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Diverse Geographical Regions Based Biodiversity Conservation by LiDAR Image with Deep Learning Model

Remote Sensing in Earth Systems sciences 2024年第4期7卷 738-749页

作者： Babu C. N, Gireesh Reddy, Chilukala Mahender Kumar, Mungara Kiran Nagaraju, Komatigunta Maheswari, G. Uma Kiran, Ajmeera Department of Computer Science and Engineering BMS Institute of Technology & amp Management Karnataka Bengaluru India Department of Artificial Intelligence and Data Science GITAM (Deemed to be University) Hyderabad India Department of CSE School of Technology GITAM (Deemed to Be University) Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P India Department of Computer Science and Engineering RMK College of Engineering and Technology Pudhuvoyal Chennai India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Reliable data on the composition and structure of forests at various spatial scales is necessary for the conservation and monitoring of forest biodiversity. However, because field sampling techniques can be challenging, comprehensive data regarding the features of forest habitat over wide areas are frequently lacking. In order to get over this restriction, we created variables that describe the structure of the forest landscape over a wide environmental gradient using a nationally accessible light detection and ranging (LiDAR) remote sensing dataset. This research proposes a novel technique in forest management based on biodiversity analysis using LiDAR image processing by deep learning techniques. Here, the input LiDAR image has been collected as temperature pattern dataset in major deforestation regions and processed for noise removal and normalization. This image feature has been extracted using recurrent graph learning-based SegNet component analysis. Then, this extracted feature has been classified using Gaussian deep belief region-based adversarial neural networks. The experimental analysis has been carried out for various temperature pattern datasets in terms of random accuracy, average precision, recall, NSE, and AUC. The results of this study will help determine policy objectives for the restoration of mountain areas and enhance the effectiveness of monitoring damaged mountain forests. With the help of the suggested method, 96% random accuracy, 94% average precision, 49% NSE, 93% recall, and 95% AUC were achieved. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial neural networks Deep learning techniques Deforestation rate LiDAR image processing Temperature rise analysis

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Dual Attention and Question Categorization-Based Visual Question Answering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 81-91页

作者： Mishra, Aakansha Anand, Ashish Guha, Prithwijit Department of Computer Science & Engineering Guwahati781039 India Department of Electronics and Electrical Engineering Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati Guwahati781039 India

Visual question answering (VQA) aims at predicting an answer to a natural language question associated with an image. This work focuses on two important issues pertaining to VQA, which is a complex multimodal AI task: First, the task of answer prediction in a large output answer space, and second, to obtain enriched representation through cross-modality interactions. This work aims to address these two issues by proposing a dual attention (DA) and question categorization (QC)-based visual question answering model (DAQC-VQA). DAQC-VQA has three main network modules: First, a novel dual attention mechanism that helps toward the objective of obtaining an enriched cross-domain representation of the two modalities;second, a question classifier subsystem for identifying input (natural language) question category. The second module of question categorizer helps in reducing the answer search space;and third, a subsystem for predicting answer depending on the question category. All component networks of DAQC-VQA are trained in an end-to-end manner with a joint loss function. The performance of DAQC-VQA is evaluated on two widely used VQA datasets, viz., TDIUC and VQA2.0. Experimental results demonstrate competitive performance of DAQC-VQA against the recent state-of-the-art VQA models. An ablation analysis indicates that the enriched representation obtained using the proposed dual-attention mechanism helps improve performance. © 2022 IEEE.

关键词： Forecasting

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Coffee Leaf Disease and Severity Prediction Using Deep Learning 38

Coffee Leaf Disease and Severity Prediction Using Deep Learn...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Chaubey, Harshit Kumar Arelli, Siri Raj, Aryan Naik, Nenavath Srinivas Iiit Naya Raipur Computer Science and Engineering Raipur India Iiit Naya Raipur Data Science and Artificial Intelligence Raipur India Iiitdm Kurnool Computer Science and Engineering Kurnool India

