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10th International Conference on Optimization and Applications, ICOA 2024

作者： El Majdouby, Karima El Mohadab, Mohamed Daoud, Rachid Ait Abouelmehdi, Karim University of Chouaib Doukkali Elites laboratory Faculty of Sciences Computer Science department Eljadida Morocco Sultan Moulay Slimane University Systems Engineering Laboratory Computer Science department Beni Mellal Morocco

ISBN: (纸本)9798350387353

Agriculture is evolving towards more sustainable practices thanks to the integration of the machine learning and Internet of Things, which addresses many of the issues related to agricultural production and leads to increased yields with reduced input and labor costs. Smart farming has developed solutions to many conventional agricultural problems to automatically manage and monitor farmland. These technologies reduce the risk of field management in the event of bad weather, and also provide benefits to farmers in the event of labor shortages. The Internet of Things ensures the connection of physical objects that are equipped with sensors to the internet, enabling them to collect and exchange data and communicate with each other and with computer systems to gather information, perform analysis and take actions in real time that require rapid data analysis, and this task can be accomplished through machine learning algorithms with greater efficiency and high-quality decision-making. In addition, we carry out an in-depth state-of-the-art survey to identify very recent work that has implemented the Internet of Things and machine learning in smart agriculture, analyzing the development of practical solutions and discussing recent research trends and future prospects. © 2024 IEEE.

关键词： Internet of things Machine learning Smart Agriculture

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Improving High Dimensional Data Streams Classification 4

Improving High Dimensional Data Streams Classification

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Bensaoula, Hadjer Imene Lounici, Maissa Bahloul, Sarah Nait University of Science and Technology of Oran Mohamed Boudiaf USTO-MB Simpa Laboratory Computer Science Department Oran Algeria University of Science and Technology of Oran Mohamed Boudiaf USTO-MB Computer Science Department Oran Algeria University of Science and Technology of Oran Mohamed Boudiaf USTO-MB Lssd Laboratory Computer Science Department Oran Algeria

ISBN: (纸本)9798331519872

Traditional data mining approaches can no longer meet the analytical needs of modern Big Data, as many of today's applications generate an infinite amount of dynamic data known as 'data streams', in real time and at rapid rates. In response, data stream mining has emerged as a new paradigm for processing and deriving real-time insights from such data. Data stream mining encompasses a variety of techniques, with stream classification being the focus of much research. Accordingly, this research aims to refine one of the key methods employed in online classification. This study explores the application of Adaptive Random Forests (ARF) to data stream classification, with a focus on the Compressed-Sensing ARF (CS-ARF) variant, which has been specifically designed to tackle the challenges of high-dimensional data streams classification. Based on the CS-ARF implementation, our research aims to further enhance its performance. Experimental results demonstrate promising outcomes, particularly in terms of improved classification accuracy. © 2024 IEEE.

关键词： Data assimilation

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Data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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Inception-UDet: An Improved U-Net Architecture for Brain Tumor Segmentation

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Annals of Data science 2024年第3期11卷 831-853页

作者： Aboussaleh, Ilyasse Riffi, Jamal Mahraz, Adnane Mohamed Tairi, Hamid LISAC Laboratory Department of Computer Science Faculty of Sciences Dhar El Mahraz University Sidi Mohamed Ben Abdellah Fez30000 Morocco

Brain tumor segmentation is an important field and a sensitive task in tumor diagnosis. The treatment research in this area has helped specialists in detecting the tumor’s location in order to deal with it in its early stages. Numerous methods based on deep learning, have been proposed, including the symmetric U-Net architectures, which revealed great results in the medical imaging field, precisely brain tumor segmentation. In this paper, we proposed an improved U-Net architecture called Inception U-Det inspired by U-Det. This work aims at employing the inception block instead of the convolution one used in the bi-directional feature pyramid neural (Bi-FPN) network during the skip connection U-Det phase. Furthermore, a comparison study has been performed between our proposed approach and the three known architectures in medical imaging segmentation;U-Net, DC-Unet, and U-Det. Several segmentation metrics have been computed and then taken into account in these methods, by means of the publicly available BraTS datasets. Thus, our obtained results have showed promising results in terms of accuracy, dice similarity coefficient (DSC), and intersection–union ratio (IOU). Moreover, the proposed method has achieved a DSC of 87.9%, 85.5%, and 83.9% on BraTS2020, BraTS2018, and BraTS2017, respectively, calculated from the best fold in fourfold cross-validation employed in the present approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Tumors

