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Just: towards jute pest classification by combination of supervised learning and triplet loss aided contrastive learning

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Iran Journal of computer Science 2025年 1-14页

作者： Kundu, Shreyan Mukhopadhyay, Souradeep Talukdar, Rahul Das, Semanti Adhikari, Subhajit Computer Science and Engineering (Artificial Intelligence) Institute of Engineering and Management Iem Management Building D-1 Street No. 13 EP Block Sector V Bidhannagar West Bengal Kolkata 700091 India Computer Science and Automation Indian Institute of Science CV Raman Road Karnataka Bangalore 560012 India Computer Science and Engineering (core) Institute of Engineering and Management Iem Management Building D-1 Street No. 13 EP Block Sector V Bidhannagar West Bengal Kolkata 700091 India Department of Lifescience Christ University Hosur Main Road Bhavani Nagar Karnataka Bangalore 560029 India Department of Information Technology RCCIIT KOLKATA West Bengal Kolkata 700015 India

Jute is a vital agricultural commodity contributing significantly to the GDP of countries like Bangladesh, India, Myanmar, and China. However, because of its inaccuracy and slowness, its vulnerability to pest infestations-which are often handled by manual inspections-poses serious cost concerns. This study suggests a unique method for early and accurate pest identification that combines contrastive and supervised learning. Contrastive learning enhances feature representation by distinguishing between positive and negative samples, ensuring that instances within the same class are closely grouped while maintaining separation between different classes. It reduces false negatives by classifying some samples as negative and others with the same label as positive. Supervised learning enables precise pest identification by aligning features with distinctive characteristics of each class. Metrics including precision, recall, F1 score, ROC curve, and confusion matrix are used to assess the hybrid model’s performance;the findings show notable accuracy gains over conventional techniques. This scalable and dependable solution lowers losses caused by pests and provides a sustainable method of growing jute using cutting-edge advanced machine-learning techniques. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Augmentation Contrastive learning Supervised learning Triplet loss

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New 5G Kaiser-Based Windowing to Reduce Out of Band Emission

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computers, Materials and Continua 2022年第2期71卷 2721-2738页

作者： Hammoodi, Ahmed Audah, Lukman Al-Jobouri, Laith Mohammed, Mazin Abed Aljumaily, Mustafa S. Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Batu Pahat86400 Malaysia School of Engineering Arts Science and Technology University of Suffolk IP4 1QJ United Kingdom College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Electrical Engineering and Computer Science Department University of Tennessee KnoxvilleTN37901 United States

OFDM based waveforms are considered as the main part of the latest cellular communications standard (namely 5G). Many inherited problems from the OFDM-Based LTE are still under investigation. Getting rid of the out of band emissions is one of these problems. Ensuring low out of band emission (OOBE) is deemed as one of the most critical challenges to support development of future technologies such as 6G and beyond. Universal Filtered Multi Carrier (UFMC) has been considered as one of the candidate waveforms for the 5G communications due to its robustness against Inter Carrier Interference (ICI) and the Inter Symbol Interference (ISI). It is also a preferred option because it is the most appropriate for low latency scenarios. In this paper, a novel approach is proposed that makes use of modified Kaiser-Bessel filter-based pulse windowing instead of standard Dolph-Chebyshev filter for UFMC based waveform. The aim of proposing the new approach is to enable the reduction of spectral leakage into nearby sub-bands. A comprehensive study for the modified Kaiser-Bessel filters is performed and the results are presented in terms of several Key Performance Indicators (KPIs). Based on the results of the simulation, the UFMC Kaiser-Hankel window demonstrated lower sidebands and better power spectral density, when compared with the traditional Orthogonal frequency-division multiplexing (OFDM) and UFMC as well as the normal UFMC Kaiser window. In addition, the real test for the kaiser window with 5G waveform is lower OOBE than conventional 5G waveform (CP-OFDM and UFMC). The OOBE reduction of 31% of the Kaiser vs. the Dolph-Chebyshev filter, 68% of Kaiser Hankel over the Dolph-Chebyshev, and 20% of Kaiser Hankel over Kaiser filter have been reported in this paper. The Power Spectral Density has been improved accordingly. © 2022 Tech Science Press. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Implementation of Industrial Automation Sustainable Training in Savannakhet University, Laos 8

Implementation of Industrial Automation Sustainable Training...

