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Distinctive Zucchini Classification in India: Leveraging CNN and RNN Models 15

Distinctive Zucchini Classification in India: Leveraging CNN...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vats, Satvik Sharma, Rishabh Computer Science and Engineering Graphic Era Hill University Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350370249

The proposed study works at three types of zucchini-Arka Sumeet, Arka Anamika, and Ahkra Manik-in India. It uses a mix of Convolutional Neural Networks (CNN) and recurrent Neural Networks (RNN) models to classify them. A big set of 11,000 high-quality pictures showing different stages of zucchini growth was gathered and cleaned up to make all the data match. The suggested new design, a mix of CNN and RNN, was made to find complex plant pictures. It can also understand how the zucchini grows over time better by looking at its patterns. The model is very good, with a success rate of 96.9%. It does an excellent job telling zucchini types apart. Measurement tools like precision, recall, and F1-score along with specificity show if the model is good at correctly classifying things. The Confusion Matrix helps us understand where mistakes happen in classification, so can improve models better. This study shows how the mix of CNN-RNN is important for the proper grouping of zucchini types. It gives a strong base to help precision farming uses. The usefulness of the model in farming choices, getting rid of diseases, and planning specific crop strategies shows its real importance. More progress is needed in making neural networks better, adding more data, and fixing models. This will make them flexible for all kinds of farming situations and improve their performance. This study shows a good, solid way to classify types of zucchini in India. It could have important effects on broader farming tech uses. © 2024 IEEE.

关键词： Agricultural technology Crop classification Cultivar classification hybrid model machine learning Neural network Precision agriculture Zucchini variety classification

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Globally Informed Graph Contrastive learning for Recommendation 20th

Globally Informed Graph Contrastive Learning for Recommendat...

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20th international conference on Intelligent computing (ICIC)

作者： Zheng, Yixing Li, Chengxi Dong, Junyu Yu, Yanwei Ocean Univ China Fac Informat Sci & Engn Qingdao 266400 Shandong Peoples R China

ISBN: (纸本)9789819756179;9789819756186

In recent years, the fusion of graph convolutional networks (GCNs) with contrastive learning (CL) has emerged as a promising approach in recommender systems, owing to its adeptness in extracting self-supervised signals from the original data, thus addressing the challenge of data sparsity. While this methodology has been proven to be effective, it predominantly focuses on capturing discrepancies in user preferences through the construction of contrastive views, often neglecting the underlying commonalities in user preferences. To address this limitation, we introduce a novel framework, Globally Informed Graph Contrastive learning (GIGCL), which aims to incorporate global features into the construction of contrastive views, facilitating the capture of shared features among nodes and refining the nodes' embedding outcomes. Central to our framework are four key modules, with the global features extractor module and the global informed fusion module serving as pivotal components. Following the initial embedding phase, local embeddings are fed into the global features extractor module to derive global features. Subsequently, in the global informed fusion module, the global features are seamlessly integrated with the local embeddings, generating a pair of contrastive views. Experimental results on two real-world datasets demonstrate the superiority of our proposed GIGCL over state-of-the-art baselines, and underscore the efficacy of GIGCL in reconciling divergent and convergent user preferences, thereby enhancing recommendation performance.

关键词： Graph Convolutional Networks Contrastive learning Collaborative Filtering Recommender System

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Blockchain-Based Secure data Sharing Algorithms for Cognitive Decision Management 4

Blockchain-Based Secure Data Sharing Algorithms for Cognitiv...