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Simultaneous improving luminescence intensity and stability of CsPbBr_(3):SCN−@Eu/Zr-Uio-66-NH_(2) with tunable emissions from blue to green and applications in indoor photovoltaics

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Nano Research 2024年第8期17卷 6879-6887页

作者： Minze Li Yao Sun Honglai Lu Peifen Zhu Ruihong Wang Guofeng Wang Key Laboratory of Functional Inorganic Material Chemistry Ministry of EducationSchool of Chemistry and Materials ScienceHeilongjiang UniversityHarbin 150080China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA

The construction of stable and efficient materials that emit blue and green light remains a *** the blue light materials reported,metal-organic framework(MOF)materials are rarely reported as blue phosphors due to their weak luminescence *** on the construction of CsPbBr_(3)@MOF(CPB@MOF),an innovative idea was proposed to simultaneously enhance the green luminescence of CPB and the blue luminescence of MOF through the interaction between CPB and MOF for the first *** expected,the blue luminescence from CPB:7%SCN−@0.5%MOF:Eu as well as the green luminescence from 5%CPB:7%SCN−@MOF:Eu was sufficient to construct high-performance light-emitting diode(LED)devices and further excite solar cells to generate stable photoelectric *** white LED(WLED)device with excellent color quality(color rendering index(CRI)=96.2)and correlated color temperature(CCT=9688 K)can be constructed by using the obtained blue-emitting CPB:7%SCN-@0.5%MOF:Eu,green-emitting 5%CPB:7%SCN−@MOF:Eu,and red-emitting PPB:30%Mn^(2+).The density functional theory(DFT)theoretical calculation results indicate that the p orbital of Pb plays the major role in the conduction band,and the p orbital of Br plays the major role in the valance band of CPB and CPB:SCN−.While the p orbital of O plays the major role in both the conduction band and valance band of *** heat capacity of CPB and CPB:SCN−separately reaches the Dulong–Petit limit at 200 and 400 K,indicating that the thermal stability of CsPbBr_(3)increases after SCN−doping.

关键词： CsPbBr_(3):SCN−@Uio-66-NH_(2) composites luminescence stability white light-emitting diodes(WLEDs) indoor photovoltaics density functional theory(DFT)calculation

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Extracting and Exploiting the Behavior Business Process Graph through Transition-Centric Event-Log data

Extracting and Exploiting the Behavior Business Process Grap...

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2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Chaima, Afifi Ali, Khebizi Khaled, Halimi Laboratory of Labstic Department of Computer Science Guelma Algeria University of 8 May 1945 Department of Computer Science Guelma Algeria

ISBN: (纸本)9781665456272

In recent years, there has been an intense interest in extracting knowledge from Business Process (BP) execution data provided by Information System (IS). In this area, a set of Process Mining (PM) approaches has been developed. While such conventional PM approaches aim to extract hand-crafted features from the event log, the Deep Learning (DL) models are used to automatically extract the features from the input data. Whereas, the graph representation is the advanced and powerful input format for these DL models. This paper focuses on the pre-processing data representation stage as a starting step for the application of any Machine Learning (ML) technique (process discovery, anomaly detection, classification, recommendation, ldots etc.). This phase aim to represent the BP event-log data transitions as Behavior Graphs (BG). This BG constitutes the backbone of our perspective hierarchical DL framework's based feature extraction and which allows to learn the unified execution of the process hidden behind the event-log data trace's. © 2023 IEEE.