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Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Georgieva, Tsvetelina Bencheva, Nina Daskalov, Plamen Yahoui, Hamed Khemmarath, Sitha Norasane, Khonesavanh Chanthanouvong, Lamnguen Leaunglith, Youlananh Keovorabouth, Vinai University of Ruse Angel Kanchev Automation and Mechatronics Department Ruse Bulgaria University of Ruse Angel Kanchev Telecommunications Department Ruse Bulgaria Automation &processing Claude Bernard University Lyon 1 Department of Mechanical Engineering Lyon France Savannakhet University Faculty of Engineering Savannakhet Laos Savannakhet University Faculty of Information Technology Technology Computer Network Dept. Savannakhet Laos Savannakhet University Faculty of Information Technology Savannakhet Laos

ISBN: (纸本)9798350396546

The paper presents part of the work fulfilled under the Asean Factori 4.0 Erasmus+ project focused on the implementation of industrial automation in the education in 6 universities from 3 countries in South-East Asia: Thailand, Laos and Cambodia. The implementation of the project tasks, related to increasing the capacity and more precisely to the training of the professors from the Asian Universities is presented. According to the planned activities, professors from the University of Ruse conduct training for teachers from two universities: Chulalongkorn University, Thailand and Savannakhet University, Laos. The phases of training included in the project activities and how many of them have already been implemented are briefly presented. The program and the tasks of the teachers practical training for building the necessary teaching capacity in Savannakhet University and implementation of sustainable training in the field of industrial automation were presented and analyzed. Conclusions and future training of the staff of Asian universities planned under the project are presented in the paper. © 2023 IEEE.

关键词： Personnel training

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PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

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Discover Artificial Intelligence 2024年第1期4卷 75页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Mazhar, Tehseen Ahmed, Noor Shahzad, Tariq Khan, Muhammad Amir Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Bahria University Karachi Campus Sindh Karachi75000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5. © The Author(s) 2024.

关键词： Health risks

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Channel Modeling for Ultraviolet Non-Line-of-Sight Communications Incorporating an Obstacle

Channel Modeling for Ultraviolet Non-Line-of-Sight Communica...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wu, Tianfeng Yang, Fang Cao, Tian Cheng, Ling Chen, Yupeng Song, Jian Cheng, Julian Han, Zhu Xidian University School of Telecommunications Engineering Xi'an710071 China University of the Witwatersrand School of Electrical and Information Engineering Johannesburg2000 South Africa Southern University of Science and Technology School of Microelectronics Shenzhen518055 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China The University of British Columbia School of Engineering KelownaBCV1V 1V7 Canada University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

Existing studies on ultraviolet (UV) non-line-of-sight (NLoS) channel modeling primarily focus on scenarios without any obstacle, which makes them unsuitable for small transceiver elevation angles in most cases. To address this issue, a UV NLoS channel model incorporating an obstacle was investigated in this paper, where the impacts of atmospheric scattering and obstacle reflection on UV signals were both taken into account. To validate the proposed model, we compared it to the related Monte-Carlo photon-tracing (MCPT) model that had been verified by outdoor experiments. Numerical results manifest that the path loss curves obtained by the proposed model agree well with those determined by the MCPT model, while its computation complexity is lower than that of the MCPT model. This work discloses that obstacle reflection can effectively reduce the channel path loss of UV NLoS communication systems. © 2024 IEEE.

关键词： Communication channels (information theory)

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Task Offloading and Resource Management for IIoT with Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

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IEEE Internet of Things Journal 2025年

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex communication environments, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov Decision Process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2025 IEEE.

关键词： Energy utilization

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Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 15453-15466页

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 IEEE.

