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Multi-domain feature-enhanced attribute updater for generalized zero-shot learning

Neural Computing and Applications 2025年第14期37卷 8397-8414页

作者： Shi, Yuyan Jiang, Chenyi Song, Feifan Ye, Qiaolin Long, Yang Zhang, Haofeng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China School of Information Science and Technology Nanjing Forestry University Jiangsu Nanjing210037 China Department of Computer Science Durham University DurhamDH1 3LE United Kingdom

The goal of generalized zero-shot learning (GZSL) is to transfer knowledge from seen classes to unseen classes. However, a significant challenge is the single-category attributes are often inadequate to capture the intra-class variability of diverse image samples. During training, the model tends to cluster samples of seen categories based solely on semantic descriptions, overlooking subtle yet important visual differences. To address these challenges, this paper introduces an instance-level attribute updating method, termed as multi-domain feature-enhanced attribute updater (MDAU), which establishes a one-to-one alignment between visual and semantic by obtaining multiple instance-level attributes that closely correspond to the natural intra-class distribution of the image, in contrast to the many-to-one alignment typically used in traditional GZSL approaches. Specifically, we initially compute feature weights for each individual image, and then, the visual information is incorporated as conditions to facilitate the personalized updating of class attributes. We evaluate our model on three benchmark datasets, the results show that our model can achieve the state-of-the-art performance and detailed analysis also illustrates the effectiveness of each proposed module. Code is available at https://***/S-Silvia/MDAU. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Zero-shot learning

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CNN-Based Machine Learning Model for Disease Prediction in Crops

CNN-Based Machine Learning Model for Disease Prediction in C...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Choudhary, Vijay Thakur, Archana Bhalse, Nisha IPS Academy Institute of Engineering & Science Department of Computer Science & Engineering Indore India School of Computer Science & IT Devi Ahilya University Indore India

ISBN: (纸本)9798331512965

Potato crops are vital to global food security, but they are susceptible to several diseases that hinder growth and yield. Traditional methods of detecting these diseases rely on labor-intensive lab tests and human observation. This research presents an enhanced machine learning model for detecting and categorizing potato leaf diseases, specifically early blight and late blight, using Convolutional Neural Networks (CNN). The model is trained on various datasets of potato leaf images, utilizing techniques such as hyper-parameter tuning, data augmentation, and transfer learning. The proposed model attains an accuracy of 96.11%, significantly improving upon existing methods. Image processing techniques, Segmentation is performed using K-means technique and feature extraction is done by utilizing gray-level co-occurrence matrix, along with multi-class support vector machines for classification, are employed. Additionally, the study shows that CNN performs better than Artificial Neural Networks (ANN) in image classification. The study demonstrates how AI and deep learning technology can be used in agriculture to improve crop management and enable early disease prediction. Experiments have been conducted using the real image dataset. © 2025 IEEE.

关键词： Adversarial machine learning

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Multi-Objective Teaching-Learning-Based Optimizer for a Multi-Weeding Robot Task Assignment Problem

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Tsinghua science and Technology 2024年第5期29卷 1249-1265页

作者： Nianbo Kang Zhonghua Miao Quan-Ke Pan Weimin Li M.Fatih Tasgetiren School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Industrial Engineering Department Baskent UniversityAnkara 06790Türkiye

With the emergence of the artificial intelligence era,all kinds of robots are traditionally used in agricultural ***,studies concerning the robot task assignment problem in the agriculture field,which is closely related to the cost and efficiency of a smart farm,are ***,a Multi-Weeding Robot Task Assignment(MWRTA)problem is addressed in this paper to minimize the maximum completion time and residual herbicide.A mathematical model is set up,and a Multi-Objective Teaching-Learning-Based Optimization(MOTLBO)algorithm is presented to solve the *** the MOTLBO algorithm,a heuristicbased initialization comprising an improved Nawaz Enscore,and Ham(NEH)heuristic and maximum loadbased heuristic is used to generate an initial population with a high level of quality and *** effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule.A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the ***,a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the *** results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.

关键词： genetic algorithm heuristic algorithm Multi-Weeding Robot Task Assignment(MWRTA) teaching optimization algorithm

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Translation English to Punjabi: A Concise Review of Significant Approaches

Translation English to Punjabi: A Concise Review of Signific...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Srivastava, Muskan Malik, Nishchay Gupta, Adarshita Chhabra, Megha Khepra, Shaveta Abidi, Ali Imam Sharda University School Of Engineering & Technology Department Of Computer Science & Engineering Greater Noida India Amrita School Of Engineering Department Of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350306446

Over the years, Machine Translation in English to Punjabi has seen significant advancements. The study emphasizes Punjabi's unique linguistic complexities, which pose challenges for accurate and contextually relevant translations. Recognizing the importance of overcoming these obstacles, the paper investigates various pre-processing methodologies within neural-based and statistical-based Machine Translation systems. It examines the methods to improve translation accuracy, addressing issues such as morphological complexity, syntactic variations, and a lack of parallel corpora. Effective pre-processing techniques are highlighted as a critical step toward improving machine translation performance for Punjabi. Ultimately, by capitalizing on existing advancements, this study highlights practical implications and potential areas for improvement, focusing on the end-user experience. © 2024 IEEE.