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Alijoyo, Franciskus Antonius Prabha, B. Aarif, Mohd Fatma, Gulnaz Rao, Vuda Sreenivasa Valavan M, Ponni School of Business and Information Technology STMIK LIKMI Bandung Indonesia SRM Institute of Science and Technology Department of CSE Tamilnadu Chennai India Aligarh Muslim University Department of Commerce Uttar Pradesh Aligarh India University College Jazan University Al Ardah Jazan Saudi Arabia Koneru Lakshmaiah Education Foundation Department of CSE Andhra Pradesh Vaddeswaram India K.Ramakrishnan College of Engineering Department of AI & DS Tamil Nadu Trichy India

ISBN: (纸本)9798350395914

Cognitive Decision Management in healthcare data management refers to the integration of artificial intelligence, machine learning, and cognitive computing to enhance decision-making processes. Secure data sharing is crucial in Cognitive Decision Management for healthcare as it enables seamless collaboration among stakeholders while ensuring the confidentiality, integrity, and availability of sensitive health data. This practice is essential for delivering timely and accurate insights, facilitating collaborative care, and maintaining patient privacy. Traditional approaches for secure data sharing in Cognitive Decision Management include role-based access control, encryption, and Virtual Private Networks (VPNs). Traditional approaches often face limitations such as complex key management, potential vulnerabilities in encryption algorithms, and scalability challenges in managing access controls. To overcome these limitations, this study introduces a novel approach leveraging blockchain technology. The proposed system incorporates Ethereum blockchain 2.0 for scalable and efficient healthcare data management, along with Elliptic Curve Cryptography for secure transaction verification. Blockchain ensures transparency, immutability, and decentralized control, addressing the drawbacks of traditional methods and providing a robust foundation for secure data sharing in Cognitive Decision Management within healthcare settings. Results of this study was implemented in Python tool. The throughput ranged from 450 to 520 transactions per second, showcasing system stability. © 2024 IEEE.

关键词： data integrity

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Textual Out-of-Distribution data Detection Based on Granular Dictionary

Textual Out-of-Distribution Data Detection Based on Granular...

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2024 IEEE international conference on Big data, Bigdata 2024

作者： Ouyang, Tinghui Amagasa, Toshiyuki Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798350362480

As an factor influencing data quality, out-of-distribution (OOD) data detection plays a critical role in AI quality assurance. This paper presents an advanced OOD detection method based on Granular computing (GrC) and dictionary learning, specifically designed for detecting textual OOD in natural language processing (NLP) systems. First, informative data structure descriptors (information granules) are generated through GrC, which are aimed to reduce the computation overhead in big data analysis. Next, granular dictionary is constructed from these granules and used to represent original data through dictionary learning and data reconstruction. Finally, OOD detection is formalized by analyzing differences between original and reconstructed data. Finally, the proposed method formulate OOD detection via the difference between original and reconstructed data. Experiments conducted on a sentiment analysis system based on a large language model (LLM) and three OOD datasets are implemented. The constructed granular dictionary is firstly demonstrated to have good representation ability supporting effective OOD detection. Furthermore, the proposed method's effectiveness, efficiency and scalability in textual OOD detection are validated through comprehensive comparative analysis. © 2024 IEEE.

关键词： Information granules

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Cyber Chronicles: Tracking Behavior Patterns for Detecting Threats in Large Networks 3

Cyber Chronicles: Tracking Behavior Patterns for Detecting T...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Ramasubbu, Senthilnathan Thangavel, Senthil Kumar Jeyakumar, G. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350372120

One of the primary challenges in cybersecurity is that even one un-detected, appropriately unanalyzed malicious security event can hide the attack vectors of a potential hacker. It is essential to detect the data breach at the earliest stage to reduce the impact on the business. The malicious actor's activities will have higher visibility during the initial attacks to compromises or exploitation at the beginning of the attack cycle. After the initial compromise, the attacker studies the environment and establishes the persistence and covert channel communication. At this successive phase of the attack cycle, the attacker camouflages with the production data flow, access, and noises in the network and application logs. It is essential to detect the attacks during the reconnaissance and initial attacks and should not miss any malicious activities. Most of our security tools focus on reducing false positives;however, reducing false negatives is critical to cyber security. Hence, this paper is focused on suitable deep learning to reduce the False Negatives (FNs) with minimal Type II error to detect the attacks well in advance. © 2024 IEEE.

关键词： Cybersecurity

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Evolving Knowledge Distillation with Large Language Models and Active learning 30

Evolving Knowledge Distillation with Large Language Models a...