ISBN: (纸本)9798350302196

Coffee production is a vital industry in many countries, but diseases affecting coffee leaves can lead to significant losses for farmers. To mitigate these losses, timely disease detection and accurate assessment of disease severity are crucial. This work proposes a deep learning approach for classifying coffee leaf diseases based on their severity levels. The proposed methodology involves several steps. Initially, U2Net removes the background from the coffee leaf images. Subsequently, the background-removed images are converted into BGR format to identify the diseased regions. DeepLabV3 is then trained to extract and mark the diseased portions of the leaves in red. Using these annotated images, various convolutional neural network (CNN) models, including VGG-16, Inception V3,and MobileNetV2, were trained to classify the diseases based on their severity levels. These models are carefully modified and fine-tuned with hyperparameters to achieve the best performance metrics. Upon model training, the modified MobileNetV2 model performs better than the other CNN models, achieving an impressive F1-score of 97.99%. This outcome highlights the effectiveness of this paper's approach in accurately classifying coffee leaf diseases according to their severity levels. The proposed methodology has significant implications for coffee farmers, enabling them to swiftly detect diseases and assess their severity, allowing for timely and appropriate actions. Furthermore, the findings indicate the superiority of the modified MobileNetV2 model in achieving high accuracy in disease severity classification. This research contributes to advancing deep learning techniques for agricultural applications, offering practical solutions for disease management in the coffee industry. © 2023 IEEE.

关键词： Deep learning

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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Towards Better Accuracy-Efficiency Trade-Offs: Dynamic Activity Inference via Collaborative Learning From Various Width-Resolution Configurations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6723-6738页

作者： Qin, Lutong Zhang, Lei Li, Chengrun Song, Chaoda Cheng, Dongzhou Wang, Shuoyuan Wu, Hao Song, Aiguo Nanjing Normal University School of Electrical and Automation Engineering Nanjing210023 China Shanghai200240 China Case Western Reserve University Department of Computer and Data Sciences ClevelandOH44106 United States University of Macau Department of Computer and Information Science Taipa999078 China Yunnan University School of Information Science and Engineering Kunming650500 China Southeast University Department of Instrument Science and Engineering Nanjing210096 China

Recently, deep neural networks have triumphed over a large variety of human activity recognition (HAR) applications on resource-constrained mobile devices. However, most existing works are static and ignore the fact that the computational budget usually changes drastically across various devices, which prevent real-world HAR deployment. It still remains a major challenge: how to adaptively and instantly tradeoff accuracy and latency at runtime for on-device activity inference using time series sensor data? To address this issue, this article introduces a new collaborative learning scheme by training a set of subnetworks executed at varying network widths when fueled with different sensor input resolutions as data augmentation, which can instantly switch on-the-fly at different width-resolution configurations for flexible and dynamic activity inference under varying resource budgets. Particularly, it offers a promising performance-boosting solution by utilizing self-distillation to transfer the unique knowledge among multiple width-resolution configuration, which can capture stronger feature representations for activity recognition. Extensive experiments and ablation studies on three public HAR benchmark datasets validate the effectiveness and efficiency of our approach. A real implementation is evaluated on a mobile device. This discovery opens up the possibility to directly access accuracy-latency spectrum of deep learning models in versatile real-world HAR deployments. © 2020 IEEE.

关键词： Budget control

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Enhancing Hadoop Performance with Q-Learning for Optimal Parameter Tuning 38

Enhancing Hadoop Performance with Q-Learning for Optimal Par...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Akshay, Garika Naik, Nenavath Srinivas Vardhan, Jai Iiit Naya Raipur Data Science and Artificial Intelligence Raipur India Iiitdm Kurnool Computer Science and Engineering Kurnool India Iiit Naya Raipur Computer Science and Engineering Raipur India

ISBN: (纸本)9798350302196

This paper presents an approach to enhance Hadoop performance by leveraging deep Q-Learning, a form of Reinforcement Learning, to optimize parameter settings. The performance of Hadoop, a widely adopted distributed computing framework, relies heavily on configuring numerous parameters. However, the complex and extensive search space poses challenges in determining the optimal settings. In response, we propose an innovative deep Q-Learning algorithm that iterative discovers and applies the most effective parameter configurations, thereby improving Hadoop's performance. Our approach involves defining state and action spaces, learning from performance feedback, and identifying the optimal configuration to maximize Hadoop's efficiency. Our proposed model consistently outperforms existing solutions by benchmarking with popular jobs such as TeraSort, WordCount, Hive Aggregate, and WordCooccur, demonstrating significant reductions in execution times and improved computational efficiency. This research accelerates big data processing in Hadoop and provides a scalable and efficient solution applicable to similar distributed computing frameworks. Furthermore, our paper reinforces the effectiveness and applicability of Reinforcement Learning in addressing complex optimization problems. By harnessing the power of deep Q-Learning, we showcase its capability to navigate the intricate parameter space of Hadoop, resulting in enhanced performance and streamlined data processing. This research contributes to advancing distributed computing frameworks, offering a novel and efficient approach to optimize parameter settings and improve overall system performance. © 2023 IEEE.

