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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Kyaw, Chan Min Thein, Nwe Nwe Myint Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar Faculty of Computer Science Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar

ISBN: (纸本)9789819615308

Nowadays, the ability to process, generate and collect various data in real-time has become more relevant for faster reactions to important events in technical enterprises. The numerous distributed stream processing engines is developed to provide the advance large streaming applications. Apache Kafka and Flink also become more popular in the processing and analyzing of large amount of data. Apache Flink is best suited for data analytics, event-driven and data pipeline applications. Applying Apache Kafka to Flink provides low idle time and low latency, and the most demanding stream processing applications in the world. This paper investigates the effective pipeline architecture for developing data driven analytics. Confirming the experimental results, the system proves to optimize the maximum latency, average latency, and idle time of the pipeline architecture. Additionally, further experiments were performed by implementing different levels of parallelism while streaming data, allowing for a comprehensive analysis of maximum latency and average latency of the pipeline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data streams

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

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EDCOPA : Enhancing DCOPA Protocol by Exploring New Criteria for Improved Clustering 7

EDCOPA : Enhancing DCOPA Protocol by Exploring New Criteria ...

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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Mir, Foudil Bounceur, Ahcene Meziane, Farid Faculty Of Exact Sciences University Of Bejaia Bejaia06000 Algeria Kfupm Information And Computer Science Department Dhahran Saudi Arabia College Of Science And Engineering Data Science Research Centre University Of Derby United Kingdom

ISBN: (纸本)9798400709036

The Internet of Things (IoT) has significantly expanded the connectivity of smart devices, emphasizing the crucial importance of clustering for optimizing device energy, particularly in data communication. The DCOPA protocol is presented as a distributed clustering protocol for data communication in IoT based on Wireless Sensor Networks (WSN). The DCOPA protocol uses competition among network nodes, based on the multicriteria aggregation approach. Residual energy and Distance from a Base Station (DistBS) criteria are used to elect Cluster-Heads (CHs). DCOPA operates in two distinct phases: a Setup phase dedicated to CH designation and a Steady-state phase that includes specific periods for normal nodes and CHs. In this article, we propose an enhancement to the DCOPA protocol called EDCOPA, in which we focus on integrating two additional parameters that can influence and optimize the selection of CHs for increased energy efficiency and a better network lifetime. This improvement preserves the distributed and multicriteria aspects of the protocol. The number of times a given node has assumed the role of CH, as well as the energy consumed in the previous round, have been incorporated into the objective function. Simulation results demonstrate that this integration has significantly improved the network's lifetime and the energy consumption of both nodes and the entire network. © 2023 ACM.

关键词： Internet of things

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Energy Efficiency in Sustainable Software Development: Clean Code Approaches

Energy Efficiency in Sustainable Software Development: Clean...

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International Conference on Electrical Engineering and computer science (ICECOS)

作者： Inayatullah Wijang Widhiarso Deris Stiawan Department Program in Engineering Science Faculty of Computer Science Universitas Multi Data Palembang Palembang Indonesia Department Program in Engineering Science Faculty af Computer Science Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Multi Data Palembang Palembang Indonesia Faculty af Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development.

关键词： Electrical engineering computer science Energy consumption Codes Software Global warming Energy efficiency Greenhouse gases Carbon emissions Software development management

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Analysis of Agricultural Product Package Recommendations Using the FP-Growth Algorithm 5

Analysis of Agricultural Product Package Recommendations Usi...

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5th International Conference on Networking, Information Systems and Security, NISS 2022

作者： Masdiyasa, I Gede Susrama Prabowo, Aris Mandyartha, Eka Prakarsa Ariefwan, Rafka Mahendra Sugiarto Idhom, Mohammad Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Data Science Surabaya Indonesia Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Informatics Surabaya Indonesia

ISBN: (纸本)9781665453639

The size of the agricultural sector in Indonesia provides opportunities for small businesses managed by local residents to provide various needs to support agricultural activities. The rise of agricultural shops has made business competition in the agricultural sector quite competitive. Therefore, a marketing strategy is needed that can increase the sales of various marketed agricultural products. The method applied in this research is Association Rule Mining using the FP-Growth algorithm. This method is applied to obtain association rules that are used as a reference in making recommendations for agricultural product packages at agricultural shops to increase sales. The system workflow starts from preprocessing data to form an itemset which is then processed using the FP-Growth algorithm. The next step is to determine the minimum value of support and minimum confidence as a limit in calculating the FP-Growth algorithm. The system will eliminate a number of itemsets that do not meet the specified threshold to produce frequent itemsets which are then mined into rules as a reference to form the most recommended package of agricultural products. From the research conducted it can be seen that there are 3 itemets that almost always appear and the most recommended agricultural product package is Prowl 250 ml (Herbicide) which is associated with Antracol 70wp 1 kg (fungicide) with a support value of 6.38% and a confidence value of 85, 71% and the lift ratio is 8.95. © 2022 IEEE.

