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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri Arfat Ahmad Khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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Battery-Induced Load Hiding and Its Utility Consequences 4

Battery-Induced Load Hiding and Its Utility Consequences

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Aly, Hussein Altamimi, Emran Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah Qatar University College of Engineering Department of Computer Science and Engineering Doha Qatar Qatar University Kindi Center for Computing Research Doha Qatar

ISBN: (纸本)9798350306262

The introduction of smart grids allows utility providers to collect detailed data about consumers, which can be utilized to enhance grid efficiency and reliability. However, this data collection also raises privacy concerns. To protect user privacy, some studies suggest using battery-based load hiding. Nevertheless, the impact of widespread adoption of this approach on utility providers remain unclear. Our paper seeks to evaluate the effects of battery-based load hiding on two critical operations: user profiling and anomaly detection. Our findings reveal that the inclusion of battery users in datasets can diminish the quality of conclusions drawn from these data. This can result in a decrease in the Area Under the Curve (AUC) by more than 10% when attempting to profile users within single-occupant and multiple-occupant households. Furthermore, our experiments demonstrate that battery-based load hiding not only conceals information about users employing the batteries but can also lead to an increased rate of false positives for other non-battery users (from 0.15 to 0.37) within the system. To mitigate these adverse effects, our study assessed various mitigation strategies. In the context of user profiling, our experiment demonstrated that identifying and removing battery users from the analytical dataset using unsupervised detection methods can effectively lessen the impact of battery users. For anomaly detection, our experiment revealed that creating separate classification models for battery and non-battery users can significantly reduce the adverse influence of battery users on the detection performance. © 2024 IEEE.

关键词： Smart power grids

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RES-EV: Identifying EV-Households under High AC Load Using a Residual-Based Model 8

RES-EV: Identifying EV-Households under High AC Load Using a...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Aly, Hussein Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

ISBN: (纸本)9798350382150

This paper introduces a novel residual-based model to identify households with Battery Electric Vehicles (EVs) under high Air Conditioning (AC) load. The considerable energy demands of AC units can obscure charging events for EVs. In this work we propose a residual based model which leverages the distinctive characteristics of EV charging patterns, marked by unpredictable spikes in energy consumption, and the more predictable nature of AC load. Our proposed approach involves training a lightweight forecasting model to predict overall household consumption and utilizes the residuals of this model for identifying household with EVs. The residual-based model, ResEV-AR, demonstrated a substantial advantage in F1 score (5.8% and 7.32%) compared to state-of-the-art models such as EVS and KBF, respectively. Additionally, a simpler residual model, ResEV-SRM, exhibited a 3.5% F1 score advantage over EVS, coupled with an impressive 11-fold reduction in computation time. ©2024 IEEE.

关键词： Energy utilization

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Mining User Behavior in Decentralized Applications for Blockchain Trust and Security Analytics 5

Mining User Behavior in Decentralized Applications for Block...

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5th International Conference on Blockchain computing and Applications, BCCA 2023

作者： You, Shengwei Joshi, Aditya Kuehlkamp, Andrey Nabrzyski, Jarek University of Notre Dame Center for Research Computing Department of Computer Science and Engineering Notre Dame United States

ISBN: (纸本)9798350339239

This paper presents a cutting-edge data mining approach to investigate user trust in decentralized applications (dApps) on public blockchains. Our innovative data analysis methodology enables a comprehensive exploration of dApp user behaviors, uncovering crucial insights. Contrary to popular belief, large-scale hacking incidents on centralized exchanges (CEX) have a limited impact on decentralized exchange (DEX) migration and inactive user rates. Additionally, our results reveal a notable trend of increased user outflow in older dApp protocols compared to their newer counterparts and emphasize the significant role of government regulations on DEX user inactivity. This research advances the understanding of dApp scalability and user trust, laying the groundwork for future inquiries in this rapidly evolving field. © 2023 IEEE.

关键词： Data mining

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Advancing general robotic manipulation with multimodal foundation models: An embodied AI paradigm

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science China Technological sciences 2025年第5期68卷 294-296页

作者： Shifeng HUANG He WANG Xing ZHOU Wenkai CHEN Haibin YANG Jianwei ZHANG Department of Mechanical and Aerospace Engineering Hong Kong University of Science and Technology Foshan Institute of Intelligent Equipment Technology Foshan Huashu Robot Co. Ltd. Center on Frontiers of Computing Studies (CFCS) School of Computer SciencePeking University Beijing Academy of Artificial Intelligence Department of Informatics Universit?t Hamburg

Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered in everyday scenarios such as services, healthcare, agriculture,construction, and numerous other fields. From the perspective of general robotic manipulation, the challenges arise from three factors.

