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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zhou, Chengliang Wang, Tian Li, Liying Sun, Jin Zhou, Junlong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai200062 China

ISBN: (纸本)9783031226762

As efficient commercial information technology, cloud computing has attracted more and more users to submit their requests to the cloud platform and pay for them based on the amount and quality of services. One of the most important concerns of cloud service providers is to minimize the monetary cost for completing users’ requests while meeting constraints on the quality of services such as makespan and security. To solve this concern, this paper proposes a makespan and security-aware workflow scheduling method based on an improved firefly optimizer that consists of a new solution initialization scheme, a firefly position updating scheme, a firefly-to-solution mapping operator, and two task-to-virtual machine assignment schemes. We carry out extensive simulation experiments on real-world workflows to validate our proposed workflow scheduling method. The results reveal that the monetary cost reduction achieved by our method can be up to 54.0% compared to a baseline and two state-of-the-art approaches. © 2023, Springer Nature Switzerland AG.

关键词： Cloud computing

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting 17

An Image Analysis-Based Automated Method using Deep Learning...

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17th IEEE International Conference on Industrial and Information Systems, ICIIS 2023

作者： Ajikaran, Ramesh Hewarathna, Ashen Iranga Palanisamy, Vigneshwaran Joseph, Charles Thuseethan, Selvarajah Sabaragamuwa University of Sri Lanka Dept. of Computing and Infromation Systems Sri Lanka Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sri Lanka Fau Dept. of Computer Science Germany School of Information Technology Deakin University Australia

ISBN: (纸本)9798350323634

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst. © 2023 IEEE.

关键词： Grain (agricultural product)

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Developing Trustworthy Reinforcement Learning Applications for Next-Generation Open Radio Access Networks

Developing Trustworthy Reinforcement Learning Applications f...

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Ahmad M. Nagib Hatem Abou-zeid Hossam S. Hassanein School of Computing Queen’s University Canada Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Electrical and Software Engineering University of Calgary Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Artificial intelligence is envisioned to transform the design and operation of 6G networks. Reinforcement learning (RL), in particular, has emerged as a fundamental approach toward this goal with strong support from the industry and the Open Radio Access Network (O-RAN) Alliance. While research efforts have demonstrated the potential of RL, the lack of trustworthiness of RL algorithms remains a challenge to its adoption in real-world networks. In this paper, we propose a trustworthy RL framework that addresses the core challenges experienced by RL-based radio resource management applications deployed in O-RANs. We then demonstrate a case study on O-RAN slicing incorporating several modules of the proposed framework. The experimental results show improvements in the average RL convergence rate, initial reward value, percentage of converged scenarios, and reward variance. Hence, the RL-based algorithms exhibit fast convergence and enhanced generalizability, safety, and robustness.

关键词： Industries Open RAN Reinforcement learning Transforms Robustness Safety Resource management

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Insider Attack Detection Using Deep Belief Neural Network in Cloud computing

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Computer Systems Science & engineering 2022年第5期41卷 479-492页

作者： A.S.Anakath R.Kannadasan Niju P.Joseph P.Boominathan G.R.Sreekanth School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science CHRIST(Deemed to be University)Bengaluru560029India Department of Information Security School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information *** use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing *** cloud is nowadays highly affected by internal threats of the *** applications such as banking,hospital,and business are more likely affected by real user *** intruder is presented as a user and set as a member of the *** becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or *** major issue in today's technological development is identifying the insider threat in the cloud *** data are lost,compromising cloud users is *** and security are not ensured,and then,the usage of the cloud is not *** solutions are available for the external security of the cloud ***,insider or internal threats need to be *** this research work,we focus on a solution for identifying an insider attack using the artificial intelligence *** insider attack is possible by using nodes of weak users’*** will log in using a weak user id,connect to a network,and pretend to be a trusted ***,they can easily attack and hack information as an insider,and identifying them is very *** types of attacks need intelligent solutions.A machine learning approach is widely used for security *** date,the existing lags can classify the attackers *** information hijacking process is very absurd,which motivates young researchers to provide a solution for internal *** our proposed work,we track the attackers using a user interaction behavior pattern and deep learning *** usage of mouse movements and clicks and keystrokes of the real user is stored in a *** deep belief neural

关键词： Cloud computing security insider attack network security privacy user interaction behavior deep belief neural network

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Imaging Modalities in Brain Cancer Detection and Diagnosis

Imaging Modalities in Brain Cancer Detection and Diagnosis

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Basheer Almuhaya Bishal Saha Mahmood A. Bazel Rehab A. A. Mohammed Ahmed A. A. Mohammed Ezzaldeen Esmail School of CSE Lovely Professional University Phagwara Punjab India Faculty of Engineering and Computing University of Science and Technology Taiz City Yemen Software Engineering Dept Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Brain cancer is a highly heterogeneous and complex illness, making diagnosis and treatment extremely difficult. The purpose of this study is to clarify how different data types—such as imaging, genomic, proteomic, and clinical data—affect brain tumor characterizations and therapeutic decision-making. This research explores the application of customized medicine, the molecular categorization of brain tumors, and the monitoring of disease progression. Additionally, a comparative examination of various imaging modalities, including fMRI, MRI, CT, PET, and SPECT, is conducted. As a result, this study highlights the importance of many data kinds in tracking disease development, identifying brain tumor subtypes, and devising tailored treatments. These data types include imaging, genomic, proteomic, and clinical data. It also emphasizes the difficulties and possibilities associated with combining multimodal data, with the goal of stimulating more research on the identification and management of brain cancer. Ultimately, the analysis highlights the current obstacles and potential paths for combining and evaluating multimodal data to improve the prognosis for brain cancer.

