Results for pedestrian identification were improved by the use of deep learning techniques. To locate human beings quickly and precisely, this study suggests employing pruning to increase the Yolo models' efficiency in pedestrian recognition. This method would allow the device to function seamlessly when used for video surveillance. This method uses one stage detector, YOLOv5, to better suit the task situations and adjust to the real-time demands. To improve the possible model sparsity, 1 × 1 kernel weights are pruned using a pruning approach. The findings show that, following pruning, this model attains a 30% sparsity in this model, which indicates that 30% of the weight parameters in the nn. Conv2d layers of the algorithm are equal to 0. Inference time is nearly constant and soft-nms is applied for better ROI results. In addition, a thorough result and discussion are given to demonstrate the efficacy of this methodology regarding mAP, latency, energy consumption, and attained sparsity. © 2024 IEEE.

关键词： Energy utilization

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Fair and Efficient Congestion Management for Low Voltage Distribution Networks 8

Fair and Efficient Congestion Management for Low Voltage Dis...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Vadavathi, Aswin Ramesh Hoogsteen, Gerwin Hurink, Johann dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

The widespread increase of photovoltaic panels in the distribution grid often leads to overvoltage. Traditional voltage regulation techniques are unfair for prosumers at the sensitive part of the grid due to larger power curtailment. Consequently, there has been considerable research on implementing fair curtailment strategies for congestion management. However, existing fairness-incorporated curtailment approaches yield varying results based on changes in the grid topology and integration of renewable energy sources. Therefore, this paper introduces proportional voltage fairness that is independent of the above-mentioned network properties and inherently distinguishes regions for fair curtailment. Additionally, there is a need to consider the underutilisation of the grid while adhering to a fair curtailment scheme. Thus this paper proposes a control algorithm incorporating proportional voltage fairness and gives the opportunity to specify the trade-off between fairness and the total power curtailed. Further, a comparative analysis with respect to state-of-the-art notions of fairness is performed. ©2024 IEEE.

关键词： Electric power distribution

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Improving Cardiac Arrest Prediction in PICU Through Gradient Boosting and Neural Networks

Improving Cardiac Arrest Prediction in PICU Through Gradient...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Viswanath, Ch. Srujan Raju, K. Nuthanakanti, Bhaskar Dept. Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India

ISBN: (纸本)9798350374933

Improving PICU patient outcomes after a cardiac arrest (CA) requires an accurate and timely prognosis. The purpose of this study is to concentrate on the use of machine learning (ML) approaches for the prediction of cardiac arrest in pediatric critical care units. We identified critical predictive parameters and evaluated the efficacy of several ML approaches after conducting a thorough literature assessment of recent advances in this sector. Vital signs, test findings, and demographic information were all part of the electronic health records (EHRs) that made up our dataset, which came from several PICUs. Heart rate, breathing rate, blood pressure, oxygen saturation, and electrocardiogram transmissions are some of the comprehensive physiological data provided by neonates and babies in the cardiac critical care unit. A variety of machine learning approaches were trained and evaluated using metrics such as sensitivity, specificity, recall, F1 score, and ROC-AUC. A few examples of these algorithms include Gradient Boosting, Support Vector Machine (SVM), Random Forest, and neural networks. Among the measures examined, the Gradient Boosting model emerged as the frontrunner, with an impressive ROC-AUC of 0.95, a recall of 91.0%, a specificity of 93.5%, an accuracy of 89.7%, and an F1 score of 90.3%. Blood pressure trends and heart rate variability were identified as key predictors using feature importance analysis. These findings demonstrate that ML can improve clinical decision-making and decrease the occurrence of cardiac arrests in children. © 2024 IEEE.