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Joint 30th international conference on Computational Linguistics and 14th international conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Chengyuan Kang, Yangyang Zhao, Fubang Kuang, Kun Jiang, Zhuoren Sun, Changlong Wu, Fei College of Computer Science and Technology Zhejiang University China Institute for Intelligent Computing Alibaba Group China School of Public Affairs Zhejiang University China Shanghai Institute for Advanced Study of Zhejiang University China

ISBN: (纸本)9782493814104

Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, previous research has attempted to distill the knowledge of LLMs into smaller models by generating annotated data. Nonetheless, these works have mainly focused on the direct use of LLMs for text generation and labeling, without fully exploring their potential to comprehend the target task and acquire valuable knowledge. In this paper, we propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of active learning to interactively enhance the process of data generation using large language models, simultaneously improving the task capabilities of small domain model (student model). Different from previous work, we actively analyze the student model's weaknesses, and then synthesize labeled samples based on the analysis. In addition, we provide iterative feedback to the LLMs regarding the student model's performance to continuously construct diversified and challenging samples. Experiments and analysis on different NLP tasks, namely, text classification and named entity recognition show the effectiveness of EvoKD. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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ANOMALY-AWARE SEMANTIC SELF-ALIGNMENT FRAMEWORK FOR VIDEO-BASED PERSON RE-IDENTIFICATION 49

ANOMALY-AWARE SEMANTIC SELF-ALIGNMENT FRAMEWORK FOR VIDEO-BA...

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49th IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ran, Zhidan Lu, Xiaobo Liu, Wei Southeast Univ Sch Automat Nanjing 210096 Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Video-based person re-identification (Re-ID) aims at matching the video snippets of the same person across multiple cameras. The ubiquitous appearance misalignment is a critical challenge in video person re-identification. Existing alignment-based methods rely on off-the-shelf human parsing models and cannot handle anomalous appearance information (e.g., obstacles and pedestrian interference) in video sequences. In this paper, we propose Anomaly-Aware Semantic Self-Alignment (ASSA), a novel video-based person Re-ID framework that seeks out body parts without prior human topology information and learns part-based feature representations against anomalous information. The proposed ASSA performs part classifier training and part-aligned representation learning alternately. For the classifier training, we design a Salient Region Extraction module to segment the entire foreground from the background in each input frame. Furthermore, a novel Anomaly-Aware Refinement module is proposed to suppress the influence of anomalous interference. Extensive experiments on three prevalent benchmarks demonstrate the effectiveness and superiority of the proposed framework.

关键词： Video person re-identification part-based representation learning feature alignment

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3AM: An Ambiguity-Aware Multi-Modal machine Translation dataset 30

3AM: An Ambiguity-Aware Multi-Modal Machine Translation Data...

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Joint 30th international conference on Computational Linguistics and 14th international conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ma, Xinyu Liu, Xuebo Wong, Derek F. Rao, Jun Li, Bei Ding, Liang Chao, Lidia S. Tao, Dacheng Zhang, Min NLP2CT Lab Department of Computer and Information Science University of Macau China Institute of Computing and Intelligence Harbin Institute of Technology Shenzhen China Northeastern University Shenyang China The University of Sydney Sydney Australia

ISBN: (纸本)9782493814104

Multimodal machine translation (MMT) is a challenging task that seeks to improve translation quality by incorporating visual information. However, recent studies have indicated that the visual information provided by existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabilities. This issue presents a significant obstacle to the development of MMT research. This paper presents a novel solution to this issue by introducing 3AM, an ambiguity-aware MMT dataset comprising 26,000 parallel sentence pairs in English and Chinese, each with corresponding images. Our dataset is specifically designed to include more ambiguity and a greater variety of both captions and images than other MMT datasets. We utilize a word sense disambiguation model to select ambiguous data from vision-and-language datasets, resulting in a more challenging dataset. We further benchmark several state-of-the-art MMT models on our proposed dataset. Experimental results show that MMT models trained on our dataset exhibit a greater ability to exploit visual information than those trained on other MMT datasets. Our work provides a valuable resource for researchers in the field of multimodal learning and encourages further exploration in this area. The data, code and scripts are freely available at https://***/MaxyLee/3AM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Towards a New Hybrid Synthetic Minority Oversampling Technique for Imbalanced Problem in Software Defect Prediction 5

Towards a New Hybrid Synthetic Minority Oversampling Techniq...

