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Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants 5

Deep Learning-Based Image Analysis for Detecting Nutrient De...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Nirasha Jayasiri, K.C. Chandrasiri, Sanjeevi Rupasinghe, Shalini Karunanayake, K.O. Rivindu Uthayachandran, Abineshh Zihara, M.I. Fathima Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350358131

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns. © 2023 IEEE.

关键词： Object detection

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Towards an integration management maturity 49

Towards an integration management maturity

引用

49th Euromicro Conference on software engineering and Advanced Applications, SEAA 2023

作者： Hyrynsalmi, Sonja M. Olsson, Helena Holmstrom Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350342352

As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside *** research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration *** address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management. © 2023 IEEE.

关键词： Integration

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Dynamic Image Deblurring based on Crosshatch Attention Adversarial Network and Hybrid Loss 5

Dynamic Image Deblurring based on Crosshatch Attention Adver...

引用

5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Aslam, Ummama Shaukat, Arslan Azam, Basim Dept. of Computer and Software Engineering H-12 Islamabad Pakistan Institute of Integrated and Intelligent Systems Griffith University Brisbane Australia

ISBN: (纸本)9798350394764

Image deblurring techniques that uses deep learning have shown great potential but due to low generalizability, noise immunity and the correlation among different pixels is not addressed in detail that results in unwanted artifact that appears in the deblurred image. To tackle this problem an end-to-end approach is proposed for the recovery of sharp image from blurred one without the estimation of blur kernel. A special type of attention module known as crosshatch attention is used after Residual Block of Generator model for removing noise and for the collection of correlation of different pixels in an image. Hybrid Loss function is defined which focus on different part of image and improve edges and texture details. The performance of the model for deblurring is measured on GoPro dataset. Our proposed model has slightly higher objective and subjective evaluation i-e PSNR, SSIM value and the visual results. © 2024 IEEE.

关键词： Pixels

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Towards a Data Business Maturity Model for software-intensive Embedded System Companies 29

Towards a Data Business Maturity Model for Software-intensiv...

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29th International Conference on engineering, Technology, and Innovation, ICE 2023

作者： Hyrynsalmi, Sami Olsson, Helena Holmström Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350315172

Data has been quickly becoming as the fuel, the new oil, of growth and prosperity of companies in the modern age. With useful data and sufficient tools, companies have the ability to enhance their current products, presents new innovations and services as well as generate new revenue streams with a secondary customer base. While there are ongoing efforts to develop machine learning and data science techniques, little attention has been paid to understanding and characterizing data-related business activities in software-intensive *** multiple-case study examines four large international embedded system companies to explore how they are utilizing data and how they have proceeded in their journey in the data business. This study identifies six distinct stages, each with unique challenges, that seems to be common for embedded system companies in their data business. As the result, this study presents an initial data business maturity model for software-intensive embedded system companies. Additionally, this research provides a foundation for future efforts to support software-intensive embedded system companies in establishing data businesses. © 2023 IEEE.

关键词： Embedded systems

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Work in Progress: Congestion Control in mmWave Fluctuating Scenarios in 5G-A/6G 10

Work in Progress: Congestion Control in mmWave Fluctuating S...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Sandoval, Jorge Ignacio Cespedes, Sandra University of Chile Department of Electrical Engineering Santiago Chile Concordia University Dept. of Computer Science and Software Eng Montreal Canada

ISBN: (纸本)9798350373011

The development of the next generation of cellular networks, 5G-Advanced and 6G (5G-A/6G), offers higher speeds, sub-millisecond latency, and providing wider coverage, which are key to meeting the requirements of new and demanding applications. As such, there are several challenges in managing and leveraging this opportunity to ensure that channel conditions do not impact the application due to mobility and obstructions. 5G introduces the use of millimeter wave (mmWave), and the commercial deployment of this technology brings to light the issues related to propagation and mobility in high-frequency bands, and their impact on applications has begun to be studied by exploring the response of the congestion control algorithms (CCAs) in representative scenarios. However, the highly variable channel conditions in mmWave require that they be defined as fluctuating bandwidth scenarios. It is necessary to determine how the transport layer can take advantage of the maximum available capacity and not affect the applications, and whether the network promises are fulfilled. This work aims to design and evaluate a CCA capable of efficiently adapting to the fluctuating bandwidth of extreme condition scenarios of 5G-A/6G mobile networks and to compare it with the state-of-the-art algorithms. As a first result, a set of simulation-based evaluation using 5GLENA was discussed. A combination of CCAs and different radio link control (RLC) buffer sizes allows us to highlight the importance of defining appropriate metrics and including convergence times as part of the evaluation performance of the algorithms. Algorithms such as HighSpeed and New Reno are among the slowest to converge, while BBR is the fastest. The analysis and characterization of the algorithms will allow us to define the requirements for designing an Machine Learning (ML) based algorithm with optimal performance and better convergence times. © 2024 IEEE.