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DPT‐tracker:Dual pooling transformer for efficient visual tracking

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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ALF - Ambient Assisted Living for Healthcare Framework Based on IoMT and Big Data 7th

ALF - Ambient Assisted Living for Healthcare Framework Based...

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7th International Conference on Smart City Applications, SCA 2022

作者： Zahoor, Kanwal Bawany, Narmeen Zakaria Center for Computing Research Department of Computer Science and Software Engineering Jinnah University for Women Karachi74600 Pakistan

ISBN: (纸本)9783031268519

A rapid technology change has significant opportunities for almost every industry, especially for healthcare. By using advanced network technologies and Internet of Medical Things (IoMT) devices, health professionals can monitor remotely and respond to emergency cases efficiently. Ambient Assistive living (AAL) aims to improve living conditions for the elderly and disabled people. Data from heterogeneous sensors allow AAL systems to continuously monitor the health status of the elderly. The main contribution of this paper is to propose an Ambient Assisted Living Framework (ALF) for health care. The framework - ALF for health care comprises four layers namely, data sources, data integration, data analytics and ambient assisted living applications for healthcare. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Privacy Leakage in Federated Learning for Smart Grid Short-Term Load Forecasting 50

Privacy Leakage in Federated Learning for Smart Grid Short-T...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Aly, Hussein Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar KINDI Computing Research Center Qatar University Doha Qatar

ISBN: (纸本)9781665464543

Federated Learning (FL) for household-level Short-Term Load Forecasting (STLF) has emerged as a solution to privacy concerns in smart grids, enabling clients to collaboratively train models without sharing their consumption data with a central server. However, sharing model updates can still introduce privacy risks. A common solution to mitigate this risk is the use of differential privacy during the federation process. However, there is a lack of empirical privacy analysis of these techniques in smart grid scenarios. This paper proposes a property inference attack utilizing a single update per client to evaluate privacy risks in federated learning within smart grid contexts. We assess the privacy risk associated with the standard FedAvg algorithm and a differentially private noise-before-aggregation (NBA) FL scheme. Furthermore, we investigate the trade-off between privacy and utility in the NBA-FL scheme. Our empirical findings reveal significant information leakage with standard FedAvg scheme. An adversary with access to a single model update can identify global data properties of the FedAvg client local dataset with an Area Under the Curve (AUC) of 72%. This privacy leakage can be reduced using NBA-FL, which reduces the AUC to 60%. However, the addition of noise to the model updates results in a utility loss of up to 70% in the model’s predictive power. This significant degradation in model performance outweighs the advantages of the scheme. © 2024 IEEE.

关键词： Federated learning

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Improving Energy Theft Detection through Time Series Segmentation and Ensemble Learning 50

Improving Energy Theft Detection through Time Series Segment...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Altamimi, Emran Al-Ali, Abdulaziz Al-Ali, Abdulla Malluhi, Qutaibah College of Engineering Department of Computer Science and Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

ISBN: (纸本)9781665464543

Non-technical loss and energy theft detection are crucial for improving the stability and reducing financial losses in smart grid and power grid utilities. Recently, the availability of massive datasets has improved detection capabilities using sophisticated techniques like deep neural networks. However, training models on extensive feature sets, such as multi-year data, can lead to confusion due to varied behavioral changes in electricity consumption. To address this, we propose a reformulation of the energy theft detection problem by segmenting the time series data and training individual models on each segment. These models’ anomaly scores are then aggregated to produce a final classification. Our framework has shown significant improvement, elevating the F1 score from 0.6 to 0.74, outperforming recent state-of-the-art techniques on the SGCC dataset, the only publicly available dataset labeled for energy theft. © 2024 IEEE.

关键词： Adversarial machine learning

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Testing the Unknown: A Framework for OpenMP Testing via Random Program Generation

Testing the Unknown: A Framework for OpenMP Testing via Rand...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Laguna, Ignacio Chapman, Patrick Parasyris, Konstantinos Georgakoudis, Giorgis Rubio-Gonzalez, Cindy Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States University of California Department of Computer Science Davis United States

ISBN: (纸本)9798350355543

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates. © 2024 IEEE.

关键词： differential testing OpenMP random program generation software testing

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