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Line Detection and Segmentation of Annual Crops Using Hybrid Method 20

Line Detection and Segmentation of Annual Crops Using Hybrid...

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20th Latin American Robotics symposium (LARS) / 15th Brazilian symposium on Robotics (SBR) / 14th Workshop on Robotics in Education (WRE)

作者： Goncalves, Erick Cardoso de Almeida, Gustavo Pereira da Silva, Eduardo Lawson Schein, Tatiana Tais Dias de Oliveira Evald, Paulo Jefferson Jorge Drews-, Paulo Lilles, Jr. Univ Fed Rio Grande Ctr Ciencias Comp Rio Grande RS Brazil Univ Fed Pelotas Ctr Engn Pelotas RS Brazil

ISBN: (纸本)9798350315387

Currently, agriculture has incorporated more and more technologies to optimize production, reducing waste and environmental impacts of this activity. Precision agriculture aims to maximize efficiency and agricultural productivity while reducing the use of chemicals. this work proposes an automatic approach based on supervised learning and image processing methods for crop line detection in frontal images obtained by a ground camera. Along with this, data augmentation techniques are applied to increase the reference method from the literature ability of the algorithm. the proposed method uses a semantic segmentation architecture based on Deeplabv3+ architecture to segment the region of interest, and the Canny Edge technique is applied to locate the image edges. Finally, the probabilistic Hough transform is applied to identify the segments from the identified boundaries. the proposed method is evaluated and compared with a variation of a reference method from the literature algorithm, where our method presented higher performance, achieving an overall accuracy of 84.1%, even on images with high weed presence and crop line gaps.

关键词： computer vision Precision agriculture Image processing Deep Learning

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Deep Ensemble Fusion For Enhanced Multi-Label Land Cover Classification 15

Deep Ensemble Fusion For Enhanced Multi-Label Land Cover Cla...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Anup Mishra, Anjana Raman, Rakesh Kumar Priyadarshi, Ayush Das, Arkadeepa C.V. Raman Global University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

Accurate identification of land cover is an important step for determining the right management strategy, planning and environmental assessment. Remote sensing images depicting land covers generally parcel information that has spatial and temporal correlation which cannot be easily identified by classical techniques. this paper proposes a new type of multi-label land cover classification model based on RNN and CNN that can efficiently cover these problems. the suggested model, which uses Fastai library for training and prediction, is capable of capturing both spatial and temporal attributes with a high level of efficiency in dealing with the UCMerced Landuse dataset. the dynamics measured by the plugin prove that the use of the hybrid architecture where LSTM is integrated with ResNet34 as the CNN backbone helps in the identification of complex relation/dependency and patterns in the changes of land cover. therefore, the degrees of accuracy and the F-beta scores are presented which are as good as experimental results confirmed the model's robustness. the paper extends the methodologies for classifying land cover and provides a promising approach for those application domain, in which the temporal and spatial information needs to be used simultaneously. © 2024 IEEE.

关键词： Convolutional neural networks

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Interpreting Deep Convolutional Neural Networks for Medical Diagnosis Using Perceptual Encoding and Retrieval 15

Interpreting Deep Convolutional Neural Networks for Medical ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kasture, Shreyas Maurya, Sudhanshu Verma, Rohan Yadav, Ajay Kumar Shukla, Amit Nagpur Campus Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India United Institute of Management Department of Computer Application Prayagraj India Centre of Artificial Intelligence & Robotics Mandi India

ISBN: (纸本)9798350370249

the robust performance of deep neural networks (DNNs) in many areas, including medical diagnosis, is not accessible from the problem of interpretability. Even though DNNs have high accuracy, they tend to operate as black boxes, usually without any explanation of which basis their predictions are made. this interpretability issue has become important, especially for high-stakes applications, such as medical image analysis and disease diagnosis, in which people's lives are at risk. the study was structured to raise novel approaches for DNNs, internal representation translation and provide visualizations to assist medical diagnosis tasks and ensure reliable and transparent decision-making procedures. Such techniques have been developed to improve the interpretability of DNN without sacrificing the performance. the models for proposed models succeeded in the clinical tasks of medical diagnosis. they reached competitive accuracy with the best model, obtaining an accuracy of 95.7% and an F1-score of 0.914 on lung abnormality detection from chest X-rays. AUC-ROC high sensitivity and specificity of 0.963 were observed when diagnosing brain tumors based on MRI scans. these models also provided improved faithfulness perceived through saliency maps, which had an average score of 0.82, and concept activation vectors, with a consistency of 0.77 from the evaluation done by medical experts. the suggested methods offer a chance for a better patient outcome via high-precision and safe diagnostics and treatment options with an expected change in misdiagnosis rate of 10-15%. © 2024 IEEE.

关键词： Active Learning Deep Neural Networks Interpretation Synthesis Medical Diagnosis Network Traffic Forecasting Saliency Map Aggregation and Saliency Maps

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high-Quality I/O Bandwidth Prediction with Minimal Data via Transfer Learning Workflow 36

High-Quality I/O Bandwidth Prediction with Minimal Data via ...

