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Comparative Analysis of Various Deep Learning Techniques for Weed Detection in Corn Crops 8

Comparative Analysis of Various Deep Learning Techniques for...

引用

8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Jiveesha, Jiya Vinothini, A. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

Weed detection is an important process in increasing the crop yield and minimizing labor input in crop management. Automatic weed detection helps in enhancing precision agriculture. The objective of this study is to explore the application of deep learning techniques for automated weed detection. It compares the performance of four deep learning models: Standard CNN, Custom Residual CNN, VGG16, and EfficientNet B0. The models were trained with a publicly available corn dataset with 24,000 images with various image processing techniques such as normalization and data augmentation to increase the model's resilience. EfficientNet B0 performed better when compared to other models with an accuracy of 96% proving the higher capability for crop and weed classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Developing ML model for early detection and prediction of Alzheimer's disease using multi modal biomarkers

Developing ML model for early detection and prediction of Al...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Bajaj, Divyansh Renjith, P.N. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348460

The early identification and outlook of Alzheimer's disease (AD) are the primary focus of this review of the literature. It emphasizes the significance of early detection, the application of machine learning approaches for AD prediction, and the utilization of multimodal biomarkers from several data sources. The review also covers resolving flaws in AD prediction algorithms, privacy and ethical issues, and the usability of machine learning models. A synopsis of the state of the field's research and the significance of interdisciplinary cooperation in advancing machine learning and multimodal biomarkers for early AD detection are included in the conclusion. For the benefit of the millions of people afflicted by this illness, the primary goal is to make early AD identification a clinical reality. © 2024 IEEE.

关键词： AI Alzheimer EEG Machine learning Prediction

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MTAGCN: Mixed Temporal Adaptive GCN for Air Pollution Prediction 20

MTAGCN: Mixed Temporal Adaptive GCN for Air Pollution Predic...

引用

20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Cheng, Yuxuan Wang, Zhanquan School of Information Science and Engineering Computer Technology East China University of Science and Technology Shanghai China

ISBN: (纸本)9798350356328

As a crucial issue for the advancement of intelligent urban centers, the urgent resolution of air pollution in urban areas is necessary. The essential task for accurate air pollution forecasting aims to aptly capture the spatiotemporal (ST) inter- dependencies of air pollution data. In recent years, numerous models grounded on the Spatial-Temporal Graph Neural Networks (STGNNs) framework have been devised to encapsulate intricate ST dependencies within air pollution data. However, traditional STGNN models are ineffective in capturing long-range temporal trends with lower complexity and in accounting for hidden spatial dependencies due to the explicit graph structure. In relation to the foregoing issue, the Mixed Temporal Adaptive Graph Convolutional Networks (MTAGCN) composed of stacked ST blocks is proposed for effectively acquiring spatial and temporal dependencies within air pollution data. More specifically, each spatial-temporal block contains two major parts: 1) the mixed temporal modules are proposed to capture the dynamic temporal patterns within the input data, which learns the global temporal dependencies of input data at a lower complexity;2) the graph generation layers produce a learnable self-adaptive graph structure that capture the hidden spatial dependencies between each nodes, while the adaptive spatial layers dynamically learn the spatial patterns of input data using both pre-defined and learnable graph structures. The exceptional performance and robustness of our method over other baseline models are illustrated by comprehensive experiments with two nationwide public datasets. © 2024 IEEE.

关键词： Spatio-temporal data

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Finite Automata Application in Monitoring the Digital Scoreboard of a Cricket Game 1

引用

1st International Conference on Computational sciences and Sustainable Technologies, ICCSST 2023

作者： Kaushik, K. Pavithra, L.K. Subbulakshmi, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9783031509933

ISBN: (纸本)9783031509926

Cricket is one of the most viewed sports in the world and tracking of this game is very important throughout the gameplay. The application of automata in sports is least explored and this paper proposes the use of automata theory in cricket. The paper sheds light upon the potential application of finite automata concept in cricket which proposes proper tracking flow of the game. The major focus of the paper is shown upon ball-tracking and on-strike batsman tracking with each tracking system being explained in detail using the state diagrams. With the help of Deterministic Finite Automata (DFA), it is possible to achieve the flow of track and thus, helps in efficient tracking of the sport. It also minimizes the rate of committing the tracking errors during the gameplay. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sports

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Design and Implementation of Deep Learning Models for Tomato Plant Leaf Disease Classification 12th

Design and Implementation of Deep Learning Models for Tomato...

