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作者： Alqahtani, Ali Khan, Surbhi Bhatia Department of Networks and Communications Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Data Science School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cyber-physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero-day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search-cascade regional convolutional neural network (hybrid TS-Cascade R-CNN) to detect cyberattacks. The proposed model combines the hybrid transit-search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards. © 2023 John Wiley & Sons Ltd.

关键词： Zero-day attack

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MPNAS: Multimodal Sentiment Analysis Pruning via Neural Architecture Search

MPNAS: Multimodal Sentiment Analysis Pruning via Neural Arch...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Binyan Ren, Ao Zhang, Zihao Duan, Moming Liu, Duo Tan, Yujuan Zhong, Kan College of Computer Science Chongqing University Chongqing China School of Big Data Software & Engineering Chongqing University Chongqing China Institute of Data Science National University of Singapore Singapore

ISBN: (纸本)9798350368741

With the rapid development of social media, sentiment analysis from multimodal posts has garnered significant attention in recent years. However, the substantial size of these models impedes their deployment on resource-constrained embedded devices. Although pruning has been extensively studied to reduce the size of unimodal models, specific challenges remain for Multimodal Sentiment Analysis (MSA) models. First, existing techniques prune fixed original models into sparse models, while our findings indicate that different model architectures of identical size yield varying performance outcomes. Second, prior studies fail to explore the unique characteristics of MSA models, resulting in suboptimal pruning performance. To address these challenges, we propose MPNAS, a unified pruning framework via Neural Architecture Search (NAS) for MSA models. Specifically, we formulate pruning as a NAS problem and analyze MSA model characteristics to guide the subnet search. We conduct an initial coarse-grained NAS on the original model, expanding the search space slightly to identify suitable subnets that enhance pruning rates and accuracy. Subsequently, we refine coarse-grained subnets in a fine-grained NAS stage, where MSA model characteristics guide the search process. Extensive experiments on three representative datasets demonstrate the superiority of our approach over existing methods. © 2025 IEEE.

关键词： Multimodal Compression NAS

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Hate Speech Detection Using Enhanced Recurrent Neural Network Based on Elephant Herding Optimization on Social Media 3

Hate Speech Detection Using Enhanced Recurrent Neural Networ...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Senthilkumar, K. Sathiyabama, S. Ayyamuthukumar, D. Thiruvalluvar Government Arts College Department of Computer Science Namakkal Rasipuram India Department of Artificial Intelligence and Data Science Muthayammal College of Engineering Rasipuram India

ISBN: (纸本)9798350369083

In the past ten years, there has been a rise in nasty behaviors on social media due to the increased use of these platforms. One of the most offensive of these behaviors is hate speech, so users must safeguard themselves against it on social media. This research presents an approach to detect hate speech from social media using an optimized Elman recurrent neural network (ERNN) based on the hybrid local search algorithm (LSA) and elephant herding optimization (EHO) algorithm called LSAEHO. The LSA-EHO algorithm is used to LSA to obtain the optimal solution of EHO, enhance the convergence rate, and avoid the local optima problem of EHO. The LSA-EHO is used to find appropriate hyperparameters for ERNN to enhance the convergence rate and accuracy. The experimental results confirmed that the developed hate speech prediction-based optimized ERNN produced higher prediction accuracy and a faster convergence rate. © 2024 IEEE.

关键词： Elman neural networks

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Application of Machine Learning for Ensuring Water Quality in Aquaculture Ponds

Application of Machine Learning for Ensuring Water Quality i...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Bharathi, S.V.Shri Sai, Paidipati Lakshman Ayyappa, Paidipati Charan Ram Reddy, Kakuru Jaya Praveen Reddy, Gangireddy Sai Preetham Rajesh, Katuri Madhava SRMIST- Ktr Department Of Data Science And Business Systems Tamil Nadu Chennai India Vit Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350352689

Aquaculture plays a pivotal role in meeting the growing global demand for seafood. However, ensuring optimal water quality within aquaculture ponds is a pressing challenge. This project proposes a paradigm shift by integrating machine learning to enhance water quality assurance. The system leverages real-time monitoring, predictive analytics, and adaptable algorithms, contributing to sustainability and responsible aquaculture practices. © 2024 IEEE.