关键词： Association rules

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Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach

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IEEE Transactions on Services Computing 2025年第03期18卷 1428-1444页

作者： Zuo, Xuhan Wang, Minghao Zhu, Tianqing Zhang, Lefeng Yu, Shui Zhou, Wanlei University of Technology Sydney School of Computer Science Ultimo2007 Australia City University of Macau Faculty of Data Science China

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. This paper introduces an innovative framework that melds blockchain with federated learning, ensuring an immutable record of unlearning requests and actions. Our approach not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Key contributions include a certification mechanism for the unlearning process, enhancement of data security and privacy, and optimization of data management. Experimental results on MNIST and CIFAR-10 datasets demonstrate the effectiveness of our approach, achieving 0% accuracy for unlearned classes while maintaining 77.74% and 42.65% overall model accuracy for MNIST and CIFAR-10, respectively. Our time complexity analysis shows that the blockchain integration introduces only 2 seconds of overhead per epoch, highlighting the practicality of our solution for IoT applications. © 2008-2012 IEEE.

关键词： Federated learning

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Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits

Transferability of a discrepancy model for the dynamics of e...

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93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2023

作者： Wohlleben, Meike Muth, Lars Peitz, Sebastian Sextro, Walter Faculty of Mechanical Engineering Dynamics and Mechatronics Paderborn University Paderborn Germany Department of Computer Science Data Science for Engineering Paderborn University Paderborn Germany

Modelling of dynamic systems plays an important role in many engineering disciplines. Two different approaches are physical modelling and data-driven modelling, both of which have their respective advantages and disadvantages. By combining these two approaches, hybrid models can be created in which the respective disadvantages are mitigated, with discrepancy models being a particular subclass. Here, the basic system behaviour is described physically, that is, in the form of differential equations. Inaccuracies resulting from insufficient modelling or numerics lead to a discrepancy between the measurements and the model, which can be compensated by a data-driven error correction term. Since discrepancy methods still require a large amount of measurement data, this paper investigates the extent to which a single discrepancy model can be trained for a physical model with additional parameter dependencies without the need for retraining. As an example, a damped electromagnetic oscillating circuit is used. The physical model is realised by a differential equation describing the electric current, considering only inductance and capacitance;dissipation due to resistance is neglected. This creates a discrepancy between measurement and model, which is corrected by a data-driven model. In the experiments, the inductance and the capacity are varied. It is found that the same data-driven model can only be used if additional parametric dependencies in the data-driven term are considered as well. © 2023 The Authors. Proceedings in Applied Mathematics and Mechanics published by Wiley-VCH GmbH.

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Learned distributed image compression with decoder side information

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Digital Communications and Networks 2025年第2期11卷 349-358页

作者： Yankai Yin Zhe Sun Peiying Ruan Ruidong Li Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai UniversityTianjin 300350China Institute of Natural Sciences Kanazawa UniversityIshikawa 9201164Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo UniversityChiba 2790013Japan NVIDIA AI Technology Center NVIDIA JapanTokyo 1070052Japan

With the rapid development of digital communication and the widespread use of the Internet of Things,multi-view image compression has attracted increasing attention as a fundamental technology for image data ***-view image compression aims to improve compression efficiency by leveraging correlations between ***,the requirement of synchronization and inter-image communication at the encoder side poses significant challenges,especially for constrained *** this study,we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during *** model integrates an encoder network,a quantization module,and a decoder network,to ensure both high compression performance and high-quality image *** encoder uses a deep Convolutional Neural Network(CNN)to extract high-level features from the input image,which then pass through the quantization module for further compression before undergoing lossless entropy *** decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder ***,we first introduce a channel-spatial attention module to capture and refine information within individual image feature ***,we employ a semi-coupled convolution module to extract both shared and specific information in ***,a cross-attention module is employed to fuse mutual information extracted from side *** effectiveness of our model is validated on various datasets,including KITTI Stereo and *** results highlight the superior compression capabilities of our method,surpassing state-of-the-art techniques.

关键词： Digital communication Image compression Side information Channel-spatial attention module Cross-attention module

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan 25

Prediction of Hypertension Disease Using Machine Learning Te...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 IEEE.

关键词： Prediction models

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Graphene oxide-coated microbottle resonators for relative humidity sensing

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Optoelectronics Letters 2024年第7期20卷 400-405页

作者： Huda Adnan Zain Malathy Batumalay Md Ashadi Md Johari Hazli Rafis Abdul Rahim Sulaiman Wadi Harun Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur 50603Malaysia Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Malaysia1 Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia MelakaMelaka 76100Malaysia

Graphene oxide(GO)is a 2D coating material used to improve fiber optics sensors’response to relative *** resonators(MBRs)have garnered more attention as sensing media *** MBR with a 190μm diameter was coated with ***,tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25℃.The MBR showed a higher Q factor before and after GO *** sensitivity of 0.115 dB/%RH was recorded with the 190μm GO-coated MBR sample compared to a sensitivity of 0.022 dB/%RH for the uncoated MBR *** results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

关键词： humidity oxide bottle

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