关键词： Machine translation

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Hannan Quinn Quantum Grasshopper Optimization and Attention Deep Intelligent Train Status Prediction

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Multimedia Tools and Applications 2024年 1-26页

作者： Singh, Ajay Dhanaraj, Rajesh Kumar Kumar, Santosh Abraham, Ajith School of Computer Science and Engineering Galgotias University Greater Noida India Pune India Department School of Computer Science Engineering & amp Technology Bennett University Uttar Pradesh Greater Noida India

Providing accurate and timely traffic information such as arriving time of train plays a significant part in intelligent train status prediction. Maximum-speed train status forecast is a significant topic as far as railway dispatching. In this paper proposes a novel train status prediction method based on the Hannan Quinn Quantum Grasshopper Optimized Attention Deep Learning (HQQGO-ADL) method introduced for accurate train status prediction. The proposed HQQGO-ADL is designed with three different processes namely preprocessing, feature selection, and prediction. This method can well capture the influence of discrete and continuous features in addition to spatiotemporal characteristics, therefore modeling train status prediction. At first, Mid-hinge Inter-Quartile Interpolation is utilized to perform preprocessing for eradicating or discarding the outliers with less train status identification time. Second, HannanQuinn Quantum Grasshopper Optimization is employed to capture the influence of discrete and continuous features on spatiotemporal characteristics, therefore modeling train status prediction. Besides, the maximum log likelihood discrete feature fine tuned to obtain continuous features. Finally, the Attention Deep Neural Train Status Prediction model is utilized for improved latency train status prediction. The overall analysis of HQQGO-ADL has better performance of higher train status prediction accuracy by 32% and lower train status prediction time, overhead, and error rate by 32%, 32%, and 27%as compared to existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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GRA: Graph Representation Alignment for Semi-Supervised Action Recognition

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IEEE Transactions on Neural Networks and Learning Systems 2024年第9期35卷 11896-11905页

作者： Huang, Kuan-Hung Huang, Yao-Bang Lin, Yong-Xiang Hua, Kai-Lung Tanveer, M. Lu, Xuequan Razzak, Imran National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Indian Institute of Technology Indore Department of Mathematics Indore Simrol453552 India La Trobe University Department of Computer Science and IT MelbourneVIC3086 Australia University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependency on huge labeled datasets. Acquiring such datasets is often prohibitive, and the frequent occurrence of incomplete skeleton data, typified by absent joints and frames, complicates the testing phase. To tackle these issues, we present graph representation alignment (GRA), a novel approach with two main contributions: 1) a self-training (ST) paradigm that substantially reduces the need for labeled data by generating high-quality pseudo-labels, ensuring model stability even with minimal labeled inputs and 2) a representation alignment (RA) technique that utilizes consistency regularization to effectively reduce the impact of missing data components. Our extensive evaluations on the NTU RGB+D and Northwestern-UCLA (N-UCLA) benchmarks demonstrate that GRA not only improves GCN performance in data-constrained environments but also retains impressive performance in the face of data incompleteness. © 2012 IEEE.

关键词： Alignment

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Event Management System Using Spatial and Event Attribute Information

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SN computer science 2025年第3期6卷 1-9页

作者： Setia, Sonia Anjli, Km Bisht, Upasana Jyoti Raj, Dharm Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science Institute of Innovation in Technology & Management Delhi Janakpuri India Department of Computer Science and Engineering ITS Engineering College Greater Noida India

The event management mechanism matches messages that have been subscribed to and events that have been published. To identify the subscriptions that correspond to the occurrence inside the category, it must first run a clustering operation on the subscription message data and determine the category to which the event belongs. In this work, we offer an on-the-fly publish-subscribe technique that can handle on-the-fly subscription changes and category modifications in a distributed environment, as well as the synchronization of spatial information and event attribute information. Additionally, without any prior information, clustering and matching processes can be carried out rapidly. The publish-subscribe method is implemented on a distributed system, and a load-balancing solution is proposed for the algorithm on the distributed system. We then constructed our cluster and used real data to empirically evaluate the suggested publish-subscribe technique. Experiments show that incorporating both similarity and relevance with frequency, compared to focusing on similarity alone, significantly decreases the number of categories which need to be matched for events. This leads to a 15.5% improvement in the speed of matching events and subscriptions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clustering algorithms Distributed systems Publish-and-subscribe systems

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Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Optimized featured swarm convolutional neural network (OFSCNN) model based dialect recognition system for Bagri Rajasthani language

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International Journal of Information Technology (Singapore) 2025年第1期17卷 121-133页

作者： Kukana, Poonam Sharma, Pooja Bhardwaj, Neeru Department of Computer Science and Engineering University School of Engineering & amp Technology Rayat-Bahra University Punjab Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Mohali India

The dialects of a language hold a significant place in speech processing (SP) applications. The objective of dialect identification is to categorize speech sample data into a specific dialect of a speaker's spoken language. A dialect recognition system must effectively distinguish between different dialects of a standard language, as they tend to possess many similarities. The dialect of a language is not a distinct characteristic, as it can be impacted by the utterer's birthplace. Researchers in the domain of automatic speech recognition (ASR) face difficulties in identifying the speech patterns unique to each dialect or language. The proposed work recognizes the dialects of the Bagri Rajasthani language from undefined expressions of speech. Rajasthani language is one of the eldest and most famous languages in the Indo-Aryan languages. It comprises the different dialects and for recognizing the dialects, it used dissimilar phases of acoustic and spectral characteristics of the speech signal (SS). The spectral and acoustic features of SSs are measured to design the system. As there is no specific speech dataset for Bagri dialects, the database is built, to verify the Bagri dialects of the Rajasthani language. To improve the accuracy rate, and error rate in recognizing the Bagri dialects, the acoustic and spectral characteristics of speech expressions are joined. To verify several Bagri dialects of the Rajasthani language, different simulations for classification and investigation are carried out i.e., OFSCNN model, GA-NN, etc. The outcomes are important and the accuracy of 96.95% for the OFSCNN model, 80.63% for GA-NN, and 93.45% for the SVM method is an achievement. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN (convolutional neural network Dialects MFCC (Mel frequency co-efficient cepstral) OFSCNN (optimized featured swarm convolutional neural network) PSO (particle swarm optimization) Speech Dialect Recognition

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