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1st International Conference on Logistics, ICL 2024

作者： Umer, Muhammad Javed, Ali Dawood, Hussain University of Engineering and Technology Department of Software Engineering Taxila47050 Pakistan School of Computing Skyline University College Sharjah United Arab Emirates

ISBN: (纸本)9798331518844

Agricultural production vitally contribute to country's economic growth and food needs;however, it depends upon the production and quality of crops. Plants are affected easily due to climate changes and low-quality water. Plants health is examined manually by farmers or pathologists;however, it is not viable to examine each plant regularly due to larger areas of field and limited human resource. If the plant disease gets diagnosed at early stage, the precautionary measures can be taken on time and quality can be improved. There are several solutions available based on modern machine learning and deep learning techniques however, precise prediction and classification is challenging due to the limitations of low light or blurry images. We propose a deep learning-based approach which classifies the healthy and diseases plant using blurry images. Our method addresses the problem using improved layers activation, lesser parameters, using radiant pixels and active neurons. We improved the classification of smaller size and blur images by adding the pooling and dropout layers. Our solution addresses the vanishing gradient and high validation loss problems using a smaller number of parameters as compared to other convolutional neural networks. We evaluated the performance of the proposed method on plant village dataset consisting of a diverse collection of 20600 images. We achieved overall 98.3 % accuracy by minimizing the vanishing gradient, high validation loss and overfitting problem. © 2024 IEEE.

关键词： Plant diseases

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As Pseudo-label Free As Possible: Leveraging Adaptive Feature Generation for Sparsely Annotated Object Detection 39

As Pseudo-label Free As Possible: Leveraging Adaptive Featur...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yao, Shuilian Liu, Yu Jia, Qi Chen, Sihong Zhuo, Wei School of Software Technology DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China AILab Tencent Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)157735897X

Compared to fully supervised object detection, training with sparse annotations typically leads to a decline in performance due to insufficient feature diversity. Existing sparsely annotated object detection (SAOD) methods often rely on pseudo-labeling strategies, but these pseudo-labels tend to introduce noise under extreme sparsity. To simultaneously avoid the impact of pseudo-label noise and enhance feature diversity, we propose a novel Adaptive Feature Generation (AdaptFG) model that generates features based on class names. This model integrates a pre-trained CLIP into a VAE-based feature generator, with its core innovation being an Adaptor that adaptively maps CLIP semantic embeddings to the object detector domain. Additionally, we introduce inter-class relationship reasoning in detector, which effectively mitigates misclassifications stemming from similar features. Extensive experimental results demonstrate that AdaptFG consistently outperforms state-of-the-art SAOD methods on the PASCAL VOC and MS COCO benchmarks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Object detection

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Landslide Displacement Prediction Based on the Random Forest and Optimized Support Vector Regression 24

Landslide Displacement Prediction Based on the Random Forest...

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4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Li, Xiaojuan Liang, Xiao He, Yongqiang Wang, Huiqin Wei, Mingqiang Zhang, Guoming School of Civil Engineering Northwest Minzu University Lanzhou Gansu China School of Computing and Communications Lanzhou University of Technology Lanzhou Gansu China

ISBN: (纸本)9798400710247

In order to improve the accuracy of landslide prediction, this paper proposes a landslide displacement prediction model based on the random forests and optimized support vector regression. Firstly, Random Forests (RF) is used for preliminary prediction of landslide displacement, and Hunter-Prey Optimizer (HPO) is used to optimize the combination prediction model (HPO-SVR) of Support Vector Regression (SVR). The validation of the landslide displacement measured data shows that the HPO algorithm can lead to better prediction performance of SVR than particle swarm optimization and genetic algorithm. Compared with RF, SVR, and the combined prediction model of SVR with conventional particle swarm optimization algorithm, genetic algorithm and HPO, the combined RF-HPO-SVR model obtains more accurate landslide displacement prediction results. © 2024 Copyright held by the owner/author(s).

关键词： Prediction models

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Long-term Landslide Displacement Prediction Based on FEDformer 24

Long-term Landslide Displacement Prediction Based on FEDform...

