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Multiscale drought assessment with analyzing agricultural, meteorological, and hydrological impacts multi-index approaches (1991–2021)

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Theoretical and Applied Climatology 2025年第5期156卷 1-21页

作者： Zhou, Mingwu Haseeb, Muhammad Tahir, Zainab Mahmood, Syed Amer Arif, Hania Abdullah-Al-Wadud, M. Tariq, Aqil Department of Water Resources and Civil Engineering Hetao College Mongolia Autonomous Region Inner Bayannur 015000 China Institute of Environmental Engineering and Research University of Engineering and Technology Lahore P.O. Box 54780 Lahore Pakistan Institute of Space Science University of the Punjab Lahore 54780 Pakistan Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Department of Wildlife Fisheries and Aquaculture College of Forest Resources Mississippi State University Mississippi State 39762 - 9690 MS United States

The increasing frequency of droughts as global temperatures rise requires the development of precise and reliable criteria for drought evaluation. This research used three standard drought indices: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI) to assess drought conditions in five districts of Punjab, Pakistan, from 1991 to 2021. A key innovation of this research is the integration of multiple timescales (3-, 6-, and 12-months) to analyze agricultural, meteorological, and hydrological droughts, providing a more comprehensive drought assessment. The RDI calculation noticed that all districts experienced moderate to extreme droughts, with values ranging from -1.0 to -2.5. Moderate to severe droughts happened in all districts, with critical drought years including 1993, 2000, 2001, 2005, 2018, and 2021 resulting in considerable wheat production decreases of 2.74 tons/ha. Based on 3-, 6-, and 12-month timescale estimations for SPI and SPEI, the research indicated major droughts occurred in 1991, 1993, 2000, 2001, 2003, 2016, 2018, and 2021, which were consistent throughout all five districts of Punjab. The study indicated that the Bahawalpur and Rahim Yar Khan districts were most vulnerable to droughts. This study’s comparative assessment of SPI, SPEI, and RDI highlights that SPI is highly effective for detecting short-term agricultural droughts, while SPEI provides a more detailed perspective on long-term climatic variability. RDI, which integrates precipitation and evapotranspiration, is particularly useful for assessing hydrological drought impacts in semi-arid regions. The study underscores the importance of selecting appropriate drought timescales, where SPI-3 is optimal for agricultural droughts, while SPI-6 and SPI-12 provide insights into hydrological and meteorological droughts, respectively. The findings of this research offer innovative insights for enhanci

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YOLO-RAW: Advancing UAV Detection With Robustness to Adverse Weather Conditions

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 7857-7873页

作者： Munir, Adnan Siddiqui, Abdul Jabbar Hossain, M. Shamim El-Maleh, Aiman Computer Engineering Department Dhahran31261 Saudi Arabia KFUPM SDAIA-KFUPM Joint Research Center for Artificial Intelligence IRC for Intelligent Secure Systems and Computer Engineering Department Dhahran31261 Saudi Arabia King Saud University College of Computer and Information Sciences Research Chair of Pervasive and Mobile Computing Department of Software Engineering Riyadh12372 Saudi Arabia KFUPM Computer Engineering Department Information and Computer Science Department IRC for Intelligent Secure Systems Dhahran31261 Saudi Arabia

With the widespread adoption of unmanned aerial vehicles (UAVs) in various applications (e.g., aerial transportation, traffic monitoring), there have been apprehensions regarding the associated risks of employing UAVs in both civilian and military contexts, including concerns about privacy infringement, safety issues, and security threats. Although several methods have been proposed to detect UAVs, a pressing open challenge is posed by varying adverse weather conditions that could degrade the performance of many existing methods. To address these limitations, this work proposes a YOLO-based (You Only Look Once) novel model, YOLO-RAW that exhibits improved performance in adverse weather conditions and considers different scales of UAVs. Furthermore, to facilitate a more comprehensive evaluation of the proposed model’s effectiveness in UAV detection, we have curated a complex background dataset and introduced three distinct test sets affected by adverse weather conditions. These three test sets comprise the Rainy Test Set (RTS), the AWGN (Additive White Gaussian Noise) Test Set (ATS), and the Motion Blurred Test Set (MBTS). The comprehensive experiments demonstrate the effectiveness of the proposed YOLO-RAW model over its counterparts in detecting UAVs under adverse conditions. © 2000-2011 IEEE.