关键词： cardiac arrest cardiovascular ICU early identification infants machine learning neonates physiological data

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Deep Learning Based Segmentation of Weed Images in crop fields using U-Net 3

Deep Learning Based Segmentation of Weed Images in crop fiel...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Naik, K. Jairam National Institute of Technology Raipur Dept. of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350354171

Weeds are problems for crop yields because they exert harmful effects on the yields by competing with cultivated crops for necessary resources like nutrients, sunlight, and soil. As a result, continual attempts are being made to create safer and more effective herbicide formulations. For precision weeding and to reduce pesticide pollution, segmenting weeds in photos is extremely important. However, weeds and crops are comparable in the field setting, making it challenging to precisely segregate weed from intricate field photos. A deep learning-based system for segmenting weeds from photos was proposed in this paper. This algorithm can distinguish between weeds and crops and soil in photos. Utilizing a condensed U-net, this semantic segmentation method was created. An image augmentation strategy was suggested because image tagging for the semantic segmentation of weeds is challenging. The classification algorithms used in the modified U-Net are Support Vector Machine (SVM), Random Forrest (RF) and Decision Tree (DT). These were also used for predicting the weed part in a sample image before their segmentation. The threshold accuracy for segmenting the image using to be around 51.85% with a segmentation time of approximately 01.02 ms whereas our modified U-Netis obtained 91.03% accuracy and the segmentation time of 50.35ms. The results showed the modified U-Net successfully differentiated weeds from images with numerous other plants. This study seeks to demonstrate that the weed-associated image segmentation technique is effectively identifying and separating weeds in intricate field conditions. © 2024 IEEE.

关键词： Semantic Segmentation

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An Efficient Approach for Maintaining and Mining Frequent Episodes 6

An Efficient Approach for Maintaining and Mining Frequent Ep...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Yen, Show-Jane Lee, Yue-Shi Ming Chuan University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350344349

Data mining technology plays a crucial role in the area of data analysis. Mining frequent episodes stands out as a pivotal task within this domain, enabling users to forecast future events based on present occurrences. Conventional methods for discovering frequent episodes typically follow a hierarchical approach, involving the generation of candidate episodes and subsequent scanning of sequence data to count their frequency, which can be quite time-consuming, as it necessitates repeated scans of the sequence data and the search for candidate episodes. In this paper, we introduce a novel approach for episode mining in a data stream. Our method distinguishes itself by scanning newly added data to update existing frequent episodes, all without the need for scanning the original data or searching for candidate episodes. The experiments also show that our approach is more efficient compared to other existing methods. © 2024 IEEE.

关键词： Data mining

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Performance Analysis of Modern Garbage Collectors using Big Data Benchmarks in the JDK 20 Environment 5

Performance Analysis of Modern Garbage Collectors using Big ...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Islam, Md. Jahidul Rahman, A.T.M Mizanur Rana, Sohel Chandpur Science and Technology University Dept of Computer Science and Engineering Bangladesh Brac University Dept of Computer Science and Engineering Bangladesh Chandpur Science and Technology University Dept of Information and Communication Technology Bangladesh

ISBN: (纸本)9798350394290

Garbage collection is a fundamental aspect of Java Virtual Machine (JVM) memory management, and choosing the optimal garbage collector is essential for attaining optimal application performance. In this work, we conduct experiments with the big data benchmarks from DaCapo and Renaissance benchmark suites in both fixed and variable heap environments to determine the efficacy of JDK 20's garbage collectors. The ZGC algorithm has the highest throughput and the shortest pause periods, whereas the Serial GC algorithm has the lowest throughput and the longest pause intervals. Additionally, it is discovered that the G1 algorithm manages the previous generation heap and metaspace less efficiently. Our work offers valuable insights into the efficacy of garbage collection algorithms and can aid application developers in selecting the optimal garbage collection algorithm. Our investigation can be expanded by analyzing additional benchmark suites and garbage collection algorithms across a broader range of heap sizes in future work. © 2023 IEEE.

关键词： Big data

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Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning 18

Reconnaissance Detection in Decentralised IoT System using M...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Rajdeep Kumar Chhetry, Bishal Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model's ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats. © 2024 IEEE.

关键词： Contrastive Learning

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