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36th IEEE International symposium on computer architecture and high-performance computing, SBAC-PAD 2024

作者： Povaliaiev, Dmytro Liem, Radita Kunkel, Julian Lofstead, Jay Carns, Philip RWTH Aachen University Chair of High Performance Computing IT Center Aachen Germany Georg-August Universität Göttingen/GWDG Göttingen Germany Sandia National Laboratory Albuquerque United States Argonne National Laboratory Lemont United States

ISBN: (纸本)9798350356168

Providing a high-quality performance prediction has the potential to enhance various aspects of a cluster, such as devising scheduling and provisioning policies, guiding procurement decisions, suggesting candidate applications for tuning, and identifying probable scaling and porting challenges. Creating such a prediction for the I/O metrics is still challenging, however, due to the intricate interplay of multiple cluster components, making this an ideal case for machine learning. Nevertheless, achieving the required accuracy level with machine learning calls for a substantial amount of high-quality data, which is often a difficult challenge for most HPC clusters. In this work we explore the use of transfer learning to predict the applications' I/O bandwidth based on a public dataset. As a result, our experiment can provide an I/O bandwidth prediction for a different cluster comparable to the current state-of-the-art result while employing 100 times less data than needed to construct the base model. Furthermore, we evaluate potential future improvements of the proposed workflow. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving Connectivity of IoT-Enabled Networks through Big Data Analytics 15

Improving Connectivity of IoT-Enabled Networks Through Big D...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kiran, K.S. Sen, Sumanta Sharma, Sunil School of Engineering and Technology Karnataka Bangalore India ARKA JAIN University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

the net of things (IoT) has quick grow to be the backbone of many corporations, taking into account real-time statistics collection and automatic tasks. However, the connectivity of these IoT-enabled networks can become without difficulty congested when huge amounts of data are generated in a brief time frame. To be able to hold a high stage of overall performance, big facts analytics can be used to research the massive volumes of generated statistics, provide insights and locate anomalies to improve community connectivity. Massive statistics analytics will permit higher records processing and evaluation competencies, become aware of regions of improvement and optimize network operations. Additionally, predictive analytics techniques can be used to forecast upcoming community congestion and take movement that could enhance network performance in real time. this, but, requires green facts collection, garage and control gear to ensure that the big records analytics answer is effective and efficient. this paper investigates how massive records analytics may be used to improve the connectivity of IoT-enabled networks through leveraging the energy of predictive analytics. © 2024 IEEE.

关键词： Predictive analytics

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Predictive Modeling of Cryptocurrency Prices Using Machine Learning Algorithms 15

Predictive Modeling of Cryptocurrency Prices Using Machine L...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahmud, Tanjim Akter, Tahmina Chakma, Rishita Barua, Tikle Hossain, Mohammad Shahadat Andersson, Karl Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati4500 Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350370249

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. this study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. this research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling. ©2024 IEEE.

关键词： Adversarial machine learning

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Alzheimer's Disease Detection through Spatially Attentive Enhanced Low-Mid-high Level Feature Modelling using Deep CNN 15

Alzheimer's Disease Detection Through Spatially Attentive En...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Tripathy, Santosh Kumar Shanmugham, Poonkuntran Sharma, Rishabh Doshi, Yash National Institute of Technology Computer Science and Engineering Bihar Patna India Vit Bhopal University Bhopal-Indore Highway School of Computing Science and Engineering Sehore Madhya Pradesh Kothrikalan India

ISBN: (纸本)9798350370249

Alzheimer's disease (AD) severely affects patients and has become a warning concern around the world. Several AI-based solutions that have extensively explored convolutional neural networks (CNN) for diagnosis. Most of these approaches only utilise high-level features for diagnosis. However, to solve any non-linear and complex problem, the low-level and mid-level features are as important as the high-level features. Such an approach is missing in the AD classification. To solve this, we proposed a novel way to utilise low, mid, and high (LMH) features for classification. We design the suggested framework using the backbone of a multilayer atrous CNN. We enhance the LMH features by exploiting features from a multicolumn atrous convolution with varying kernel shapes, which can extract features at multiple scales. We adopt, modify, and apply a spatial attention mechanism over the enhanced LMH features to emphasise important features or attributes in the spatial domain. A multilayer of dense neurons receives the fused and enhanced spatially attentive LMH features for AD classification. We demonstrate extensive experiments using an open-source AD dataset available on Kaggle. the proposed framework achieves better performance than current best practices. © 2024 IEEE.

关键词： Alzheimer's disease atrous CNN deep learning spatial attention

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Circular Reconfigurable Parallel Processor for Edge computing Industrial Product 51

Circular Reconfigurable Parallel Processor for Edge Computin...