关键词： Drones

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Optimal Multi-Bitrate Video Caching and Processing in Edge Computing: A Stackelberg Game Approach

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IEEE Internet of Things Journal 2025年

作者： Guo, Min Zhang, Di Xing, Weiwei Shao, Xun Liu, Zhi Zhang, Yaoxue Beijing Jiaotong University School of Software Engineering Beijing100044 China Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Aichi441-8580 Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan Tsinghua University Department of Computer Science and Technology Beijing100084 China

By caching and transcoding video files on edge servers, video edge caching (VEC) can alleviate network congestion and improve user experience. To achieve this, VEC needs to address resource allocation and pricing problems and develop caching and transcoding strategies. However, existing works have not effectively coordinated and resolved these issues. Towards this end, we propose a game-theoretic multi-bitrate video caching and processing method for VEC. Specifically, to address these interdependent issues concurrently, we first incorporate multidimensional strategies into the system model and formalize the profit maximization problems of the network service provider and multiple video providers (VPs) as a constrained multi-objective optimization problem. Second, we transform the problem into a Stackelberg game and identify a Nash equilibrium as the solution. To solve the game problem, we decompose the game process into two stages focusing on resource competition and price determination, respectively. Then, we address the problems of transcoding and caching strategies development, and resource allocation in parallel across varying resource price conditions. The transcoding strategy is determined based on the constraints imposed by delay, value, and the source video. For the caching strategy development and resource allocation, we transform them into two grouped knapsack problems and apply the "dominant strategy» from game theory to identify the "feasible strategy» of VPs. Finally, we determine the resource prices in a traversal manner. Theoretical analysis shows that our proposed method achieves the optimal solution. Experimental results show that our algorithm achieve a 6.04% increase in overall profit compared to the greedy strategy algorithm. © 2025 IEEE.

关键词： Costs

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A Big Data Analytic and IoT-Based Approach for Addressing Water Scarcity in Jordan

A Big Data Analytic and IoT-Based Approach for Addressing Wa...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Abdelraouf Ishtaiwi Nuha El-Khalili May Alnashashibi Mohammad Abu Arqoub Dept. of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Software Engineering University of Petra Amman Jordan Department of Computer Science University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Water scarcity critically threatens agricultural sus-tainability in Jordan, necessitating innovative technological solutions. This study presents a comprehensive framework that integrates Internet of Things (IoT) sensors with Big Data Analytics to optimize water usage in agriculture. Employing a multi-stage approach, we conducted real-time data acquisition from diverse farms, processed the data using clustering algorithms, and implemented intelligent decision-making systems. Our analysis focused on key parameters such as soil moisture, temperature, rainfall, and wind speed across various farming locations. The methodology encompassed extensive field data collection, advanced analytics, and the deployment of smart irrigation systems. Results demonstrated a significant 25 % reduction in water consumption while maintaining optimal crop yields. Additionally, clustering identified three distinct farm categories, each requiring tailored water management strategies. This research contributes to the field of smart agriculture by providing practical, scalable solutions for regions facing severe water scarcity. The findings highlight the potential of advanced technologies to enhance agricultural sustainability and address critical water management challenges.

关键词： Temperature sensors Smart agriculture Irrigation Wind speed Soil moisture Big Data Real-time systems Internet of Things Sustainable development Water resources

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Profit Maximization of Delay-Sensitive, Differential Accuracy Inference Services in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Yuncan Liang, Weifa Xu, Zichuan Jia, Xiaohua Yang, Yuanyuan City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Dalian University of Technology School of Software Dalian China Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY United States

