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26th International Conference on Automation and computing, ICAC 2021

作者： Shaukat, Kamran Alam, Talha Mahboob Hameed, Ibrahim A. Khan, Wasim Ahmed Abbas, Nadir Luo, Suhuai The University of Newcastle School of Electrical Engineering and Computing Australia Virtual University of Pakistan Department of Computer Science Lahore Pakistan Norwegian University of Science and Technology Department of ICT and Natural Sciences Trondheim Norway National College of Business Administration Economics School of Computer Science Lahore Pakistan University of Engineering and Technology Department of Computer Science and Engineering Lahore Pakistan

ISBN: (纸本)9781860435577

The Internet of Things (IoT) is the new appearing technology and produces big data nowadays. Today, IoT is primarily a vision that dictates that everything should be interconnected over the Internet. IoT is currently and for sure will be the foundation for future development because it brings new opportunities for novel services. The IoT industry is flourishing since the number of computations that a computer can execute almost doubles biyearly. In contrast, the size and the amount of power needed are nearly half for the same period. This means that smaller and more powerful devices are available for interconnection and data exchange offering a wide range of applications. This aspect leads to significant security challenges, which will be treated from now on. IoT is being used in numerous applications like health care, home automation, smart car, industrial automation, etc. IoT features bring convenience to us, but threats also emerge with them. It shows plenty of security troubles like securing these gadgets, information and correspondence from unapproved to access these gadgets. To enable analysts to pursue the forward works in this paper, what are the security challenges the IoT features face, what solutions are provided yet, and the open challenges. © 2021 Chinese Automation and computing Society in the UK-CACSUK.

关键词： Wireless sensor networks

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FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning

arXiv

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arXiv 2023年

作者： Yi, Liping Yu, Han Shi, Zhuan Wang, Gang Liu, Xiaoguang Cui, Lizhen Li, Xiaoxiao College of Computer Science TMCC SysNet DISSec GTIISC Nankai University Tianjin China College of Computing and Data Science Nanyang Technological University Singapore Switzerland School of Software Shandong University Jinan China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system heterogeneity. Model-Heterogeneous Personalized FL (MHPFL) has emerged to address this challenge. Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs. To address these limitations, we propose the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks, which splits each client’s model into a heterogeneous (structure-different) feature extractor and a homogeneous (structure-same) classification header. It performs local-to-global knowledge transfer via semantic similarity-based header parameter aggregation. In addition, global-to-local knowledge transfer is achieved via an adaptive parameter stabilization strategy which fuses the seen-class parameters of historical local headers with that of the latest global header for each client. FedSSA does not rely on public datasets, while only requiring partial header parameter transmission to save costs. Theoretical analysis proves the convergence of FedSSA. Extensive experiments present that FedSSA achieves up to 3.62% higher accuracy, 15.54 times higher communication efficiency, and 15.52 times higher computational efficiency compared to 7 state-of-the-art MHPFL baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT

arXiv

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arXiv 2025年

作者： Lang, Zifan Liu, Guixia Sun, Geng Li, Jiahui Sun, Zemin Wang, Jiacheng Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Optg: Optimizing Gradient-Driven Criteria in Network Sparsity

SSRN

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SSRN 2024年

作者： Zhang, Yuxin Lin, Mingbao Chen, Mengzhao Chao, Fei Tian, Yonghong Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Tecent Youtu Lab China Department of Computer Science and Technology Peking University China Peng Cheng Laboratory China Department of Artificial Intelligence School of Informatics Xiamen University China

Network sparsity receives popularity mostly due to its capability to reduce the complexity of the network. Extensive studies excavate gradient-driven sparsity. Typically, these methods are constructed upon the premise of weight independence, which however, is contrary to the fact that weights are mutually influenced. Thus, their performance remains to be improved. In this paper, we propose to optimize gradient-driven sparsity (OptG) by solving this independence paradox. Our motive comes from the recent advances in supermask training which shows that high-performing sparse subnetworks can be located by simply updating mask values without modifying any weight. We prove that supermask training is to accumulate the criteria of gradientdriven sparsity for both removed and preserved weights, and it can partly solve the independence paradox. Consequently, OptG integrates supermask training into gradient-driven sparsity, and a novel supermask optimizer is further proposed to comprehensively mitigate the independence paradox. Experiments show that OptG can well surpass many existing state-of-the-art competitors, especially at ultra-high sparsity levels. Our project is available at \url{https://***/zyxxmu/OptG}. © 2024, The Authors. All rights reserved.