关键词： Convex optimization

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Cloud-Edge-End Collaborative Computing-Enabled Intelligent Sharding Blockchain for Industrial IoT Based on PPO Approach

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IEEE Transactions on Mobile Computing 2025年

作者： Xiong, Xin Li, Meng Yu, F. Richard Zhang, Haijun Wang, Kan Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Carleton University Department of Systems and Computer Engineering ON OttawaK1S 5B6 Canada University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China

The security and reliability risks of industrial data have constrained the advancement of the Industrial Internet of Things (IIoT). Although blockchain can protect the security and reliability of industrial data through hash verification mechanisms, there are numerous challenges in the existing blockchain enabled IIoT systems, such as the trilemma of scalability, decentralization and security, high computational power consumption of consensus protocols and limited computational resources of industrial devices. To address these problems, an intelligent sharding blockchain enabled IIoT framework is proposed, in which the intelligent sharding based on the reputation mechanism and the adaptive switching for multi-consensus protocols are utilized to enhance the decentralization, security and scalability of blockchain. Considering higher requirement of computational power of the sharding blockchain, a cloud-edge-end collaborative computing framework is introduced, in which the parallel computational offloading and the Terahertz communication technology are utilized to enhance the cooperation of the cloud-edge-end networks. Furthermore, due to the highly dynamic nature of industrial devices and industrial data, we consider and design the optimization problem as a Markov decision process (MDP), which is solved via the Proximal Policy Optimization (PPO) algorithm. Simulation results show that our proposed scheme can minimize total delay and maximize transaction throughput while guaranteeing the safety as well as decentralization of blockchain enabled IIoT systems. © 2002-2012 IEEE.

关键词： Markov processes

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Extending RIS Life Span for Reliable Communication Under Hardware Ageing Effects

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3395-3409页

作者： Mondal, Atiquzzaman Ghosh, Shyamal Singh, Keshav Biswas, Sudip Duong, Trung Q. Indian Institute of Information Technology Guwahati Department of Electrical Communication Engineering Guwahati781015 India Indian Institute of Science Education and Research Thiruvananthapuram Department of Data Science Thiruvananthapuram695551 India National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

In this paper, we address the critical aspect of hardware ageing effects in reconfigurable intelligent surfaces (RISs), whereby the problem of extending the RIS's life cycle under the impact of non-residual stochastic hardware impairment is considered. Through the replication of the RIS hardware and diverse wireless environments within a statistical simulation environment, we formulate an electronic maintenance framework (EMF) for RIS, where we incorporate non-residual impairments through stochastic modeling and determine the electronic reliability of the system. Accordingly, we derive optimal analytical solutions within the EMF to determine whether systematic maintenance of the RIS hardware should be done immediately or postponed in order to extend the expected life cycle of the RIS system. Furthermore, for the scenario with imperfect maintenance of the RIS-aided system, a reliable communication framework (RCF) is also introduced with residual impairments to assess the error probability of the RIS-aided communication. The RCF is established by deriving the distribution of the received signal-to-interference-plus-noise ratio in the presence of residual hardware impairment arising due to imperfect maintenance of the RIS system. Extensive numerical examples are provided to elucidate the derived solutions and illustrate the reliability performance of the proposed framework under hardware impairments. © 2020 IEEE.

关键词： Wireless sensor networks

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DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3295-3307页

作者： Leilei Li Yansheng Fu Dongjie Zhu Xiaofang Li Yundong Sun Jianrui Ding Mingrui Wu Ning Cao Russell Higgs Artificial Intelligence Academy Wuxi Vocational College of Science and TechnologyWuxi214068China School of Computer Science and Technology Harbin Institute of TechnologyWeihai204209China Department of Mathematics Harbin Institute of TechnologyWeihai264209China College of Information Engineering Shandong Vocational and Technical University of International StudiesRizhao276826China School of Mathematics and Statistics University College DublinDublinD04 V1W8Ireland

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval *** and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge *** the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge *** representation learning method is sig-nificant to the application of multi-modal knowledge *** paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data ***,use distributed technology to classify and store the data in the knowledge graph to improve retrieval ***,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of ***,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image *** paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.

关键词： Multi-modal retrieval distributed storage knowledge graph

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