The integration of Artificial Itelligence (AI) and edge computing has sparked significant interest in edge inference services. In this paper, we consider delay-sensitive, differential accuracy inference services in a Mobile Edge Computing (MEC) network while meeting user stringent delay and accuracy requirements. We formulate two novel profit maximization problems under static and dynamic settings of service request arrivals, with the aim of maximizing the accumulative profit of admitted requests. We assign differential accuracy service requests to the corresponding resolution instances of their requested service models, assuming that each resolution instance can serve up to L≥ 1 the same type of service requests. Since the profit maximization problem is NP-hard, we first formulate an Integer Linear Program (ILP) solution if the problem size is small or medium;otherwise, we devise a constant randomized algorithm with high probability. Then, we consider dynamic service request admissions without the knowledge of future request arrivals for a given finite time horizon, for which we develop a simple yet effective prediction mechanism to accurately predict the number of different resolution instances of each model needed, and pre-deploy the predicted number of resolution instances into cloudlets to reduce instantiating delays. We then devise an online algorithm with a provable competitive ratio for the dynamic profit maximization problem by leveraging the primal-dual dynamic updating technique. Finally, we evaluate the performance of the proposed algorithms by simulations. The simulation results demonstrate that the proposed algorithms are promising. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Energy-Efficient Tactile-Driven Rule Configuration and Anomaly Detection in Industrial IoT Systems

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IEEE Internet of Things Journal 2025年

作者： Tan, Lizhuang Singh, Amritpal Zhang, Wei Pei, Hongjuan Zhang, Peiying Chahal, Prabhjot Kaur Singh, Maninderpal Ji'nan250014 China Pohang University of Science and Technology Department of Computer Science and Engineering Pohang37673 Korea Republic of Northumbria University Department of Computer and Information Sciences United Kingdom University of Chinese Academy of Sciences School of Engineering Science Beijing100049 China China University of Petroleum Qingdao Institute of Software College of Computer Science and Technology East China Qingdao266580 China Chandigarh Group of Colleges CEC Punjab Mohali India Southern Methodist University Electrical and Computer Engineering Department Dallas United States

The Industrial Internet of Things (IIoT) enables communication among automation systems, machinery, and sensors in an industrial setting. To optimize critical industrial operations, a substantial volume of data concerning diverse in-factory activities and automation services is generated by IoT devices and sensors. This data is subsequently transferred to distant processing systems for analysis and decision-making. Nevertheless, a substantial latency in data transmission or any abnormality in the generated data may result in delayed or erroneous decisions, consequently impacting the efficacy of essential industrial systems. To address these challenges, we established an intelligent network architecture utilizing software-defined networking that achieves tactile latencies efficiently while handling industrial data traffic in an energy-efficient manner. To address the initial challenge, the suggested architecture utilizes the Self-Organized Maps approach to distinguish between industrial traffic requiring tactile latencies and non-tactile traffic. We utilize a binary tree-based flow table mapping method to enhance flow table matching and decrease lookup times. To address the second challenge, we employ the Support Vector Machine technique to identify anomalies in real-time industrial data traffic. The Hadoop system and Mininet emulator are utilized to evaluate the proposed architecture using the UNSW dataset. The results demonstrate the effectiveness of the suggested solution in providing energy-efficient tactile assurances and identifying anomalies in traffic. © 2014 IEEE.

关键词： Table lookup

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Monge-Ampere Regularization for Learning Arbitrary Shapes from Point Clouds

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Yang, Chuanxiang Zhou, Yuanfeng Wei, Guangshun Ma, Long Hou, Junhui Liu, Yuan Wang, Wenping The School of Software Shandong University Jinan250100 China The Department of Computer Science City University of Hong Kong Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore The Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States

As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capable of representing various surfaces. However, its inherent theoretical shortcoming, i.e., the non-differentiability at the zero-level set, would result in sub-optimal reconstruction quality. In this paper, we propose the scaled-squared distance function (S2DF), a novel implicit surface representation for modeling arbitrary surface types. S2DF does not distinguish between inside and outside regions while effectively addressing the non-differentiability issue of UDF at the zero-level set. We demonstrate that S2DF satisfies a second-order partial differential equation of Monge-Ampere-type, allowing us to develop a learning pipeline that leverages a novel Monge-Ampere regularization to directly learn S2DF from raw unoriented point clouds without supervision from ground-truth S2DF values. Extensive experiments across multiple datasets show that our method significantly outperforms state-of-the-art supervised approaches that require ground-truth surface information as supervision for training. The code will be publicly available at https://***/chuanxiang-yang/S2DF. © 2025 IEEE. All rights reserved.

