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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

Journal of Electronic science and Technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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Analyzing the effects of congestion on planning time index–Grey models *** forest regression

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International Journal of Transportation science and Technology 2023年第2期12卷 578-593页

作者： Shahriar Afandizadeh Zargari Navid Amoei Khorshidi Hamid Mirzahossein Hanif Heidari School of Civil Engineering Iran University of Science and Technology(IUST)TehranIran Department of Civil Engineering-Transportation Planning Faculty of Engineering and TechnologyImam Khomeini International University(IKIU)QazvinIran School of Mathematics and Computer Science Damghan UniversityDamghanIran

Travel time and its predictability level are important factors influencing travel behavior and the role of traffic congestion as an important factor in the unreliability of travel time is *** congestion is divided into two categories:Recurring and *** this paper,the effect of recurring congestion,defined as the ratio of the traffic speed over one hour to the free flow speed,will be investigated on the reliability of travel time in terms of the planning time index(PTI)on a 1.467-mile segment along the IS-64 freeway in Chesapeake,*** do so,two methods have been analyzed in this study:the grey models(GM)and the random forest regression(RFR).By using mean absolute percentage error(MAPE)as a criterion to judge,RFR could show a better and more satisfying performance in predicting PTI values when congestion *** the following,to make prediction results of RFR more understanding and easier to use,bagging and bootstrapping are used to improve the model results and more accurately predict the ***,the outputs were drawn using scatter plots for both peaks *** graphs has shown that for congestion values in the range of 1 to 0.9,PTI is reliable in both *** congestion starts to decrease from 0.9 and reaches 0.7 or 0.75,depending on peak type,PTI is moving in the unreliable area,but it isn’t ***,when the congestion value becomes smaller,the rate of change in PTI in both peaks increases.

关键词： Congestion Travel time Reliability Grey models Random forest

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INVESTIGATION OF DEMI-ab CONTINUOUS OPERATORS

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Journal of Mathematical sciences (United States) 2024年第1期289卷 59-66页

作者： Erkurşun Özcan, Nazife Eryüksel, Ezgi Han Department of Mathematics Faculty of Science Hacettepe University Ankara 06800 Turkey Department of Computer Engineering School Of Engineering and Natural Sciences Ankara Medipol University Ankara Turkey

In this paper, we define the class of demi ab-continuous operators on Banach lattices where a and b are given as n, w, o, ru, uo, un, and uaw. The relations between ab-continuous and demi ab-continuous operators are given. Moreover, the relations between the class of demi ab-continuous operators for different a, b convergence are investigated. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： 46B42 47B60 Banach lattice Convergence Demi operators Order convergence Primary 47B65 Relatively uniformly convergence Secondary 28A20 Unbounded absolutely weak convergence Unbounded norm convergence Unbounded order convergence

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Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

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computer Modeling in engineering & sciences 2023年第10期137卷 939-964页

作者： M.C.Shanmukha Sokjoon Lee A.Usha K.C.Shilpa Muhammad Azeem Department of Mathematics Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Computer Engineering(Smart Security) Gachon UniversitySeongnam13120South Korea Department of Mathematics Alliance School of Applied MathematicsAlliance UniversityBangalore562106India Department of Computer Science and Engineering Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahore54660Pakistan

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical *** molecules of chemical compounds are modelled as a graph to study the properties of the *** geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular *** article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and *** topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological *** study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological ***,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

关键词： Topological descriptors weighted entropy graphenylene

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Leveraging Semantic Parsing using Text Embeddings and Reinforcement Learning

Leveraging Semantic Parsing using Text Embeddings and Reinfo...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Piza-Londono, Jessenia Andrade-Lotero, Edgar J. Universidad Del Rosario Applied Mathematics and Computer Science Bogotá Colombia Universidad Del Rosario School of Engineering Science and Technology Bogotá Colombia

ISBN: (纸本)9798350374575

Representing natural language information is a key challenge in Artificial Intelligence and Cognitive science, requiring the transformation of unstructured data into formats suitable for computational tasks. While logical formalisms offer robust methods for information representation, their complexity often limits widespread adoption. Conversely, transformer architectures provide strong generalization capabilities but struggle with logical inference tasks. To address both the need for generalization and reliable logical inference, we propose a novel approach using deep reinforcement learning, enabling agents to autonomously learn the rules of semantic parsing. Our preliminary results indicate successful generation of appropriate representations for simple queries. Future work will extend the environment to handle a wider range of real-world sentences. © 2024 IEEE.

关键词： Deep reinforcement learning

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Don’t Forget Pairing-Friendly Curves with Odd Prime Embedding Degrees

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2023年第4期2023卷 393-419页

作者： Dai, Yu Zhang, Fangguo Zhao, Chang-An School of Mathematics Wuhan University Wuhan China School of Mathematics Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Guangzhou China

Pairing-friendly curves with odd prime embedding degrees at the 128bit security level, such as BW13-310 and BW19-286, sparked interest in the field of public-key cryptography as small sizes of the prime fields. However, compared to mainstream pairing-friendly curves at the same security level, i.e., BN446 and BLS12-446, the performance of pairing computations on BW13-310 and BW19-286 is usually considered inefficient. In this paper we investigate high performance software implementations of pairing computation on BW13-310 and corresponding building blocks used in pairing-based protocols, including hashing, group exponentiations and membership testings. Firstly, we propose efficient explicit formulas for pairing computation on this curve. Moreover, we also exploit the state-of-art techniques to implement hashing in G1 and G2, group exponentiations and membership testings. In particular, for exponentiations in G2 and GT, we present new optimizations to speed up computational efficiency. Our implementation results on a 64-bit processor show that the gap in the performance of pairing computation between BW13-310 and BN446 (resp. BLS12-446) is only up to 4.9% (resp. 26%). More importantly, compared to BN446 and BLS12-446, BW13-310 is about 109.1% − 227.3%, 100% − 192.6%, 24.5% − 108.5% and 68.2% − 145.5% faster in terms of hashing to G1, exponentiations in G1 and GT, and membership testing for GT, respectively. These results reveal that BW13-310 would be an interesting candidate in pairing-based cryptographic protocols. © 2023, Ruhr-University of Bochum. All rights reserved.