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2024 International Academic Conference on Edge computing, Parallel and Distributed computing, ECPDC 2024

作者： He, Yongqiang Zhang, Jianliang Wang, Huiqin Li, Xiaojuan Wei, Mingqiang Zhang, Guoming School of Civil Engineering Northwest Minzu University Lanzhou730030 China School of Computing and Communications Lanzhou University of Technology Lanzhou730050 China

ISBN: (纸本)9798400718168

In order to improve the problem of long-term landslide displacement prediction accuracy, this paper applies the Frequency enhanced decomposed transformer (FEDformer) model to landslide displacement prediction, and proposes a landslide displacement prediction method based on the FEDformer model. The method takes rainfall and displacement as inputs to the model, extracts data features using Fourier transform and wavelet transform, and trains the model's learning prediction ability with knowledge distillation methods, aiming to improve the model's prediction accuracy. Finally, an example validation was carried out using landslide displacement monitoring data from Yuanshi Village, Huichuan Town, Dingxi City, Gansu Province. The results show that the model has higher prediction accuracy. Compared with the Autoformer model, the average absolute error and mean square error of the FEDformer model on cumulative displacement were improved by 10.12% and 12.04%, respectively. © 2024 ACM.

关键词： Prediction models

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Enhanced Interpretability in Project Risk Management: Interpreting the Antecedents by Capturing the Interdependencies in Risk Factors 51

Enhanced Interpretability in Project Risk Management: Interp...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Badhon, Bodrunnessa Anavatti, Sreenatha G. Chakrabortty, Ripon K. School of Systems & Computing UNSW ADFA CanberraACT2610 Australia School of Engineering & Technology UNSW ADFA CanberraACT2610 Australia

This paper proposes two frameworks-Interpretive Structural Modelling with Local Interpretable Model-agnostic Explanations (ISM-LIME) and Knowledge Graph with LIME (KG-LIME)-to enhance interpretability in project risk management. While machine learning has proven to be a powerful tool for predicting and analyzing risks in increasingly complex projects, its"black box" nature often obscures decision-making processes. Current explainable AI (XAI) techniques, such as LIME, SHAP (SHapley Additive exPlanations), and G-LIME (Global-LIME), provide valuable insights into feature importance but struggle with capturing the intricate relationship and cascading effects among multiple risk factors. Our proposed frameworks address these limitations by not only elucidating the impact of individual risk factors and interpreting the antecedents but also by revealing their interconnections and cascading effects. Through a comparative analysis, this study demonstrates how ISM-LIME and KG-LIME overcome the shortcomings of traditional XAI methods. ISM-LIME offers a clear hierarchical representation of risk interactions, while KG-LIME provides a detailed network-based view that captures complex interdependencies. This comparison highlights the strengths of each approach in improving risk assessment and decision-making. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Knowledge graph

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Human-Centric Principles for Computational Systems Supporting Collaborative Creativity 15th

Human-Centric Principles for Computational Systems Supportin...

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15th Advanced Doctoral Conference on computing, Electrical and Industrial Systems, DoCEIS 2024

作者： Ebadpour, Zahra Nikghadam-Hojjati, Sanaz Barata, Jose Department of Electronic and Computing Engineering Nova School of Science and Technology NOVA University Lisbon Caparica Portugal Caparica Portugal

ISBN: (纸本)9783031638503

Neglecting human values, needs, and priorities in computational systems that support collaborative creativity and innovation may result in diminished user trust and engagement, creativity barriers, and a potential disconnect between users and technology. This absence might also result in a lack of inclusion and neglect to discuss the complex facets of interpersonal cooperation that are necessary to optimize creative potential. To mitigate this issue this explorational paper aimed to identify and investigate integration of human-centric principles in computational systems dedicated to supporting collaborative creativity. A set of guiding principles are highlighted within the human-centric principles to guarantee that computational systems supporting collaborative creativity are designed, developed, and implemented in a way that is consistent with human traits, behaviours, and preferences. The result of this paper enhances user engagement in collaborative creativity and innovation practices, optimize creativity and innovation, improve users trust and satisfaction, enhance inclusivity, and improve effective communication, adaptability, and feasibility. © IFIP International Federation for Information Processing 2024.