引用

12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Mittal, Mihir Santhi, H. Anuradha, J. Boominathan, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819732913

Agriculture is an integral part of the community and making sure that plants can grow and produce fruits and vegetables is necessary for the survival of the human race. Here we look into the classification of various tomato plant diseases such as early blight, Septoria leaf spot, bacterial leaf spot, late blight, mosaic virus, yellow virus, mold leaf, and two spotted spider mite leaf. We have selected tomato plants as they are the world's most used and common fruit as it is used in every household throughout the world. The dataset being used is a combination of various datasets such as the PlantVillage dataset and the PlantDoc Dataset along with augmentation. The dataset has been taken from Kaggle and contains more than 40,000 images of tomato plant leaves. Various deep learning models such as the pre-trained (such as ResNet50, ResNet152, EfficientNetB3, Xception, VGG19, and InceptionV3) and a suggested Convolutional Neural Network model are all compared to see which provides the best results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Semantic Attacks based on both Style and Frequency Restrictions 5

Semantic Attacks based on both Style and Frequency Restricti...

引用

5th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2024

作者： Yu, Jiahao Chen, Deng School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331518677

Adversarial attacks can generate adversarial examples by adding perturbation information to benign samples, causing neural networks to misjudge. Semantic-based adversarial attacks are a type of attack algorithm that aims to disrupt the semantic information represented by sample features. However, most semantic attacks do not distinguish between style-related features and content-related features of the sample, resulting in unstable attack effects and perturbations that are easily perceived by the human visual system (HVS). Our attack algorithm generates adversarial examples by perturbing the semantic information of benign examples and then obtains the stylized adversarial examples through style transformations technology. The adversarial examples and stylized adversarial examples compete with each other to suppress perturbations caused by style-related features, while frequency constraints are introduced to limit the perturbations, thereby generating the optimal adversarial examples. Our attack algorithm suppresses the style features of the samples, which can improve the stability of the attack and ensure that perturbations do not appear in areas easily perceived by the HVS. Extensive experiments on the CIFAR-10, CIFAR-100, and ImageNet-1K datasets demonstrate that our attack algorithm can achieve a maximum improvement of 0.41 in ASR, 0.19 in L2, and 2.05 in FID. Therefore, our attack algorithm has a better attack effect and can generate perturbations that are more difficult to detect. © 2024 IEEE.

关键词： Generative adversarial networks

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Leveraging Data Analytics in Azure for Effective Churn Management 2

Leveraging Data Analytics in Azure for Effective Churn Manag...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Gokulnath, Shreejith Suthraye Revathi, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

This research aims to develop optimized and cost-effective Azure architecture solutions by strategically selecting and utilizing Microsoft Azure services for specific use cases. The study leverages a simulated data-driven churn management case study within a social network context, emulating real-world data scenarios. The methodology employed involves a comprehensive understanding of the existing churn management script, enabling adaptability to diverse scenarios. This necessitates an in-depth exploration of Microsoft Azure's service offerings to ascertain their necessity and relevance. As the case study entails real-time streaming of event data, it is channeled into a staging area while concurrently undergoing batch processing for both account and subscription data. This synthesis culminates in a unified storage space tailored for analytics purposes. The implications of this research are twofold. First, the study yields a high-impact automated asset from a business standpoint. Second, it contributes expertise in the intricate process of migrating an entire asset from on-premises infrastructure to the cloud. Furthermore, using streamlined cost optimization achieved by judiciously selecting and configuring only essential resources, culminating in efficient outcomes. This paves the way for client-side stakeholders to make informed, data-driven decisions, leveraging the insights harnessed from the system. © 2024 IEEE.