关键词： Feature extraction

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Improvement of LEACH Protocol: W-LEACH Approach

Improvement of LEACH Protocol: W-LEACH Approach

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Chafi, Safia Amina Abbad, Houda Benhaoua, Mohammed Kamel El Hassan Benyamina, Abou Oranl University Lapeci Laboratory Computer Science Department Oran Algeria Djillali Liabes University Eedis Laboratory Computer Science Department Sidi Bel Abbes Algeria Mustapha Stambouli University Computer Science Department Lapeci Laboratory Mascara Algeria

ISBN: (纸本)9798350347050

Wireless Sensor Networks (WSNs) are autonomous, self-configurable and self-organizing embedded systems designed to provide innovative services. Their use is growing in several fields, such as military, environment, medicine, etc. The energy consumed by sensor nodes presents a major challenge in this type of network, since they have a limited lifetime. For this reason, several routing protocols have been designed, in particular W-LEACH protocol [6]. W-LEACH is an improved version of LEACH protocol [8]. The purpose of our paper is to highlight the improvements made by W-LEACH protocol, through a development stage under MATLAB language, simulation step of LEACH and W-LEACH protocols and finally a summary results validating the improvements in terms of lifetime claimed by W-LEACH designers. © 2023 IEEE.

关键词： Base stations

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Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

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computers, Materials & Continua 2023年第8期76卷 1947-1976页

作者： Asif Rahim Yanru Zhong Tariq Ahmad Sadique Ahmad Mohammed A.ElAffendi School of Computer and Information Security Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic TechnologyGuilin541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of *** Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of *** this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship *** conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 *** compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN *** results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like *** findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this ***,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic *** findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

关键词： Convolutional Neural Network(CNN) hyper-tuning authorship verification digital forensics

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How to Leverage Diverse Demonstrations in Offline Imitation Learning 41

How to Leverage Diverse Demonstrations in Offline Imitation ...

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41st International Conference on Machine Learning, ICML 2024

作者： Yue, Sheng Liu, Jiani Hua, Xingyuan Ren, Ju Lin, Sen Zhang, Junshan Zhang, Yaoxue Department of Computer Science and Technology Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science University of Houston Texas United States Department of Electrical and Computer Engineering University of California Davis United States

Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental problem in this scenario is how to extract positive behaviors from noisy data. In general, current approaches to the problem select data building on state-action similarity to given expert demonstrations, neglecting precious information in (potentially abundant) diverse state-actions that deviate from expert ones. In this paper, we introduce a simple yet effective data selection method that identifies positive behaviors based on their resultant states - a more informative criterion enabling explicit utilization of dynamics information and effective extraction of both expert and beneficial diverse behaviors. Further, we devise a lightweight behavior cloning algorithm capable of leveraging the expert and selected data correctly. In the experiments, we evaluate our method on a suite of complex and high-dimensional offline IL benchmarks, including continuous-control and vision-based tasks. The results demonstrate that our method achieves state-of-the-art performance, outperforming existing methods on 20/21 benchmarks, typically by 2-5x, while maintaining a comparable runtime to Behavior Cloning (BC). Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Leveraging Deep Learning to Automate the Approval Process for Durum Wheat Grain Quality 1

Leveraging Deep Learning to Automate the Approval Process fo...

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1st International Conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Merzougui, Ghalia Boulelouah, Nadia Mokhtari, Abdelheq Maalem, Selsabil Merzougui, Hanine Hebira, Abderraouf Saadoune, Imene Leila, Saadi Koulib, Aya Lamie Laboratory University of Batna 2 Computer Science Department Algeria University of Batna 1 Agricultural Sciences Department Algeria University of Batna 2 Computer Science Department Algeria

ISBN: (纸本)9798331541064

This study addresses the urgent need for accurate and efficient quality control of durum wheat grains in Algeria, where traditional inspection methods are often insufficient. To tackle this, we trained and evaluated seven convolutional neural network (CNN) models on a comprehensive dataset of 1 8, 3 6 2 images, aiming to classify wheat species, varieties, and impurities. Our findings demonstrate the ResNet50 model's superior performance in species and variety classification, achieving accuracies of 99.59% and 98.11%, respectively. Although its accuracy for impurity detection was comparatively lower, likely due to the limited size of the impurity's dataset. ResNet50 still demonstrated its potential in addressing this challenging classification task. Future directions for this study include integrating the ResNet50 model into an automated quality control system, representing a significant step forward in enhancing the efficiency, accuracy, and consistency of wheat quality assessments within the Algerian agricultural sector. This adaptable solution could extend to other cereal types, offering a scalable impact on agricultural quality control practices more broadly. © 2024 IEEE.

关键词： Convolutional neural networks

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