关键词： Reinforcement learning

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Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems 9

Graph Neural Networks for Optimizing Reliability in Vehicle-...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Neelamegam, G. Revathy, J. Kannukkiniyal, M. Devendran, M. Akshya, J. Choudhry, Mani Deepak Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Artificial Intelligence and Data Science Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Hindustan institute of technology Department of Computer Science and Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350377972

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models;it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data. © 2024 IEEE.

关键词： Graph neural networks

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A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

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computer Modeling in engineering & sciences 2023年第10期137卷 363-383页

作者： Yanjun Zhang Yongqiang He Jingbo Zhang Yaru Zhao Zhihua Cui Wensheng Zhang School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China Department of Big Data and Intelligent Engineering Shanxi Institute of TechnologyYangquan045000China School of Computer Science Beijing University of TechnologyBeijing100124China The Institute of Automation Chinese Academy of Sciences(CAS)Beijing100049China

The video compression sensing method based onmulti hypothesis has attracted extensive attention in the research of video codec with limited ***,the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging *** resolve this problem,this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction *** includes the optimization of prediction blocks(OPBS),the selection of searchwindows and the use of neighborhood ***,the OPBS consists of two parts:the selection of blocks and the optimization of prediction *** combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video *** addition,most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on ***,Block-level search window(BSW)is constructed to cover the position of the optimal hypothesis block as much as *** maximize the availability of reference frames,Nearby reference frame information(NRFI)is designed to reconstruct the current *** proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction *** results showthat the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

关键词： Compressed sensing OPBS block-level search window nearby reference frame information evolutionary algorithm

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Evaluating Pretrained Deep Learning Models for COVID-19 Detection in Medical Imaging 1

Evaluating Pretrained Deep Learning Models for COVID-19 Dete...

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1st Renewable Energy Technologies and Modern Communications Systems: Future and Challenges Conference, RETMCS 2024

作者： AL-Shehri, Bushra A. Sabbeh, Sahar F. Dessouky, Mohamed M. Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Faculty of Computers and Artificial Intelligence Information Systems Department Benha University Benha13518 Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9798331519445

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography dataset (Kaggle), the SARS-CoV-2 CT-Scan dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography dataset and 99% accuracy on the SARS-CoV-2 CT-Scan dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions. ©2024 IEEE.

关键词： COVID-19

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M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought

M3Hop-CoT: Misogynous Meme Identification with Multimodal Mu...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kumari, Gitanjali Jain, Kirtan Ekbal, Asif Department of Computer Science and Engineering India Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In recent years, there has been a significant rise in the phenomenon of hate against women on social media platforms, particularly through the use of misogynous memes. These memes often target women with subtle and obscure cues, making their detection a challenging task for automated systems. Recently, Large Language Models (LLMs) have shown promising results in reasoning using Chain-of-Thought (CoT) prompting to generate the intermediate reasoning chains as the rationale to facilitate multimodal tasks, but often neglect cultural diversity and key aspects like emotion and contextual knowledge hidden in the visual modalities. To address this gap, we introduce a Multimodal Multi-hop CoT (M3Hop-CoT) framework for Misogynous meme identification, combining a CLIP-based classifier and a multimodal CoT module with entity-object-relationship integration. M3Hop-CoT employs a three-step multimodal prompting principle to induce emotions, target awareness, and contextual knowledge for meme analysis. Our empirical evaluation, including both qualitative and quantitative analysis, validates the efficacy of the M3HopCoT framework on the SemEval-2022 Task 5 (MAMI task) dataset, highlighting its strong performance in the macro-F1 score. Furthermore, we evaluate the model's generalizability by evaluating it on various benchmark meme datasets, offering a thorough insight into the effectiveness of our approach across different datasets. © 2024 Association for Computational Linguistics.

关键词： Chains

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