关键词： Cost effectiveness

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An Integrated Dynamic Behavior Analysis and Adaptive Detection Framework for Enhanced Cloud Malware Detection

An Integrated Dynamic Behavior Analysis and Adaptive Detecti...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Keerthika Devi, S. Sandhya, G.R. Krithika, J. Joseph, Linda School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331543617

As malware threats continue to evolve and pose significant risks to cloud computing environments, there is a pressing need for advanced detection systems that can effectively address these challenges. This research introduces the Dynamic Behavior Analysis and Adaptive Detection Network (DBA-ADN), a novel framework designed to enhance malware detection through a combination of dynamic behavior analysis and adaptive detection techniques. DBA-ADN integrates real-time behavioral data with sophisticated adaptive algorithms to achieve superior detection performance. The study involves a comprehensive comparison of DBA-ADN against several benchmark systems, including MDCD, GloVe with CNN, CloudIntellMal, Hybrid Deep Learning Models, and DenseNet-121. The results reveal that DBA-ADN outperforms these established methods, achieving an impressive accuracy of 98.20% and an F1 Score of 0.99. This enhanced performance is attributed to DBA-ADN's ability to accurately identify both known and novel malware threats while minimizing false positives and false negatives. The framework's innovative approach addresses key limitations observed in existing solutions, as difficulties in handling sophisticated malware and inefficiencies in traditional detection techniques. By leveraging dynamic behavior analysis and adaptive algorithms, DBA-ADN not only improves detection accuracy but also reduces the complexity of deployment compared to its counterparts. The findings highlight DBA-ADN's potential to set a new benchmark in malware detection within cloud environments, offering a robust solution to the ongoing challenges of cybersecurity. This research contributes significantly to the field by providing a highly effective and adaptable approach to malware detection, laying the groundwork for future advancements and continued innovation in the domain. © 2024 IEEE.

关键词： Cloud security

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The Method for Detecting Abnormal Actions in Industrial Robots Based on Robots Spatio-Temporal Graph Normalizing Flow 5

The Method for Detecting Abnormal Actions in Industrial Robo...

引用

5th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2024

作者： Xiao, Wen Chen, Deng School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331518677

Rapid detection and response to abnormal movements of industrial robots can effectively reduce the risk of injury. Abnormal action detection requires modeling and distinguishing the complex and variable action patterns of industrial robots, which needs to take into account the spatio-temporal coupling and dynamic characteristics of industrial robots' actions, while efficient processing of spatio-temporal features and complex dynamics models requires a large amount of computational resources, which limits the accuracy and response speed of detection. To this end, we propose a robots spatio-temporal graph normalized flow (RSTG-NF) model for abnormal action detection of industrial robots. The model uses a Robots Spatio-Temporal Convolutional Network (RSTGC) to capture and differentiate the complex action relationships and motion patterns of industrial robots actions in both temporal and spatial dimensions. The frame-level action sequences are placed in a bi-directional mapping between the high-dimensional data space and the low-dimensional potential space through normalized flow, and the anomaly scores are evaluated based on the mapped data distributions for localization and detection of anomalies. Extensive experiments on a home-made six-axis industrial robots action dataset (IRADs) show an AUC score of 92.18% for frame-level detection and an inference speed of 79 FPS. © 2024 IEEE.

关键词： Industrial robots

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CO-LEACH: Cooperative Data Collection Protocol for Data-Heterogeneous WSNs 18th

CO-LEACH: Cooperative Data Collection Protocol for Data-Het...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Xinyu Wu, Gaojie Fu, Luwei Mao, Wenliang Zhao, Zhiwei School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9783031714696

Wireless Sensor Networks (WSNs) are indispensable for providing crucial sensing and data collection services to IoT systems. The diversity of IoT applications leads to data heterogeneity, which has become a prevalent characteristic of WSNs. Most existing work can only cope with data-homogeneous WSNs, where the same policy for clustering and data processing is applied to all network nodes. However, in practical IoT systems, different policies are desired to support the diverse types of IoT data. The neglect of data heterogeneity may result in poor data collection performance or even non-functionality. In this paper, we propose a cooperative data collection protocol (CO-LEACH) for data-heterogeneous WSNs. Specifically, 1) To mitigate the impact of data heterogeneity, CO-LEACH introduces an efficient cooperative clustering algorithm, in which heterogeneous nodes jointly form clusters, nodes with a higher proportion of intra-cluster type are more likely to be elected as cluster heads, thereby improving data collection efficiency. 2) To cope with the heterogeneous data, CO-LEACH presents an adaptive reprogramming strategy, making cluster heads capable of processing heterogeneous data. Simulation experiments show that CO-LEACH makes a better energy balance of all devices than state-of-the-art protocols, and prolongs the network lifetime by 3–5 times in data-heterogeneous WSNs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data collection

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