关键词： Reviews Imaging Brain cancer Genomics Proteomics Functional magnetic resonance imaging Bioinformatics

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Schrödinger Mechanisms: Optimal Differential Privacy Mechanisms for Small Sensitivity

Schrödinger Mechanisms: Optimal Differential Privacy Mechan...

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IEEE International Symposium on Information Theory

作者： Wael Alghamdi Shahab Asoodeh Flavio P. Calmon Juan Felipe Gomez Oliver Kosut Lalitha Sankar School of Engineering and Applied Sciences Harvard University Department of Computing and Software McMaster University School of Electrical Computer and Energy Engineering Arizona State University

We consider the problem of designing optimal differential privacy mechanisms with a favorable privacy-utility tradeoff in the limit of a large number n of compositions (i.e., sequential queries). Here, utility is measured by the average distance between the mechanism's input and output, evaluated by a cost function c. We show that if n is sufficiently large and the sensitivities of all queries are small, then the optimal additive noise mechanism has probability density function fully characterized by the ground-state eigenfunction of the Schrödinger operator with potential c. This leads to a family of optimal mechanisms, dubbed the Schrödinger mechanisms, depending on the choice of the cost function. Instantiating this result, we demonstrate that for c(x) = x 2 the Gaussian mechanism is optimal, and for c(x) = |x|, the optimal mechanism is obtained by the Airy function, thereby leading to the Airy mechanism.

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Semantic learning and attention dynamics for behavioral classification in police narratives

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Smart Health 2024年 32卷

作者： Attota, Dinesh Chowdary Azmee, Abm Adnan Khan, Md. Abdullah Al Hafiz Pei, Yong Thomas, Dominic Nandan, Monica College of Computing and Software Engineering - Kennesaw State University 1100 South Marietta Pkwy Marietta 30060 GA United States

The proactive identification of behavioral health incidents concerns from police reports is a critical yet underexplored area. The law enforcement officers provide follow-up services to improve community life by manually analyzing and identifying generated public narrative reports after the 911 incident calls. Therefore, automatically identifying these behavioral health calls from public narrative reports helps reduce the current manual labor-intensive identification process for law enforcement officers. In this work, we introduce a novel, multi-faceted approach that combines manual expert annotations, natural language processing (NLP), and cutting-edge machine learning strategies to classify and understand these incidents within police narratives efficiently. Our proposed method retrieves relevant narrative reports utilizing domain knowledge as behavioral health cues as terms/keywords. Our approach automatically extracts different categories of behavioral health cues by utilizing the limited domain knowledge enabled behavioral health terms using a cosine similarity-based thresholding approach. The behavioral health classification model employs an automatic attention-aware feature representation of terms/keywords, categories, and narrative reports to identify behavioral health cases with an accuracy of 85%. Extensive evaluation shows that our proposed model outperforms all the state-of-the-art models by approximately 4%. © 2024 Elsevier Inc.

关键词： Attention Behavioral health Bi-LSTM Inverted index Police narratives Text classification

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： Computer vision

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Frequency Filtering Network for Undersampled MRI Reconstruction

Frequency Filtering Network for Undersampled MRI Reconstruct...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Xinren Chen Zhaohui Liu Dongping Xiong Lijun Ouyang Xiaozhi Zhang School of Computing/Software University of South China Hengyang China School of Electrical Engineering University of South China Hengyang China

Magnetic resonance imaging (MRI) is an imaging technology widely used in medical clinical diagnosis. Nevertheless, it always takes a long imaging time to obtain the high spatial resolution MR image. Image reconstruction has been playing a crucial role for accelerated MRI. Recently, deep neural networks show potential to significantly speed up MRI. However, these deep learning methods usually learn the feature information in the spatial domain. Although some works have attempted to utilize the frequency information from the k-space to reconstruct MR images, they still suffer from the inability to consider the global information. To address these limitations, in this work, we propose a new residual filter channel attention network (RFCAN) for undersampled MRI reconstruction. Specifically, the proposed model consists of a novel filter channel attention (FCA) in the frequency domain and the multiscale cascade convolution in the spatial domain to learn both the dual-domain features. This model employs the filtering instead of the traditional self-attention mechanism in Transformer to learn k-space information. It can avoid the limitations of the Transformer window to better learn the global information. We also introduce a frequency channel attention to fine-tune the filtering results to better balance the global and local information. Experiment results show that our model consistently outperforms other state-of-the-art methods and can achieve high-quality MRI reconstruction.

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Data-Driven Analysis of Climate Change Impacts on Public Health: A Systematic Review and Future Directions

Data-Driven Analysis of Climate Change Impacts on Public Hea...

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IEEE International Conference on Big Data

作者： Sahar Yarmohammadtoosky Ying Xie School of Data Science and Analytics Kennesaw State University Marietta Georgia USA College of Computing and Software Engineering Kennesaw State University Marietta Georgia USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Climate change profoundly impacts human health, ecosystems, and food and water security. This review provides a comprehensive analysis of climate change's effects on human health using data science methods, including statistical and machine learning models, to assess current knowledge. It identifies critical gaps in data collection and methodological limitations across different regions and climates. Key recommendations include improving data resolution and duration to enhance model accuracy, as well as fostering collaboration between data scientists, public health experts, and climate specialists. The review underscores the need for innovative, data-driven models to better predict and address the health impacts of climate change.

关键词： Climate change Analytical models Biological system modeling Ecosystems Machine learning Data science Data models Thermal pollution Public healthcare Meteorology

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