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5th international conference on data Analytics for Business and Industry, ICDABI 2024

作者： Saheed, Yakub Kayode Abdulsalam, Sulaiman Olaniyi Ibrahim, Mohammed Babatunde Baba, Usman Ahmad American University of Nigeria School of ITand Computing Adamawa State Nigeria Kwara State University Dept. of Computer Science Malete Nigeria Nigerian Extractive Industries Transparency Initiative ICT Unit Abuja Nigeria North-Eastern University Dept. of Computer Science Gombe Nigeria

ISBN: (纸本)9798350368710

The software industry strives to improve software quality through continuous bug prediction, bug elimination, and module fault prediction. This issue has piqued researchers' interest because of its significant relevance in the software industry. Frequently, Software Defect Prediction (SDP) models contain significantly skewed data, making it difficult for classifiers to recognize defective occurrences. The machine learning (ML) community has put a lot of effort into solving the problem of learning from imbalanced SDP data, though less so in empirical software engineering. The over-sampling strategy is one of many recent solutions to this problem. This strategy balances the number of defective and non-defective cases by creating new defective instances. Unfortunately, these methods would result in non-diverse synthetic instances as well as a large number of unneeded noise instances, creating an imbalanced class problem. As a result, we propose a hybrid synthetic minority oversampling (HSMOTE) to address the problem of imbalance in SDP. In this paper, we introduce the hybrid Synthetic Minority Oversampling Technique (HSMOTE), a method that utilizes Extra Tree, Random Forest (RF), and Extreme Gradient Boosting (Xgboost) for classification. We develop and deploy the proposed method on the National Aeronautics and Space Administration (NASA) dataset, evaluating its performance on three datasets: JM1, KC1, and PC3. We compared the parameters for accuracy, precision, AUC, recall, F-measure, and Mathew Correlation Coefficient to those of the existing SDP. The findings from the simulations on the JM1 data showed that the proposed techniques work better than the current best models. The proposed SMOTE+RF technique surpasses the existing techniques with an accuracy of 93.69%, an AUC of 82.70%, and an F-measure of 32.98%. Similarly, the proposed SMOTE+Xgboost method outperforms the existing techniques with an accuracy of 93.432%, an AUC of 82.64%, and an F-measure of 34.13%, whi

关键词： NASA

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Revolutionizing Brain Tumor Analysis: Unleashing the Potential of Residual UNet for Enhanced Segmentation Accuracy 4

Revolutionizing Brain Tumor Analysis: Unleashing the Potenti...

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4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Tiwari, Pradeep Kumar Johri, Prashant Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350360165

Advances in diagnosis have been made, leading to significant changes in the approach to mental health diagnosis. This update delivers the powerful segmentation algorithms needed to provide accurate diagnosis and treatment planning. Within the scope of this research, a new technology that uses the Residual UNet deep learning architecture is proposed to increase segmentation accuracy. Our method solves the problem of incompleteness while ensuring that the model captures the correct data. This is done by taking advantage of the fixed connection available in the UNet design. The results show that this method is more efficient than other methods. This is done through long-term experiments on large datasets, including multimodal neuroimaging. An unlikely oncology surrogate is Residual UNet, which has proven to be accurate and useful in most cases. Specifically, our strategy achieved 0.9897 accuracy, 0.9726 accuracy, 0.9985 accuracy, and 0.8665 accuracy. BraST 2020 profile is used for this purpose. Additionally, our study proves that Residual UNet exceeds standards for brain tumors. This shows that it can provide superior segmentation for existing and specific technologies. © 2024 IEEE.

关键词： Brain Tumor Medical Imaging Residual UNet Segmentation

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