关键词： Water quality

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Design, Mathematical Modeling, and Control of an Underactuated 3-DOF Experimental Helicopter

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IEEE Access 2024年 12卷 55568-55586页

作者： Makki, Osamah Talal Moosapour, Seyyed Sajjad Mobayen, Saleh Nobari, Jafar Heyrani Shahid Chamran University of Ahvaz Faculty of Engineering Ahvaz*** Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu Yunlin640301 Taiwan K. N. Toosi University of Technology Department of Electrical and Computer Engineering Tehran19697 Iran

This article covers the design, implementation, mathematical modelling, and control of a multivariable, underactuated, low-cost, three-degrees-of-freedom experimental helicopter system (namely a 3-DOF helicopter). The system is considered an essential benchmark for the development and evaluation of control algorithms and identification methods as it mimics real helicopters and UAVs. The system is multivariable and difficult to control due to its underactuation characteristics. In addition, it presents further challenges in modelling and control analysis in terms of high nonlinearity, coupling, uncertainty, and unmodeled dynamics. In this scene, the system is designed, developed, and operated virtually in SolidWorks using the necessary hardware and software. A new accurate multivariable coupled nonlinear mathematical model is derived and proposed. The model takes into account the complex dynamics and interactions between the system components, providing a more comprehensive representation of the real system compared to previous models. System parameters are then identified using the Sum of the Square Error (SSE) algorithm, and the mathematical model is compared to the practical results in many different scenarios via Simulink. Experimental tests confirmed the mathematical model and robust performance of the implemented experimental 3-DOF helicopter system, as well as the ability of the proposed controller to stabilize the helicopter and track different trajectories in the presence of uncertainties and cross-coupling effects. © 2013 IEEE.

关键词： Iron

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Reliable knowledge graph fact prediction via reinforcement learning

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 304-317页

作者： Fangfang Zhou Jiapeng Mi Beiwen Zhang Jingcheng Shi Ran Zhang Xiaohui Chen Ying Zhao Jian Zhang School of Computer Science and Engineering Central South UniversityChangshaHunan 410083China School of Target and Data Information Engineering UniversityZheng ZhouHenan 450001China

Knowledge graph(KG)fact prediction aims to complete a KG by determining the truthfulness of predicted *** learning(RL)-based approaches have been widely used for fact ***,the existing approaches largely suffer from unreliable calculations on rule confidences owing to a limited number of obtained reasoning paths,thereby resulting in unreliable decisions on prediction ***,we propose a new RL-based approach named EvoPath in this *** features a new reward mechanism based on entity heterogeneity,facilitating an agent to obtain effective reasoning paths during random *** also incorporates a new postwalking mechanism to leverage easily overlooked but valuable reasoning paths during *** mechanisms provide sufficient reasoning paths to facilitate the reliable calculations of rule confidences,enabling EvoPath to make precise judgments about the truthfulness of prediction *** demonstrate that EvoPath can achieve more accurate fact predictions than existing approaches.

关键词： Knowledge graph Fact prediction Reinforcement learning Entity heterogeneity Postwalking mechanism

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Advancements in Environmental data Analysis for Climate-Resilient Agriculture Using Remote Sensing and Deep Learning

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 670-680页

作者： Nirmal, Khyati R. Reddy, Chilukala Mahender P, Jose Tulasi, Rajesh Maheswari, G. Uma Sapthami, I. SNJB’S Late Sau KBJ CoE Maharashtra Nashik India Department of Artificial Intelligence and Data Science GITAM Deemed to Be University Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai Avadi India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P Vaddeswaram Guntur Dist. India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Pudhuvoyal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Weather significantly influences agricultural productivity. Plant biotic and abiotic stressors are primarily induced by climate change, resulting in a detrimental effect on worldwide agricultural productivity. These two components are closely interconnected. This paper presents an innovative approach in smart agriculture for climate change combining remote sensing and a deep learning algorithm. The input is obtained as a multispectral environmental image and then subjected to noise reduction and normalisation processing. The image has been retrieved using a primary convolutional component with a stacked encoder model. The retrieved features are identified using ResNet graph reinforcement neural networks. The resulting classification output displays environmental imagery depicting climate variations. The agriculture sector has been analysed based on the classified climate analysis. The experimental results have been conducted on diverse farm datasets related to climate change, evaluating the detection accuracy, recall, mean average precision, normalized correlation, and F-1 score. The proposed method achieved a detection accuracy of 98%, a normalised correlation of 95%, a mean average precision of 92%, a recall of 97%, and an F-MEASURE of 94%. Machine learning can assist in monitoring and predicting the impact of climate change on food security, as indicated by the findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change Deep learning model Remote sensing Smart agriculture Stacked encoder model