关键词： Supervised learning

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Privacy Enhanced Edge-AI Healthcare Devices Authentication: A Federated Learning Approach

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IEEE Transactions on Consumer Electronics 2025年

作者： Ahmed, Syed Thouheed Kaladevi, A.C. Kumar, V. Vinod Shankar, Achyut Alqahtani, Fayez REVA University School of Computing and Information Technology India Vellore Institute of Technology School of Computer Science Engineering Vellore632014 India Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida201310 India King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia

Medical devices connected via internet are open to cyber-attacks, hence the privacy preservation of the user and the devices plays an important role. In this paper, Medical Edge-devices privacy enhanced technique based on the federated learning principles is proposed on the distributed coordination of devices and computational system. The technique generates a dedicated token via Centralized Authentication Authority (CAA) for the active users at the local Federated Learning (FL) cum training model. Further the technique introduces dual layer validation of tokens via Authentication Authority Valuation Layer (AAVL) and Centralized Server Firewall Authentication Layer (CSFAL). The purpose of these layers is to optimize the active and inactive tokens, assigning tokens and revoking the inactive tokens from the user groups via distributed federated learning model. The technique is validated on 1500 edge devices of IoT/IoMT on 5 clusters servers of global model. The technique has demonstrated an accuracy of 96% in training and optimizing the IoMT device operations via privacy policies proposed in this paper. © 1975-2011 IEEE.

关键词： Federated learning

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Enhancing Human Posture Detection with Hybrid Deep VGG16 and Attention Mechanism Network

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Procedia computer science 2025年 258卷 1209-1218页

作者： Ogundokun Roseline Oluwaseun Rytis Maskeliūnas Federick Oscar Oluwakayode A. Oki Department of Software Engineering Kaunas University of Technology Kaunas 44249 Lithuania Department of Computer Science Landmark University Omu-Aran Nigeria Department of Computer Systems Engineering Tshwane University of Technology (TUT) Pretoria South Africa Adioo Technology Abuja Nigeria Department of Information Technology East London Walter Sisulu University South Africa

Human Posture Detection (HPD) and classification are the initial steps toward various computer vision applications related to security, advertisement, and healthcare. Even though more than three decades have passed, most methods have been focused only on detecting standing people. However, in real applications, human postures may be significantly different, such as standing, sitting, lying, and crouching, and the shapes of a human are varied with viewpoints, thus making detection and classification difficult. Moreover, the gradual transition from one posture to another further complicates the determination of the number of postures to be classified. This paper uses a pre-trained model to determine the number of unique human postures in activities. We approached PC as a multiclass detection problem using the very deep VGG 16 Convolutional Neural Network (CNN) empowered by an attention mechanism. Extract frames from video data recorded, followed by the derivation of features through a convolutional neural network. Classify different postures—bending, exercise, lying, sitting, and standing—with VGG 16 and attention mechanism. It is trained on the ETRI-Activity3D-LivingLab Dataset. Our proposed (VEACT-CNN) framework has shown stability in HPD with an overall accuracy of 95% and an F-measure of 98% while generating a very low level of false alarms rate (FAR) of 0.02 (2%). For comparison purposes, the accuracy obtained by other models on the same dataset is VGG 16.

关键词： Vision-based human detection Posture classification VGG 16 Deep learning Pre-trained model Human posture detection

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An Intelligent Machine Learning Approach for Prediction of Road Accident Severity

An Intelligent Machine Learning Approach for Prediction of R...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Prashanth Ragam Namana Sri Rahul Pandi Balaji Sankati Jyothi Pavithra Danaveni School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Software Development Cell VIT-AP University Amaravati Andhra Pradesh India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety.

关键词： Support vector machines Radio frequency Road accidents Machine learning algorithms Predictive models Nearest neighbor methods Prediction algorithms Road safety Public security Random forests

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