关键词： Image classification

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Mustafa A. Al Sibahee Chengwen Luo Jin Zhang Yijing Huang Zaid Ameen Abduljabbar National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen PR China Computer Technology Engineering Department Iraq University College Basrah Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen PR China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections.

关键词： Privacy Data privacy Cloud computing Memory Search problems Cryptography Servers

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Generative AI Empowered Semantic Feature Multiple Access (SFMA) Over Wireless Networks

arXiv

引用

arXiv 2024年

作者： Wang, Jiaxiang Yang, Yinchao Yang, Zhaohui Huang, Chongwen Chen, Mingzhe Zhang, Zhaoyang Shikh-Bahaei, Mohammad Department of Engineering King’s College London London United Kingdom College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China Zhejiang Hangzhou310007 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

This paper investigates a novel generative artificial intelligence (GAI) empowered multi-user semantic communication system called semantic feature multiple access (SFMA) for video transmission, which comprises a base station (BS) and paired users. The BS generates and combines semantic information of several frames simultaneously requested by paired users into a signal. Users recover their frames from this combined signal and input the recovered frames into a GAI-based video frame interpolation model to generate the intermediate frame. To optimize transmission rates and temporal gaps between simultaneously transmitted frames, we formulate an optimization problem to maximize the system sum rate while minimizing temporal gaps. We observe that the standard signal-to-interference-plus-noise ratio (SINR) equation does not accurately capture the performance of our semantic communication system. Therefore, we introduce a weight parameter into the SINR equation to better represent the system’s performance. Due to the complexity introduced by the weight parameter’s dependence on transmit power, we propose a three-step solution. First, we develop a user pairing algorithm that pairs two users with the highest preference value, a weighted combination of semantic transmission rate and temporal gap. Second, we optimize inter-group power allocation by formulating an optimization problem that allocates proper transmit power across all user groups to maximize system sum rates while satisfying each user’s minimum rate requirement. Third, we address intra-group power allocation to enhance the performance of each user. Simulation results demonstrate that our method improves transmission rates by up to 24.8%, 45.8%, and 66.1% compared to fixed-power non-orthogonal multiple access (F-NOMA), orthogonal joint source-channel Coding (O-JSCC), and orthogonal frequency division multiple access (OFDMA) schemes, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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CNNs in Crop Care: A Comparative Analysis of Tomato Disease Detection Models

CNNs in Crop Care: A Comparative Analysis of Tomato Disease ...

引用

Cyber Resilience (ICCR), International Conference on

作者： Ali M Ali Shadi Nashwan Ahmad Al-Qerem Amjad Aldweesh Mohammad Alauthman Zenab Elgamal Ammar Almomani Communications and Computer Engineering Department Faculty of Engineering AlAhliyya Amman University Amman Jordan Cybersecurity Department Faculty of Information Technology Middle East University (MEU) Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia Department of Information Security Faculty of Information Technology University of Petra Amman Jordan School of Computing Skyline University College University City of Sharjah Sharjah United Arab Emirates School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Timely and accurate detection of plant diseases is critical for sustainable crop production. This paper presents a comparative analysis of convolutional neural network (CNN) models for automated tomato leaf disease detection, including VGG16, VGG19, AlexNet and Google Net. A dataset of tomato leaf images with multiple disease types was utilized to train and test the performance of each CNN architecture. Our results demonstrate that the VGG16 model achieved the highest accuracy of 99.38% in classifying tomato leaf diseases. Compared to AlexNet and VGG19 models which obtained 98.67% and 99.29% accuracy respectively, the superior performance of VGG16 highlights its suitability for deploying robust tomato disease detection systems to enable prompt disease control interventions. Our work provides valuable insights on harnessing deep learning for agricultural disease surveillance, allowing farmers and agronomists to identify crop infections early and implement timely management practices to reduce yield losses.