关键词： Public key cryptography

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Early Fire Detection Based on Local Morphological Knowledge Matching 24

Early Fire Detection Based on Local Morphological Knowledge ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Wang, Xinzhi Li, Mengyue Zhu, Nengjun Qian, Jiayan Zheng, Zhanyi School of Computer Engineering and Science Shanghai University Shanghai China School of Information and Mathematics Anhui International Studies University Anhui Hefei China

ISBN: (纸本)9798331506681

Among various disasters, fire poses one of the most widespread threats to public safety. The early stage of fire, marked by small and slow-spreading fire objects, is the ideal time for firefighting intervention. Therefore, early fire detection is crucial to prevent potential hazards and reduce loss of life and property. However, due to the variable shapes and small size, existing methods struggle to precisely locate smoke and flame in the initial stage. Compared to rigid objects, flame has unique local morphology, such as sharp tip, irregular edge and cavity. These prior knowledge could guide the model to focus on the local morphological features of the flame, thereby improving early fire detection ability. To address the current challenges, the paper proposes a Local Morphological Knowledge Matching based Early Fire Detector(LMKMFD), which accurately detects early fires and locates the multi-scale fires by exploring local flame morphology in fire images. Firstly, the local morphological features are extracted by matching the input fire image with the knowledge templates of the designed local morphological knowledge base. Secondly, fire multi-scale semantic features at four scales are mined by a Transformer-based backbone. Finally, fire local morphological features and multi-scale semantic features are aggregated by single-level depth prediction module to achieve region-level localization of fire objects. Experimental results on private and public datasets show that LMKMFD exhibits high detection precision for fires of different scales, particularly small early fires. LMKMFD outperforms baseline models, with mean Average Precision(mAP) of 88.38% and 83.12% on two datasets. Notably, due to the significance of local morphology of flame, the method performs better in detecting flame than smoke. © 2024 IEEE.

关键词： Premixed flames

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Dynamic Client Selection for Over-the-Air Federated Learning Network

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IEEE Internet of Things Journal 2025年第12期12卷 18508-18519页

作者： Shi, Fang Lin, Weiwei Peng, Chaoda Zhong, Cankun Wang, Dong Cheng, Mingyue South China Agricultural University College of Mathematics and Informatics Guangzhou510006 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Shenzhen518000 China

As a privacy-preserving solution, Federated Learning (FL) demonstrates great potential in distributed model training, but limited bandwidth, particularly in NFC-based systems, emerges as a key bottleneck by restricting the number of participating clients. To address this challenge, over-the-air FL leverages the superposition property of wireless multiple-access channels, enabling faster model training and accommodating more clients, even in bandwidth-constrained scenarios like NFC. However, due to its analog-integrated nature, the FL performance is also affected by other factors, such as channel noise. These motivate us to consider how the selected client set and channel noise affect FL performance. To explore this concern, in this paper, we consider an over-the-air FL system with analog gradient aggregation and analyze the impact of the selected client set and channel noise on FL training performance. The theoretical analysis effectively shows the importance of the clients' number and the power scaling factor to the FL training performance. Based on the theoretical analysis, we transform the global optimization problem into the client selection problem and propose a dynamic client selection scheme to optimize the training performance under the aggregation error constraint. Experimental results demonstrate that our proposed scheme can boost FL by speeding up the convergence of the global model (at least 35%) and saving energy consumption. © 2025 IEEE.

关键词： Mean square error

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Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 333-353页

作者： Rao, Bosen Zhang, Jiale Wu, Di Zhu, Chengcheng Sun, Xiaobing Chen, Bing Yangzhou University School of Information Engineering Yangzhou225127 China University of Southern Queensland School of Mathematics Physics and Computing ToowoombaQLD4350 Australia Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

The emergence of new machine learning methods has led to their widespread application across various domains, significantly advancing the field of artificial intelligence. However, the process of training and inferring machine learning models relies on vast amounts of data, which often include sensitive private information. Consequently, the privacy and security of machine learning have encountered significant challenges. Several studies have demonstrated the vulnerability of machine learning to privacy inference attacks, but they often focus on specific scenarios, leaving a gap in understanding the broader picture. We provide a comprehensive review of privacy attacks in machine learning, focusing on two scenarios: centralized learning and federated learning. This article begins by presenting the architectures of both centralized learning and federated learning, along with their respective application scenarios. It then conducts a comprehensive review and categorization of related inference attacks, providing a detailed analysis of the different stages involved in these attacks. Moreover, the article thoroughly describes and compares the existing defense methods. Finally, the article concludes by highlighting open questions and potential future research directions, aiming to contribute to the ongoing competition between privacy attackers and defenders. © 2020 IEEE.

关键词： Data privacy

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