关键词： Radio links

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Deep Learning-Based Energy Efficiency Maximization in Multi-STAR-RIS-Assisted Massive MIMO-NOMA Networks

Deep Learning-Based Energy Efficiency Maximization in Multi-...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van Pramitarini, Yushintia Nguyen, Duy H. N. An, Beongku Hongik University Dept. of Software and Communications Engineering in Graduate School Korea Republic of San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798331507022

This paper studies deep learning (DL)-based energy efficiency maximization (EEM) in multi-simultaneous transmission and reflection-reconfigurable intelligent surfaces (STAR-RISs) assisted massive multiple-input multiple-output (mMIMO)-non-orthogonal multiple access (NOMA) networks. We formulate the EEM problem to jointly optimize the precoding matrix and STAR-RIS phase shifts subject to the power budget at the base station, STAR-RIS phase shift constraints, and minimum quality-of-service (QoS) requirements. The formulated EEM problem belongs to the mixed-integer programming class, which is difficult to solve optimally. Thus, we develop an alternative optimization approach by dividing the original EEM problem into two sub-problems such as phase shift and beamforming optimization, and solve them alternatively. A bisection search algorithm is proposed to solve the phase shift optimization, while the inner approximation method is employed to address the non-convex beamforming problem through our newly tractable transformations. To enable real-time optimization, we design a DL framework that predicts optimal phase shifts and precoding matrices under various parameter settings. Simulation results demonstrate that the DL-based approach accurately predicts the optimal solutions and is significantly faster than conventional methods. We also evaluate the impact of the essential parameters on the system's performance. © 2025 IEEE.

关键词： Convex optimization

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PLCD: Policy Learning for Capped Service Mobility Downtime 32

PLCD: Policy Learning for Capped Service Mobility Downtime

引用

32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Mwasinga, Lusungu J. Raza, Syed M. Le, Duc-Tai Kim, Moonseong Choo, Hyunseung Sungkyunkwan University Dept. of Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Seoul Theological University Dept. of It Convergence Software Bucheon Korea Republic of

ISBN: (纸本)9798350336184

Service mobility in Multi-access Edge Computing (MEC) paradigm is necessary to provide ultra-Reliable Low Latency Communications for the erratically roaming MEC users. It involves relocation of containerized application services to a strategically selected optimal edge host. During relocation, service containers are unavailable (downtime), resulting in the interruption of ongoing user sessions and increased operational expenses for the network operator. Prolonged service downtime degrades perceived quality of experience for users, and this study handles this problem by proposing a downtime-aware Policy Learning based Capped Downtime (PLCD) service mobility strategy. It exploits Deep Actor-Critic prowess for effectively deciding when and where to relocate a containerized application service while taking user mobility and MEC server resource fluctuations into account. Efficacy of the proposed PLCD strategy is confirmed through simulation experiments, and results indicate over 90% average reduction in service downtime comparing to a baseline scheme. © 2023 IEEE.

关键词： Reinforcement learning

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The Early Detection of Dementia Disease Using Machine Learning Approach

The Early Detection of Dementia Disease Using Machine Learni...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kabir, Md. Shariar Khanom, Jakia Bhuiyan, Md. Atik Tumpa, Zerin Nasrin Rabby, S.K. Fazlee Bilgaiyan, Saurabh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350348217

Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia. © 2023 IEEE.

关键词： Machine learning

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Establishing a Correlation of Clinical Characteristics with the Level of Tumor Mutation Burden in Urothelial Bladder Carcinoma

Establishing a Correlation of Clinical Characteristics with ...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Alshomali, Lujain Khorma, Rania Al-Refai, Abdullah Alkhateeb, Abedalrhman Princess Sumaya University for Technology Software Engineering Dept. Amman Jordan Lakehead University Computer Science Dept. Thunder Bay Canada

ISBN: (纸本)9798350337488

Bladder cancer is a complex disease and one of the most lethal types of cancer. Recently, some malignancies, including bladder carcinomas, have shown better results with immunotherapy using immune checkpoint inhibitors. Tumor mutational burden (TMB) is a potential biomarker for predicting tumor behavior and immunotherapy response as an outcome. Publicly available clinical data from the bladder cancer of TCGA project is used to analyze correlations of clinical variables with an increased tumor mutation burden (TMB) number compared with those with a lower number of mutations. The threshold for the high mutation burden in the analysis was set at 10 mutations (Mut) per Megabase (Mb). The Chi-Square test (χ2) was used to compare categorical data. The Chi-Square 'Best first' method was used to find a correlation between clinical variables and TMB, then compared with the p-value of significance (p © 2023 IEEE.

关键词： biomarkers Chi-Square clinical characteristics Tumor mutational burden urothelial bladder carcinoma

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Sinhala Sign Language Learning System for Hearing Impaired Community 4

Sinhala Sign Language Learning System for Hearing Impaired C...

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4th International Informatics and software engineering Conference, IISEC 2023

作者： Idushan, K.A.S. Dilshan, P.G.T. Jayasundera, P.S. Madhushan, K.L.B. Krishara, Jenny Wijendra, Dinuka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350318036

This research aims to develop a comprehensive system for Sinhala Sign Language (SSL) that includes a learning system, dynamic sign detection, audio/video to sign conversion, and vocal training. SSL plays a crucial role in facilitating communication for individuals who are deaf or hard of hearing in Sri Lanka. The learning system provides a platform for learning SSL and includes a text-To-sign language interpreter. The dynamic sign detection system uses computer vision techniques to identify and interpret dynamic signs accurately. The audio/video to sign conversion system bridges the gap between spoken language and SSL by converting auditory information into visual representations. The vocal training system focuses on enhancing the vocal skills of cochlear implanted children. This research contributes to the development of effective communication and language skills for SSL users. © 2023 IEEE.

关键词： Learning systems

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