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51st ACM/IEEE Annual International symposium on computer architecture, ISCA 2024

作者： Li, Yuan Zhu, Jianbin Fu, Yao Lei, Yu Nagata, Toshio Braidwood, Ryan Fu, Haohuan Zheng, Juepeng Luk, Wayne Fan, Hongxiang AzurEngine Technologies China Tsinghua University China Sun Yat-Sen University China Imperial College London United Kingdom University of Cambridge United Kingdom

ISBN: (纸本)9798350326581

Graphics Processing Units (GPUs) have emerged as the predominant hardware platforms for massively parallel computing. However, their inherent von-Neumann architecture still suffers performance inefficiency stemming from the sequential instruction execution and frequent data transfer overheads within the memory system. these intrinsic architectural flaws lead to heavy overhead on the latency, area, and energy efficiency, rendering GPUs suboptimal for edge computing applications. To tackle these challenges, this paper introduces a novel circular Reconfigurable Parallel Processor (RPP) to enable massively parallel applications in edge computing with high efficiency. RPP features a novel circular array of reconfigurable compute engines, enabling efficient streaming dataflow processing. In contrast to traditional Coarse Grained Reconfigurable architecture (CGRA), the circular network topology of RPP is formed by linear switch networks with an innovative gasket memory, which reduces complicated network routing overheads while allowing versatile datapath mapping and optimized data reuse. A dedicated hierarchical memory system is proposed to support different memory access patterns and address mapping strategies, enabling flexible data access with high memory efficiency. Several hardware optimizations are further introduced to improve hardware utilization and performance such as concurrent kernel execution, register split&refill and heterogeneous scalar&vector computing. To fully utilize the hardware capability of RPP, we develop an end-to-end software stack consisting of a compiler, runtime environment, and different RPP libraries. this software stack is designed to be compatible with the GPGPU computing paradigm, enhancing its potential for broader adoption. Fabricated in a 14nm process, RPP occupies an area of 119 mm2 and operates at a maximum power of 15W with a 1GHz clock frequency. From the runtime measurement of various workloads, RPP achieves up to 27.5 × higher ene

关键词： Edge computing

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Metrics for Packing Efficiency and Fairness of HPC Cluster Batch Job Scheduling 34

Metrics for Packing Efficiency and Fairness of HPC Cluster B...

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34th IEEE International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Goponenko, Alexander, V Lamar, Kenneth Peterson, Christina Allan, Benjamin A. Brandt, Jim M. Dechev, Damian Univ Cent Florida Dept Comp Sci 211 Harris CtrBldg 1164000 Cent Florida Blvd Orlando FL 32816 USA Sandia Natl Labs POB 5800MS 0823 Albuquerque NM 87185 USA

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Development of job scheduling algorithms, which directly influence high-performance computing (HPC) clusters performance, is hindered because popular scheduling quality metrics, such as Bounded Slowdown, poorly correlate with global scheduling objectives that include job packing efficiency and fairness. this report proposes Area Weighted Response Time, a metric that offers an unbiased representation of job packing efficiency, and presents a class of new metrics, Priority Weighted Specific Response Time, that assess both packing efficiency and fairness of schedules. the provided examples of simulation of scheduling of real workload traces and analysis of the resulting schedules with the help of these metrics and conventional metrics, demonstrate that although Bounded Slowdown can be readily improved by modifying the standard First Come First Served backfilling algorithm and by using existing techniques of estimating job runtime, these improvements are accompanied by significant degradation of job packing efficiency and fairness. In contrast, improving job packing efficiency and fairness over the standard backfilling algorithm, which is designed to target those objectives, is difficult. It requires further algorithm development and more accurate runtime estimation techniques that reduce frequency of underpredictions.

关键词： high performance computing parallel job scheduling performance metrics schedule quality runtime estimates packing efficiency fairness weighted flow time weighted response time

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A Detailed Analysis of Machine Learning Models to Predict Water Potability 15

A Detailed Analysis of Machine Learning Models to Predict Wa...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sreekumar, Vishnu Ihsan, Faraz Reghuram, Surya Sarath, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Water is one of the most indispensable things in our life. However, getting clean drinking water has become more difficult for a lot of people lately. this research paper uses machine learning techniques to tackle the crucial problem of predicting the potability of water. Reliability in water potability assessment is critical, as concerns about water quality and its effects on public health are becoming more widespread worldwide. We present a novel predictive model to assess the potability of water sources by utilizing an extensive dataset that includes various water quality parameters. In order to examine intricate relationships within the dataset, the study uses a cutting-edge machine learning framework that integrates algorithms like Random Forest, KNN, SVM, Decision tree, and Gradient boosting. To extract pertinent information, feature engineering is used, and model performance is rigorously tuned using hyper parameters. To determine the best method for predicting water potability, a comparative analysis of several models is carried out. the proposed model is validated on different datasets, which guarantees its robustness and generalizability in a range of environmental conditions. the outcomes show that the predictions of water potability have a high degree of sensitivity, specificity, and accuracy. © 2024 IEEE.

关键词： Support vector machines

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