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16th Innovations in Software engineering Conference, ISEC 2023

作者： Godboley, Sangharatna Mohapatra, Durga Prasad Department of Computer Science and Engineering National Institute of Technology Warangal India Department of Computer Science and Engineering National Institute of Technology Rourkela India

ISBN: (纸本)9798400700644

The conference on Program Analysis and Software Testing is a special session under the workshop category which will be held in conjunction with ISEC-2023, Indian institute of Information technology, Allahabad Prayagraj, UP, India. It is in line with the scope of ISEC-2023 which will be specially focused on the theme of the event. The theme is based on the research work specific to the areas mentioned in the scope of this conference. RAPAST-2023 is expected to be a good avenue where the researchers from Academia and Industries will participate by presenting their most recent research findings and experimental results in the domain of the scope. © 2023 Owner/Author.

关键词： Software testing

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Beyond boundaries: Unifying classification and segmentation in wildfire detection systems

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Multimedia Tools and Applications 2024年 1-33页

作者： Singh, Swapnil Vazirani, Vidhi Singhania, Sanvika Suroth, Vaishnavi Singh Soni, Vaibhav Biwalkar, Ameyaa Krishnan, Deepa Computer Science Department Virginia Tech BlacksburgVA United States Computer Science Department Northwestern University EvanstonIL United States Computer Engineering Department Mukesh Patel School of Technology Management & ampEngineering NMIMS University Maharashtra Mumbai India

Forest fires, a dangerous natural phenomenon, cause large-scale destruction in forests and nearby communities. In this paper, we leverage the capabilities of classification and fast prediction of machine learning and deep learning to detect forest fires by proposing a dual-stage approach of identification and segmentation of burnt/burning regions. The initial identification step uses simple classification by harnessing custom convolutional neural network and AlexNet to detect satellite images displaying signs of burning, followed by the next step of segmentation, which employs UNet and SegNet architectures to precisely localize the burnt/burning areas. We chose UNet considering its ability of efficient feature preservation, and SegNet prominent use for precise boundary delineation. For training our models, we have curated a dataset using Google Earth Engine, querying latitude, longitude, and dates. Image processing techniques facilitated the extraction of segmentation masks, bounding box images, and segmented images. The post-training results portray that CNN outperforms AlexNet with an 88.19% accuracy for classification, while in segmentation, UNet excelled over SegNet with a dice score of 0.6869. If deployed, this study stands to benefit forest officials, local communities, and biodiversity. Moreover, it aids afforestation teams in identifying burnt zones for targeted reforestation efforts, ensuring a more effective response to forest fires. © The Author(s) 2024.

关键词： Landsat

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Comparative Analysis of Early Warning System for Landslide Prediction and Mitigation 7

Comparative Analysis of Early Warning System for Landslide P...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Krishna, Gayathri R. Nandini, D. Usha Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India St.Joseph's College of Engineering & Technology Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9798350372816

An Early Warning System (EWS) is an integrated system that disseminates early warning information to lessen a natural catastrophe's impact, facilitating preparedness and reaction processes. To reduce the likelihood of landslides occurring, researchers can take several measures. These include proactive measures to reduce the probability of landslides, the placement of EWS where, in some situations, the risk to life exceeds acceptable limits, and structural engineering initiatives aimed at reducing the susceptibility of sensitive elements. The benefits and drawbacks of the current landslide EWS are the main objectives of this study. It first clarifies the early warning system framework's obvious standpoint. The overall analysis will help the researchers who want to focus on landslide early warning systems. © 2024 IEEE.

关键词： Landslides

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A versatile acoustically active surface based on piezoelectric microstructures

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Microsystems & Nanoengineering 2022年第3期8卷 51-60页

作者： Jinchi Han Mayuran Saravanapavanantham Matthew R.Chua Jeffrey H.Lang Vadimir Bulovic Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA

We demonstrate a versatile acoustically active surface consisting of an ensemble of piezoelectric microstructures that are capable of radiating and sensing acoustic waves.A freestanding microstructure array embossed in a single step on a flexible piezoelectric sheet of polyvinylidene fluoride(PVDF)leads to high-quality acoustic performance,which can be tuned by the design of the embossed *** high sensitivity and large bandwidth for sound generation demonstrated by this acoustically active surface outperform previously reported thin-flm loudspeakers using PVDF,PVDF copolymers,or voided charged polymers without *** further explore the directivity of this device and its use on a curved *** addition,high-fidelity sound perception is demonstrated by the surface,enabling its microphonic application for voice recording and speaker *** versatility,high-quality acoustic performance,minimal form factor,and scalability of future production of this acoustically active surface can lead to broad industrial and commercial adoption for this technology.

关键词： surface. piezoelectric versatile

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Deep Convolutional Multi-Relational Graph Attention Network for Autonomous Drone Navigation in Urban Wind Zones

Deep Convolutional Multi-Relational Graph Attention Network ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pabitha, C. Joshua, K. Paul Pradeep, S. Sophia, S. Sharma, R. Rajesh Prabhu, D. Karthik Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India MAlla Reddy Engineering College for Women Department of Computer Science and Engineering Telangana Secunderabad India Karunya Institute of Technology and Sciences Department of computer Science and Engineering Tamil Nadu Coimbatore India Alliance University Department of Computer Technology Alliance College of Engineering and Design Bangalore India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalaiyam India

ISBN: (纸本)9798331509828

Drones are essential for civil engineering operations like logistics and data collecting. Current autonomous drone studies mainly concerns itself with safe path planning in static scenarios;however one of the major challenges of over urban environment is often dynamic factors such as wind, building layouts, and signal coverage that can significantly impact direction stability. For autonomous navigation across dynamic wind zones, this study proposed a new Shape-aware Mesh Dual Lightweight Deep Convolutional Multi-Relational Graph Attention Network with Growth Optimizer (SAMDLDCMRGANet-GO) that leverages local data from RGB cameras and GPS. The method incorporates a Dual Aggregation Transformer for feature extraction, Shape-aware Mesh Normal Filtering for preprocessing, and the LDCMRGAN et for efficient path planning. The proposed approach demonstrates its efficacy in navigating complicated surroundings while retaining high precision in obstacle avoidance and target acquisition, achieving an impressive 99.14% recall, 99.15% accuracy, an outstanding EVA of 0.99, and a low RMSE of 0.41. © 2025 IEEE.