关键词： Behavioral research

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A hybrid personality-aware recommendation system based on personality traits and types models

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Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 12775-12788页

作者： Dhelim, Sahraoui Chen, Liming Aung, Nyothiri Zhang, Wenyin Ning, Huansheng School of Computer Science University College Dublin Dublin Ireland School of Computing Ulster University Belfast United Kingdom School of Information Science and Engineering Linyi University Linyi China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

Personality-aware recommendation systems have been proven to achieve high accuracy compared to conventional recommendation systems. In addition to that, personality-aware recommendation systems could help alleviate cold start and data sparsity problems by adding the user’s personality traits in the recommendation process. The majority of the literature works used Big-Five personality model to represent the user’s personality, this is due to the popularity of Big-Five model in the literature of psychology. However, from personality computing perspective, the choice of the most suitable personality model that satisfy the requirements of the recommendation application and the recommended content type still needs further investigation. In this paper, we study and compare four personality-aware recommendation systems based on different personality models, namely Big-Five traits model, Eysenck model and HEXACO model from the personality traits theory, and Myers–Briggs Type Indicator (MPTI) from the personality types theory. Furthermore, we propose a hybrid personality model for recommendation that takes advantage of the personality traits models, as well as the personality types models. Through extensive experiments on recommendation dataset, we prove the efficiency of the proposed model, especially in cold start settings. Our proposed hybrid personality-aware recommendation model improves the precision and recall in cold start settings by 21% and 18% respectively compared to the widely used Big-Five traits model. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Recommender systems

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Wrapper Based Linear Discriminant Analysis(LDA)for Intrusion Detection in IIoT

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Computer Systems Science & engineering 2023年第5期45卷 1625-1640页

作者： B.Yasotha T.Sasikala M.Krishnamurthy Department of Computer Science and Engineering MNM Jain Engineering CollegeChennai600097India Department of School of Computing Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering KCG College of TechnologyChennai600097India

The internet has become a part of every human ***,various devices that are connected through the internet are ***,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their ***,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy *** is mandatory that various industrial fields require highly reliable security and preventive measures against *** detection is defined as the detection in the network of security threats targeting privacy information and sensitive *** Detection Systems(IDS)have taken an important role in providing security in the field of computer *** of intrusion is completely based on the detection functions of the *** an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network *** research works have been done for preventing network *** day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly *** this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.

关键词： Intrusion detection IIoT wrapper support vector machine(SVM) LDA random forest(RF) feature selection

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Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

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Computers, Materials & Continua 2024年第5期79卷 2035-2062页

作者： Qiong Yang Li Ma Shanshan Tu Sami Ullah MuhammadWaqas Hisham Alasmary Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different *** BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security ***,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of *** decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing *** this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their ***,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security ***,we discuss the challenges posed by BGP security problems and outline prospects for future research.

关键词： BGP security blockchain prefix hijacking trust model secure routing

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Deep Learning - Based Prediction Models for Colon and Rectum Cancer Disease: Enhancing the Precision Diagnosis

Deep Learning - Based Prediction Models for Colon and Rectum...

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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Suman, Sunil Kumar Kumaraswamy, D. Sahu, Sunil Kumar Akella, Prasanna Lakshmi Santhoshkumar, S.P. Karthika, R. Supaul College of Engineering Department of Computer Science and Engineering Supaul India Ellenki College of Engineering and Technology Telangana Sangareddy India Hyderabad India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Information Technology Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331538538

Colon and rectal cancer are among the leading causes of cancer-related illness and death worldwide. The detection and treatment of colon cancer are viewed as social and economic issues due to the high fatality rates. Every year, around half a million people worldwide develop cancer, including colon cancer. Although improving patient outcomes requires an accurate and timely diagnosis, traditional diagnostic methods usually suffer from subjectivity and variability problems. An extensive summary of colon cancer diagnosis is given here. Deep learning, a branch of artificial intelligence, has become a powerful tool for increasing cancer detection accuracy by utilizing large datasets and complex algorithms. This study combines genomic data, medical imaging, histopathological analysis, and clinical decision-making tools to better comprehend the creation and application of deep learning-based prediction models in the diagnosis of colon and rectal cancer. Through the use of Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and hybrid models, deep learning can improve the accuracy of cancer detection, anticipate the course of a disease, and assist with customized treatment planning. By automating and enhancing diagnostic precision, these models have the potential to revolutionize healthcare workflows, reduce diagnostic errors, and ultimately increase patient survival rates. This research also discusses the challenges and possible advantages of using deep learning to clinical practice for colon and rectal cancer. Last but not least, the problems that exist today and possible directions for future research are examined. © 2025 IEEE.

关键词： Diseases

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