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Anthracnose Detection and Classification in Chilli Leaves Using Mobile Net: UAV Images 9

Anthracnose Detection and Classification in Chilli Leaves Us...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bandaru, Prudhvi Narayana Anuradha, G. Bora, Himavarshini Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence And Data Science Andra Pradesh India Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science And Engineering Andra Pradesh India

ISBN: (纸本)9798350394474

The health of a plant has remained an indispensable factor for sustainable and enriching crop production to fulfill the Nation's food demands. A degradation in the health of the crop leads to low crop yields. Out of a wide range of factors affecting the health of the crop the most concerning factor remains the plant diseases and pests. More than 50% of India's population is engaged in agricultural practices. In the agricultural sector, India is among the top 3 exporters of Dried Chilli. However, Chilli exports have faced a descent in recent years owing to crop yield loss due to diseases caused by fungi or pests. Anthracnose is the most prevalent disease in Chilli plants. The process of anthracnose disease detection in Chilli crops is a tedious process due to a lack of laboratory facilities and expertise. Such a situation demands for development of an 'automated detection of anthracnose disease in Chilli plant' system which puts the process of early and precise detection of plant disease at ease. computer Vision advancements make it possible for automatic disease detection using visible symptoms on the crop. Keeping this in view, this paper proposes an automated model for the detection of anthracnose in the Chilli plant using a Mobile Net-based convolutional neural network (CNN). Over the last few years researchers such as Boulent et al. stated in their article that the integration of aerial image data and AI-based methods holds promise for offering an effective, high-throughput technique for detecting plant diseases in real-world conditions. Hence the dataset utilized for our model is aerial images using UAV (unnamed aerial vehicle). The proposed system for anthracnose detection in Chilli developed using Mobile Net achieved an accuracy of 99.6%. © 2024 IEEE.

关键词： Crops

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Scalable and adaptive log manager in distributed systems

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Frontiers of computer science 2023年第2期17卷 45-62页

作者： Huan ZHOU Weining QIAN Xuan ZHOU Qiwen DONG Aoying ZHOU Wenrong TAN The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu UniversityChengdu 610041China School of Data Science and Engineering East China Normal UniversityShanghai 200062China

On-line transaction processing(OLTP)systems rely on transaction logging and quorum-based consensus protocol to guarantee durability,high availability and strong *** makes the log manager a key component of distributed database management systems(DDBMSs).The leader of DDBMSs commonly adopts a centralized logging method to writing log entries into a stable storage device and uses a constant log replication strategy to periodically synchronize its state to *** the advent of new hardware and high parallelism of transaction processing,the traditional centralized design of logging limits scalability,and the constant trigger condition of replication can not always maintain optimal performance under dynamic *** this paper,we propose a new log manager named Salmo with scalable logging and adaptive replication for distributed database *** scalable logging eliminates centralized contention by utilizing a highly concurrent data structure and speedy log hole *** kernel of adaptive replication is an adaptive log shipping method,which dynamically adjusts the number of log entries transmitted between leader and followers based on the real-time *** implemented and evaluated Salmo in the open-sourced transaction processing systems Cedar and *** results show that Salmo scales well by increasing the number of working threads,improves peak throughput by 1.56×and reduces latency by more than 4×over log replication of Raft,and maintains efficient and stable performance under dynamic workloads all the time.

关键词： distributed database systems transaction log-ging log replication scalable adaptive

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