关键词： Mesh generation

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Joint Dual Learning With Mutual Information Maximization for Natural Language Understanding and Generation in Dialogues

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 2445-2452页

作者： Su, Shang-Yu Chung, Yung-Sung Chen, Yun-Nung Rakuten Institute of Technology Rakuten Tokyo158-0094 Japan Massachusetts Institute of Technology Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Computer Science and Information Engineering Taipei106319 Taiwan

Modular conversational systems heavily rely on the performance of their natural language understanding (NLU) and natural language generation (NLG) components. NLU focuses on extracting core semantic concepts from input texts, while NLG constructs coherent sentences based on these extracted semantics. Inspired by information theory in digital communication, we introduce a one-way communication model that mirrors human conversations, comprising two distinct phases: (1) the conversion of thoughts into messages, similar to NLG, and (2) the comprehension of received messages, similar to NLU. This paper presents a novel algorithm that trains NLU and NLG collaboratively by concatenating their models and maximizing mutual information between inputs and outputs. This approach efficiently facilitates the transmission of semantics, leading to enhanced learning performance for both components. Our experimental results, based on three benchmark datasets, consistently demonstrate significant improvements for both NLU and NLG tasks, highlighting the practical promise of our proposed method. © 2014 IEEE.

关键词： Semantics

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Categorisation by Leveraging CNNs and Remote Sensing Satellite Imagery for Crop Analysis in Arid Environments

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Remote Sensing in Earth Systems Sciences 2024年第2期7卷 66-79页

作者： Malhan, Anu Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Department of Computer Engineering New Horizon Institute of Technology and Management

The classification of walnuts in the dry areas of Ganquan Township, Awati County, Xinjiang, is investigated in this research using traditional approaches, remote sensing photos taken by satellite, and convolutional neural networks (CNNs). Thorough field sampling and the collecting of Landsat-8 Level-2 data were necessary for correct analysis due to the different environmental features of the research region, which presented major hurdles. Despite some early difficulties, CNN design revisions led to significant gains in classification accuracy. These modifications included AlexNet, VGG, GoogleNet, ResNet, and EfficientNetV2. When compared to more traditional approaches, ResNet and EfficientNetV2 stood up as the most successful models. Nevertheless, challenges including computational complexity and low-resolution imaging highlight the necessity for more research to improve detection efficiency without sacrificing accuracy. The study emphasises the need to keep looking for new ways to analyse and monitor crops, and it suggests that combining remote sensing with advanced machine learning methods could help improve precision agriculture management and increase crop yields in dry areas. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Categorisation of crop Machine learning Precision agriculture Remote sensing Walnut

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Multivariate Time series Forecasting for Cloud Machine Utilization 3

Multivariate Time series Forecasting for Cloud Machine Utili...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Naik, Archana Sooda, Kavitha Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9798331527549

The cloud machine utilization prediction using a multivariate time series model is explored in this study. The cloud machine usage data set samples are selected with varied patterns. The long sequence forecasting model Informer is investigated for the forecasting. The model results are benchmarked against LSTM, GRU, Prophet, and XGBoost across the diverse datasets. The performance is evaluated using RMSE. Experimental results show that the Prophet model performs best overall due to its ability to capture both seasonal and irregular patterns accurately. The RMSE value for Informer is 0.13, Prophet is 0.097, XGBoost is 0.096, LSTM is 0.1725, and GRU is 0.1547. © 2025 IEEE.

关键词： Time series

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Parallel Computation of Dominance Scores for Multidimensional Datasets on GPUs

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IEEE Transactions on Parallel and Distributed Systems 2024年第6期35卷 764-776页

作者： Chen, Wei-Mei Tsai, Hsin-Hung Ling, Joon Fong National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan

The dominance scoring problem in a multidimensional dataset is to return the number of points dominated by a given point, which is a common metric for evaluating the quality of a data point. Dominance scoring is an elementary operator for variations of the skyline operator, including top k dominating and k-skyband queries. This study proposes query processing for dominance scores that operates primarily on the graphics processing unit (GPU) to fully utilize its massive processing resources and restricted memory space while reducing the transfer overhead between the central processing unit (CPU) and GPU. We introduce a heap-based multidimensional data structure with complete and well-balanced characteristics. Using our preprocessed data, we can construct a complete R-tree with the non-overlapping property, ensuring that the bounding boxes of internal nodes of the same level do not overlap, thereby reducing redundant operations. In addition, we propose two algorithms based on depth-first and breadth-first traversals to accumulate the dominance score on GPUs in parallel. Both take full advantage of the GPU's computing resources and memory space supported by the non-overlapping tree structures. Experiments on synthetic and real-world datasets demonstrate that the proposed algorithms implemented on GPUs dramatically improve the efficiency of dominance scoring. © 1990-2012 IEEE.

关键词： Graphics processing unit

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ABES: attention bi-directional ensemble SVM for early detection of brain tumors

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Neural Computing and Applications 2024年第26期36卷 16179-16193页

作者： Subramaniam, Erana Veerappa Dinesh Krishnasamy, Valarmathi Department of Computer Science and Business Systems Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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