关键词： Deep learning Plant diseases Analytical models Visualization Surveillance Crops Training data

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Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

引用

computers, Materials & Continua 2021年第4期67卷 1033-1049页

作者： Shahan Yamin Siddiqui Iftikhar Naseer Muhammad Adnan Khan Muhammad Faheem Mushtaq Rizwan Ali Naqvi Dildar Hussain Amir Haider School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan School of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea School of Computational Sciences Korea Institute for Advanced Study(KIAS)85 HoegiroDongdaemunguSeoul02455Korea Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality *** to clinical statistics,one woman out of eight is under the threat of breast *** and inheritance patterns may be a reason behind its spread among ***,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances *** diagnosis and initial stage treatment can help increase the survival *** that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)*** study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based *** multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%***,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.

关键词： Fusion feature breast cancer prediction deep learning convolutional neural network multi-modal medical image fusion decision-based fusion

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An Early Warning System to Predict Earthquakes based on WT-ARIMA-ANN Model

An Early Warning System to Predict Earthquakes based on WT-A...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Uppara Raghu Babu Shrinwantu Raha Mujtaba Ali Khan C. Yamini R. Jayadurga Gourav Kalra Department of Civil Engineering Srinivasa Ramanujan Institute of Technology Andhra Pradesh India Department of Geography Bhairab Ganguly College Belgharia Kolkata India Department of Computer Science College of Computing and Informatics University of North Carolina Charlotte Department of Mathematics PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of MBA Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Mullana India

A devastating earthquake is one of the Earth’s most destructive natural disasters. Real-time seismic monitoring facilities can evolve into Earthquake Early Warning (EEW) systems with the help of modern technology, property damage, reducing the number of casualties, and economic losses. The proposed EEW system’s goal is to supply a precise damage assessment just before an earthquake hits a certain area. The proposed method involves preprocessing, feature extraction, and model training. It is recommended to preprocess the data by first filtering it and then reformatting it. Filtering is essential for getting rid of irrelevant or noisy information. In feature extraction, the Principal Component Analysis (PCA) provides a useful perspective on the data by establishing a new coordinate system, and it also offers a method for reducing the data’s dimensionality. Once the features have been obtained, the models are trained with WT-ARIMA-ANN. The proposed method outperforms two widely used alternatives, ARIMA and ANN.

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An energy efficient selection of cluster head and disease prediction in IoT based smart agriculture using a hybrid artificial neural network model

引用

Measurement: Sensors 2024年 32卷

作者： Ara, Tabassum Bhagappa Ambareen, Javeria Venkatesan, S. Geetha, M. Bhuvanesh, A. Department of Artificial Intelligence and Machine Learning HKBK College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering Brindavan College of Engineering Karnataka Bangalore India School of Computing and Information Technology REVA University Karnataka Bangalore India Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

The advent of new technologies paved the way for the proliferating productivity of agriculture and farming activities in a cost effective way. Internet of Things is the one among them and used for automatic smart farming applications. Hence to overcome the issues faced in the wide agriculture sector most of the farmers are now changed to IoT based applications and in the meantime many scientists also used artificial intelligence techniques to predict the diseases of the crops from the data collected using the IoT sensors in the agriculture farming. The sensors gather data from crops and fused with the local unit and forwarded to the cluster heads of network. To achieve the disease free crops and therein improve the productivity we have proposed a novel energy efficient IoT based smart farming approach, in which we have utilized K-means algorithm for the cluster formation and Adaptive Mud Ring optimization algorithm (AMR) for CH and energy efficient optimal path. The collected data via the CH are then stored in the cloud storage. Subsequently, the data are accessed via the proposed Hybrid Artificial Neural Network (HANN) to predict the diseases. The Artificial Neural Network (ANN) is hybridized using the Google Net in order to extract the exact features to predict the diseases. Performance validation is effectuated with Network Simulator-2 (NS-2) software and the results are compared with the state-of-art works. Several parameters such as energy efficiency, delay, network lifetime, accuracy and etc, are used for analyzing and our approach surpasses all the other approaches. © 2024 The Authors

关键词： Neural networks

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